The Acoustic World of Sharks

Lucille Chapuis

BSc (Biology), MSc (Evolutionary and Conservation Biology)

This thesis is presented for the degree of Doctor of Philosophy of

The University of Western Australia

School of Animal Biology and the UWA Oceans Institute

2017

i

La mer est un tableau qui change de couleur et de sentiment à

chaque minute du jour et de la nuit. Il y a ici des gouffres remplis de

clameurs dont vous ne pouvez vous représenter l'effroyable variété:

tous les sanglots du désespoir, toutes les imprécations de l'enfer s'y

sont donné rendez-vous, de ma petite fenêtre j'entends dans la nuit

ces voix de l'abîme qui tantôt rugissent une bacchanale sans nom,

tantôt chantent des hymnes sauvages encore redoutables dans leur

plus grand apaisement.

George Sand, Elle et lui

“

”

Blue, green, grey, white, or black; smoothed, ruffled, or

mountainous; that ocean is not silent. All my days have I

watched it and listened to it, and I know it well. At first it told

me only the plain little tales of calm beaches and near ports,

but with the years it grew more friendly and spoke of other

things ; of things more strange and more distant in space and

in time.

H. P. Lovecraft, The White Ship

”

“

ii

iii

Abstract

Underwater sound travels rapidly over great distances and thus represents an important

sensory cue for aquatic species. Elasmobranch fishes are known to be sensitive to relatively

low frequency sounds present in their environment. However, we still know little of the

acoustic world of sharks and their use of their hearing sense. In particular, assessing hearing

thresholds and understanding the behaviour of these apex predators towards sounds is

essential, in an era where the anthropogenic noise in the ocean is exponentially increasing.

The aims of this thesis were to investigate the acoustic world of sharks. First, the

methodology of determining hearing thresholds, typically achieved through auditory evoked

potentials (AEPs), was examined to optimise for data accuracy and animal welfare. Secondly,

we aimed to investigate the effect of sounds on the behaviour of sharks by assessing their

reactions to playbacks of various sounds in the wild. The combined effect of bright flashing

lights and sounds was also tested on wild and captive sharks. Finally, our knowledge of shark

hearing was applied to investigate whether sharks would mistake the acoustic signatures of

humans for prey in the water, hence confirming or refuting an acoustic role in negative

interactions between humans and sharks.

The findings highlight the difficulty of acquiring accurate electrophysiological data to assess

hearing thresholds in sharks while ensuring the animals’ welfare and has emphasised the

paucity of information available on the hearing system of elasmobranchs. The interspecific

differences in the reactions of sharks towards sounds are consistent with the differences in

inner ear morphology and the need for more comparative studies is discussed. Although

sharks present a relatively narrow sensitivity range, the fact that their behaviour seems to be

altered by sound suggests a significant role for the auditory modality in their life history.

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v

Acknowledgements

This PhD is the result of a challenging journey, filled with overwhelming experiences. From

tremendous excitation to extreme sadness, the emotions encountered along the way have

inundated my senses, allowing me to become a stronger and, I hope, better person. Many

people have contributed to making the path smoother and more enjoyable.

First and foremost, I would like to express my special appreciation and thanks to my principal

supervisor Professor Shaun Collin. With his enthusiasm, his inspiration and his values on

research and life in general, Shaun has been a tremendous mentor for me. I feel privileged to

have learned alongside a Master Jedi like Shaun and I have no doubt that this preparation

has shaped the start of my career in mastering the Force.

Secondly, I would like to sincerely thank Assistant Professor Kara Yopak, for graciously

hopping into the middle of the journey, providing her energising brain cells to the project.

Kara has been a constant source of illumination and mentorship and I only wish I could

follow half of her steps as a wonderful scientist. Her boundless passion will stay with me, as

well as, I hope, a friendship for life.

I am grateful to Associate Professor Nathan Hart, for his guidance and patient help on all

the experiments undertaken in this thesis. Nathan’s immense knowledge and ingenuity have

been an inspiration and stimulation to be a better scientist and to seek outside of my

boundaries.

I would like to express my gratitude to Associate Professor Robert McCauley in collaborating

on this project. Rob has tactfully introduced me to the complex world of underwater

acoustics with a lot of patience. His elucidations have arisen my motivation to progress and

broaden my knowledge and I hope that I will be able to continue learning alongside Rob in

the future.

I must, at this level, acknowledge Caroline Kerr, who made such a big difference to the

journey. Not only her professional help in multiple aspects of the thesis has greatly improved

and disentangled the project, but her vital emotional support during all the steps of the

voyage have allowed me to conquer my summit. I would like to deeply thank Caroline for

carrying my heavy backpack through some of the steep and dangerous pathways, as well as

lending her friendly shoulder in each basecamp.

Besides my supervisors, I have been very privileged to encounter a few wonderful scientists

during my time as a PhD student. Firstly, I would also like to express my deep gratitude for

Dr Jan Hemmi, who generously shared hours of his brain time with me to answer all my

vi

questions with unbelievable knowledge and intelligence. I admire Jan’s commitment to teach

with boundless openness, inspiring students to grow and practice quality science with full

integrity. Secondly, I am thankful to Dr Kate Sanders, who I met by chance around some

acoustic non-sense, and has since become a fabulous mentor and friend. I would like to thank

her for her trust and inspiration and I am looking forward to share more fun in the field,

surrounded with gorgeous sea snakes and Pinot Noir. I would also like to thank Associate

Professor Julian Partridge, who has been a continuous source of encouraging discussions

and insights, and whose way of seeing science has truly been refreshing and motivating. I am

fortunate to have met Dr Wayne Davies, whose both scientific and artistic creations are an

inspiration. Finally, I am thanking Dr Jessica Mountford, whose intellectual humility makes

her one of the best scientist I’ve known and who has been so insightful and kind to me.

I would like to sincerely thank Carl Schmidt, who taught me all I know about elasmobranch

husbandry and helped me in building some essential parts of the experiments, and whom

I’ve missed greatly since his departure. I also thank Michael Archer for his kind help during

my lost errands in the lab and his offers to explore the underwater world of Perth.

I am grateful to The University of Western Australia for financially supporting my

candidature through the Scholarship for International Research Fees (SIRF) and the UWA

Safety-Net Top-Up Scholarship. I also would like to acknowledge the Sea World Research

and Rescue Foundation, the WA State Government, and National Geographic Australia for

funding different parts of this project.

An accomplished support group has been essential to survive and stay as healthy and sane as

possible during this journey. Firstly, I have been extremely lucky to benefit from skilful and

dedicated professionals, from the few years that preceded my departure to Australia, via the

toughest moments of the journey, until now when I am finally ready to arrive. I would like

to take the opportunity to formally thank some of these people: Françoise R., Elisabeth C.,

Anne-Françoise C., Liz B., Paula B., Shazzy T., Anita F. and Kelly G. I also would like to

thank the people from the graduate school of UWA, most especially Dr Sato Juniper for her

mentorship, wisdom and guidance into the wild world of academia.

Secondly, I would like to express all my gratitude to the core of this support group: my

friends, old and new, who accompanied me through the highs and lows of this experience. I

cannot list you all, my good friends from home, but you know how much I owe you: Carole,

Aude, Tiziana, Sarah, Carole, Sarah, Elena&Yann, Val&Duf, Gordon, Flavien.

It has been incredible how many good souls I have encountered since arriving in Perth and

how much their friendship means to me. I would like to make a group hug and thanks all the

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members of the neuroecology lab. Special shout out to my brother Nic whose emotional and

intellectual support has been vital to reach every step; to my brother João Paulo whose

wisdom and Brazilian warmth have touched my heart; to my brother Eduardo whose

resilience and energy have been so inspiring; to my brother Matt F S whose calming and wise

aura enlightened my days in the office; to my brother Carlos, with whom I enjoyed some

peaceful walks along the Swan River. Big hugs and thanks to my sister Laura, whose hidden

love is overwhelming, with whom I enjoyed to work a lot and who helped proofread this

thesis; to my sister Fanny, with whom it has been such a pleasure to share a bit of ‘home’ at

times, and Vic whose welcomed coffees kept me warm and alive! I would also like to thank

Audrey, Tony, Lauren, Rachael and Ryan from the lab, and my good friend Richie in Perth.

I will finish with Switzerland, where the most basic source of my life energy resides, my

family. Their unconditional trust and support have allowed me to realise my dream by taking

a passion to the next degree. I thank them for their strength, their consistency, their

intellectual spirit and never-ending support, their constant interest and positive stimulation,

and finally, their patience. Grand-maman, maman, papa, Jonas, Valériane, Agnès, Edith,

Cathy, Maryline, Nathan, Theo, Cédric: merci!

Five great beings have not seen me finish. Pascal (1970 - 2012), Grand-maman Lucienne

(1929 – 2012), Laeticia (1982 – 2012), Francois (1959 - 2012) and Stella (1918 – 2012): I miss

you. You are the determination in every page.

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Statement of candidate contribution

In accordance with the University of Western Australia’s regulations regarding Research

Higher Degrees, this thesis is presented as a series of papers prepared for publication. The

bibliographical details of the work and where it appears in the thesis are outlined below.

Chapter 2: Assessing the effects of anaesthesia on auditory evoked potentials (AEPs)

in a benthic shark model

Lucille Chapuis, Caroline C. Kerr, Kara E. Yopak, Robert D. McCauley, Jan M. Hemmi,

Nathan S. Hart, Shaun P. Collin.

Study concept and design: LC, SPC, NSH, CCK; equipment and implementation: NSH, LC,

RDM; collection and acquisition of data: LC, CCK, KEY, SPC; equipment calibration: RDM,

LC; data analysis: LC, JHG; interpretation of data: LC; drafting of the manuscript: LC; critical

revision of the manuscript: SPC, NSH, CCK; funding to: SPC, NSH, RDM, LC.

Overall estimated contribution of the candidate: 85%

Chapter 3: Effects of auditory and visual stimuli on shark feeding behaviour: the

disco effect

Lucille Chapuis, Laura A. Ryan, Kara E. Yopak, Robert D. McCauley, Jan M. Hemmi, Shaun

P. Collin, Nathan S. Hart.

Study concept and design: NSH, SPC, LC, LAR; equipment and implementation: NSH, LC,

LAR; collection and acquisition of data: LC, LAR, NSH, SPC; equipment calibration: RDM,

LC, LAR; analysis: LC, LAR, JMH; interpretation of the data: LC, LAR; drafting of the

manuscript: LC, LAR; critical revision of the manuscript: SPC, KEY, RDM; funding to:

NSH, SPC, RDM, LC.

Overall estimated contribution of the candidate: 45%

Chapter 4: Effects of acoustic stimuli on the behaviour of wild sharks

Lucille Chapuis, Kara E. Yopak, Robert D. McCauley, Nathan S. Hart, Shaun P. Collin

Study concept and design: LC, SPC, NSH; equipment and implementation: LC, NSH, RDM;

collection and acquisition of data: LC, SPC, NSH; equipment calibration: LC, RDM; analysis

and interpretation of data: LC; drafting of the manuscript: LC; revision of the manuscript:

SPC, NHS, RDM, KEY; funding to: NSH, SPC, RDM, LC.

Overall contribution of the candidate: 90%

x

Chapter 5: Examining the mistaken identity hypothesis: do sharks mistake the

acoustic signature of humans for pinnipeds?

Lucille Chapuis, Kara E. Yopak, Jan M. Hemmi, Robert D. McCauley, Nathan S. Hart, Shaun

P. Collin

Study concept and design: NSH, LC, SPC; equipment and implementation: NSH, LC;

collection and acquisition of data: LC, NSH; equipment calibration: RDM, LC; analysis: LC,

JMH; interpretation of the data: LC; drafting of the manuscript: LC; revision of the

manuscript: SPC, KEY, RDM, NSH; funding to: NSH, SPC.

Overall estimated contribution of the candidate: 90%

Lucille Chapuis Shaun P. Collin

PhD Candidate Coordinating supervisor

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Scientific contributions not included in the thesis

Peer reviewed journal articles

R.M. Kempster, C.A. Egeberg, N.S. Hart, L. Ryan, L. Chapuis, C.C. Kerr, C. Schmidt, C.

Huveneers, E. Gennari, K.E. Yopak, J.J. Meeuwig, S.P. Collin. (2016) How close is too close?

The effect of a non-lethal electric shark deterrent on white shark behaviour. PLoS ONE, 11,

e0157717.

T.B. Letessier, J.J. Meeuwig, M. Gollock, L. Groves, P.J. Bouchet, L. Chapuis, G.M.S.

Vianna, K. Kemp, H.J. Koldewey. (2013) Assessing pelagic fish populations: The application

of demersal video techniques to the mid-water environment. Methods in Oceanography, 8, 41-

55.

Conference oral presentation

L. Chapuis, R. D. McCauley R., N. H. Hart, S.P. Collin S.P. (2014) Shark! What big ears you

have: Functional and morphological differences in the auditory system of elasmobranchs.

Sharks International 2014, Durban.

Scientific reports

N.S. Hart, S.P. Collin, R. Kempster, C.C Kerr, L. Chapuis, L.A. Ryan. (2015) Report on

‘Development and testing of novel shark deterrents’ for the WA State Government Applied

Research Program on Shark Hazard Mitigation. July 2015.

S.P. Collin, N.S. Hart, L. Chapuis, L.A. Ryan, C. Kerr. (2016) Report on ‘A case of Mistaken

identity? Discovering the sensory cues that triggers shark attacks’ for the WA State

Government Applied Research Program on Shark Hazard Mitigation. June 2016.

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Table of contents

Abstract ....................................................................................................................... iii

Acknowledgements ..................................................................................................... v

Statement of candidate contribution .......................................................................... ix

Table of contents ...................................................................................................... xiii

List of figures ........................................................................................................... xvii

List of tables ............................................................................................................. xix

List of abbreviations ................................................................................................. xxi

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

1.1. The hearing system of elasmobranchs ......................................................................... 3

1.1.1. Underwater sound ........................................................................................................... 3

1.1.2. The inner ear of elasmobranchs ........................................................................................ 5

1.1.3. Hearing abilities of elasmobranchs ................................................................................... 7

1.1.4. Auditory processing in the brain of elasmobranchs ............................................................ 9

1.1.5. Acoustic behaviour of elasmobranchs .............................................................................. 10

1.1.6. What do they listen to? The soundscape of elasmobranchs ............................................... 13

1.1.7. Functional anatomy and physiology ................................................................................ 15

1.2. Rationale ......................................................................................................................... 17

1.2.1. Standardising bioacoustics studies................................................................................... 17

1.2.2. Conservation and neuroecology ....................................................................................... 17

1.2.3. Technical limitations ...................................................................................................... 18

1.3. Structural presentation of the thesis ........................................................................... 19

Chapter 2 Assessing the effects of anaesthesia on auditory evoked potentials (AEPs) in a benthic shark model, Chiloscyllium punctatum .................................... 21

2.1. Abstract .......................................................................................................................... 22

2.2. Introduction ................................................................................................................... 23

2.3. Methods .......................................................................................................................... 25

2.3.1. Animal Ethics statement ............................................................................................... 25

2.3.2. Fish acquisition and maintenance .................................................................................. 25

2.3.3. Anaesthesia .................................................................................................................. 26

2.3.4. Experimental setup ....................................................................................................... 27

2.3.5. AEP stimulation and recordings ................................................................................... 28

2.3.6. Sound and particle calibration ....................................................................................... 29

2.3.7. Signal analysis .............................................................................................................. 30

2.3.8. Statistical analysis ......................................................................................................... 32

2.3.9. Audiogram estimation ................................................................................................... 32

2.4. Results ............................................................................................................................. 32

xiv

2.4.1. Sound stimuli ................................................................................................................ 32

2.4.2. Anaesthetic effects on AEPs amplitude, latency and thresholds ....................................... 33

2.4.3. Audiogram .................................................................................................................... 34

2.5. Discussion ....................................................................................................................... 36

2.5.1. The effects of tricaine methanesulfonate (MS-222) .......................................................... 36

2.5.2. Interaction of anaesthetics with particle acceleration levels ................................................. 38

2.5.3. Audiogram .................................................................................................................... 39

2.5.4. Limitations and future directions .................................................................................... 40

2.5.5. Recommendation for AEP procedures in fish and anaesthesia ......................................... 40

2.6. Conclusion ...................................................................................................................... 41

2.7. Acknowledgements ....................................................................................................... 42

Chapter 3 Effects of auditory and visual stimuli on shark feeding behaviour: the disco effect ................................................................................................................. 43

3.1. Abstract ........................................................................................................................... 44

3.2. Introduction ................................................................................................................... 45

3.3. Methods .......................................................................................................................... 48

3.3.1. Ethics statement and animal acquisition ......................................................................... 48

3.3.2. Description of sensory stimulation ................................................................................... 49

3.3.3. Experiment on captive benthic sharks ............................................................................ 53

3.3.4. Field experiment on white sharks ................................................................................... 55

3.4. Results ............................................................................................................................. 57

3.4.1. Electrical field of the devices ............................................................................................ 57

3.4.2. Experiment on captive benthic sharks ............................................................................ 58

3.4.3. Field experiments on white sharks .................................................................................. 61

3.5. Discussion ....................................................................................................................... 62

3.5.1. Species-specific effects of the strobe lights .......................................................................... 63

3.5.2. Sound as a sensory disturbance ....................................................................................... 65

3.5.3. Multisensory stimulation ................................................................................................ 66

3.5.4. Individual differences in white shark behaviour ............................................................... 67

3.6. Conclusion ...................................................................................................................... 67

3.7. Acknowledgements ....................................................................................................... 67

Chapter 4 Effect of acoustic stimuli on the behaviour of wild sharks ............ 69

4.1. Abstract ........................................................................................................................... 70

4.2. Introduction ................................................................................................................... 71

4.3. Methods .......................................................................................................................... 74

4.3.1. Ethics statement ............................................................................................................ 74

4.3.2. Experimental apparatus ................................................................................................ 75

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4.3.3. Experimental design and sound treatments ..................................................................... 76

4.3.4. Calibration of sound device ............................................................................................ 78

4.3.5. Field sites ...................................................................................................................... 80

4.3.6. Video analysis .............................................................................................................. 81

4.3.7. Data analysis ................................................................................................................ 82

4.4. Results ............................................................................................................................. 83

4.4.1. Reef sharks ................................................................................................................... 83

4.4.2. White sharks ................................................................................................................ 83

4.5. Discussion ...................................................................................................................... 90

4.5.1. Sound as an aversive stimulus ........................................................................................ 90

4.5.2. Interspecific effects of sound ............................................................................................. 91

4.5.3. Intraspecific effects of sound ............................................................................................ 93

4.5.4. Tolerance....................................................................................................................... 95

4.5.5. Challenges of playback studies and limitations ................................................................ 96

4.5.6. Predictions on shark sensitivity to anthropogenic noise ..................................................... 97

4.5.7. Future of acoustic shark mitigation devices ..................................................................... 98

4.5.8. Conclusion .................................................................................................................... 98

4.5.9. Acknowledgements ........................................................................................................ 99

Chapter 5 Examining the mistaken identity hypothesis: do sharks mistake the acoustic signatures of humans for pinnipeds? ......................................................... 101

5.1. Abstract ........................................................................................................................ 102

5.2. Introduction ................................................................................................................. 103

5.3. Methods ........................................................................................................................ 105

5.3.1. Ethics statement .......................................................................................................... 105

5.3.2. Study sites and animals ............................................................................................... 105

5.3.3. Recordings ................................................................................................................... 106

5.3.4. Signal processing .......................................................................................................... 107

5.3.5. Frequency domain analysis .......................................................................................... 107

5.3.6. Time domain analysis .................................................................................................. 109

5.3.7. Statistical analysis ....................................................................................................... 109

5.4. Results ........................................................................................................................... 110

5.4.1. Frequency domain analysis .......................................................................................... 110

5.4.2. Time domain analysis .................................................................................................. 112

5.5. Discussion .................................................................................................................... 112

5.5.1. The question of mistaken identity ................................................................................. 113

5.5.2. Limitations ................................................................................................................. 115

5.6. Conclusions .................................................................................................................. 116

5.7. Acknowledgements ..................................................................................................... 116

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Chapter 6 General discussion ......................................................................... 119

6.1. Implications and future directions ........................................................................... 121

6.1.1. Auditory evoked potential audiometry: criticisms and recommendations ......................... 121

6.1.2. Using sound as a mitigation strategy ............................................................................ 126

6.1.3. Anthropogenic noise .................................................................................................... 127

6.1.4. Acoustic ecology .......................................................................................................... 132

6.2. Conclusion ................................................................................................................... 133

References ................................................................................................................ 135

Appendices ............................................................................................................... 161

Appendix A ............................................................................................................................... 161

Appendix B ............................................................................................................................... 161

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List of figures

Chapter 1

Figure 1-1 Schematic diagram of (A) particle motion and (B) pressure changes .................... 4

Figure 1-2 (A) Dorsal view and (B) lateral view of the external cartilaginous structure, and

(C) membranous labyrinth of the spiny dogfish ........................................................................... 6

Figure 1-3 Existing audiograms for elasmobranchs ..................................................................... 9

Figure 1-4 Examples of elasmobranch inner ears from each of the four groups (A-D) ...... 16

Chapter 2

Figure 2-1 Auditory evoked potential test chamber and setup ................................................ 28

Figure 2-2 Parameters measured in each AEP signal ................................................................ 31

Figure 2-3 For Chiloscyllium punctatum, average AEP amplitudes (maximum p-p) (A), latencies

(B) for each anaesthetic treatment in response to 100 Hz (left) and 50 Hz (right) tone bursts

............................................................................................................................................................ 35

Figure 2-4 Particle acceleration audiograms for the brownbanded bamboo shark Chiloscyllium

punctatum ............................................................................................................................................ 36

Chapter 3

Figure 3-1 Laboratory-based recordings ...................................................................................... 52

Figure 3-2 Field-based recordings................................................................................................. 53

Figure 3-3 Experimental setups and ReMoRA ........................................................................... 57

Figure 3-4 In-water voltage gradients measured at increasing distance from the underwater

speaker .............................................................................................................................................. 58

Figure 3-5 Percentage of trials in which H. portusjacksoni took the bait for each of the stimulus

treatments ......................................................................................................................................... 59

Figure 3-6 Responses of H. ocellatum to the stimuli treatments ................................................ 60

Figure 3-7 Time C. carcharias spent in the field of view of the camera (time on screen) for

each of the stimulus treatments .................................................................................................... 62

Chapter 4

Figure 4-1 Schematic diagram of a Remote Monitoring Research Apparatus (ReMoRA) .. 75

Figure 4-2 Field recording of ‘Artificial Sound’ .......................................................................... 76

Figure 4-3 Field recording of ‘Orca’ sound ................................................................................. 77

Figure 4-4 Field calibration of the speaker setup ....................................................................... 80

Figure 4-5 Maps of deployment areas in Exmouth, Western Australia (left) and Mossel Bay,

South Africa (right) ......................................................................................................................... 81

Figure 4-6 Proportions of presence and absence of sharks conditional on treatments ...... 85

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Figure 4-7 Boxplots representing the data for reef sharks (blue, left column) and for the great

white sharks (pink, right column) .................................................................................................. 88

Figure 4-8 Scatter plots representing the development of scores (A) and time on screen (B)

through experience of great white sharks (C. carcharias) ............................................................. 89

Chapter 5

Figure 5-1 Recording setup at Taronga zoo ............................................................................. 106

Figure 5-2 Examples of acoustic signatures .............................................................................. 108

Figure 5-3 Parameters measured in this study, shown here in examples from different

recordings of the human crawling stroke .................................................................................. 109

Figure 5-4 Boxplots showing the differences for the frequency domain analysis ............... 111

Chapter 6

Figure 6-1 Hearing range of elasmobranch species for which an audiogram has been

published (in blue) and frequency range for some examples of anthropogenic noise sources

(in orange) ...................................................................................................................................... 129

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List of tables

Chapter 1

Table 1-1 Summary of experiments in which sharks were attracted to or repelled by an

underwater speaker ......................................................................................................................... 12

Chapter 2

Table 2-1 SPL and particle acceleration levels in the three orthogonal Cartesian directions

............................................................................................................................................................ 33

Table 2-2 Results of the mixed models for the AEPs amplitudes, latencies and thresholds34

Table 2-3 Mean (± SEM) hearing thresholds for each treatment and each frequency ......... 34

Chapter 3

Table 3-1 Sound parameters for the artificial sound, the Control sound (10,000 Hz tone) and

the ambient background noise ...................................................................................................... 51

Table 3-2 Results showing the levels of significance found from the mixed models for

H. portusjacksoni ................................................................................................................................ 59

Table 3-3 Results showing the levels of significance found from the mixed models for

H. ocellatum ........................................................................................................................................ 60

Table 3-4 Results showing the levels of significance found from the mixed models for

C. carcharias ........................................................................................................................................ 62

Chapter 4

Table 4-1 Sound parameters for Artificial Sound, and Orca .................................................... 79

Table 4-2 Table summarising all data recorded from the reef sharks in Exmouth, Australia

(in blue), and from the white sharks in Mossel Bay, South Africa (in pink) .......................... 85

Table 4-3 Results of mixed models for reef sharks .................................................................... 86

Table 4-4 Results of mixed models for great white sharks ....................................................... 87

Chapter 5

Table 5-1 Results of the mixed model ....................................................................................... 110

Table 5-2 Summary of means for parameters calculated ........................................................ 112

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List of abbreviations

∆sig Magnitude of the difference between two pressures A Artificially produced pulsed sound a acceleration ABR Auditory brainstem response ARP Applied research program AU Audio unit b Voltage gradient BnB Broadband noise BRS Behavioural Response Unit CNS Central nervous system DF Degrees of freedom E Electrosensitivity EEG Electroencephalogram EST Estimate of regression coefficients FN Filtered random or white noise FS Fish sound GLM General linear model GLMM Generalised linear mixed model HD High definition HF Hooked struggling fish HFC High-frequency component

LED Light-emitting diodes LFC Low-frequency component LMS Least mean squares MN Macula neglecta MOSFET Metal oxide semiconductor field effect transistor N Naturally recorded pulsed sound NI National Instruments NSW New South Wales NW North West PSD Power spectral density PT Pure tone QLD Queensland r Distance RMS Root mean square SD Standard deviation SEL Sound exposure level SNR Signal-to-noise ration SO Sudden onset SpF Speared struggling fish SPL Sound pressure level SqW Square wave StF Stampeded group of fish UWA The University of Western Australia vs Versus WA Western Australia x Particle acceleration on x-axis y Particle acceleration on y-axis z Particle acceleration on z-axis ZA South Africa

𝜌 Density of the medium

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1

Chapter 1

General Introduction

2

“Nothing but peace and quiet in nature…”

This was the message advertised on the learn-to-dive brochures years ago to encourage

people to enrol and take a break from their busy and loud lifestyle. In those days, the

underwater world was routinely referred to as a ‘silent heaven’. Even Jacques Cousteau, in

1956, named one of his most famous documentaries ‘Le Monde du Silence’, The Silent World.

But the more we explored this so-called “quiet realm”, the more this description seemed

absurd. The ocean soundscape is, in fact, home to a wide range of sounds from a variety of

organisms and physical phenomena, some of which may travel long distances (often tens,

hundreds or theoretically thousands of kilometres) (Urick, 1979; Kinsler et al., 2000; Cato,

2012). In order to detect these ambient sounds, aquatic vertebrates have evolved

sophisticated auditory abilities to determine and locate prey, predators and reproductive

partners. In comparison to other sensory modalities, hearing underwater can operate at

longer distances and even provide orientation and navigation cues (Gans, 1992; Fay &

Popper, 2000; Fritzsch et al., 2000; Collin & Marshall, 2003).

The study of hearing and sound communication in marine animals (marine bioacoustics) has

been vital towards understanding the relationship between organisms and their aquatic sound

environment. Bioacousticians have intensively studied the hearing systems of marine

mammals and bony fishes, notably driven by the interests of the U.S Navy after both World

Wars, when underwater acoustics played a major role in mastering the oceans. However,

elasmobranchs (sharks, rays and skates) have received comparatively little attention,

potentially due to their inability to vocalise. In the 1960s, some early studies describing the

anatomy of the inner ear of elasmobranchs, issued predictions on its physiology, and

observed the behaviour of sharks to sound in the wild (reviewed by Popper & Fay, 1977;

Myrberg, 1978; Corwin, 1981a; Myrberg, 2001). Today, although the current bioacoustics

techniques have become much more advanced and the rationale for such research is higher

than ever, there remains a paucity of information on the acoustic world of sharks and their

relatives, resulting in an immense number of unsolved questions pertaining to variation in

anatomy, hearing sensitivity and thresholds, and biological relevance of sound to this unique

group of fishes.

The present thesis aims to investigate many of the unexplored aspects of the elasmobranch

hearing sense, in the context of today’s challenges. Notably, our goal is to revisit the currently

available techniques to assess hearing sensitivity in fishes, and provide a framework for better

practice and standardisation in the future. We also explore the use of sound in the context

of negative interactions between sharks and humans. Lastly, we discuss the results in the

context of conservation and the potential impact of noise on elasmobranchs species.

3

This chapter will summarise current knowledge of the hearing systems of elasmobranchs,

describe the rationale for the thesis, and finally, provide an outline of each chapter.

1.1. The hearing system of elasmobranchs

Sharks, rays and skates (subclass: Elasmobranchii), like all vertebrates, possess an inner ear

which, along with the lateral line and the electrosensory systems, comprise part of the

octavolateralis system. This grouping reflects similarities in receptor morphology (i.e. hair

cells), innervation and ontogenetic development (reviewed by Robert, 1978). In this review,

we will focus solely on the structures responsible for receiving acoustic stimuli from the

environment: the hearing or auditory system.

1.1.1. Underwater sound

To understand how the hearing system of an elasmobranchs works, a perception of the

physics of how sound is transmitted underwater is beneficial. Some researchers have

provided excellent summaries of the key concepts of underwater sound for bioacousticians

(Parvulescu, 1964; Kalmijn, 1988; Rogers & Cox, 1988; Schellart & Popper, 1992), which will

only be briefly summarised here.

Sound obeys the same physical principles whether it is in air or in water. However, there are

some significant distinctions due to the differences between the two media: water is

approximately 830 times more dense than air and its acoustic impedance is around 3,600

times greater (Urick, 1975). Consequently, the speed of sound in water is 4.8 times faster

than in air, and wavelengths of any given frequency are 4.8 times longer. Sound is a

longitudinal mechanical wave; a ‘disturbance’, which propagates within an elastic medium

(e.g. within a gas, liquid or solid). This acoustic disturbance involves both changes in the

states of the medium (density, temperature and pressure – scalar variables) and the motion

of the medium (displacement, velocity, and acceleration of particles – vectorial variables)

(Figure 1-1). In Figure 1-1, the amplitude variation of a longitudinal (acoustic) wave as a

function of distance is shown along with the compression (increase in particle density) and

rarefaction (decrease in particle density) of the fluid particles as the wave propagates.

Practically, acoustic pressure is the most often used quantity in underwater acoustics (Urick,

1975). Aquatic microphones, known as hydrophones, are, actually, pressure transducers.

However, marine organisms do not all use sound pressure. In fact, with the exception of

marine mammals, a lot of aquatic animals primarily detect and use the particle motion aspect

of the sound (Chapman & Hawkins, 1973; Fay, 1984; Popper et al., 2001; Bleckmann, 2004;

Kaifu et al., 2008).

4

Figure 1-1 Schematic diagram of (A) particle motion and (B) pressure changes due to a vibrating sound source, which results in a sound (longitudinal) wave.

In addition to generating a propagating sound wave, a vibrating sound source (e.g. an

underwater speaker) also produces hydrodynamic flow in the immediate vicinity. These

proximate particle motions decay very steeply with distance from the source. Consequently,

near the source, particle motions are composed of both hydrodynamic flows and the motions

generated by the propagating sound. This zone close to the source, where the hydrodynamic

particle motions dominate, is known as the acoustic near field, and is usually within about two

wavelengths of a noise source. In the near field, there is no simple relationship between

sound level and distance, and the particle velocity is not in phase with the sound pressure

(Kalmijn, 1988). The region beyond the near field, where the sound pressure-associated

particle motions dominate, is named the acoustic far field. In this zone, under ideal conditions

(free field), the sound pressure level obeys the inverse-square law, whereby sound pressure

level decreases 6 dB with each doubling of distance from the source (Kalmijn, 1988). In this

region, the particle velocity is also in phase with the sound pressure. The boundary of the

near and far fields is frequency-dependent: the lower the frequency, the larger the near-field

(Kalmijn, 1988).

5

1.1.2. The inner ear of elasmobranchs

All vertebrate have an inner ear composed of three semicircular canals that are used for the

determination of angular acceleration (equilibrium) and several chambers, or endorgans,

mediating hearing, notably the sacculus and utriculus (Fay & Popper, 1980). Another end

organ, the lagena, is present in all vertebrates except mammals, where it has been considered

to have been replaced by the cochlea (Wever, 1974). Each end organ possesses a sensory

epithelium, or macula, housing the mechanoreceptors of the hearing system: the hair cells

(Flock, 1971). In elasmobranchs, the inner ear is located in a cartilaginous otic capsule, just

posterior to the optic capsules (Figure 1-2 A, B). Figure 1-2 C shows the membranous

labyrinth of the spiny dogfish, Squalus acanthias, with the endorgans (sacculus, lagena,

utriculus), and the three orthogonally arranged semicircular canals. In elasmobranchs, each

chamber contains a mucilaginous cupula or an otoconia, which is composed of calcium

carbonate crystals embedded in a gelatinous matrix (Carlström, 1963). The otoconia usually

covers the major portion of the maculae in the endorgans. Some species, like the spiny

dogfish, Squalus acanthias, and the Port Jackson shark, Heterodontus portusjacksoni, have been

found to incorporate exogenous sand grains as a way to increase the endogenous otoconial

mass (Lychakov et al., 2000; Mills et al., 2011). Additional sensory epithelium, the macula

neglecta (MN), are housed in the inner ear (one patch of MN epithelial tissues in rays, two

patches in sharks) (Lowenstein & Roberts, 1951; Tester et al., 1972; Corwin, 1977; Popper

& Fay, 1977). The macula neglecta, however, does not contain any otoconia (Tester et al.,

1972). An endolymphatic duct links the sacculus of the inner ear to the external environment

via an opening pore on the dorsal surface of the head. The functional significance of the

endolymphatic duct is still unknown, although it may provide a means for fluid exchange

between the surrounding seawater and the inner ear (Tester et al., 1972). The precise role of

each of the endorgans in hearing is also still uncertain (Myrberg, 2001).

The mechanoreceptive hair cells of the macular epithelium are all innervated by the eighth

cranial nerve, also known as the octaval or statoacoustic nerve (Lowenstein et al., 1964)

(Figure 1-2 A, B). Each hair cell has an apical ciliary bundle, consisting of a single, long

kinocilium, arising at one end of a bundle of microvilli-like stereocilia (Retzius, 1881; Wersäll,

1956; Lowenstein et al., 1964). There are two proposed pathways, which are non-mutually

exclusive for sound detection in elasmobranchs (reviewed by Corwin, 1981a). The first is

known as the ‘otolithic pathway’. This pathway is dependent on the fact that the bodies of

fishes (including elasmobranchs) are composed mainly of water, and thus are presumably

directly coupled to the surrounding medium. As a sound wave propagates through the water,

the whole body vibrates. The particle motion associated with the sound wave elicits an inertia

6

between the tissues within and surrounding the inner ear and the otoconia, which are of

much denser mass. The relative motion between the animal’s body and the denser tissues

results in the bending of the ciliary bundles and stimulation of the hair cells, leading to the

detection of the sound (Flock, 1971). The mechanism of this differential motion resembles

that of an accelerometer: a mass (otoconia) that moves in a relative manner to some form of

receptor (sensory hair cells) (Popper & Fay, 2011).

Figure 1-2 (A) Dorsal view and (B) lateral view of the external cartilaginous structure, and (C) membranous labyrinth of the spiny dogfish, Squalus acanthias (adapted from Retzius, 1881; Schaeffer:1981vm, and Maisey, 2001). aa, anterior ampulla; asc, anterior semicircular canal; ed, endolymphatic duct; ef, endolymphatic fossa; fo, fenestra ovalis; ha, horizontal ampulla; hsc, horizontal semi-circular canal; l, lagena; lm, lagenar macula; mn, macula neglecta; op, optic capsule; ot, otic capsule; pa, posterior ampulla; pac, pre-ampullary canal; psc, posterior semicircular canal; s, sacculus; sm, saccular macula; u, utriculus; um, utricular macula; VIII, course of 8th (statoacoustic) nerve; IX, course of 9th (glossopharyngeal) nerve; X, course of 10th (vagal) nerve. Note that the VIII (8th) nerve is only visible from the ventral side, not shown here.

The second - ‘non-otolithic’ - pathway involves the macula neglecta. The macula neglecta is

a sensory epithelium situated near the junction of the posterior vertical canal and the sacculus

in the parietal fossa, close to the fenestra ovalis, which is a dorsal, membrane-covered,

opening in the otic capsule cartilage (Lowenstein & Roberts, 1951; Tester et al., 1972;

Corwin, 1977; Popper & Fay, 1977) (Figure 1-2). This epithelium is particularly well

developed, with a high number of hair cells all aligned perpendicular to the plane of the

fenestra ovalis, and has been shown to play an essential role in hearing in elasmobranchs

(Tester et al., 1972; Fay et al., 1974; Corwin, 1977; 1981b). This epithelium lacks an otoconia,

but is covered by a light gelatinous cupula, similar to the cupula covering the neuromasts of

the lateral line system (Lowenstein & Roberts, 1951; Tester et al., 1972; Corwin, 1977). The

non-otolithic mechanism would rely on sound transmission through the dorsal surface of

7

the head into the loose connective tissue of the parietal fossa and the fenestra ovalis (Corwin,

1977; 1981a). A directed portion of the particle motion from the sound wave would move

the fluids and the cupula inside the posterior canal duct with a greater amplitude and a smaller

phase lag, relative to the movements of the skull (Corwin, 1981b).

Some bony fishes possess accessory organs that can contribute to hearing function, notably

the swim bladder. The swim bladder is an organ filled with gas, which changes volume in

response to the sound pressure of a sound wave passing through. It thus acts as a pressure-

to-displacement transducer, by transforming the acoustic pressure stimulus into a

displacement of the bladder’s surface (Enger & Andersen, 1967). The displacement can then

be transmitted to the hair cells of the ear, so that they indirectly respond to the pressure

component of a sound. Elasmobranchs lack a swim bladder, making them unable to

transduce and detect pressure from a sound wave, and are thus thought to only respond to

particle motion (reviewed by Corwin, 1981a; Myrberg, 2001).

1.1.3. Hearing abilities of elasmobranchs

Audible thresholds for standardised frequencies, also known as audiograms, have been

obtained for only a few species of elasmobranchs, work dating back to the 1960s. At that

time, audiograms were obtained by behavioural conditioning techniques. Classical

conditioning was the most commonly used technique, using a form of a reflexive response,

such as defense response or a suppression of ventilation or cardiac activity (reviewed by

Bhandiwad & Sisneros, 2016). The fish associate a sound stimulus (e.g. tone) with a shock

(e.g. electrical shock) during a conditioning period. In subsequent trials, the fish exhibits a

reflexive response upon hearing the tone alone. For example, in the case of ventilation

suppression, a tone-shock paradigm is used and the temporary reduction of the frequency of

opercular ventilation movements is measured. The frequency and intensity of the tone can

be altered to determine the fish hearing threshold. The audiogram of the bull shark,

Carcharhinus leucas (Kritzler & Wood, 1961), the lemon shark, Negaprion brevirostris (Banner,

1967; Nelson, 1967), and the horn shark, Heterodontus francisci (Kelly & Nelson, 1975), were

described with this method (Figure 1-3 A). Those studies established the low frequency range

(10 – 1500 Hz) of the hearing sensitivity of sharks. Banner (1967) was the first investigator

to use a particle motion sensor in addition to a pressure hydrophone to characterise the

sound field in his experimental studies. His results showed that elasmobranchs were only

receptive to the particle motion, rather than the pressure of the sound wave. However, van

den Berg and Schuijf (1983) later confounded that point by denoting that the catshark,

Chiloscyllium griseum, could detect both sound pressure and particle motion. This study

especially aimed to elucidate sound discrimination based on phase differences existing

8

between acoustically generated particle motion and pressure and followed seminal work on

bony fishes to resolve a 180˚ ambiguity about the localisation of a source (Schuijf & Buwalda,

1975; Schuijf et al., 1977). Since then, however, no study has demonstrated pressure

sensitivity in elasmobranchs, where calibrating the audiograms with particle motion level, in

addition to sound pressure level, has became more widely accepted (Figure 1-3 B).

In the 1980s, a new electrophysiological technique called auditory brainstem response (ABR)

was developed to measure audiograms, which are now referred to as auditory evoked

potentials (AEPs) in fishes (reviewed by Ladich & Fay, 2013). This method involves the

recording of electrical activity evoked by acoustic stimuli via electrodes inserted beneath the

skin overlying the inner ear. The technique was applied to study the hearing abilities of

elasmobranchs (Bullock & Corwin, 1979; Corwin et al., 1982; Corwin & Northcutt, 1982),

and later adapted by Kenyon (1998) to produce audiograms in bony fishes. As the animal

does not need to be trained, audiograms produced from AEPs are quick to generate and

have thus become increasingly popular in all vertebrate hearing studies. In 2003, Casper et

al. (2003) produced both behavioural and AEP audiograms for the little skate, Raja erinacea

(Figure 1-3 A), which showed best hearing sensitivity between 100 and 300 Hz.

Since then, Casper and Mann (2006; 2007a; 2007b; 2009) have been the only team of

researchers to investigate the hearing abilities of elasmobranchs, using AEPs to produce

audiograms. They generated audiograms for the nurse shark, Ginglymostoma cirratum, the

yellow stingray, Urobatis jamaicensis (Casper & Mann, 2006), as well as the Atlantic sharpnose

shark, Rhizoprionodon terraenovae (Casper & Mann, 2009) (Figure 1-3 B). They also investigated

the directional hearing abilities of bamboo sharks Chiloscyllium plagiosum and Chiloscyllium

punctatum by using a shaker table instead of sound stimuli to record AEPs (Casper & Mann,

2007b). The shaker table applies directional whole body accelerations to stimulate the inner

ears independently in three dimensions. Finally, they used dipole stimuli to record AEPs on

the horn shark Heterodontus francisci and the whitespotted bamboo shark Chiloscyllium plagiosum

(Casper & Mann, 2007a)(Figure 1-3 B). A dipole stimulus differs from the commonly used

monopole sound source (i.e. underwater speaker), as it is directional along the axis of motion

and attenuates more rapidly in the near field than monopole sources, where sound radiates

out in all directions equally. Dipole sound sources are known to better represent a biological

motion (e.g. a fish swimming) than monopole sources (Bass & Clark, 2003).

Figure 1-3 summarises all the audiograms collected for elasmobranchs. Unfortunately, it is

difficult to reliably compare between audiograms generated with different methods

(behavioural, AEPs), from different laboratories, with different stimuli

(monopole/dipole/shaker), and different calibration approaches (particle motion, sound

9

pressure levels) (Ladich & Fay, 2013; Maruska & Sisneros, 2016; Sisneros et al., 2016).

Nevertheless, these audiograms give us an estimation of the general hearing abilities of

elasmobranchs, which lie in the low frequency spectrum, from about 40 Hz to 1500 Hz, with

peak sensitivities between 200 and 400 Hz. This acoustic range is in concordance with the

hearing abilities of documented bony fishes that are only or predominantly detecting particle

motion (Popper & Fay, 2011), like flatfishes, which lack a swim bladder (Chapman & Sand,

1974), and fishes where the swim bladder is separated from the ears, like the eels and some

species of salmon (Hawkins & Johnstone, 1978; Jerkø et al., 1989).

Figure 1-3 Existing audiograms for elasmobranchs in response to (A) sound pressure, and (B) particle acceleration levels, generated with behavioural conditioning techniques (‘behavioural’) or auditory evoked potentials (‘AEP’), with a monopole, dipole or shaker table stimulus. (Modified from Kritzler & Wood, 1961; Nelson, 1967; Kelly & Nelson, 1975; Casper et al., 2003; Casper & Mann, 2006; 2007a; 2007b; 2009)

1.1.4. Auditory processing in the brain of elasmobranchs

Integration of sensory information (including sound) in the elasmobranch central nervous

system (CNS) ultimately leads to a behavioural response at the level of the animal. The brain

of elasmobranchs contains six major areas: the olfactory bulbs, telencephalon, diencephalon,

mesencephalon, cerebellum, and medulla oblongata (Northcutt, 1978; Yopak, 2012;

10

reviewed by Collin et al., 2015). Auditory information projects from the hair cells of the inner

ear to the laterodorsal wall of the medulla oblongata via the eighth (octaval) nerve, which

also relays the input from the vestibular system. The projections of the eighth nerve to the

medulla have been described in several species of elasmobranchs, including the dogfish,

Scyliorhinus canicula (Montgomery & Roberts, 1979; Boord & Roberts, 1980), the carpet shark,

Cephaloscyllium isabellum, (Housley & Montgomery, 1983), the clearnose skate, Raja eglanteria,

(Koester, 1983), the guitarfish, Rhinobatos sp. (Dunn & Koester, 1987), and the Atlantic

stingray, Dasyatis sabina, (Puzdrowski & Leonard, 1993). In the medulla oblongata,

projections go to regions in the dorsolateral medulla known as the octaval nuclei, arranged

in a columnar fashion (Boord & Roberts, 1980; Northcutt, 1980). Due to the complex nature

of the eighth nerve and its ganglion, the actual input of individual axonal projections has

been difficult to assess. However, four octaval nuclei are known to receive the primary

afferent input from the octaval nerve: the anterior, magnocellular, descending and posterior nuclei.

Barry (1987) described a fifth octaval nucleus, the periventricular nucleus, in R. eglanteria. The

secondary projections of the octaval nuclei to the medial mesencephalic complex, in an area

of the mesencephalon called the tegmentum, have been described in only two species, the

clearnose skate, R. eglanteria (Barry, 1987) and the Atlantic stingray, D. sabina (Puzdrowski &

Leonard, 1991). It is not completely known how processing occurs beyond that level,

although electrophysiological studies have shown tertiary projections reach the

telencephalon (Bullock & Corwin, 1979), and much work remains to be done regarding the

auditory pathways in the CNS of elasmobranchs.

1.1.5. Acoustic behaviour of elasmobranchs

Between the 1960s and 1980s, numerous studies investigated the behaviour of sharks

towards acoustic stimuli in the wild. ‘Attraction’ of sharks to low-frequency, pulsed sounds,

where the animals would swim towards the sound source, has notably been demonstrated in

the species studied in the field to date (Nelson & Gruber, 1963; Banner, 1968; Richard, 1968;

Myrberg, 1969; Myrberg et al., 1969; Nelson et al., 1969; Nelson & Johnson, 1970; Evans &

Gilbert, 1971; Banner, 1972; Myrberg et al., 1972; Nelson & Johnson, 1972; Myrberg et al.,

1975a; 1976; Nelson & Johnson, 1976). Table 1-1 summarises the species and sound stimuli

investigated. These studies showed that pulsed, synthesised or naturally produced sounds

below 800 Hz are attractive to many sharks from a variety of habitats, as opposed to pure

tones and continuous sounds, which do not appear to attract any sharks (reviewed by

Myrberg, 1978). The natural stimuli presented in these studies were recordings of struggling

fishes (i.e. fishes which had just been speared or hooked), fish vocalisations, or fast swimming

fish (‘stampeded fish’) (Nelson & Gruber, 1963; Banner, 1972). These sounds all shared the

11

characteristics of being rapidly pulsed and in the low frequency range (< 400 Hz), and these

were used to build the synthetic, pulsed sounds used for playbacks (Nelson & Gruber, 1963;

Banner, 1968; 1972). Overall, attractiveness seemed to increase with lower frequencies,

particularly when there were more repetitive pulses in the stimulus, and if those pulses were

irregular (Myrberg, 1978). Natural sounds often display irregularity in their pulse structures,

according to whether the animals creating these sounds have erratic movements, and

attraction of predators to these types of sound signatures would be expected (Myrberg, 1978).

During his experiment with juvenile lemon sharks, Negaprion brevirostris, Banner (1972)

reported the sudden withdrawal of the animal when the sound playback started as they were

approaching. This aspect was further investigated with silky sharks, Carcharhinus falciformis,

and oceanic whitetip sharks, Carcharhinus longimanus in the field, where a sudden change in

the sound levels of the playbacks similarly elicited a withdrawal (Myrberg et al., 1975b; 1978).

In these studies, playback included either a recording of killer whale ‘screams’, or some pure

tones of similar frequencies of the killer whale sounds. Given their status as natural predators

of some shark species in the wild, killer whales’ sounds may elicit a withdrawal response in a

species they predate upon (Myrberg et al., 1978; Visser, 2005). However, it may be that the

abrupt changes in intensity levels, rather than the biological nature of the sounds, were

triggering the startle and response in the animals studied, as the pure tones also elicited a

retreat (Myrberg et al., 1978). This response to changes in sound intensity may not be

restricted to those species in the wild, as the same results from the same stimuli were

observed in lemon sharks, N. brevirostris, in a captive study (Klimley & Myrberg, 1979). Table

1-1 summarises the species and sound stimuli investigated.

While all species studied thus far seemed equally attracted to the pulsed sound (Myrberg,

1978, see also Table 1), the repelling effect might be species-dependent, as withdrawal was

less obvious in oceanic whitetip sharks than in silky sharks, for example (Myrberg et al.,

1978). Habituation to both stimuli (attractants and repellents) were observed in a number of

these studies, whereby sightings of sharks (e.g. Rhizoprionodon sp.) rapidly decreased when the

trials were massed over a short period and in the absence of reinforcement (Myrberg et al.,

1969; Nelson & Johnson, 1970; Myrberg et al., 1978). In summary, the magnitude of the

signal and the abruptness by which that magnitude is achieved (onset) seemed to act together

towards determining whether a shark would initiate an approach, continue its approach or

suddenly withdraw from a sound source (Klimley & Myrberg, 1979). In addition, the effect

on the behaviours of sharks did not necessarily result from the recognition of a biological

sound (i.e. predator’s call).

12

Table 1-1 Summary of experiments in which sharks were attracted to or repelled by an underwater speaker during playback of low frequency sounds. Taken in part from Myrberg (1978).

Family, genus and species

Common name

Sound stimuli tested

Behavioural

Effects

Author(s)

Alopidae Alopias sp. Thresher HF (N) Attraction Nelson & Johnson

(unpublished)

Carcharhinidae Carcharhinus sp. FN (A) Attraction Nelson & Gruber

1963 FN(A) Attraction Richard 1968 FN, SqW (A) Attraction Myrberg et al. 1969 C. albimarginatus Silvertip FN(A) Attraction Nelson & Johnson

1972 C. falciformis Silky FN(A) Attraction Nelson et al. 1969 SpF (N) Attraction Evans & Gilbert

1971 FN (A) Attraction Myrberg et al. 1972,

1975a, 1976 KW(N, SO),

BnB, PT(A, SO) Withdrawal Myrberg et al.

1975b, 1978 C. leucas Bull FN (A) Attraction Nelson & Gruber

1963 C. longimanus Oceanic

whitetip FN(A) Attraction Myrberg et al.

1975a, 1976 BnB, PT(A, SO) Withdrawal Myrberg et al.

1975b, 1978 C. melanopterus Blacktip reef FN (A) Attraction Nelson & Johnson

1970 FN (A) Attraction Nelson & Johnson

1972 C. amblyrhynchos + Grey reef FN(A) Attraction Nelson & Johnson

1970

FN(A) Attraction Nelson & Johnson 1972

C. perezii * Caribbean reef FN, SqW (A) Attraction Myrberg et al. 1969 Galeocerdo cuvier Tiger FN(A) Attraction Nelson & Gruber

1963 Negaprion brevirostris Lemon FN(A) Attraction Nelson & Gruber

1963 BbN (A) Attraction Banner 1968 FS(N) Attraction Banner 1972 KW(N, SO),

BnB, PT(A, SO) Withdrawal Klimley & Myrberg

1979 Negaprion acutidens † Sicklefin

lemon FN(A) Attraction Nelson & Johnson

1972 Prionace glauca Blue HF, StF (N),

FN(A) Attraction Nelson & Johnson

(unpublished) Rhizoprionodon porosus Sharpnose FN(A) Attraction Richard 1968

FN, SqW(A) Attraction Myrberg et al. 1969 Triaenodon obesus Reef whitetip SpF, StF(N),

FN(A) Attraction Nelson & Johnson

1970 FN(A) Attraction Nelson & Johnson

1972 Lamnidae

Isurus oxyrinchus Mako HF, StF(N) Attraction Nelson & Johnson (unpublished)

Orectolobidae Ginglymostoma cirratum Nurse FN(A) Attraction Richard 1968

13

FN, SqW (A) Attraction Myrberg et al. 1969 FN (A) Attraction Nelson et al. 1969 Sphyrnidae

Sphyrna sp. FN(A) Attraction Nelson & Gruber 1963

Sphyrna tiburo Bonnethead FN(A) Attraction Nelson et al. 1969

Types of artificially produced (A) and naturally recorded (N) pulsed sounds: FN, filtered random or white noise; BnB, broadband noise; SqW, square waves; PT, pure tones (unpulsed); SpF, speared struggling fish; HF, hooked struggling fish; StF, stampeded group of fish; and FS, fish sounds. SO, sudden onset. +*† : Species previously referred as + C. menisorrah, * C. springeri, † N. fosteri.

Some of the studies mentioned above (Myrberg et al., 1972) have been met with scepticism

regarding the distance (e.g. 400 m) and configuration of the sound fields that the sharks were

responding to. Monopoles (i.e. underwater speakers) were used to attract the animals, by

presenting stimuli, which represent ‘stronger-than-natural’ sound fields (Kalmijn, 1988). A

struggling fish, for example, is not expected to produce low-frequency sounds detectable

against the ambient noise at distances of 100 m or more (Richard, 1968; Myrberg, 1978;

Kalmijn, 1988) and would be better represented by a dipole source (Bass & Clark, 2003).

Sounds emitted by monopole sources ‘promote’ the far field over the near field, and the

particle velocities fall off less rapidly than with dipole sources (Kalmijn, 1988); thus, it is

unlikely these studies represent a comprehensive analysis of sound detection in sharks.

Although these studies constituted the first evidence that sound plays an important role in

shark ethology, we are still far from understanding how specific sound stimuli elicit certain

behaviours, and even further from predicting and/or controlling shark behaviours such as

attraction or repulsion in the wild.

1.1.6. What do they listen to? The soundscape of elasmobranchs

Elasmobranchs are not known to vocalise unlike some bony fishes, which emit sounds in a

variety of behavioural contexts, such as courtship, mating and agonistic interactions (Ladich,

2014; Mann, 2016). However, not all teleost species vocalise and auditory function cannot

be fully understood only with regard to sound cues used in animal communication (Fay,

2009). The goldfish, Carassius auratus, for example, does not produce sound but is known to

show one of the most sensitive audiograms amongst the taxa, ranging from about 100 Hz to

5 kHz (reviewed by Fay, 1988). Even if the ‘interception’ of the sounds used in

communication between species would represent an important role for the auditory system

(Myrberg, 1981), especially for predatory species, there must be more general functions of

hearing.

Rather than the detection of specific signals, the most general function of hearing might be

to ‘image’ the immediate scene and resolve the individual objects within it (Popper & Fay,

1993), a strategy which would usually be reserved for the visual system. Objects in the

14

underwater environment not only reflect and scatter light, but also sound. This sound

phenomenon is also more significant in an underwater scene than in a terrestrial

environment, because of the intrinsic physics of sound underwater. This concept has been

described as the ‘acoustic daylight’ (Buckingham et al., 1992; Fay, 2009) and has led to the

new field of acoustic ecology, which endeavours to understand the relationship between an

organism and its ‘soundscape’ (or acoustic daylight) (Pijanowski et al., 2011; Hastings & Au,

2012; Merchant et al., 2015). Identifying and locating objects might thus be the primary role

of the hearing system, rather than decoding an acoustic message. An awareness of the general

structure of the sound environment would allow animals to make important and rapid

decisions or plan long-term behaviours appropriate for feeding, travel/migration, social

interaction, the avoidance of predation, and reproduction. A well-established example of

such a phenomenon in teleosts is the role environmental sounds play in larval recruitment,

whereby acoustic signatures of the reef serve as an attractant to larval fish, where they will

settle for their adult life (reviewed by Montgomery et al., 2006; Mann et al., 2007; Leis et al.,

2011).

The primary literature on acoustic ecology has thus far focused on teleost fishes, where no

studies have explored the soundscape of elasmobranchs. Many fundamental issues pertaining

to the hearing abilities of sharks and rays are currently lacking, limiting our abilities to predict

the relationship between individual species and their soundscape. There is only limited

information on how well elasmobranchs process temporal patterns and sound levels,

discriminate frequencies, and determine the nature of a sound source (Nelson, 1967; Popper

& Fay, 1977). Similarly, the directional hearing abilities of elasmobranchs is poorly

understood. Elasmobranchs, like all fishes, lack the adaptations of most land animals, such

as widely separated ears and external pinnae, to process the time-arrival differences in order

to estimate the direction of a sound source (Thompson, 1882; Batteau, 1967). Moreover,

these animals are only sensitive to low frequency sounds, which have very long wavelengths

underwater, and thus cannot be used to assess directionality by using the difference in phase

detected between the ears (Fay, 1988). However, the inner ear hair cells in teleosts are

arranged in distinct patches, often with the same directional orientation (Flock, 1964; Popper,

1977; Lu et al., 1998), which allows for directional sensitivity along the axis of particle motion.

Such polarities have also been demonstrated in the inner ears of elasmobranchs, providing a

potential mechanism for directional hearing in elasmobranchs (Barber & Emerson, 1980;

Corwin, 1981b).

However, two behavioural experiments have investigated the directional hearing abilities of

sharks. Nelson (1967) showed that the lemon shark, N. brevirostris, could differentiate

15

between speakers with an error of only 9.5º at a distance of about two meters. Casper and

Mann (2007b) studied the directional hearing thresholds of the whitespotted bamboo shark,

C. plagiosum, and showed that there were no differences between thresholds in each of the

different directions. These results suggest that at least some species of shark may have

omnidirectional ears, which support the ‘acoustic daylight’ theory, as they would be able to

sample their three-dimensional acoustic environment.

1.1.7. Functional anatomy and physiology

Elasmobranchs comprise more than 1200 different species, which are subdivided into two

major groups: the Selachii (sharks) and the Batoidea (rays, skates, and sawfishes) (Last, 2007;

White & Last, 2012). These organisms occupy a range of ecological niches in both freshwater

and marine ecosystems, and possess large anatomical, morphological, physiological, and

behavioural variations (Compagno, 1990; Stevens et al., 2009). The field of neuroecology

aims to determine the evolutionary influence of ecology on shaping the sensory systems and

attributes of each species of elasmobranch (reviewed by Collin, 2012). These differences in

both peripheral and central anatomy suggest that various species of sharks and rays may

detect and process sound differently, depending upon their peripheral auditory structures,

the acoustic characteristics of their environment (their ‘soundscape’), and their phylogeny, as

hypothesised for bony fishes (Popper & Coombs, 1982; Popper, 1983; Schellart & Popper,

1992).

Variations in the inner ear of elasmobranchs has been noted by numerous studies, which

have notably described differences in the size of the maculae, the location of the endorgans

and semicircular canals, the number of epithelial patches in the macula neglecta and the

orientation of the hair cells (Tester et al., 1972; Corwin, 1978; Barber & Emerson, 1980;

Corwin, 1981b; 1989; Mulligan & Gauldie, 1989; Lychakov et al., 2000; Maisey, 2001;

Evangelista et al., 2010). The latest study by Evangelista (2010) proposed a classification in

four distinct groups, assessed from quantitative measurements of the inner ears of 17 species

of sharks and rays. Figure 1-4 illustrates the morphology of the inner ears of example species

for each of the four groups (A-D). The defining features of Groups A and B were the clearly

visible canal ducts separating the endorgans from the semicircular canals (Figure 1-4 A, B).

Group B was differentiated from Group A by the inner ears presenting larger saccular organs

(Figure 1-4 B). Group C and D were defined by the semicircular canals being bound in part

to the dorsal surface of the sacculus, giving the ears a triangular appearance (Figure 1-4 C,

D). The definition of a larger sacculus distinguished Group C from Group D (Figure 1-4 C).

However, the study could not provide any clear indication as to whether the morphological

16

differences are a direct result of phylogeny and/or predatory ecology (Evangelista et al.,

2010).

Figure 1-4 Examples of elasmobranch inner ears from each of the four groups (A-D). Photographs (left) and diagrammatic tracings (right) of the inner ear of the spotted eagle ray, Aetobatus narinari (A), the smooth butterfly ray, Gymnura micrura (B), the bull shark, Carcharhinus leucas (C), and the brownbanded bamboo shark, Chiloscyllium punctatum (D). Modified from Evangelista et al. 2010. l, lagena; s, sacculus; u, utriculus. Scale= 2mm. Elasmobranch illustrations not to scale.

As mentioned above, the audiograms from a few species of elasmobranchs have shown

differences in hearing abilities. However, it is difficult to distinguish the variations due to the

methodology used by different investigators from that of the actual sensitivities of the species

(Ladich & Fay, 2013). The hypothesis of ‘ecoacoustic constraints’, postulated by

Ladich (2013), states that species living in quiet aquatic environments should have high

auditory sensitivities, whereas species occupying noisy areas would not benefit from and thus

not develop an acute hearing system. For example, in a number of freshwater teleost species

living in quiet environments (e.g. stagnant waters), it has been shown they possess hearing

specialisations and superior hearing abilities in contrast to species which inhabit noisier

habitats (e.g. flowing rivers) (reviewed by Ladich, 2014). The paucity of data on the hearing

17

sensitivities of elasmobranchs and the impracticality of comparing audiograms do not allow

for confirmation of such a pattern for elasmobranchs, but the adaptation of each species to

its ‘soundscape’ would be expected.

1.2. Rationale

This brief review of the acoustic biology of elasmobranchs reveals the successes and

challenges faced by the pioneers who first explored the hearing system of sharks and rays.

However, many questions still remain to be investigated. This thesis will concentrate on two

main topics.

1.2.1. Standardising bioacoustics studies

Technological advances to assist bioacousticians have expanded extensively over the last few

decades. Underwater acoustic technologies are now essential in understanding the

soundscape of the oceans, with scientific, military and industrial applications being developed

for the detection, tracking, imaging, transmission and measurement of sound underwater at

a range of scales (Lurton, 2002). Another crucial technological revolution occurred at the

end of the 1960s, with the introduction of digital signal processing. The development of

electrophysiological techniques has also played an important role in the study of hearing in

marine organisms. However, while most researchers have readily embraced those new

methodologies, the technological boom has created confusion. Due to a lack of

communication and standardisation, results from different laboratories are very often not

directly comparable (Erbe et al., 2016a). Lately, an effort to issue guidelines, protocols and

recommendations has arrested this trend (Popper et al., 2014; Gray et al., 2016) through

multiple international meetings, conferences and workshops, which are finally uniting experts

around the world. This approach will hopefully persist to ensure that the value of the research

outputs from time-consuming and expensive bioacoustic studies, is recognised and used in

establishing a collaborative structure between researchers, stakeholders and policy makers.

1.2.2. Conservation and neuroecology

This study encompasses the general framework of ‘conservation physiology’ (Wikelski &

Cooke, 2006). As apex predators, elasmobranchs play an important role in structuring marine

communities by influencing the balance between species abundance and biodiversity

(Heithaus et al., 2008). Many elasmobranch populations are threatened by high rates of

directed fishing and bycatch mortality in global fisheries (Stevens et al., 2000; Dulvy et al.,

2008; 2014). The life history of many of these fishes is characterised by late maturity, long

life spans, and long gestation periods, making them particularly vulnerable to over-

exploitation (Stevens et al., 2000). The need for experimental studies on shark repellents and

18

other aspects of sensory physiology relevant to shark bycatch reduction has been largely

expressed (Collin, 2012; Molina & Cooke, 2012; Jordan et al., 2013; Hart & Collin, 2015).

However, the increased attention on negative interactions between sharks and humans has

triggered a demand for effective shark repellents to both reassure ocean users and protect

sharks from lethal programs in place for mitigation (e.g. shark nets, drum lines, ‘catch-and-

kill’) (Gibbs & Warren, 2015; Hart & Collin, 2015). In parallel, the reasons behind negative

interactions between sharks and humans are unclear. By understanding the cause, more

appropriate measures could be developed to reduce negative encounters. In these

circumstances, neuroecological approaches can provide important insights into the sensory

systems of elasmobranchs, resulting in more effective mitigation applications (Collin, 2012).

1.2.3. Technical limitations

Until recently, bioacoustic studies have focused on describing the pressure component of

the sound field with instruments like hydrophones, for example. However, as mentioned

previously, elasmobranchs are thought to be only sensitive to the particle motion component

of the sound and therefore, a description of the acoustic field in sound pressure levels is not

adequate. Instruments to record particle motion have only recently become available

commercially and their use in tank experiments and field studies is still in its infancy (Nedelec

et al., 2016). Because of the difficulty to get access to an instrument recording particle

motion, we have estimated particle motion throughout this thesis by calculating the pressure

gradient between two hydrophones, as used in the studies by Casper and Mann (2006),

Horodysky et al. (2008), and Wysocki et al. (2009a).

This thesis examines the acoustic world of sharks within these two contexts, and specifically

targets three questions:

Is there scope to revise the current electrophysiological techniques available to

study hearing abilities in elasmobranchs, to improve both the validity of the data

and the welfare of the animal?

Can sound (and other sensory cues) be considered as a means to repel sharks

from specific areas?

Does the sound produced by humans in the water potentially resemble that of a

typical shark prey object, and thus serve as an attractant?

Although this study has a general aim to gain fundamental knowledge on the acoustic ecology

of sharks, it also aspires to provide applicable solutions in a real world context.

19

1.3. Structural presentation of the thesis

In Chapter 2, we explore auditory evoked potentials (AEPs) and their use as an

electrophysiological technique to study hearing in elasmobranchs. In the context of

standardisation, we test the use of anaesthetic agents on the brownbanded bamboo shark,

Chiloscyllium punctatum, with the aim of acquiring comparable results to previous studies that

have not used anaesthesia, while addressing the welfare of the animal.

Chapter 3 investigates the use of artificial sound and strobe lights as potential shark

repellents. We test these stimuli on captive benthic sharks (Port Jackson,

Heterodontus portusjacksoni, and epaulette sharks, Hemiscyllium ocellatum) and on great white

sharks, Carcharodon carcharias in the wild. The results are discussed within the framework of

shark mitigation for bycatch reduction and shark-human interactions.

The study presented in Chapter 4 builds on the results of Chapter 3, by presenting other

artificial and natural sounds in the wild to numerous species of reef sharks. We examine the

outcomes by issuing concerns towards the effects of anthropogenic noise on wild sharks.

In Chapter 5, we test the ‘mistaken identity’ hypothesis, which postulates that negative

interactions between sharks and humans occur when sharks misidentify humans as their

usual prey. We compare the acoustic signatures of ocean-users and typical prey (sea lion) for

some species of adult sharks and present the results in the context of the other sensory cues,

which may also play a role in negative shark encounters with humans.

Finally, Chapter 6 provides a synthesis of the study and its significance. It summarises the

prospects and recommendations for future research.

20

21

Chapter 2

Assessing the effects of anaesthesia on auditory evoked potentials (AEPs) in a

benthic shark model, Chiloscyllium punctatum

22

2.1. Abstract

The recording of auditory evoked potentials (AEPs) is a commonly used electrophysiological

method to study hearing in fishes. However, for fear of altering the AEP waveforms,

investigators usually do not anaesthetise the fish during the procedure, or only administer

neuromuscular blockers. This presents some animal welfare concerns, as well as the potential

for not obtaining reliable data, since prolonged pharmacological paralysis, in the absence of

anaesthesia, does not eliminate sensory awareness. It is well known that any anaesthetic agent

has the potential to alter the basic physiology of an animal and may thus confound results

and therefore any consequent interpretations. There is a specific need to understand how

the auditory system of fishes is affected by anaesthetic agents in order to formulate protocols

that ensure the animal’s welfare with minimal effects on the auditory evoked potentials. In

this study, we tested three anaesthetic agents while recording AEPs on the brownbanded

bamboo shark Chiloscyllium punctatum: tricaine methanesulfonate (MS-222), alphaxalone

(Alfaxan) and isoeugenol (AQUI-S). The amplitude, latency and estimated thresholds were

measured and compared to AEPs recorded without anaesthesia. We observed no significant

differences of the AEP waveforms generated using the three anaesthetic agents to those

acquired without anaesthesia. Although surprising, these results support the notion of

dosage- and/or species-dependent effects of anaesthesia on AEPs, and on sensory function

in general. Despite the need to integrate a wider range of dosages and investigate other

species to confirm our results, we recommend that anaesthesia becomes part of AEP

procedures to standardise data collection while addressing important animal welfare issues.

23

2.2. Introduction

The recording of auditory evoked potentials (AEPs) is a widely used non-invasive technique

for the study of animal hearing, where synchronous neural activity elicited by acoustic stimuli

is recorded with extracellular electrodes from the surface of the head. The ease and rapidity

of collecting AEP data have made this a popular method for building audiograms, notably

in fishes but also in a range of other vertebrates (Corwin et al., 1982; Kenyon et al., 1998).

AEPs in fishes have been extensively reviewed by Ladich & Fay (2013), not only to estimate

hearing sensitivity for inter- and intraspecific comparisons (Kenyon et al., 1998; Hadjiaghai

& Ladich, 2015), but also for investigating the function of accessory hearing structures (e.g.

swim bladder, Weberian ossicles) (Kratochvil & Ladich, 2000; Yan & Curtsinger, 2000;

Ladich & Wysocki, 2003; Wilson et al., 2009), assessing the effect of noise and masking on

hearing (Scholik & Yan, 2000; 2002; Amoser & Ladich, 2003; Ramcharitar & Popper, 2004;

Smith et al., 2004; Amoser & Ladich, 2005; Wysocki & Ladich, 2005; Popper et al., 2007;

Smith et al., 2011; Halvorsen et al., 2012; Jørgensen et al., 2012), studying the ontogenetic

development of hearing (Wysocki & Ladich, 2001; Higgs et al., 2004; Egner & Mann, 2005;

Vasconcelos & Ladich, 2008; Belanger et al., 2010; Wang et al., 2015), and investigating

acoustic communication (Wysocki & Ladich, 2003; Codarin et al., 2009; Vasconcelos et al.,

2011; Alves et al., 2016).

In the assessment of AEPs, it is necessary for the fish to be immobilised for a variable period

of 30-150 minutes, depending on the aim of the experiment, while the responses to sounds

of different frequencies and intensities are recorded. However, the most commonly used

anaesthetic in fishes, tricaine methanesulfonate (MS-222), is known to interfere with sensory

function (Hensel et al., 1975; Späth & Schweickert, 1977; Arnolds et al., 2002; Palmer &

Mensinger, 2004). To avoid potential measurement artefacts caused by anaesthesia, the

traditional approach taken is to instead restrain the animal pharmacologically by

administrating neuromuscular blocking agents such as pancuronium bromide or gallamine

triethiodide, which paralyse the animal and also reduces myogenic noise (Kenyon et al.,

1998). These blockers are not known to affect neural sensitivity (Sloan, 1998;

Ramlochansingh et al., 2014) and provide a long-lasting immobilisation of the subject. In

some cases, light anaesthesia is used prior to the administration of the blocker to facilitate

positioning and restraint of the animal, causing the AEP experiment to be delayed for

approximately 60 minutes to allow the animal to recover from anaesthesia and avoid

interference with recording and data collection. However, prolonged pharmacological

paralysis in the absence of anaesthesia does not eliminate sensory awareness and

neuromuscular blocking agents should not be used to provide surgical restraint in lieu of

24

adequate anaesthesia (Drummond et al., 1996; Marsch & Studer, 1999). Although fishes are

not always included in the regulations applicable for animal welfare or the use of laboratory

animals, they are increasingly recognised as sentient animals experiencing pain in a similar

manner to other vertebrates (Sneddon, 2009; Brown, 2015; Sneddon, 2015). In Australia,

fishes are included in the Australian code for the care and use of animals for scientific

purposes (2013), and neuromuscular blocking agents are not to be used as an immobilising

option for a procedure on a fish without anaesthesia. Cordova and Braun (2007) recognised

this issue and suggested the use of Fentanyl as an anaesthetic agent in the AEP technique in

goldfish (Carassius auratus). Fentanyl is an opioid agonist, which proved not to alter auditory

sensitivity during AEP recordings, while providing proper anaesthesia (Cordova & Braun,

2007). Unfortunately, Fentanyl will not induce anaesthesia in fish at commercially available

concentrations (except in the USA, see Cordova & Braun 2007), making it unusable for most

researchers using small fish species in other countries. While there are numerous choices of

anaesthetic agents for use in mammals, and their mechanisms of action are generally well

understood, there are only a limited number of approved drugs for anaesthesia in fish (Ross

& Ross, 2008; Sneddon, 2012; Zahl et al., 2012), which are generally governed by country-

specific guidelines. To add to the difficulty of finding the most appropriate anaesthetic agent

for an electrophysiological procedure, extrapolation between different species on the effects

of each agent is usually difficult and inappropriate (Bazin et al., 2004). Although the goldfish

has been the most popular species in hearing physiology and has been the preferred choice

for most AEP studies so far (Ladich & Fay, 2013), an increasing number of different species

are being investigated. It is thus important to develop anaesthetic protocols for a range of

different fish taxa (both bony and cartilaginous).

AQUI-S (AQUI-S New Zealand Ltd.) is an isoeugenol-based (50%) liquid anaesthetic used

in New Zealand and Australia as a safe and inexpensive fish anaesthetic. Thanks to its zero

withholding period, it is commonly used to sedate fishes destined for human consumption,

but has also been successfully utilised as an anaesthetic agent for research purposes on fishes

and amphibians (Hill et al., 2002; Iversen et al., 2003; Hill & Forster, 2004; Young, 2009;

Gladden et al., 2010; Iversen et al., 2013; Speare et al., 2014). However, the mechanism of

action of isoeugenol as an anaesthetic is unknown and its use for electrophysiological

investigations has never been tested.

Alfaxan (Jurox Pty. Australia) is a steroid anaesthetic commonly used in veterinary science

on dogs and cats. Its active ingredient, alphaxalone appears to preserve afferent responses in

the lateral line nerve in the pollock (Pollachius virens) (Oswald, 1978) and does not alter

spontaneous activity of electroreceptor organ primary afferents in the catfish Clarias gariepinus

25

(Peters & van Ieperen, 1989; Peters et al., 1997a; 1997b). On the basis of these earlier studies,

alphaxalone has been used successfully in both mechanoreception and chemoreception

research in fishes (reviewed by Peters et al., 2001).

In this study on the brownbanded bamboo shark Chiloscyllium punctatum, we investigated the

use of isoeugenol (AQUI-S) and alphaxalone (Alfaxan) in recording auditory-evoked

potentials, and compared the responses to those obtained using the commonly used fish

anaesthetic, tricaine methanesulfonate (MS-222). All three anaesthetic agents were compared

to control animals that received no anaesthesia and were simply restrained physically.

The brownbanded bamboo shark is a benthic species, commonly found in tidal pools on

coral reefs and intertidal zones of the Indo-West Pacific (Last & Stevens, 2009) and is easy

to maintain in captivity. This species was selected because of its demersal lifestyle, staying

motionless for long periods of time and thus amenable for our control treatment. C. punctatum

is also a common elasmobranch model for physiological, genetic, behavioural and ecological

studies (Harahush et al., 2007; Chapman et al., 2011; Schwarze et al., 2013; Chen et al., 2014;

Harahush et al., 2014; Masstor et al., 2014; Bernal et al., 2015; Cramp et al., 2015). We used

measurements of latency (time between stimulus onset and the appearance of the AEP

wave), amplitude (maximum peak-to-peak voltage of the AEP), as well as estimated sound

detection thresholds to characterise the AEPs recorded in response to tone bursts. An

audiogram for C. punctatum was also estimated from the thresholds obtained under MS-222

anaesthesia.

2.3. Methods

2.3.1. Animal Ethics statement

This study was carried out with the approval of The University of Western Australia Animal

Ethics Committee (Application RA/3/100/1153) and in strict accordance with the

guidelines of the Australian Code of Practice for the Care and Use of Animals for Scientific

Purposes (8th Edition, 2013).

2.3.2. Fish acquisition and maintenance

All sharks in this study were brownbanded bamboo sharks Chiloscyllium punctatum, sourced

from an approved commercial breeding colony in Queensland (Australia), and acquired as

juveniles or egg cases. The animals were held in holding tanks at The University of Western

Australia (UWA), for a maximum of 12 weeks, with seawater at a temperature held between

23 and 24˚C, and a mean pH of 8.0. The sharks were maintained together in a 2800 L aerated

and filtered rectangular tank and fed a diet consisting of squid and small oily fish three times

26

per week. A total of 12 bamboo sharks were tested (precaudal length, PCL, ranging from

16.5 to 22.5 cm, weight ranging from 33 to 64 g; n = 7 females, n = 5 males).

2.3.3. Anaesthesia

Three anaesthetic agents were used in this study and tested against a control (no anaesthesia):

tricaine methanesulfonate MS-222 (ethyl 3-aminobenzoate methanesulfonate 98%, Sigma-

Aldrich, immersion/inhalant), AQUI-S (AQUI-S New Zealand Ltd., Narangba QLD

Australia, isoeugenol 50%, immersion/inhalant) and Alfaxan (Jurox Pty., Rutherford NSW

Australia, 10 mg/mL alfaxalone, intra-muscular injection). Four individuals were randomly

attributed to each treatment (AQUI-S, Alfaxan, MS-222). Some of the individuals used for

the treatments were randomly chosen and attributed to the Control tests as well. Individuals

were tested twice for each experiment (including for the Control), with a minimum of one

week recovery between each trial. In total, 12 individuals were used and 36 experiments were

performed: 8 Alfaxan trials, 8 AQUI-S trials, 8 MS-222 trials and 12 Control trials.

Individuals were fasted for a minimum of 48 hours prior to each procedure to minimise the

risk of gastric regurgitation during or post-anaesthesia. Subjects were netted from their

holding tank, weighed and measured and transferred to a small treatment tank before an

anaesthetic agent was administered (except for the Controls). Depending on the attributed

treatment, the animals were intramuscularly injected with Alfaxan (induction dose 50-55

mg/kg) on the caudal peduncle, immersed in a bath of MS-222 (induction dose 100 mg/L,

buffered with sodium bicarbonate) or immersed in a bath of AQUI-S (induction dose 60-80

mg/L). The level of anaesthesia was monitored until the animal reached an early Stage III.1

anaesthesia level, corresponding to a light anaesthesia, suitable for minor surgical procedure

(Brown, 1993). Since each individual would respond to the treatment differently, the dose

was adapted accordingly so that the animal would reach the desired level of anaesthesia.

Effective stage of anaesthesia was achieved where a loss of equilibrium and no reaction to

tactile stimulation of the eyes, skin and caudal peduncle (pinch) was observed. The

respiration rate and heart rate were also monitored. All animals reached stage III within 20

minutes of induction. Once anaesthetised, the subject was transferred to the test chamber

and secured in a sling 200 mm below the water surface. Aerated seawater was delivered with

a gravity-fed flow, into the mouth and over the gills of the shark via a soft plastic tube. For

inhalant (immersion) anaesthetics, maintenance anaesthesia was delivered through the water

flow throughout the duration of the AEP experiment (maintenance doses MS-222: 60 mg/L,

AQUI-S: 25 mg/L). For the Alfaxan treatment, maintenance injections were performed if

required (maintenance dose 15-25 mg/kg). An observer monitored the depth of anaesthesia

27

of the animal at 5-minute intervals in between recordings to assess and adjust the level of

anaesthesia maintenance.

2.3.4. Experimental setup

Hearing responses were determined for each fish by measuring an auditory evoked potential

(AEP) in response to a tonal stimulus at a known sound pressure level (SPL) and particle

acceleration level. AEPs were performed on-site at UWA in a test chamber, which comprised

of a 400-mm Schedule 60 steel pipe, 1 m high and 370 mm inside diameter (Figure 2-1 A).

The pipe was flanged to a steel plate at the bottom and oriented upright. An underwater

sound transducer (Diluvio from Clark Synthesis, frequency response 20 Hz – 17 kHz in air,

undetermined in water) was placed at the bottom on rubber mounts. The test chamber was

mounted inside a plywood enclosure lined with foil to minimise electromagnetic interference

and with damping material to minimise airborne noise interference. A PVC pipe frame was

placed and attached inside the enclosure to support the shark, isolated with a layer of rubber

patches to reduce vibrations from the pipe. In addition, the enclosure sat on top of four

automobile tires to diminish interference from structural vibration though the floor of the

building. The enclosure had a hinged lid for access to the test chamber. Cables and wires

were fed through small holes in the wall of the enclosure so that the lid could be closed and

secured during AEP recordings to minimise background electromagnetic noise. The

enclosure was grounded to mains earth to reduce electromagnetic interferences. The test

chamber was filled with the same seawater as the holding tank to a height of 800 mm. The

seawater was drained and refilled before each experiment. Water temperature was held

between 23 and 24˚C throughout the testing.

Each individual shark was held in the PVC pipe frame with a rubber-padded sling, fully

submerged, and attached with velcro straps to minimise movement while allowing for normal

respiration. Stainless steel needle electrodes were used to record the AEP signal. A recording

electrode was inserted subdermally into the dorsal surface of the animal, directly over the

brainstem and in between the endolymphatic pores (Figure 2-1 B). A reference electrode was

inserted subdermally anterior to the dorsal fin. A ground electrode was placed directly in the

water near the fish. The identical AEP setup, including all instrumentation and experimental

tank, was used to perform AEPs on all animals (Figure 2-1).

28

Figure 2-1 Auditory evoked potential test chamber and setup. (A) Wooden chamber enclosing a 1 m high steel pipe, with underwater speaker at the bottom. (B) Head stage comprising one recording electrode (red) and one reference electrode (black) subdermally inserted in a restrained shark, as well as a ground wire (green) loose in water. (C) Power amplifier (from underwater speaker). (D) Decibel attenuator. (E) Data acquisition board. (F) Differential amplifier. (G) Laptop.

2.3.5. AEP stimulation and recordings

Sound stimuli were produced and AEP waveforms recorded using a National Instruments

(NI; USB 6353 X-Series) data acquisition system controlled by custom software (scripts

written in Microsoft Visual Studio 2015 using NI Measurement Studio libraries by NSH).

Evoked biopotentials were amplified (10,000 times) and bandpass filtered (10 Hz- 3 kHz)

with an AC-coupled differential amplifier (DAM50; World Precision Instruments). The

system was implemented on a HP ProBook 6570b laptop computer. Sound stimuli were

created by generating a voltage output from the USB 6353, which was subsequently passed

through a Pi-pad passive attenuator used to control acoustic stimulus intensity in steps of

1dB. The resultant signal was amplified using a power amplifier (Response Precision

AA0452) connected to the Diluvio underwater sound transducer. Figure 2-1 (C-G) shows

the recording and sound stimulation setup.

Stimuli were sinusoidal tone bursts at fundamental frequencies of 50, 100, 500 and 1000 Hz.

A minimum of 5 intensities were tested for each frequency. All bursts were windowed in the

time domain using a 2 ms Hanning window to reduce spectral leakage and provide a ramped

onset and decay. The number of cycles in a tone burst was adjusted according to frequency.

Three cycles were used for 50 and 100 Hz tone bursts (60 and 30 ms respectively), and 5

29

cycles for 500 and 1000 Hz (10 and 5 ms, respectively). Tones were presented in one phase,

then in the other phase, and this stimulus pair was repeated for averaging. The 50 Hz tones

were presented within a 200 ms recording epoch, and averaged over 300 repetitions (of the

stimulus pair), whereas the others frequencies (100, 500, 1000 Hz) were presented within a

100 ms recording epoch and averaged over 500 repetitions. Mean AEPs corresponding to

each phase of the tone were subtracted from one another to remove electrical or inductive

electrical artefacts caused by the speaker. Experiments were also conducted with dead sharks

to ensure that identified AEP responses were not stimulus artefacts.

2.3.6. Sound and particle calibration

As sharks are sensitive to the particle motion component of the sound (see review by

Myrberg, 2001), we used particle acceleration, rather than pressure levels, to calibrate sound

intensity. Particle acceleration in the test chamber was calibrated in the absence of a shark by

measuring the pressure gradient over two closely spaced sound receivers (Gade, 1982). Two

hydrophones (HTI 90U, High Tech, Inc.), with responses that were considered linear from

2 Hz – 20 kHz, were vertically spaced 11 cm apart, and fixed at the location of the shark’s

head. System gain was estimated with a white noise calibrator. Acoustic stimuli from all

frequencies and intensity levels used for the study were measured with the hydrophone array

subsequently oriented in all three orthogonal directions. Consistent with previous studies

(Casper & Mann, 2006; Horodysky et al., 2008; Wysocki et al., 2009a), the x-axis was

considered to be anterior-posterior along the animal’s body, the y-axis was considered to be

lateral (right-left) relative to the animal, and the z-axis to be vertical (i.e. up-down) relative to

the animal. Particle acceleration was computed from the pressure gradient across the two

hydrophones, with the Euler equation (Rasmussen, 1989; Mann, 2006):

𝑎 = −Δ𝑠𝑖𝑔/(𝜌 Δ𝑟),

where 𝑎 is the acceleration (in m/s2), Δ𝑠𝑖𝑔 is the magnitude of the difference between the

waveforms of the two hydrophones (in Pa), 𝜌 is the density of the medium (seawater

1025.176 kg/m3), and 𝑟 is the distance between the hydrophones (0.11 m). The magnitude

of the three directions combined was calculated by the following equation:

‖𝑎‖ = √(𝑥2 + 𝑦2 + 𝑧2),

where ‖𝑎‖ is the magnitude of acceleration (in m/s2), and 𝑥, 𝑦 and 𝑧 are the particle

acceleration (in m/s2), measured on the x-, y- and z-axes, respectively. Sound pressure level

(SPL) was measured from the first hydrophone (nearest to the surface and the shark head)

in the z-axis orientation and stimulus intensity was determined following the method of

30

Burkhard (1984). The root-mean-squared amplitude of the largest single cycle (360°) in the

hydrophone recording was used as the calibrated SPL in dB re: 1μPa. Background noise was

also measured and its particle acceleration, although variable, was consistently below

10-6 m/s2.

2.3.7. Signal analysis

All signal processing analysis described below was done with custom-made scripts in

MATLAB (The MathWorks, Inc., 2015). The mean signal was FFT-transformed from its

start point to the end of the entire window to enable consistency across all recordings, since

for some of the low frequencies tested (i.e. 50 Hz) the recorded signal filled the entire

recording epoch. The resulting AEP power spectrum was analysed for signal amplitude at

twice the stimulus frequency. In fishes, this phenomenon is well known in AEP testing and

occurs at all low frequency tone bursts in bony and cartilaginous species (Bullock & Corwin,

1979; Mann et al., 2001; Higgs et al., 2003; Casper & Mann, 2006; Sisneros, 2007), due to the

opposite orientation of the hair cells in the maculae of the fish inner ear (Flock, 1965;

Wersaell & Flock, 1965; Furukawa & Ishii, 1967). The noise floor was estimated from the

power spectrum with a window of 60 Hz around the doubling frequency (i.e. 30 Hz on each

side of the peak), where the peak itself was isolated within a 30 Hz window (i.e. 15 Hz each

side of the peak before the noise window started). An AEP was determined to be present if

the signal-to-noise ratio (SNR) of the signal amplitude was a minimum of 4. This SNR

threshold criterion was established after visually examining all AEP responses and power

spectra and an estimation of background noise from trials to low sound level presentations.

To assess the effects of anaesthesia on hearing sensitivity, AEP signals were examined for

latency (i.e., the time delay between presentation of the stimulus and AEP waveform) and

amplitude (maximum peak-to-peak voltage) of the AEP waveform (Figure 2-2 A). The

latencies were detected visually on each individual AEP waveform. The maximum peak-to-

peak (p-p) amplitude was computed for each waveform, from the start of the signal (i.e.

latency) to the end of the window. Each measurement was inspected visually before being

selected for statistical analysis.

31

Figure 2-2 Parameters measured in each AEP signalLatency and maximum peak-to-peak (p-p) amplitude (A) shown here on an AEP response to a 100 Hz tone burst with the anaesthetic treatment Alfaxan on an individual Chiloscyllium punctatum. Red horizontal bar indicates the stimulus onset and duration (30 ms). (B) Threshold estimation was made by calculating the intersection with a linear extrapolation of the AEP p-p amplitudes and the calculated noise level. Example presented here for an individual C. punctatum tested with Alfaxan in response to 100 Hz tone bursts of 5 different particle acceleration levels.

Hearing thresholds were estimated from the amplitude (maximum p-p) measurements for

each individual and every frequency tested. Logged p-p amplitude values and particle

acceleration levels were plotted for each frequency and treatment condition. A linear fitting

was confirmed for all conditions (individuals, frequencies and treatments). A noise level was

set at 20% of the amplitude measured for the highest sound level for each frequency

(constant throughout all conditions). A linear extrapolation was computed with all the true

AEP values. The particle acceleration level where the extrapolation/interpolation values

crossed the noise line was defined as the hearing threshold (Figure 2-2 B).

32

2.3.8. Statistical analysis

Statistical analyses were performed in R (R Development Core Team, 2015). As every

individual shark was tested twice for the same treatment, the values (i.e. latencies, p-p

amplitudes and thresholds) were averaged per individual. A linear mixed model was

constructed with anaesthetic treatment as the independent variable, frequency and particle

acceleration as fixed effects and the individual identity as a random effect. The contribution

of each model term was judged by comparing equivalent models that did or did not contain

the term in question, but contained all other terms. All terms in the final model made a

statistically significant contribution to that model at a 5% probability level. The final model

included an interaction term of the treatment with the particle acceleration level, the

frequency as fixed factor, and the identity as random effect. Post-hoc multivariate

comparisons by means of Tukey contrasts were performed with the package ‘multcomp’

(Hothorn et al., 2008). Given that there were less variables (no particle acceleration term), a

different model was used to test the effect of the treatments on the thresholds. The frequency

was considered fixed effects and individuals as random effects. Both models were fitted with

the ‘lme4’ package (Bates et al., 2015).

2.3.9. Audiogram estimation

To build a complete audiogram, additional stimuli were presented to three bamboo sharks

undergoing the treatment ‘MS-222’. Additional tone bursts at 40, 200, 300, 400, 600, 800 Hz

were generated as detailed above. Three cycles of 40 and 200 Hz tones were presented, while

five cycles were presented for the remaining frequencies, all within a 100 ms presentation

period, except for 40 Hz, which was presented in a 200 ms window (like 50 Hz). A minimum

of 5 intensities were tested at each frequency. Thresholds were determined as explained

above, except that the noise level was set at 5% of the amplitude measured for the highest

intensity at each frequency (rather than 20% used for the treatments comparison). This

permitted the determination of a less conservative value to approach absolute thresholds.

Individual threshold values were averaged amongst the three individuals to produce an

audiogram.

2.4. Results

2.4.1. Sound stimuli

Due to the vertical speaker axis, the vertical component (z-axis) of particle acceleration had

substantially greater amplitudes than the two horizontal axes (x- and y-axes) at each

frequency and intensity level tested (Table 2-1). Consequently, the overall magnitude of all

three directions was approximately equal to the z direction. Therefore, and as in previous

33

studies (Casper & Mann, 2006; Horodysky et al., 2008; Wysocki et al., 2009a), the two axes

perpendicular to the speaker axis were considered negligible and only the particle motion

value in the speaker axis (z-axis) was used for plotting the results from this study. As none

of the stimulus intensities at 1000 Hz elicited an AEP the results for this frequency are not

shown in all tables and figures presented here.

Table 2-1 SPL and particle acceleration levels in the three orthogonal Cartesian directions and for the magnitude of the three axes combined at each tested frequency and sound intensity level (excluding 1000 Hz which did not evoke any AEPs). Note the overall magnitude is close to the z-axis particle acceleration level, which was consequently used for the plots below. SPL is the sound pressure level,

𝒙, 𝒚 and 𝒛 are particle acceleration for x-, y- and z-axes. ‖𝒂‖ is the magnitude of particle acceleration for all axes.

Frequency (Hz)

SPL (dB re 1 μPa)

𝑥 (m/s2)

𝑦 (m/s2)

𝑧 (m/s2)

‖𝑎‖ (m/s2)

50 135.82 0.0088 0.0097 0.1077 0.1085

140.04 0.0139 0.0145 0.1743 0.1754

146.61 0.0272 0.0273 0.3672 0.3692

157.53 0.0755 0.0841 1.2842 1.2891

173.27 0.5612 0.4774 7.5878 7.6235

178.31 1.2436 1.2436 9.8641 10.000

100 125.95 0.0006 0.0018 0.0325 0.0326

128.13 0.0063 0.0054 0.0645 0.0650

133.20 0.0047 0.0164 0.1640 0.1649

139.12 0.0096 0.0333 0.3366 0.3383

140.78 0.0120 0.0416 0.4262 0.4283

149.16 0.0309 0.1048 1.0756 1.0811

165.01 0.2080 0.6930 7.7100 7.7438

168.94 0.3239 1.0386 10.785 10.840

180.76 1.0695 2.6553 16.633 16.877

500 141.00 0.0655 0.0632 0.2941 0.2946

147.36 0.1296 0.1231 0.5504 0.5519

149.37 0.1610 0.1221 0.6679 0.6761

151.29 0.1979 0.1734 0.7951 0.8007

164.93 0.8736 0.7645 2.7932 2.8250

170.41 1.5908 1.4093 4.9879 5.0422

2.4.2. Anaesthetic effects on AEPs amplitude, latency and thresholds

There was a significant effect of the treatment on the maximum p-p amplitude, in interaction

with the particle acceleration level (i.e. effect of treatments on amplitudes depended on

particle acceleration levels) (mixed model, Chi-squared6 = 17.42, p = <0.01, Table 2-2).

However, the post-hoc tests showed that the significance was driven from the difference

between two anaesthetic agents, namely Alfaxan and AQUI-S (multivariate post-hoc

comparisons, Z = 2.62, p = 0.04, Table 2-2). This means that there was no difference

between the AEP p-p amplitudes of any anaesthetic agent and the control treatment (no

anaesthesia). The results from the fitted model are displayed in Table 2-2. The interaction

between the anaesthetic effects and the particle acceleration is visible in an example of the

100 Hz tone bursts in Figure 2-3 At higher intensities, the anaesthetics have a different effect

34

on the amplitude than at low intensities, even if overall, the amplitude increased with the

sound level.

There were no effects of the treatments on the latency of the AEP responses (mixed model,

Chi-squared6= 5.11, p= 0.52, Table 2-2). The latency decreased with the particle acceleration

level, as seen on Figure 2-3 B for the 100 Hz frequency. Equally, the treatments did not affect

the thresholds of the AEPs (mixed model, Chi-squared3= 4.12, p= 0.24, Figure 2-3 C, Table

2-2, Table 2-3).

Table 2-2 Results of the mixed models for the AEPs amplitudes, latencies and thresholds. Stars indicate significance: *: p≤0.05, **: p≤0.01. ×: interaction between terms.

Response Fixed effect

df F p

AEP amplitude (maximum peak-to-peak) log transformed

Treatment × particle acceleration

6 17.42 <0.01**

Comparison Estimate SE Z p

Alfaxan – AQUI-S 0.067 0.026 2.621 0.04*

Alfaxan – Control 0.044 0.024 1.77 0.28

Alfaxan – MS-222 0.049 0.026 1.916 0.22

AQUI-S – Control -0.024 0.024 -0.991 0.75

AQUI-S – MS-222 -0.017 0.025 -0.687 0.90

Control – MS-222 0.006 0.024 0.259 0.99

AEP latency Treatment × particle acceleration

6 5.11 0.52

Threshold Treatment 3 4.12 0.24

Table 2-3 Mean (± SEM) hearing thresholds for each treatment and each frequency (50, 100 and 500

Hz) expressed in terms of particle acceleration levels (PAL) in dB re 1μPa (from z-axis).

Alfaxan AQUI-S MS-222 Control

Frequency (Hz)

PAL (dB re 1 μPa)

PAL (dB re 1 μPa)

PAL (dB re 1 μPa)

PAL (dB re 1 μPa)

50 97.01 ± 2.84 106.54 ± 1.76 94.86 ±12.25 98.50 ± 6.21

100 104.42 ± 1.094 110.81 ± 1.42 108.77 ± 6.57 105.04 ± 13.66

500 109.58 ± 1.45 114.25 ± 1.21 113.52 ± 3.02 109.87 ± 9.7

2.4.3. Audiogram

Particle acceleration thresholds from 40 – 1000 Hz were obtained and averaged from three

individual C. punctatum anaesthetised with MS-222. AEPs were obtained successfully with

stimuli from 40 Hz to 800 Hz, but not with stimuli at 1000 Hz. Figure 2-4 shows the

calculated audiogram (black line), plotted alongside an audiogram produced by Casper and

Mann (2007b) on the same species, using a shaker table. Shaker tables apply directional whole

35

body accelerations to stimulate the inner ears of the fishes strapped to the table. It only

produces particle motion (no sound pressure) and allows the measurement of directional

hearing abilities by moving the table independently in the three axis directions.

Figure 2-3 For Chiloscyllium punctatum, average AEP amplitudes (maximum p-p) (A), latencies (B) for each anaesthetic treatment in response to 100 Hz (left) and 50 Hz (right) tone bursts. Note that 500 Hz is not represented as only one particle acceleration level for each treatment was obtained. (C): Calculated thresholds for three frequencies (50, 100, 500 Hz).

36

Figure 2-4 Particle acceleration audiograms for the brownbanded bamboo shark Chiloscyllium punctatum estimated with AEPs thresholds (black line) from the present study with data from sharks anaesthetised with MS-222 (grey line) from Casper and Mann 2007 using a shaker table (no anaesthesia). Note the large threshold offset, probably due to differences in methodology, rather than the anaesthesia (see text for details).

2.5. Discussion

We investigated whether the three anaesthetic agents, tricaine methanesulfonate (MS-222),

isoeugenol (AQUI-S) and alfaxalone (Alfaxan) affected the amplitude, latency and/or

threshold estimations of auditory evoked potentials (AEPs) in the brownbanded bamboo

shark Chiloscyllium punctatum. Our results showed that none of the anaesthetic agents

significantly altered AEP responses in comparison with a control treatment of the same

procedure excluding anaesthesia. We found a significant interaction of the anaesthetics with

the intensity level of the stimuli presented (in particle acceleration), exposing the nonlinear

action of anaesthetics at different intensities tested during the AEP procedure.

2.5.1. The effects of tricaine methanesulfonate (MS-222)

The lack of effects of MS-222 on the AEP characteristics was unexpected and in conflict

with prior evidence that the agent alters sensory function in fishes. This anaesthetic is known

to depress neuronal activity in both the peripheral and central nervous systems by blocking

sodium channels (Frazier & Narahashi, 1975). MS-222 has been shown to reduce efferent

activity in the lateral-line nerve of the roach (Rutilus rutilus) and the cichlid (Tilapia leucosticta),

in which its effects extended to other systems and also affected sound-sensitive neurones

(Späth & Schweickert, 1977). The response to electrical stimulation of the electroreceptors

of the dogfish (Scyliorhinus canicula) is completely inhibited by MS-222 (Hensel et al., 1975).

37

In single cell recordings on the cunner (Tautogolabrus adspersus), Arnolds et al. (2002) found

that MS-222 altered afferent input and the current needed to elicit an action potential in

supramedullary/dorsal cells, which are neurones known to respond to the sense of touch. In

the toadfish (Opsanus tau), lateral line afferent fibres were also affected by MS-222 (Palmer &

Mensinger, 2004). Given these results, we were expecting MS-222 to alter AEPs amplitude

significantly.

Cordova and Braun (2007) were the first researchers to test the effect of MS-222 in AEPs in

fishes. They compared the peak-to-peak amplitudes of AEP responses to 1000 Hz tone pips

over a time course of one hour following the induction of 0.01% concentration (100 mg/L)

anaesthesia in goldfish. No significant variation in amplitude or threshold estimation was

found, and there was no consistent trend of increasing amplitudes over time, which was

possibly expected as MS-222 effects would decrease without maintenance (Cordova &

Braun, 2007). Our results support Cordova and Braun’s (2007) findings that MS-222 may

not affect AEPs, at least under certain conditions. In both studies, the concentration of the

agent might never have been high enough to alter significantly the AEPs. In O. tau, Palmer

and Mensinger (2004) observed a significant decrease in spontaneous and evoked activity of

lateral line fibres only above a concentration of MS-222 of ≥ 0.01%. In both this study and

Cordova and Braun’s (2007) study, the animals were initially anaesthetised with 0.01% MS-

222. However, there is usually some time from the moment the fish is taken out of the

anaesthetic bath to the moment the AEPs recording starts. If anaesthesia is not maintained

at the same depth as the induction, it is possible and expected that the anaesthetic effects will

decrease with time. Palmer and Mensinger (2004) showed the gradual recovery of lateral line

afferents, starting immediately after MS-222 withdrawal and recovering to 90% of pre-

anaesthetic levels within 90 minutes. Cordova and Braun (2007) did not administer any

maintenance anaesthesia, and in our study, the sharks were maintained at a concentration of

0.006% (60 mg/L). Therefore, we might have been constantly under the threshold of

anaesthetic concentration needed to alter auditory sensory function in any significant way.

Furthermore, AEP recordings are unlike most procedures cited above, in its averaging and

multi-level nature, as suggested by Cordova and Braun (2007). The effects of an anaesthetic

agent on AEPs might be different from that of single cell recordings or voltage clamp

techniques on nerve activities. The AEP technique averages the responses across multiple

levels of auditory processing, in contrast to neural recordings which examine auditory

responses at specific points along the pathway of the auditory system (Maruska & Sisneros,

2016). Anaesthetic effects might subsequently be ‘averaged’ in the EEG and therefore not

appear as clearly detrimental for AEP responses as in single cell recordings. Anaesthesia

38

might also affect different properties of sensory neurons, which might not be evident in AEP

recordings but might appear in single neuron data. The effects could be altering the

excitability of the cells, spectral coding or temporal response properties. To our knowledge,

there are no studies that directly compare the neural coding properties in anesthetised and

awake fishes. In songbirds, it was demonstrated that while urethane, one of the common

anaesthetics used on mammals and birds, depressed neuronal excitability, the midbrain

encoding of sounds remained largely intact despite the anaesthetic treatment (Schumacher et

al., 2011). Conversely, inhalant anaesthetic isoflurane proved to shift significantly the

response thresholds and decrease response quality of acoustic neuronal activities in rats

(Noda & Takahashi, 2015). Overall and across taxa, it appears that the effects of a specific

anaesthetic agent are highly dependent on its mechanism of action, the brain area under

investigation, the species investigated, and on the dosage used (Ter-Mikaelian et al., 2007;

Schumacher et al., 2011). Therefore, important considerations must be made when

comparing results from studies using different procedures, different species, various stimuli,

different anaesthetic agents and focusing on different parts of the brain or acoustic pathway.

2.5.2. Interaction of anaesthetics with particle acceleration levels

Our results showed a significant interaction of the anaesthetic effects on amplitudes with the

particle acceleration (sound intensity). Figure 2-3 A shows that, although in general the

amplitude increases with particle acceleration, different anaesthetic agents behave differently

at different levels. Alfaxan, particularly, seems to have a nonlinear effect through different

levels, compared to the control, and both MS-222 and AQUI-S (Figure 2-3 A). This might

be caused by the mode of administration, i.e. MS-222 and AQUI-S were dispensed by

immersion, while Alfaxan was intramuscularly injected into the caudal peduncle of the

sharks. The maintenance anaesthesia of Alfaxan was thus intermittent, as the shark was re-

injected with the maintenance dose, general every 5 to 10 minutes or when the depth of

anaesthesia appeared to lighten. On the other hand, immersion anaesthetic agents were

constantly supplied through gravity-fed maintenance, unless the depth of anaesthesia

exceeded the required level. The different offset that the injectable maintenance might cause,

even if non-significant, would affect the relative constancy of the amplitude increase through

intensity levels, as shown in Figure 2-3. Immersion anaesthetic agents might consequently

offer more stable effects over the whole AEP procedure. Other advantages of immersion

anaesthesia include the relatively safe delivery and the opportunity to change dosages easily

by addition or dilution of the solution (West et al., 2008). However, it usually requires larger

quantities of drugs, especially when anaesthetising larger fishes and/or when using large

bodies of water. MS-222 is an expensive chemical compound, while AQUI-S is a more

39

economical immersion agent. Alfaxan was used as an injectable in the present study, but has

been successfully experimented as an immersion anaesthetic in fishes (Minter et al., 2014;

Bugman et al., 2016).

2.5.3. Audiogram

We present an audiogram generated from three individuals of C. punctatum anaesthetised with

MS-222 in Figure 2-4. The hearing thresholds have only been examined once by other

investigators in this species (Casper & Mann, 2007b), who used a shaker table to measure

AEPs and produce an audiogram (see Figure 2-4 for a comparison) with non-anaesthetised

animals. Although the overall shape of the lower frequency part of the curve in the

audiogram is similar, our data shows a large threshold offset of about 30 dB (re: 1 µm/s2)

compared to the shaker table audiogram. We also obtained responses at frequencies higher

than 200 Hz (up to 800 Hz), which Casper and Mann (2007b) did not, although they tested

frequencies up to 1000 Hz. Any interpretation of the cause of this difference is hazardous,

as too many variables are implicated. In fact, unless the audiograms are made by the same

lab using equal acoustic conditions for the measurements, comparisons are probably not

valuable (Ladich & Fay, 2013; Sisneros et al., 2016). However, we argue that the threshold

offset revealed in this study is most probably not due to the differences in anaesthesia used

(MS-222 versus none), but rather a consequence of the different stimuli (sound versus

hydrodynamic) and the threshold estimation, as suggested by Ladich and Fay (Ladich & Fay,

2013). For example, similar disparities are observed in audiograms of the goldfish (Carassius

auratus) generated in different labs, with sometimes more than 30 dB (re: 1 µPa) differences

in sound level thresholds for the same frequency (Ladich & Fay, 2013). The offset of 30 dB

(re: 1 µm/s2) between this study and Casper and Mann study (2007b) is thus not surprising,

especially when a completely different methodology is involved (shaker table versus

underwater transducer).

The sound field in our tank induced by the underwater speaker comprised the particle motion

component of the propagating sound wave and the sound pressure. An additional source of

particle motion is generated by hydrodynamic flow from the motion of the speaker (Gade,

1982; Au & Hastings, 2009). The relative action of acoustic stimuli on the inner ears of the

shark and the lateral line system was not addressed in this study. There could be a significant

contribution to the AEP from the hydrodynamic motions across the lateral line canals (Higgs

& Radford, 2013; Tricas & Boyle, 2015). Therefore, our AEP responses and the audiogram

should be considered as multimodal rather than only derived from the inner ear.

40

2.5.4. Limitations and future directions

This study tested AEPs on the brownbanded bamboo shark C. punctatum. Although one of

the goals of the study was to test a species that had not been tested before, this work would

benefit from a comparison with a well-established animal model, like the goldfish (C. auratus).

Firstly, It would allow us to produce a baseline audiogram comparable to the numerous

goldfish AEP audiograms generated in the literature so far, consequently positioning our

AEP instrumentation and measurement procedures amongst those from different labs, as

suggested by Ladich and Fay (2013). Secondly, if the results we found with the brownbanded

bamboo sharks were replicated in the goldfish (or another teleost species), we could eliminate

the possibility that these results are only due to the choice of an elasmobranch species.

Obtaining similar findings would allow safer extrapolation for application of anaesthesia in

the AEP procedure on other fish species. Furthermore, performing the same study with

higher doses of anaesthetic agents, potentially to reach a stage III.2 anaesthesia level (rather

than stage III.1 sought after in this study), which would correspond to a level of surgical

anaesthesia (levels of anaesthesia as described in Brown, 1993), would also greatly improve

our interpretation. If, as hypothesised above, the anaesthetic dosages used in the present

experiments were too low to significantly alter any AEP characteristics, higher dosages may

trigger changes in AEPs amplitudes, latencies and/or thresholds. These tests would give us

important insights on whether undesirable effects of anaesthetics on AEP recordings are

dosage-dependent and/or specific to each fish taxa.

Another variable in the AEP procedure is the use of a muscle relaxant to decrease myogenic

noise and immobilise the animal. For example, the agent Flaxedil has been commonly used

in AEP studies (Kenyon et al., 1998; Yan & Curtsinger, 2000; Lu & Tomchik, 2002; Cordova

& Braun, 2007). These blockers are not known to affect neural sensitivity (Sloan, 1998;

Ramlochansingh et al., 2014), but Kenyon et al. (1998) showed that auditory thresholds were

lower in goldfish treated with Flaxedil during the AEP. However, no study has specifically

explored the effects of muscle relaxants on hearing thresholds, in combination with or

without an anaesthetic agent. As the use of a blocker without anaesthesia is unethical, a future

study may target this question by testing the effects of a muscle relaxant on the hearing

thresholds of anaesthetised fishes.

2.5.5. Recommendation for AEP procedures in fish and anaesthesia

Our demonstration that none of the examined anaesthetic agents at the dosages tested here

altered AEP recordings in the shark C. punctatum indicates that it is possible to ensure the

animal’s welfare, while maintaining the quality and reliability of the experimental data

acquired (AEPs in this case). Anaesthetising a fish during an AEP procedure provides the

41

investigator with certainty that the animal will not experience stress from prolonged restraint

and electrode insertion. In fact, stress responses can profoundly impact physiological

processes and outcomes (Harper & Wolf, 2009). Therefore, inducing light anaesthesia in the

subject may allow the collection of more robust datasets and a closer representation of the

responses of the animal under natural (unanaesthetised) conditions.

When assessing the hearing abilities of a fish, AEPs provide a good estimate of the range of

frequencies a species is sensitive to, in a relatively short time. However, as demonstrated in

this study, thresholds determined in AEP audiograms have been found to differ in the same

species between those generated electrophysiologically from different laboratories. Similar

differences are also found for audiograms generated behaviourally (Ladich & Fay, 2013;

Maruska & Sisneros, 2016; Sisneros et al., 2016). Differences in setups and methodology can

significantly affect AEP recordings, most probably due to the variation in tank acoustics

(Rogers et al., 2016), but also in threshold criteria, sound stimuli and electrode placements.

Intraspecific variation, like sex, age or size, which is sometimes difficult to control for in

fishes, might equally produce large differences. Therefore, AEP investigations would benefit

from a standardised procedure, especially when the purpose is to compare with other studies,

or estimate absolute hearing thresholds of a species. Anaesthesia, in addition to the potential

use of neuromuscular blockers, should be included in such standardised protocols as not

only a measure for fish welfare but also to ensure the acquisition of reliable data. Ultimately,

when investigating the perceptual hearing of an organism, behavioural measures should be

preferred to AEPs, as hearing capapility is often directly related to a behaviourally relevant

function of an animal (Ladich & Fay, 2013; Bhandiwad & Sisneros, 2016).

2.6. Conclusion

Anaesthetic agents alphaxalone (Alfaxan), isoeugenol (AQUI-S) and tricaine

methanesulfonate (MS-222) did not alter peak-to-peak amplitudes, latencies and estimated

thresholds on auditory evoked potentials (AEPs) of the brownbanded bamboo shark

Chiloscyllium punctatum. This appears in contradiction with the findings that some anaesthetic

agents, like MS-222, severely affect sensory function in fishes. We hypothesise that there

might be a dosage-dependent threshold under which the alterations provoked by anaesthesia

on AEPs are negligible. We aim to pursue our testings with other species and different

dosages to be able to confirm this proposition. However, within the context of this study,

we argue that anaesthesia should be considered as being an integral part of fish AEP

protocols, as it will not only ensure the welfare of the animal but could also yield more robust

datasets with respect to stable and standardised recordings. Immersion anaesthesia, in

particular, appears to offer an opportunity for constant maintenance through circulation of

42

aerated anaesthetic solution during the experiment, thereby producing valid and comparable

measurements throughout the recording.

2.7. Acknowledgements

This research was supported by a grant from the Sea World Research and Rescue Foundation

to SPC, RDM, NSH and LC and funding from the WA State Government Applied Research

Program to NSH and SPC We are highly indebted to Dr Jan Hemmi for his guidance and

help in signal processing and analysis. We thank Nicolas Nagloo for insightful discussions

and support with the Matlab scripts. We are grateful to Carl Schmidt for his invaluable help

with the care and husbandry of the animals and the help with the acoustic room setup. We

also would like to thank The University of Western Australia (UWA) Animal Ethics

Committee for their advice and knowledge on fish welfare and best practice. This research

project led to a winning application of the UWA Animal Ethics Award, led by CCK.

43

Chapter 3

Effects of auditory and visual stimuli on shark feeding behaviour: the disco effect

44

3.1. Abstract

Sensory systems play a central role in guiding normal animal behaviour, including the

detection of prey, predators and conspecifics, and can sometimes be manipulated to alter

behavioural outcomes. Repellent stimuli are widely used to limit negative interactions

between humans and certain terrestrial animals (e.g. chemical insect repellents), however few

repellents exist for aquatic organisms. Sharks are often perceived as a threat to humans and

although serious attacks and fatalities are rare, there has been increasing interest in

developing shark attack mitigation devices. Research into shark deterrents and repellents has

concentrated predominantly on the electrosensory and olfactory systems, whereas other

senses such as vision and audition have received relatively less attention. In this study, an

assessment was made of the effects of bright flashing (strobe) lights and a loud artificial

sound composed of mixed frequencies and intensities, presented both individually and

simultaneously, on shark predatory behaviour to assess their potential as shark repellents.

We tested these stimuli on wild-caught captive Port Jackson sharks (Heterodontus portusjacksoni)

and epaulette sharks (Hemiscyllium ocellatum) in aquaria, and wild white sharks (Carcharodon

carcharias) in Mossel Bay, South Africa. The behavioural response of the sharks to the stimuli

was assessed based on their willingness to take a bait. A mixed-model statistical analysis

showed that when presented alone and concurrently with the sound, the strobe lights reduced

the number of times the bait was taken by H. portusjacksoni and H. ocellatum compared to

control trials. In C. carcharias, the strobe lights did not affect behaviour when presented alone,

but sharks spent less time around the bait when both stimuli were presented concurrently.

The variable effectiveness of the strobe lights may reflect the sensitivity of the different shark

species to light or the different experimental conditions. None of the shark species tested

were deterred by the sound stimulus alone. The lack of any behavioural responses to auditory

cues may be due to insufficient energy presented by the underwater speaker in the low

frequencies to which sharks are sensitive, or the auditory signal may not be recognised as

either an attractive or repulsive cue. As the lights and artificial sound presented in this study

did not show a drastic effect on C. carcharias, we do not currently advise their use as deterrents

for mitigating shark-human interactions. However, our results suggest that the strobe lights

may have potential for use as fisheries bycatch reduction devices for certain shark species.

45

3.2. Introduction

Animals use a range of sensory modalities to obtain information about their physical

environment. The detection and accurate interpretation of this information likely contributes

greatly to the success of an individual within its ecological niche (Collin, 2012). Cartilaginous

fishes (sharks, batoids, and chimaerids) possess a diverse range of highly developed sensory

systems, including vision, olfaction, audition, electroreception, the mechanosensory lateral

line and possibly even magnetoreception (Hueter et al., 2004; Meyer et al., 2005; Wetherbee

et al., 2012). As in other vertebrate classes, sharks exhibit great interspecific variation in the

morphology and physiology of each of these sensory modalities such as different sensory

thresholds and anatomical differences from the receptors to the central nervous system,

which reflects their phylogeny and sensory ecology (Hueter et al., 2004; Lisney & Collin,

2007; Lisney et al., 2007; Litherland & Collin, 2008; Wetherbee et al., 2012). For example,

there is significant variation in the ratio of rod to cone photoreceptors present in the retina

across species (Hart & Lisney, 2006; Litherland & Collin, 2008). Deep-sea and nocturnal

sharks possess a higher proportion of rod photoreceptors, which are capable of detecting a

single quantum of light and are used for low light (scotopic) vision but process visual

information at a slower speed compared to cone photoreceptors, which can function at much

higher light levels and are used for bright light (photopic) vision (Stell, 1972; Gruber &

Cohen, 1985; Litherland & Collin, 2008; Schieber et al., 2012). Similarly, there is a high degree

of variation in inner ear morphologies across shark species (Corwin, 1978; 1989; Evangelista

et al., 2010; Mills et al., 2011), and behavioural and physiological studies have also shown

that different species possess different acoustic thresholds (Kritzler & Wood, 1961; Banner,

1967; Kelly & Nelson, 1975; Bullock & Corwin, 1979; Corwin, 1989; Kenyon et al., 1998;

Casper et al., 2003; Casper & Mann, 2006; 2007a; 2007b; 2009).

Existing knowledge of the peripheral nervous system and the way in which organisms receive

and process sensory information may inform initiatives to manipulate behavioural outcomes

in order to develop management strategies (Madliger, 2012). Several attempts have been

made to manipulate shark behaviour through their finely tuned sensory systems, for instance

to reduce interaction with fishing gear (Brill et al., 2009; Robbins et al., 2011) or to prevent

shark-human interactions (Hart & Collin, 2015) by either altering the signals produced by

the object so that it becomes undetectable (camouflaged), or produces an aversive cue that

repels the shark. Most currently available devices work by stimulating the electrosensory

(electrical and magnetic deterrents) or chemosensory (chemical deterrents) systems

(Marcotte & Lowe, 2008; Stoner & Kaimmer, 2008; Brill et al., 2009; Huveneers et al., 2013).

However, current shark repellent devices have a number of limitations and therefore, both

46

research and industry sectors are continually looking for innovative ways to overcome these

problems. For example, strong magnets have been trialled to reduce depredation and bycatch

on fishing gear (Robbins et al., 2011) and rare earth metals have been shown to have a

deterrent effect on Galapagos sharks Carcharhinus galapagensis (Robbins et al., 2011), and

sandbar sharks Carcharhinus plumbeus (Brill et al., 2009). However, the metals were found not

to be effective on all species (Godin et al., 2013) and their effectiveness was reduced when

animals were highly motivated to feed (Tallack & Mandelman, 2009) or when there were

high densities of sharks in the immediate area (Robbins et al., 2011). A commercially-

available electronic deterrent, the Shark Shield (Shark Shield Pty Ltd), emits a repetitive high

voltage pulse that has been shown to deter white sharks, Carcharodon carcharias, from

approaching static baits and seal-shaped decoys (Huveneers et al., 2013; Kempster et al.,

2016). However, the effective repellent range of the Shark Shield is approximately 130–

200 cm, which means that the electrical field emitted by the device attached to the rear of a

surfboard may not be sufficient to protect the entire body of the surfer (Kempster et al.,

2016). The device can also be uncomfortable for the wearer because of the high voltage

discharge. In addition, there is also evidence that sharks can habituate to the electronic pulse

(Kempster et al., 2016) and there are lingering concerns over whether the electronic field

attracts sharks (Bedore & Kajiura, 2013).

The development of sensory-based deterrent devices is important, not only from a public

safety perspective but also for shark conservation. Negative encounters with sharks have led

to invasive methods of beach protection, such as culling and beach netting. These invasive

methods negatively impact vulnerable shark populations, as well as other animals through

bycatch of non-target species (Wetherbee et al., 1994; Gribble et al., 1998; Reid et al., 2011)

and indirectly through effects on trophic cascades (Myers et al., 2007; Ferretti et al., 2010).

Additionally, studies have shown population declines in certain shark species as a result of

bycatch from a number of fisheries, hence the reduction of shark bycatch remains a major

challenge to both conservationists and fishermen (Lewison et al., 2004; Gilman et al., 2007;

Dulvy et al., 2008; Gilman et al., 2008; Camhi et al., 2009; Collin, 2012). However, there has

been little focus on shark deterrents that take advantage of vision and hearing, and even

fewer studies investigating multisensory approaches.

For many sharks, vision plays a vital role in their ecology, particularly in detecting and

identifying prey (Hobson, 1963; Gilbert, 1970; Strong, 1996), communication (Ritter &

Godknecht, 2000; Martin, 2007) and in navigating the complex aquatic environment (Parker,

1910; Fuss et al., 2014). The visual system of numerous sharks is relatively sensitive to light

and they possess many visual adaptations that enhance the detection of light. For example,

47

many sharks possess a rod-dominated retina (Litherland & Collin, 2008; Schieber et al., 2012).

Rods are more sensitive and operate at lower light levels than cones (Land & Nilsson, 2012).

A reflective tapetum lucidum is also common in a number of sharks and provides a second

opportunity for photoreceptors to capture light (Gilbert, 1970). The high absolute sensitivity

of their eyes to light may cause them to avoid bright flashing (strobe) lights. As a potential

deterrent, strobe lights produce abrupt flashes with alternating levels of light irradiance over

a short duration, which may limit retinal adaptation. A strobing light may cause

overstimulation of retinal photoreceptors (Chalupa & Werner, 2004; Land & Nilsson, 2012)

or may be a novel stimulus that is avoided (i.e. neophobia) (Mettke-Hofmann et al., 2002;

Sneddon et al., 2003). Strobe lights have been found to deter a range of teleost fishes

(Johnson et al., 2005; Marchesan et al., 2005) and have been deployed in many areas to reduce

the entrapment of fish around dams and power plants (Anderson et al., 1998; Johnson et al.,

2005). However, strobe lights may potentially have an attractant effect as seen in Antarctic

krill (Wiebe et al., 2004) and some teleosts (Johnson et al., 2005). No studies to date have

looked at the effect of strobe lights on shark behaviour.

Underwater sound is also an important sensory stimulus and represents a directional signal

that is able to propagate over large distances. Sharks are known to be sensitive to low

frequency sounds up to 2000 Hz and have a peak sensitivity at around 100 Hz (Popper &

Fay, 1977; Corwin, 1989; Myrberg, 2001; Hueter et al., 2004; Wetherbee et al., 2012). Mixed

low frequency sounds (resembling a struggling fish) have been shown to attract sharks in the

field (Nelson & Gruber, 1963; Myrberg et al., 1972; 1975a). In contrast, the sudden onset of

an intense sound or transmission switch from an attractive sound to that of another sound

can have a repellent effect on sharks (Banner, 1972; Myrberg et al., 1978; Klimley & Myrberg,

1979). As sharks do not possess a swim bladder or any other structure that can convert

acoustic pressure into particle movement, they are thought to respond only to the particle

motion component of a sound (reviewed by Myrberg, 2001), although refuted by van den

Berg and Schuijf (1983). This suggests that sharks may be particularly sensitive to the near

field (the sound field within two wavelengths) of the sound source. The distance at which

sharks can detect a sound ultimately depends on the type of the sound source, the frequency

spectrum of the sound and of the surrounding soundscape, and habitat acoustics. In most

of the studies cited here on shark behaviour towards acoustic signals, the received sound

pressure levels were likely to belong to the far field and were well below detection thresholds

obtained from laboratory studies. If sharks are actually able to perceive propagated pressure

waves in the far field (van den Berg & Schuijf, 1983), the mechanism remains unresolved.

48

Very few studies have used a multisensory approach to manipulate shark behaviour. In an

experiment investigating the behaviour of three different species of sharks towards prey,

Gardiner et al. (2014) demonstrated that sharks use their senses simultaneously, but switch

the primary modality in a hierarchical way as they approach their prey. Therefore, the

combination of different sensory stimuli may amplify avoidance behaviour in sharks by

increasing the likelihood of detecting the stimuli through multiple mechanisms. A

multisensory stimulus also provides the flexibility to be effective in a range of situations, for

example, a sound stimulus may be more effective over a greater distance, especially in turbid

water, than a visual stimulus, even if the visual stimulus is a more potent deterrent at close

range (Stein et al., 2005). Such a multisensory approach has proven to be an effective

management strategy for a number of species of freshwater fishes. For instance, increased

avoidance behaviour is seen in freshwater teleosts in response to the combination of strobe

lights and bubbles when compared to the response when each treatment is presented in

isolation (Patrick et al., 1985; McIninch & Hocutt, 1987; Sager et al., 1987; Nestler et al.,

1995; Ploskey et al., 1995).

This study assessed the behavioural responses of three species of shark to strobe lights and

an artificial sound, presented both separately and simultaneously. Experiments were

performed in captivity on two benthic species, the Port Jackson shark, Heterodontus

portusjacksoni and the epaulette shark, Hemiscyllium ocellatum, and in the wild on a population

of white sharks, C. carcharias, in Mossel Bay, South Africa. We hypothesised that sharks would

less likely and/or more slowly take or interact with the bait while under the intense light and

sound stimuli, when compared to a control.

3.3. Methods

3.3.1. Ethics statement and animal acquisition

The sensory stimuli were tested both in an aquarium facility on six wild caught Port Jackson

sharks, H. portusjacksoni, (pre-caudal length, PCL, ranging from 20.5 to 44.0 cm, n = 3

females, n = 3 males) and five wild caught epaulette sharks, H. ocellatum, (PCL ranging from

41.0 to 49.0 cm, n = 2 females, n = 3 males), as well as in a wild population of white sharks,

C. carcharias, in Mossel Bay, South Africa. Accurate assessments of sex and size were not

possible, but the Mossel Bay white shark population is dominated by sharks 275–325 cm in

length (Kock & Johnson, 2006) and, the population was recently recorded as 56% male and

44% female (E. Gennari, unpublished data). This study was carried out with the approval of

The University of Western Animal Ethics Committee (Applications RA/3/100/1193 and

RA/3/100/1220) and in strict accordance with the guidelines of the Australian Code of

Practice for the Care and Use of Animals for Scientific Purposes (8th Edition, 2013). The

49

work was also approved by the South African Department of Environmental Affairs:

Biodiversity and Coastal Research, Oceans and Coasts Branch (Permit RES2014/91).

3.3.2. Description of sensory stimulation

The sensory stimuli consisted of an artificial sound and/or a flashing ‘strobe’ light. The

artificial sound was generated by an underwater speaker (Diluvio from Clark Synthesis,

frequency response 20 Hz–17 kHz in air, undetermined in water), which was connected to

an amplifier (PBR300X4, Rockford Fosgate) and an MP3 player (Philips GoGEAR) powered

by a 12 V battery. The artificial sound profile was constructed digitally with Adobe Audition

CS5.5 and the digital audio workstation software Reaper v.4.62. Following the work of

Myrberg et al. (1978), who showed that a sound with a rapidly changing amplitude and

frequency content, elicited a withdrawal behaviour in free swimming sharks, the sound was

built by mixing tones of different frequencies and intensities, from 20 Hz to 20 kHz (Figure

3-1). The relative amplitude ranged from -1 to 1, with random silence intervals (Figure 3-1).

The control sound consisted of a sound inaudible to sharks, based on the current literature:

10 kHz tones, repeated every second (Figure 3-1) at a maximum relative amplitude. A video

showing the evolving spectrum of a three-minute extract of the artificial sound is provided

in Appendix A.

The sound stimuli were quantified in the experimental aquarium as well as in open water. A

pair of HTI 90U hydrophones (High Tech, Inc.), oriented parallel to one another and

positioned 11 cm apart, were used to measure the sound pressure and particle velocity along

the x, y and z axes, at specific distances from the speaker. In the experimental tank

(320 × 120 × 75 cm) the speaker was positioned 50 cm from the nearest wall of the tank, at

a depth of 45 cm, and the hydrophones were positioned at a 1 m distance from the speaker,

at the same depth. The field calibration was performed in a calm location in the Swan River

(WA, Australia), where the hydrophones were deployed from a jetty to avoid boat noise

polluting the recordings. The water depth at this location averaged 3.5 m. The speaker was

located at a depth of 3 m and the hydrophones were deployed at a depth of 1.6 m from the

surface. The spectrograms and amplitude spectra of the artificial sound and background are

presented in Figure 3-2. The root mean square (rms) average pressure was measured as well

as the sound pressure level (peak to peak and rms), and the signal-to-noise ratio of the

different sound stimuli, and the magnitude of the particle acceleration were estimated. The

sound parameters were calculated with a custom MATLAB code (LC) (The MathWorks,

Inc., 2015) (Table 3-1). This calibration could not be considered as an exact reference for the

field data in South Africa, as the different field conditions and unpredictable variations

(Shapiro et al., 2009) would affect the sound field significantly, but are likely to be a close

50

approximation. Although no audiograms are available for our target species, we assumed that

hearing thresholds in H. portusjacksoni and H. ocellatum were similar to those measured in

closely related species, the Horn shark, H. francisci, and brownbanded bamboo shark,

Chiloscyllium punctatum (Casper & Mann, 2007a; 2007b). Across these different shark species,

particle acceleration thresholds at 100 Hz are approximately 1×10-3 m/s2, observed at a

distance of approximately 10 m from the sound source for our artificial sound. The white

shark audiogram is also unknown and no audiograms are available for any other members of

the Lamnidae family. In an attempt to ensure that the sound stimuli were detectable by the

white sharks, the sound playback was started only when the sharks had approached within

two meters of the source.

The strobe light was constructed from eight constant-current-controlled white light-emitting

diodes (LEDs; Vero 13, Bridgelux), with four LEDs (eight in total) on two opposing sides

(eight in total) of a rectangular aluminium tube (10 × 45 cm). The LEDs and all wiring were

insulated and waterproofed by encasing the tube with clear epoxy resin. The intensity of the

strobe was measured using a light meter (International light technologies, ILT1700) to be

1.22 × 10 3 W sr- 1 cm-2. Current to the LEDs was switched on and off at a rate of either 5 or

10 Hz (duty cycle of 50%) using an N-channel MOSFET controlled by the output of a

programmable microcontroller (Arduino Nano v3.0). These frequencies were chosen based

on the visual temporal resolultion of the species of sharks used in the study. The critical

flicker fusion frequency is the frequency at which a series of light pulses are perceived as a

constant stream. Therefore, the strobe light needed to flicker at a frequency lower than the

flicker fusion frequency. H. portusjacksoni and H. ocellatum have a critical fusion frequency of

28 and 40 Hz, respectively (Ryan et al., 2017).

Measurements were made to determine whether the strobe lights or speaker emitted any

electrical fields into the surrounding water that might affect shark behaviour. One at a time,

each device was placed in a 250 L plastic tank filled with seawater and a voltage probe was

used to measure the electrical field. The probe consisted of either a platinum or silver chloride

electrode positioned 10 cm from the device and a reference electrode was positioned at the

opposite end of the tank, 70 cm away. Platinum wire electrodes were used to measure the

strobe light to minimise current generated by the photoelectric effect, whereas silver chloride

electrodes were used to measure the speaker. Voltages generated during stimulus

presentation were amplified (1000 times) and bandpass filtered (1 Hz–10 kHz) with an AC-

coupled differential amplifier (WPI DAM50). Voltages were digitised at 100 kHz using a

National Instruments (USB 6353 X-Series) data acquisition system, and stored using a

51

customised Visual Studio script (NSH, Microsoft Visual Studio 2015). A custom MATLAB

code (LAR) was used to analyse the data and provide an estimate of the peak-to-peak voltage.

The strobe lights did not emit a measurable electrical field when active. However, the speaker

did emit a measurable electrical field during sound playback (see results). To determine the

distance at which sharks would likely detect the electrical output of the speaker, the voltage

gradient was measured during playback of two different sounds generated by sinusoidal

voltages with a fundamental frequency of 100 Hz and 1000 Hz, respectively. A voltage

gradient probe was constructed from two silver-chloride electrodes positioned 4 cm apart

and oriented perpendicular to the front surface of the speaker. Voltage recordings were made

with the centre of the probe positioned at 10, 20, 40 and 70 cm away from the speaker. A

power function was fitted to the data (Bedore & Kajiura, 2013).

Table 3-1 Sound parameters for the artificial sound, the Control sound (10,000 Hz tone) and the ambient background noise in the laboratory environment (aquaria), and in the field. All measurements were made at a distance of 1 m from the speaker. Sampling rate: 100 kHz.

Lab Field

Sound parameters Background noise

Digital sound

Audio control

Background noise

Digital sound

Audio control

RMS(Pa) 0.96 3.04 3.94 9.38 9.94 2.95 SPLpeak + (dB re luPa) 114.08 156.64 140.00 146.11 156.36 138.66 SPLpeak - (dB re luPa) 127.62 156.51 133.17 131.51 156.52 139.85 SPLpp (dB re luPa) 127.80 159.58 140.81 146.26 159.45 142.31 SPLrms (dB re luPa) 119.66 141.69 131.90 139.44 139.95 129.40 SNR(dB) - 22.05 12.27 - 19.94 9.78

Particle acceleration:

X-axis acceleration (m/s2) 0.0037 0.1261 0.0361 0.0094 0.1097 0.0331 Y-axis acceleration (m/s2) 0.0050 0.1206 0.0483 0.0096 0.1348 0.0565 Z-axis acceleration (m/s2) 0.0046 0.0992 0.0337 0.0086 0.0947 0.0638 Magnitude of acceleration (m/s2) 5.9E-05 0.0403 0.0048 2.5E-04 0.0392 0.0084

RMS: Root mean square in Pa. SPLpeak+: Sound pressure level of the highest peak in dB re luPa. SPLpeak-: Sounds pressure level of the highest negative peak in dB re luPa. SPLpp: Sound pressure level of the peak to peak in dB re luPa. SPLrms: sound pressure level of the root mean square value in dB re luPa. SNR: signal-to-noise ratio in dB.

52

Figure 3-1 Laboratory-based recordings (from left to right: waveforms, spectrograms and amplitude spectra) of a representative 10 s extract of the artificial sound (A, B, C), the control sound (D,E,F) and the ambient background sound (G, H, I). Magnitudes are in dB re: 1 µPa.

53

Figure 3-2 Field-based recordings (spectrograms on the left and amplitude spectra on the right) of a representative 10 s extract of the artificial sound (A, B) and ambient background sound (C, D). Magnitude are in dB re: 1 µPa.

3.3.3. Experiment on captive benthic sharks

3.3.3.1. Experimental design

The behavioural responses to six different stimulus treatments were assessed for six

H. portusjacksoni and five H. ocellatum. The six treatments consisted of: (1) a 5 Hz strobe light,

(2) a 10 Hz strobe light, (3) the audible artificial sound, (4) a combination of the 10 Hz light

and the artificial sound, (5) a sound control which consisted of 10 kHz tone repeated every

second (assumed to be inaudible for those species) and (6) a control without any stimuli. The

effects of the light and sound treatments were assessed by comparing interactions to those

observed in the control treatments.

The experimental aquarium was a rectangular fibreglass tank filled with 2800 L of seawater.

The tank floor was covered to a depth of 5 cm with sand substrate. It contained a shelter

(PVC tube) located at one end of the tank and a V-shaped polypropylene barrier positioned

at the other end (Figure 3-3 A). The speaker was inserted into the vertex of the ‘V’ and the

light was positioned horizontally, 20 cm in front of the speaker. A length of clear

monofilament fishing line was suspended 10 cm in front of the strobe light device with a

small sinker attached 15 cm above the bottom of the line, and bait, used to attract sharks,

54

was attached below the sinker. The barrier was used to funnel sharks towards the bait and

prevent them from swimming into areas of the tank behind the speaker and lights. A rope,

embedded in the sand and positioned at the opening of the V-shaped-barrier, delimited the

entrance to the testing arena.

3.3.3.2. Procedure

Prior to each experiment, the sharks were held in a holding tank (identical to the experimental

tank) for a minimum of two weeks. Tanks were on a 12:12 hour, light/dark cycle and ambient

illumination was provided by filtered daylight and overhead fluorescent tubes. Every day, the

water parameters were measured so that both experimental and holding tanks were stable

and identical (temperature = 24.8 0.8°C; salinity = 36.6 1.7 ppt, pH = 8.07 0.5; mean

and SD).

Each shark was moved to the experimental tank and allowed to acclimatise for 24 h prior to

the start of the trials. In both the experimental and holding tanks, neither underwater artificial

lights nor aeration were used, to prevent either overstimulation or habituation of their

hearing and visual systems. For each trial, an approximately 2 × 1 cm piece of squid was

attached to the fishing line and lowered into position whilst the shark was not in the testing

arena. When the shark entered the testing arena by crossing the rope, the trial started and the

assigned treatment was initiated. Sharks were given 40 s to take the bait. If the shark entered

and left the trial area three times without touching the bait, the trial ended. A GoPro camera

positioned above the tank recorded all interactions. The observer was not visible and

monitored the experiment from a computer positioned away from the experimental tank and

linked to a live stream of the GoPro.

A Latin square experimental design restricted the randomisation of the order in which

treatments were presented and therefore, controlled for an individual’s previous experience

(Winer, 1962). This testing pattern ensured that the treatments were repeated every day in a

different order, such that each treatment was presented once at each of the possible time

slots per day. Each individual was tested for each of six treatments six times. For the

H. portusjacksoni, six trials (treatments) were performed per day, for six days. Since H. ocellatum

took much longer to approach the bait in comparison to H. portusjacksoni, only three

treatments were performed per day, for a total of 12 days, although the order followed the

same Latin square design. The treatment days were alternated with resting (fasting) days for

both species, to ensure that the sharks were motivated to feed during the trials.

55

3.3.3.3. Data analysis

The video footage was reviewed to determine whether the bait was taken and the latency in

taking the bait. The effect of the sensory treatments were determined using a mixed model

analysis performed in R (R Development Core Team, 2015) using the package ‘lme4’ (Bates

et al., 2015). The effect of the stimulus treatments was assessed by setting treatment as a

fixed factor and setting the random factors as the order of presentation, which was nested

inside the number of times the treatment had been encountered (experience), and also the

identification code for each individual shark. Of a range of models investigated, this

particular model was adopted as it produced the lowest Akaike Information Criterion. To

determine differences between treatments, a multiple comparison for parametric models was

performed using the R package ‘multcomp’ (Hothorn et al., 2008). The other factors,

individual, day, experience, and order of presentation, were also tested as fixed factors, but

were found to be insignificant in explaining variation in the data.

3.3.4. Field experiment on white sharks

3.3.4.1. Experimental design

The behavioural response to four different sensory stimulation treatments were assessed in

white sharks (C. carcharias) using a stereo camera rig deployed from a boat anchored at various

locations within 2 km of Seal Island, Mossel Bay. The four treatments were: (1) a 10 Hz

strobe light, (2) the audible artificial sound, (3) a combination of the 10 Hz light and artificial

sound, and (4) a control without any stimuli. The effects of the light and sound treatments

were assessed by comparing interactions to those of the control treatment. A trial consisted

of interactions occurring within the field of view of the cameras for a period of three minutes,

whilst a single treatment was presented. Treatments were presented in a randomised order.

A custom-built, downward-facing stereo camera rig (Figure 3-3 B) was used to record

interactions between sharks and a bait canister: the Remote Monitoring Research Apparatus

(ReMoRA). The ReMoRA was adapted from a mid-water stereo camera system previously

developed by Letessier et al. (2013), and has been used and described in Kempster et al.

(2016). It comprised of two GoPro Hero 3 video cameras, mounted 0.7 m apart on a

horizontal aluminium tube (Figure 3-3 B). The cameras were positioned such that their

optical axes were angled eight degrees towards the centre of the apparatus to create an

optimised field of view (Harvey & Shortis, 1996; 1998). A PVC canister containing 0.5 kg of

crushed sardines was mounted in a central position, 1 m in front of the cameras. A 2 kg

weight was placed in the bait canister to keep the rig in a stable vertical orientation. The

56

strobe light was mounted directly above the bait canister and the underwater speaker was

positioned between the cameras, pointing towards the bait canister.

3.3.4.2. Procedure

The stereo-camera rig was positioned approximately 2 m off the stern of the boat with the

cameras positioned just below the surface of the water. The rig remained in the water for

approximately one hour, which will be referred to hereafter as a ‘drop’. Between 2-4 drops

were performed each day over a period of five days. Individual sharks were initially identified

from the boat and identification was later confirmed from video analysis, using their scars

and markings, which allowed us to identify the same sharks interacting in our experiment

during the five days. Sharks were lured close to the rig using a tuna head attached to the end

of a rope. Once a shark had approached within 2 m of the rig, a trial was initiated.

3.3.4.3. Data analysis

Videos of white sharks, C. carcharias, were reviewed to record the total amount of time a shark

was in the field of view and any interactions of the shark with the camera rig. Behaviours

were classified and scored according to the level of interaction as: 1 = pass (individual is in

the field of view but does not make contact with the rig), 2 = touch rig (individual touched

any part of the rig), 3 = bump sensory stimulus device (individual touched the device with

their head or mouth), 4 = bump bait (individual touched the bait canister with their head or

mouth), 5 = taste bait (individual touched the bait canister with an open mouth), and 6 =

bite bait (individual was observed to make full contact with the bait canister in the form of a

bite).

To determine if shark behaviour was altered by any of the sensory treatments, a mixed model

analysis was performed. The day, time of day and previous experience had no effect as fixed

factors, however, to account for their variance they were included as random factors. The

model including these factors had the lowest Akaike Information Criterion. Individual was

also treated as a random factor. The treatment was set as a fixed factor. To determine which

treatments differed, a multiple comparison for parametric models was performed. In order

to assess differences in behaviour exhibited by the C. carcharias individuals, we also performed

mixed models, with individual as a fixed factor.

57

Figure 3-3 Experimental setups and ReMoRA. (A) Plan view of the apparatus used for the aquarium-based experiments on benthic sharks. (B) Side-view of the stereo camera rig (ReMoRA) used on white sharks in the field.

3.4. Results

3.4.1. Electrical field of the devices

The average peak-to-peak amplitude of the voltages recorded when the strobe light was

active (0.07 mV) was not detectable above the background noise (0.08 mV) (t-test, t24 = 0.22,

p = 0.82). The speaker was found to produce an electrical signal during stimulus playback,

although the artificial sound had a smaller peak-to-peak voltage amplitude (0.12 mV) than

the control (10 kHz) sound stimulus (0.17 mV) (t-test, t33 = -29.84, p = <0.0001) (Figure

3-4).

58

Figure 3-4 In-water voltage gradients measured at increasing distance from the underwater speaker when playing back a sinusoidal voltage waveform with a fundamental frequency of either 100 Hz (o) or 1000 Hz (x). The voltage gradient–distance relationship for both sounds were fitted with a power function (Bedore & Kajiura, 2013); the function for the 100 Hz sound (solid line) took the form voltage gradient = 3.66×10-5 × distance-0.19, and the function for the 1000 Hz sound (dashed line) took the form voltage gradient = 1.21×10-4 × distance-0.34.

3.4.2. Experiment on captive benthic sharks

Both H. portusjacksoni and H. ocellatum readily approached and took the bait in the control

experiment trials. H. portusjacksoni took the bait in 86% of the control treatments and

H. ocellatum took the bait in 100% of the control treatments.

H. portusjacksoni exhibited a clear change in behaviour when presented with the strobe light

treatments, often freezing at the onset of the strobe light. H. portusjacksoni took the bait in

42% of the 10 Hz strobe light trials, in 27% of the 5 Hz strobe light trials and in 28% of the

combined light and sound trials (Figure 3-5). The results of the mixed model showed the

treatment had a significant effect on the percentage of sharks to take the bait (mixed model,

Chi-squared5 = 91.24, p = <0.001) (Table 3-2). The 5 Hz light, 10 Hz light and 10 Hz light

and sound combination were all significantly different to the controls. The sound treatment

alone did not differ significantly from the controls. The sharks took the bait in 92% of the

audio trials, in 86% of the no-light/no-sound control trials and in 94% of the inaudible sound

control trials.

59

Figure 3-5 Percentage of trials in which H. portusjacksoni took the bait for each of the stimulus treatments. Vertical bars represent 95% confidence intervals.

Table 3-2 Results showing the levels of significance found from the mixed models for H. portusjacksoni (n = 6) based on 36 interactions for each individual. Degrees of Freedom (DF), chi-squared values (Chi-sq) and P-values. The multiple comparisons for the mixed models is also presented. Estimates of regression coefficients (EST), standard errors (SE), Z-values (Z) and P-values are presented. Stars indicate significance: *: p≤0.05, **: p≤0.01, ***: p≤0.001

H. ocellatum also took the bait less during the light treatments (80% of the 5 Hz strobe light

trials and 81% of the 10 Hz strobe light trials) and the light and sound combined treatment

(74%) compared to the control trials (100%) (Figure 3-6 A) (mixed model, Chi-squared5 =

18.53, p = 0.002) (

Table 3-3). Multiple comparisons revealed that the 5 Hz strobe light, 10 Hz strobe light and

the 10 Hz light and sound combination were all significantly different to the controls. Since

the bait was taken in a large number of the light trials, we were able to analyse the latency in

which the animals took the bait. H. ocellatum took longer to take the bait during the 5 Hz (26

s) strobe light, the 10 Hz (26 s) strobe light and the combined light and sound trials (25 s)

compared to the control trial (14 s) (Figure 3-6 B) (mixed model, Chi-squared5 = 28.55, p-

Response Effect DF Chi-sq P- value

Bait taken + Treatment 5 91.24 <0.001***

Comparison EST SE Z P- value

Control vs 5 Hz light 2.87 0.61 4.68 <0.001***

Control vs 10 Hz light 2.21 0.59 3.72 <0.001***

Control vs audio -0.58 0.77 -0.75 0.46

Control vs light & audio -2.83 0.61 -4.61 <0.001***

Control vs audio control -0.98 0.87 -1.12 0.26

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value = <0.001). The sound treatment and both the inaudible sound control and no-

light/no-sound controls were not significantly different in terms of the number of times the

bait was taken or the latency for the bait to be taken (Table 3-3).

Figure 3-6 Responses of H. ocellatum to the stimuli treatments. (A) percentage of trials in which the bait was taken for each of the stimulus treatments and (B) the latency to take the bait (in seconds) for each of the stimulus treatments. Vertical bars represent 95% confidence intervals.

Table 3-3 Results showing the levels of significance found from the mixed models for H. ocellatum (n = 5) based on 36 interactions for each individual. Degrees of Freedom (DF), chi-squared values (Chi-sq) and P-values. The multiple comparisons for the mixed models is also presented. Estimates of regression coefficients (EST), standard errors (SE), Z-values (Z) and P-values are presented. Stars indicate significance: *: p≤0.05, **: p≤0.01, ***: p≤0.001

Response Effect DF Chi-sq P- value

% Bait taken + Treatment 5 18.53 0.002**

Comparison EST SE

Z P- value

Control vs 5 Hz light 0.20 0.08 2.35 0.02*

Control vs 10 Hz light 0.19 0.08 2.23 0.03*

Control vs audio 0.03 0.01 0.30 077

Control vs light & audio 0.23 0.09 2.37 0.02*

Control vs audio control 0.03 0.10 0.38 0.71

Time to take + Treatment 5 28.55 <0.001***

bait Control vs 5 Hz light -0.26 0.07 -3.64 <0.001***

Control vs 10 Hz light -0.23 0.07 -3.22 0.001**

Control vs audio -0.06 0.07 -0.84 0.40

Control vs light & audio 0.22 0.07 3.14 0.002**

Control vs audio control 0.03 0.07 0.414 0.68

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3.4.3. Field experiments on white sharks

A total of 25 individual white sharks, C. carcharias, interacted with the rig, with a total of 242

interactions. Most interactions between C. carcharias and the experimental rig consisted of

passes (151 interactions). Individuals made contact with the bait on 72 of the observed

interactions (bump bait = 31, taste bait = 24, bite bait = 17) and interacted with the rig on

19 of the interactions (touch rig = 14, bump sensory stimulus device = 5). The number of

interactions for a single individual ranged from one to 39 events (over the five days period).

There was no significant difference in the amount of time C. carcharias spent in the field of

view (time on screen), regardless of treatment (Table 3-4, Figure 3-7). As sound had no effect

on the benthic species tested in the lab, as well as no effect on C. carcharias, we performed an

alternative analysis by pooling the sound and control data, which we then analysed with a

mixed model and multiple comparisons. The amount of time C. carcharias spent on screen

was significantly different between the light and sound combined treatment and the pooled

sound and control data (multiple comparison, z = 1.96, p = 0.05). The pooling of the control

and sound trials provided sufficient data to expose the small variation in the time that sharks

spent on screen in the combined light and sound treatment. However, this difference only

represents 0.8 s (Figure 3-7). C. carcharias spent, on average, 3.0 s on screen during the

combined light and sound treatment, compared to 3.8 s during the control. No further

differences in C. carcharias behaviour were detected between treatments.

Individuals of C. carcharias showed significantly different behaviours when interacting with

the rigs (mixed model, Chi-squared24= 45.01, p= 0.005) (Table 3-4). Four of the 25

individuals presented behaviours with significantly greater interactions with the rigs (based

on higher classification scores). However, by removing these sharks from the analysis, there

was still no significant effect of the sensory treatments on shark behaviour, even when

behaviours 1–3 and 4–6 were pooled.

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Figure 3-7 Time C. carcharias spent in the field of view of the camera (time on screen) for each of the stimulus treatments. Vertical bars represent 95% confidence intervals.

Table 3-4 Results showing the levels of significance found from the mixed models for C. carcharias (n = 25), based on 242 interactions Degrees of Freedom (DF), chi-squared values (Chi-sq) and P-values. The multiple comparisons for the mixed models are also presented. Estimates of regression coefficients (EST), standard errors (SE), Z-values (Z) and P-values are presented. Stars indicate significance: *: p≤0.05, **: p≤0.01, ***: p≤0.001

Response Effect DF Chi-sq P- value

Time on screen + Treatment 3 5.54 0.13

Time on screen + Treatment1 2 5.54 0.06

Comparison EST SE Z P- value

Control vs light 0.07 0.04 1.74 0.09

Control vs light & audio -0.08 0.04 -2.01 0.05*

Behaviour + Individual 24 45.01 0.005*

1 audio and control were pooled

3.5. Discussion

We investigated the effect of strobe lights and an artificial sound on the motivation and

latency of sharks to take or interact with bait. Captive Port Jackson sharks,

Heterodontus portusjacksoni, and epaulette sharks, Hemiscyllium ocellatum, took the bait less often

during the 5 Hz strobe light, 10 Hz strobe light and combined light and sound trials than the

control: 54% less for the Port Jackson and 22% less for the epaulette shark. In fact, we often

observed that Port Jackson sharks completely stopped swimming as soon as the strobe lights

began and would resume only when the lights were turned off. Although the epaulette sharks

still took the bait in some of the strobe light trials, they were 11 s slower than in the control

treatments. Wild white sharks, C. carcharias, interacted with the bait equally regardless of the

63

treatment, but spent less time around the light and sound combined treatment, although this

significative difference is only 0.8 s. Both the response of the sharks to the sound control

stimuli and the measurements of the electronic field suggest that electronic emissions did not

impact the behaviour of sharks.

The strobe lights indicated some potential as a deterrent, although not for all species. The

white shark, C. carcharias, is one of the main species of sharks responsible for human fatalities

(West, 2011). However, in this study, none of the stimuli reduced the interactions between

C. carcharias and the bait canister, as sharks still bit the bait in all of the treatments. The delay

in time of less than a second compared to the control and the combined light and sound

treatment would probably not be long enough to prevent injury in the case of a shark

encounter with humans. The current design of the strobe lights, the sound stimulus or the

combination of the strobe light and sound stimuli would not be recommended as a form of

shark deterrent for reducing the frequency or severity of human-shark interactions. However,

the strobe lights may have a greater deterrent effect on C. carcharias with the use of stronger

lights, different strobe frequencies and/or under different conditions, for example, at night

or in the absence of the olfactory cues given off by the bait.

3.5.1. Species-specific effects of the strobe lights

The difference in the effect of the strobe light between species may be related to differences

in their visual systems and/or trophic level. Rod photoreceptors are more sensitive to light

than cones and readily saturate in bright light, whereas cones are not easily saturated

(Campbell et al., 2005; Land & Nilsson, 2012). It is assumed that species with more rods

relative to cones are adapted for lower light conditions, such as in nocturnal or deep sea

species (Litherland & Collin, 2008; Land & Nilsson, 2012). H. portusjacksoni, which displayed

the strongest responses to the strobing lights, is the only one of the three species that

potentially has a rod-only retina (Schieber et al., 2012). It is possible that the bright strobe

lights may have saturated their rod photoreceptors, leaving H. portusjacksoni momentarily

blinded.

H. ocellatum possesses cones and has a peak rod to cone ratio of 18:1 (Litherland & Collin,

2008) and were comparatively less affected by the strobe lights than H. portusjacksoni. They

took the bait during most of the strobe light treatments, although they were slower and still

less successful than in the control treatment. Although both H. portusjacksoni and H. ocellatum

are predominantly nocturnal (Last & Stevens, 2009; Froese & Pauly, 2016), H. ocellatum may

be less sensitive to strobe lights as they occupy tropical environments which are often

brighter (McFarland, 1990; Froese & Pauly, 2016). The strobe lights may limit their vision by

64

saturating a large number of rod photoreceptors, but still leave functionally significant

numbers of cones to navigate and/or find the bait, even in the presence of bright light.

C. carcharias has a relatively low peak rod to cone ratio of 5:1 (Gruber & Cohen, 1985) and

no meaningful behavioural difference was observed in C. carcharias in response to the sensory

treatments. C. carcharias is thought to be predominantly diurnal (Klimley et al., 1992; Dewar

et al., 2004), thus is likely to be less sensitive to light than H. portusjacksoni and H. ocellatum,

and may rely on greater temporal and spatial visual information (McComb et al., 2010;

Kalinoski et al., 2014). Experiments on C. carcharias were performed during the day, when

the ambient light was almost certainly brighter, relative to the strobe lights and compared to

the experiments conducted in the indoor aquaria. The effects of the strobe lights on shark

behaviour are likely to be enhanced in low light environments, such as in turbid water or at

night. In previous studies on teleost fishes, strobe lights are more effective in turbid

environments and dark-adapted individuals (McIninch & Hocutt, 1987; Sager et al., 1987).

Although the strobe light may have partially affected their rod-dominated vision, it was not

bright enough to cause any significant changes in their behaviour.

The strobing speed of the light may also influence the sharks’ behavioural response. Animals

with rod dominated retinas typically have slower temporal resolution (Land & Nilsson, 2012).

H. portusjacksoni, which has a rod only retina (Schieber et al., 2012), has slower temporal

resolution than H. ocellatum (critical flicker fusion frequency, 28 and 40 Hz, respectively)

(Ryan et al., 2017) and also produced the strongest behavioural response to the strobe light.

Thus, the speed of the strobe light relative to the temporal sensitivity of the species may

affect the strength of the behavioural response. Although, temporal resolution has not been

measured in C. carcharias, they are likely to have greater temporal resolution than both H.

portusjacksoni and H. ocellatum as a result of their higher rod:cone ratio and their lifestyle

(McComb et al., 2010; Land & Nilsson, 2012; Kalinoski et al., 2014; Ryan et al., 2017). A

faster strobing light may have a greater impact on the behaviour of C. carcharias.

The different responses exhibited by these shark species to the strobe lights may also reflect

their size and trophic level in terms of the perceived predatory threat that the strobe light

posed. For instance, C. carcharias are large sharks (Mossel Bay population dominated by

sharks 275–325 cm (Kock & Johnson, 2006)), from a high trophic level and have few

predators (Compagno, 1990; Last & Stevens, 2009; Froese & Pauly, 2016), which has led

them to be commonly referred to as an ‘inquisitive’ or ‘curious’ species (Peschak & Scholl,

2006; Hammerschlag et al., 2012). Thus, they may even be attracted to, rather than deterred

from some novel stimuli. In contrast, the two benthic species tested were smaller (individuals

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tested ranged from 20 – 49 cm) and from lower trophic levels (Compagno, 1990; Last &

Stevens, 2009; Froese & Pauly, 2016), potentially making them more vulnerable to predators

and thus perhaps cautious around novel sensory stimuli. Further investigations on a greater

range of shark species performed in different light environments is required to better

understand the reasons behind the effect(s) of the strobe lights.

Our results show that the deterrent effect of strobe lights is species-specific, which suggests

strobe lights may be manipulated to create species-specific bycatch mitigation devices. Lights

are used in a number of fishing practices to either increase catch (Clarke & Pascoe, 1985;

Pascoe, 1990; Wiebe et al., 2004) or reduce bycatch (Southwood et al., 2008; Wang et al.,

2010), although how these sensory strategies affect sharks is unknown. Further research on

the response of a range of shark species to strobe lights may provide additional insights into

this intriguing phenomenon.

3.5.2. Sound as a sensory disturbance

The artificial sound alone did not have any direct effect on the percentage of bait taken or

the time spent in the arena, for any of the species tested. This result may be explained by a

number of factors. Firstly, the speaker might have lacked the capacity to produce enough

energy (particle motion) in the low frequencies that sharks are known to respond to (Popper

& Fay, 1977; Corwin, 1989; Myrberg, 2001; Hueter et al., 2004; Wetherbee et al., 2012), and

sounds with more low frequency components (<400 Hz), at higher intensities, might have

induced a greater response. Underwater low-frequency transducers still represent a

technological challenge: a transducer able to produce large particle motion levels at very low

frequencies would be both very large and expensive. Currently, the sound levels (i.e. particle

acceleration) required to elicit a behavioural response by sharks are unknown, and the

speaker used in this experiment may not have produced particle motion of sufficient

magnitude. Secondly, different species may have distinct acoustic thresholds and sensitivities

resulting in contrasting responses towards auditory stimuli. Variations in the structure of the

inner ear have been described (Evangelista et al., 2010), and auditory thresholds show some

inter-specific variability in sensitivity (Kritzler & Wood, 1961; Banner, 1967; Kelly & Nelson,

1975; Bullock & Corwin, 1979; Kenyon et al., 1998; Casper et al., 2003; Casper & Mann,

2006; 2007a; 2007b; 2009). However, as observed in Chapter 2 of the present thesis,

comparisons between the absolute thresholds and audiograms generated from different

laboratories and methodologies are invalid (Ladich & Fay, 2013; Sisneros et al., 2016).

However, Casper and Mann (2006; 2009) have examined three species of elasmobranchs

with similar setup, showing significant threshold differences. For example, the Nurse shark

(Ginglymostoma cirratum) has a higher sensitivity (threshold at peak sensitivity 300 Hz = 82 dB

66

re 1µm/s2) than the yellow stingray (Urobatis jamaicensis) (threshold at peak sensitivity 500 Hz

= 92 dB re 1µm/s2)(Casper & Mann, 2006). It is still unclear as to which ecological factors

are driving those differences and how much sharks rely on the auditory modality. Thirdly,

there is a possibility that the animals were not stimulated enough by the sound source to

show any change in the amount of bait taken. Unlike the strobe lights, which may have

potentially blinded the sharks in our captive experiment, the sound was not powerful enough

to cause a major disturbance to the animals and provoke an observable reaction. If that is

the case, the development of transducer technology in the future may help to build efficient

auditory attractant and/or repellent devices. Finally, the particular sound played may not

have been recognised as either an attractive or repulsive cue in the three species tested. More

research focusing on auditory sensitivity and frequency discrimination of sharks in a range

of species, and following a standardised protocol (Chapter 2), would allow a better

understanding of the behaviour of those animals towards sounds and facilitate the design of

auditory based deterrents.

3.5.3. Multisensory stimulation

The addition of the sound treatment may have enhanced the effect of the strobe light. In

trials where the two treatments were combined, C. carcharias spent less time in the field of

view (0.8 s, 21% less time than the control), although ultimately none of the light/sound

treatments deterred the sharks from interacting with the bait. Previous studies have shown

that sharks are able to process multiple sensory cues simultaneously and can alternate

between different sensory cues to accomplish tasks (Wetherbee et al., 2012; Gardiner et al.,

2014). A combination of sensory stimuli may be the most effective strategy to influence shark

behaviour. In addition, a multisensory approach may be more effective to repel a number of

different species, as there are interspecific differences in the hierarchy of sensory cues used

during predation (Gardiner et al., 2014). Combining multiple sensory cues has also proven

to be valuable in the management of teleost fishes (Patrick et al., 1985; Sager et al., 1987;

Nestler et al., 1995; Ploskey et al., 1995; Popper & Carlson, 1998; Wetherbee et al., 2012).

For example, increased avoidance is seen in many freshwater and estuarine species in

response to the combination of strobe lights and bubbles (Patrick et al., 1985). Likewise, the

combination of sound and bubbles has been shown to deter a number of species of fish in

various applications (Lambert et al., 1997; Welton et al., 2002; Maes et al., 2004; Taylor et al.,

2005; Ruebush et al., 2012).

67

3.5.4. Individual differences in white shark behaviour

Four C. carcharias individuals displayed significantly different behaviours than the other 21

individuals. Although these observations are based on the characteristic responses to this

particular experimental design and cannot be empirically extrapolated to all contexts, these

results support the notion of intraspecific variation in behaviour between sharks. Many

diving operators and field researchers have noted that certain white sharks can be recognised

by their unique actions and responses to stimuli (e.g. bait rope or shark cage). Ontogenetic

changes in the peripheral and central nervous system (Lisney et al., 2007) suggests that the

relative importance of difference sensory systems can change as the animal ages (Jacoby et

al., 2012; 2014) and as well as between sexes (Wearmouth & Sims, 2008), which could further

drive these behavioural differences. In our study of C. carcharias, it is possible that four

‘bolder’ individuals were encountered, which resulted in a higher degree of proactive action

towards the bait despite the aversive sensory stimuli. This highlights the importance of

incorporating individual variations when testing shark mitigation devices, especially in cases

where fatal outcomes may occur.

3.6. Conclusion

Evidence from this study does not support the use of strobe lights or loud underwater

sounds as an effective deterrent to prevent negative interactions between C. carcharias and

humans. However, the strobe lights show potential for reducing bycatch of certain shark

species if fitted to the appropriate fishing gear. Although the sound used in this study had

no effect on the behaviour of H. portusjacksoni, H. ocellatum and C. carcharias, we emphasised

that more knowledge is required on the auditory abilities of sharks to fully assess the

effectiveness of acoustic deterrents in cartilaginous fishes.

3.7. Acknowledgements

We would like to acknowledge the financial support of the Western Australian State

Government Applied Research Program to NSH and SPC and Sea World Research & Rescue

Foundation (Grant SWR/6/2014 to NSH, LAR, JMH and SPC and Grant SWR/3/2013 to

SPC, LC, RM and NSH). We thank Dr. Enrico Gennari, Ms. Lauren Peel and all the

volunteers at Oceans Campus who helped us in Mossel Bay, Ms. Caroline Kerr, Dr. Kara

Yopak, Dr. Ryan Kempster, and Ms. Channing Egeberg. We would also like to thank Mr.

Carl Schmidt for the help with animal husbandry.

68

69

Chapter 4

Effect of acoustic stimuli on the behaviour of wild sharks

70

4.1. Abstract

The effect of sound on the behaviour of sharks has not been investigated since the 1970s.

However, there is an urgent need for mitigation strategies to address fisheries bycatch of

sharks, and reduce negative interactions between sharks and humans. Sound offers an

advantage over other sensory stimuli, as it can spread in all direction quickly and further than

any other sensory cue. We used a baited underwater camera system to record the responses

of wild sharks, including the great white shark, Carcharodon carcharias, and some species of reef

sharks, to the playback of two sounds: killer whale calls and a custom-made artificial sound.

Using linear mixed models to analyse behavioural responses, we found interspecific

differences in the effect of underwater sounds on shark behaviour. The behaviour of reef

sharks was significantly altered following exposure to both sound playbacks. However, this

was not the case for great white sharks, which did not demonstrate any significant

behavioural change in response to either sound. When the sounds were playing, reef sharks

were less numerous, showed less interactions with the baited test rigs, and displayed less

aggressive behaviour, compared to during a silent control. We also revealed intraspecific

variation with individual great white shark behaving significantly differently from each other

towards the test rigs. We discuss our results within a neuroecological framework and in the

context of anthropogenic noise and shark mitigation technologies. Although we do not

advise the use of acoustic stimuli alone as an effective shark deterrent, we suggest a

multisensory approach may be more effective by combining sounds with other sensory cues

like light, electrical fields and/or chemicals.

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4.2. Introduction

As apex predators, sharks occupy an important trophic level and play a vital role in balancing

aquatic ecosystems, yet one in four species of elasmobranchs are currently threatened with

extinction (Dulvy et al., 2014). In addition to overfishing, bycatch in the longlines and gillnets

of commercial fisheries targeting teleost species is thought to be responsible for a large

proportion of the observed decline in shark population sizes (Lewison et al., 2004). Various

shark repellents have been also explored as a potential solution to reducing shark mortality

in the fishing industry (reviewed by Molina & Cooke, 2012). The search for effective shark

repellents has been also motivated by an increase in the negative interactions between sharks

and humans, resulting in deterrent technologies focussed on protecting surfers, swimmers

and divers (reviewed by Hart & Collin, 2015).

Sharks have a range of sensory modalities and each have the potential to be manipulated in

order to modify behaviour. To date, the most effective shark repellents utilise strong

electrical fields to overstimulate the sensitive electrosensory system of sharks (Huveneers et

al., 2013; Kempster et al., 2016). However, such devices generally operate over a limited

range, are quite large and relatively expensive and thus are impractical for large-scale

deployment for fisheries bycatch/depredation mitigation, or the protection of water users

over large areas. Acoustic methods of modifying behaviour are particularly appealing because

sound stimuli can propagate much further than any chemical, electrical or visual cue.

Investigating the behaviour of sharks in response to acoustic stimuli represents a new

challenge with regards to developing sound mitigation devices against bycatch and/or

human-shark interactions.

Although the effects of sound on the behaviour of marine organisms has received much

attention, sound-induced behavioural changes in sharks are very poorly understood. Previous

studies have focused on marine mammals (Weilgart, 2007; reviewed by Popper & Hawkins,

2012) and bony fishes (reviewed by Popper & Hastings, 2009a) with only some early studies

from the 1960s and 1970s having investigated the potential of attracting or deterring sharks

with sounds (reviewed by Myrberg, 2001). Low frequency and pulsed sounds appear to be

attractive to sharks (Nelson & Gruber, 1963; Banner, 1968; Nelson & Johnson, 1970;

Banner, 1972; Nelson & Johnson, 1972; Myrberg et al., 1975a; Myrberg, 1978), whereas

withdrawal has been observed in sharks exposed to killer whale screams and abrupt, loud,

irregular sounds (Banner, 1972; Myrberg et al., 1978; Klimley & Myrberg, 1979). Myrberg et

al. (1978) first induced the withdrawal of silky sharks Carcharhinus falciformis, with playback of

killer whale screams and pulsed noise-band differing in magnitude. These results were

72

replicated with lemon sharks, Negaprion brevirostris (Klimley & Myrberg, 1979). In summary,

the magnitude of the signal and the abruptness by which that magnitude is achieved (onset)

seemed to act together towards determining whether a shark would initiate an approach,

continue its approach or suddenly withdraw from a sound source (Klimley & Myrberg, 1979).

The auditory apparatus of sharks comprises the paired inner ears that, as in all fishes, detect

the particle motion component of a sound. Unlike most bony fishes, sharks do not possess

a swim bladder. In bony fishes, the swim bladder responds to the pressure component of a

sound and generates motion of the bladder wall, which is transduced to the inner ear

endorgans. Therefore, in theory, sharks are only sensitive to the particle motion component

of a sound. Sharks are known to be sensitive to low frequency sounds up to 1 kHz, peaking

between 200 and 600 Hz, depending on the species (Kritzler & Wood, 1961; Banner, 1967;

Nelson, 1967; Kelly & Nelson, 1975; Bullock & Corwin, 1979; Corwin et al., 1982; Corwin,

1989; also see review by Myrberg, 2001; Casper et al., 2003; Casper & Mann, 2006; 2007a;

2007b; 2009).

However, there are numerous records (Wisby & Nelson, 1964; Myrberg et al., 1972), stories

and anecdotal information of sharks being attracted by sounds from hundreds of meters

away (i.e. by prey, boats, and swimmers), which contradicts current knowledge of the shark’s

auditory system. This highlights the paucity of knowledge and the associated misconceptions

of the effects of sounds on shark behaviour. In Chapter 3, we presented an artificial sound

at different frequencies (from 20 Hz to 20 kHz) and intensities to captive Port Jackson

(Heterodontus portusjacksoni) and epaulette (Hemiscyllium ocellatum) sharks, and to wild great white

sharks (Carcharhinus carcharias). The sounds did not directly elicit any changes in the feeding

behaviour of these species. We hypothesised that the artificial sound used for these

experiments might not have generated enough energy in the low frequency range that sharks

are known to respond to (reviewed by Myrberg, 2001). In order to gain a more informed

view of the effects of sound on shark behaviour, we tested two new sound stimuli on eight

representative reef shark species in the wild: (1) a reproduction of the killer whale call and

(2) an artificial custom-made sound with more low frequency components than was

presented in Chapter 3.

Killer whales (Orcinus orca) are known to predate on cartilaginous fishes and reports of

predation exist on a number of different species of large sharks and rays (Visser et al., 2000;

Visser, 2005; Fertl et al., 2006; Ford et al., 2011; Rabearisoa et al., 2015). Killer whales are

highly vocal, and mostly produce pulsed calls, in addition to whistles and echolocation clicks

(Schevill & Watkins, 1966; Steiner et al., 1979). The pulsed calls possess a complex frequency

73

and time structure and can spread for long distances (Schevill & Watkins, 1966; Wellard et

al., 2015). Most importantly, while some calls may possess a high-frequency component

(HFC), the pulsed calls always contain a low-frequency component (LFC) (Miller & Bain,

2000; Miller et al., 2007). The LFC is composed of multiple temporal parts separated by shifts

in the pulse repetition rate, lower than 3 kHz (Ford, 1987; Miller & Bain, 2000; Grebner,

2009), which sharks may be sensitive to and may potentially avoid. Myrberg et al. (1978)

showed that recordings of killer whale ‘screams’ elicited withdrawal in wild silky sharks,

Carcharhinus falciformis. Specific avoidance to killer whale sounds have also been demonstrated

in teleosts (Doksaeter et al., 2009), pinnipeds (Deecke et al., 2002) and some other cetacean

species (Cummings & Thompson, 1971; Fish & Vania, 1971; Hutchings, 2002; Curé et al.,

2013). Myrberg (1978) demonstrated withdrawal of silky (C. falciformis) and oceanic whitetip

sharks (Carcharhinus longimanus) from sounds with a loud onset of noise between 150 and 600

Hz. The abrupt change in intensity level, rather than the actual nature of the sound (i.e. killer

whale call), appeared to trigger the flight response. These results were confirmed in a study

with captive lemon sharks, Negaprion brevirostris, which were deterred by the sudden onset of

killer whale screams, broadband synthetic sounds and 500 Hz pure tones (Klimley &

Myrberg, 1979).

Extending studies from Myrberg et al. (1978) and Klimley and Myrberg (1979), this chapter

aims to explore the potential effects of continuous sound playback on deterring multiple

shark species from a baited target. One of the stimuli is composed of continuous killer whale

calls (or screams), containing natural changes in intensity and frequency. The second stimulus

is an artificially generated sound, which tests the theory that an irregular, non-natural, and

novel sound can elicit a withdrawal effect in sharks. Extending earlier studies, the artificially

constructed sound is rich in low frequencies, is audible to sharks, and most importantly, has

a very disordered temporal configuration. Temporal structure has been shown to play a

crucial role in triggering behavioural responses in teleost fishes (Nelson & Johnson, 1972;

Myrberg, 1978; Neo et al., 2014). Most of the frequencies present in this artificial sound are

lower than 500Hz, in comparison to the sounds generated in Chapter 3, which also

incorporated higher frequencies.

Although our sounds contain disjunct components (mixed frequencies and intensities), it is

still played continuously and does not contain silences, i.e. there is no sudden onset from

silence to sound on. Therefore, we predict an ‘orienting’ response rather than a startle or a

defence reaction, which may be expected following a novel sensory stimulus (Sokolov, 1963).

Typically, orienting responses are evoked by low or medium intensity stimuli and can be a

sign of alerting an animal’s interest in a changing environment (Post & Emde, 1999). In our

74

experiments, we predict a withdrawal effect due to the nature of the sound, which is chaotic,

extraordinary, non-biological noise.

This study examined the behavioural reactions of eight species of wild sharks to two playback

sounds: (1) killer whale calls and (2) a custom-made artificial sound. Using an underwater

camera system, we recorded the behaviours of great white sharks (Carcharodon carcharias) and

seven species of benthopelagic reef and coastal sharks: sicklefin lemon sharks

(Negaprion acutidens), bronze whalers (Carcharhinus brachyurus), grey reef sharks

(Carcharhinus amblyrhynchos), dusky sharks (Carcharhinus obscurus), sandbar sharks

(Carcharhinus plumbeus), scalloped hammerhead sharks (Sphyrna lewini), and zebra sharks

(Stegostoma fasciatum).

Specifically, we examined the results of four scenarios: 1) We tested if either acoustic stimuli

(presented separately) had an effect on the behaviour of the sharks encountered, and if the

sharks were less likely to approach a baited camera while the sounds were playing. 2) We

determined if the sounds had an effect on all species and/or if there were interspecific

differences in their behavioural responses to each sound. 3) We compared individual

behaviours of white sharks (C. carcharias) to each presented stimulus to assess any

intraspecific variation. 4) Finally, we evaluated the incidence of tolerance to the introduced

sounds as an aversive stimulus. Playback of the two different sound stimuli was performed

in the field to assess the effects on the behaviour of numerous species of wild sharks. We

present the results and conclusions of the study in the contexts of understanding the neural

basis of behaviour (neuroecology), the effects of anthropogenic noise on shark behaviour

and the development of future mitigation strategies for reducing negative interactions

between sharks and humans.

4.3. Methods

4.3.1. Ethics statement

This study was carried out with the approval of The University of Western Animal Ethics

Committee (Application RA/3/100/1193) and in strict accordance with the guidelines of the

Australian Code of Practice for the Care and Use of Animals for Scientific Purposes (8th

Edition, 2013). The work was also approved by the Western Australia Department of Parks

and Wildlife (Permits SF009446 & CE003980, 2014; SF010295 & CE004834, 2015) and the

South African Department of Environmental Affairs: Biodiversity and Coastal Research,

Oceans and Coasts Branch (Permit RES2014/91).

75

4.3.2. Experimental apparatus

To record the behaviour of sharks in the field, we used a downward facing midwater stereo-

video system, the Remote Monitoring Research Apparatus (ReMoRA). The ReMoRA system

was adapted from a mid-water stereo camera system previously developed by Letessier et al.

(2013), also described in Kempster et al. (2016) and in Chapter 3. Each ReMoRA comprised

two GoPro Hero 3 high-definition video cameras mounted inside waterproof camera

housings located 0.7 m apart on a square aluminium crossbar (Figure 4-1 D). The crossbar

was mounted on a vertically oriented hollow steel tube, through which was threaded the rope

that suspended the apparatus from a surface buoy (Figure 4-1). The cameras were positioned

so that their optical axes converged eight degrees towards the centre of the apparatus to

create an optimised field of view (Harvey & Shortis, 1996; Shortis & Harvey, 1998). A bait

bag or a PVC canister containing 0.5 kg of crushed sardines was mounted in a central

position, 1 m in front of the camera (Figure 4-1 F). A 2 kg weight was attached to the end of

the suspended rope to keep the rig in a vertical orientation with the cameras pointing towards

the substrate (Figure 4-1 G). The ReMoRAs were deployed from an anchored boat, and

suspended in mid-water with a pair of buoys at the surface (Figure 4-1 B). Once deployed,

the boat left the area and the ReMoRA recorded continuously for a maximum of 90 minutes.

Figure 4-1 Schematic diagram of a Remote Monitoring Research Apparatus (ReMoRA), from below (left) and from the side (right). A, esky containing battery, power amplifier and MP3 player; B, surface buoy; C, chain to anchor at seabed; D, GoPro Hero3 video camera; E, underwater speaker in protective cage; F, bait bag; G, weights.

76

The sound device was composed of an underwater speaker (Diluvio from Clark Synthesis,

frequency response 20 Hz–17 kHz in air, undetermined in water), positioned between the

cameras pointed towards the bait (Figure 4-1 E). The speaker was powered by a 12 V battery,

through an automotive audio amplifier (PBR300X4; Rockford Fosgate), linked to an MP3

player (PhilipsGoGear). The MP3 player, amplifier and battery were enclosed within a

waterproof ‘Esky’ floating at the

surface of the water, next to the

ReMoRA buoys (Figure 4-1 A), and the

audio signal delivered to the submerged

speaker via electrical cables.

4.3.3. Experimental design and

sound treatments

The experiment involved the

presentation of two treatment sounds

and one control. The control treatment

comprised a ReMoRA system with a

visually identical, but non-functional,

speaker and floating ‘Esky’.

The first sound treatment consisted of

an artificially produced sound

(hereafter referred to as the ‘Artificial

Sound’), composed with Adobe

Audition CS5.5, the digital audio

workstation software Reaper v.42

(Cockos Inc.) and the virtual

instrument Granite (New Sonic Arts

Inc.) as an audio unit (AU) instrument

plug-in. The sound consisted of mixed

tones of different intensities and

frequencies, from 20 Hz to 10 kHz

(Figure 4-2). The sound was specifically

designed with the objective of deterring

sharks away from a defined area,

Figure 4-2 Field recording of ‘Artificial Sound’. For a better resolution, only a 120 seconds extract of the sound is shown. (A) waveform, (B) spectrogram, (C) power spectral density (PSD) of the signal, with 99% of the power highlighted.

77

following the work of Myrberg (1978), who showed that a sound with a rapidly changing

amplitude and frequency, elicited a withdrawal of free swimming sharks. The Artificial Sound

contained most of its power in the lower frequencies (overlapping with the peak sensitivity

of the auditory system in sharks), chaotic rhythms (i.e abrubt changes, no temporal domain

pattern) and sudden increases and decreases in intensity (Figure 4-2). It was critical to ensure

that most of the frequencies present in this sound were lower than 500 Hz, and thus represent

a different artificial sound from that presented in Chapter 3, with higher energy in the lower

part of the spectrum. With the

aid of the granular texture

generator (Granite, New Sonic

Arts Inc.), we used a technique

known as granular synthesis

(Roads, 1978; Truax, 1990; 1992)

to build the sound, which

allowed us to randomly alter the

temporal domain without

changing the desired frequency

(pitch) to restrict to the low range

(de Poli, 1983). A video showing

the evolving spectrum of a three-

minute extract of the Artificial

sound is provided in Appendix

B.

The second sound treatment

consisted of a combination of

killer whale calls (Orcinus orca)

(hereafter referred to as ‘Orca’)

(Figure 4-3). The calls were

recorded by David and Jennene

Riggs (Riggs Australia,

www.riggsaustralia.com) in

South Australia in February

2014, in collaboration with the

Centre of Marine Science and

Technology (CMST) of Curtin

Figure 4-3 Field recording of ‘Orca’ sound. For a better resolution, only a 30 seconds extract of the whole sound is presented. (A) waveform, (B) spectrogram, (C) power spectral density (PSD). LFC, Low Frequency Component

78

University, within a mile of a pod of approximately 20-30 individuals. These killer whale calls

were recorded prior to a mass predation on ocean sunfish (Mola ramsayi). Killer whale

populations usually specialise in one or several prey species of mammals, penguins or fishes

(Boran & Heimlich, 1999), and their acoustic behaviours are known to vary with the type of

prey hunted (Deecke et al., 2005; Simon et al., 2006). More specifically, groups of killer whales

communicate extensively while hunting bony fishes (Deecke et al., 2005; Simon et al., 2006).

The pattern of the recorded pulsed calls would thus represent calls typically used when

predating on large fishes like ocean sunfishes or sharks. Although killer whale calls can peak

to about 25 kHz, we preferentially selected low frequency components (LFC, <1000 Hz) in

the recordings (Figure 4-3). For both sounds, we built a recording of 15 minutes duration

(of unique sound waves), which we repeated four times to make two final sound files of an

hour each.

4.3.4. Calibration of sound device

The sound device was calibrated in the field. We chose a calm location in a river (Swan River,

Western Australia, salinity 33 ppt, 21°C) where the hydrophones were deployed from a jetty

to avoid boat noise polluting the recordings. The water depth at this location averaged 3.5 m.

The speaker was located at a depth of 3 m. Both sounds were recorded at a depth of 1.6 m

with two HTI 90U hydrophones (High Tech, Inc.), where responses were considered linear

from 2 Hz – 20 kHz. The recordings were made with a NI USB 6353 Data Acquisition device

(National Instruments), and the system gain was estimated with a white noise calibrator. The

sounds were measured at three distances (2 m, 4 m and 6 m) from the speaker, to estimate

propagation loss. We calculated the sound parameters of both sounds (Root mean square

[RMS], Sound pressure level [SPL peak positive, peak negative, peak to peak, RMS], sound

exposure level [SEL cumulative], and signal-to-noise ratio [SNR],Table 4-1) with a custom-

made code in MATLAB (The MathWorks, Inc., 2015). Our calculations were verified with

the Aquatic Acoustics Metrics Interface (Ren et al., 2012). Particle accelerations were

estimated from pressure gradient measurements using the Euler equation, as described by

Mann (2006)(Table 4-1). For the estimation of particle acceleration, the two hydrophones

were mounted in an array, 11 cm apart. For measurements on the X and Y-axis, the

hydrophones were positioned side by side, while, for measurements in the Z-axis, the

hydrophones were positioned vertically on top of each other. The pressure of each

hydrophone was subtracted and divided by 11 cm to calculate the pressure gradient. To

calculate the acceleration, the gradient was divided by the density of the medium (𝜌 =

1025.176). The magnitude of the particle acceleration was calculated as the sum of the

squared accelerations for each axis (Table 4-1). Figure 4-4 shows the loss of the sound

79

pressure level (RMS) and the particle acceleration magnitude, with the distance from the

underwater speaker. This calibration could not be considered as an exact reference for all

our data, as field conditions were not consistent each day and at each location, which would

have impacted the acoustic behaviour and propagation loss of the sound device. However,

we considered these sound parameters to represent a general reference for stimulus

magnitude.

Table 4-1 Sound parameters for Artificial Sound, and Orca at a distance of 2 m, 4 m and 6 m from the speaker and the background noise recorded at the calibration location, at a depth of 1.5 m. Sampling rate: 10 kHz.

Sound parameters Background

noise

Artificial Sound,

2 m

Artificial Sound,

4 m

Artificial Sound,

6 m

Orca, 2 m

Orca, 4 m

Orca, 6 m

RMS (Pa) 9.38 38.07 30.50 7.62 36.61 29.86 7.92 SPLpeak + (dB re 1µPa) 146.11 158.95 155.75 148.83 158.95 154.71 155.11 SPLpeak – (dB re 1µPa) 131.51 151.63 125.37 145.27 138.46 139.56 147.21 SPLpp (dB re 1µPa) 277.62 310.59 281.12 294.11 297.42 294.28 302.32 SPLrms (dB re 1µPa) 139.44 150.81 149.69 137.63 151.27 149.50 137.98 SELcum (dB re 1µPa2) 155.74 164.62 162.70 150.65 164.28 162.51 150.98 SNR (dB SNR) -- 12.15 10.24 -1.81 11.83 10.06 -1.46

Particle acceleration: X-axis accel. (m/s2) 0.0108 0.0163 0.0118 0.0103 0.0117 0.0106 0.0088 Y-axis accel. (m/s2) 0.0075 0.0183 0.0123 0.0096 0.0147 0.0120 0.0115 Z-axis accel. (m/s2) 0.0086 0.0245 0.0197 0.0116 0.0136 0.0130 0.0128

Magnitude of acceleration (m/s2)

2.45E-04 1.20E-03 6.79E-04 3.32E-04 5.38E-

04 4.28E-

04 3.73E-

04

RMS, Root mean square; SPLpeak+, Sound pressure level of the highest positive peak; SPLpeak-, Sound pressure level of the highest negative peak; SPLpp, Sound pressure level of the peak to peak; SPLrms, sound pressure level of the root mean square value; SELcum, Sound exposure level cumulative; SNR, signal-to-noise ratio.

To determine the effect of any electronic signal emanating from the speaker, the electrical

signature of the sound device was characterised (for details of the method, refer to Chapter

3). The Artificial Sound and the Orca sound produced a peak-to-peak signal of 0.115 mV

and 0.118 mV, respectively. We estimated that sharks would only be able to detect those

voltage signals within 26 cm of the speaker, based on the sensitivity of their electroreceptive

system (5 nV/cm, (Johnson et al., 1984; Kalmijn, 2000; Kajiura & Holland, 2002) and on the

estimated voltage loss of the system (see Chapter 3). Therefore, we considered that the

electrical signature of the speaker would not significantly influence the behaviour of the

sharks encountered, unless they came within 30 cm of the speaker, which never occurred in

the sound (active) treatments.

80

Figure 4-4 Field calibration of the speaker setup. Loss of (A) particle acceleration level [dB re 1 μm/s2],

and (B) SPLrms [dB re 1 μPa] with distance from the sound source for both sounds (Artificial Sound and Orca). The background noise is shown in red.

4.3.5. Field sites

The fieldwork was conducted in two different areas in order to target different shark species.

To investigate the effect of sound stimuli on reef and coastal shark species, we travelled to

Exmouth, Western Australia, where three field sites were visited over a period of seven days

in April 2014, and five days in April 2015: VLF Bay, Burrows Reef and North West of the

Murion Islands (Figure 4-5).

To allow sufficient time to attract the reef and coastal sharks to the area, the sound stimuli

were only triggered 30 minutes after the system was deployed in the water, and played for 60

minutes. Overall, 70 deployments of ReMoRA systems (35 Control, 16 Orca, 19 Artificial

Sound; 28 at Burrows Reef, 34 at VLF Bay, 8 at NW Murion) were carried out offshore from

Exmouth, at depths ranging from 12 – 19m (mean = 14.1 ± 1.75m). The ReMoRAs were

equipped with bait bags filled with 0.5kg of crushed sardines to attract the sharks. In addition,

two tuna heads were attached to the bait bags as a visual attractant, to initiate more

interactions with the reef sharks.

To specifically investigate the effect of sound stimuli on great white sharks, we travelled to

Mossel Bay in South Africa, where two field sites were visited over a period of 12 days in

June 2014: Seal Island and Hartenbos river mouth (Figure 4-5). During deployment of the

ReMoRA systems, additional bait (chum) was introduced into the water to attract great white

81

sharks into these areas. The sound stimuli were triggered as soon as the system was placed

into the water, and continued to play for 60 minutes. Overall, 101 deployments of ReMoRA

systems were carried out in South Africa (44 Control, 24 Orca, 33 Artificial Sound; 58 at

Hartenbos, 43 at Seal Island), at depths ranging from 10 – 18 m (mean= 15.0 ± 1.6 m). The

ReMoRAs were equipped with a PVC canister filled with 0.5 kg of crushed sardines to attract

the sharks into the area.

Figure 4-5 Maps of deployment areas in Exmouth, Western Australia (left) and Mossel Bay, South Africa (right). AU, Australia; ZA, South Africa; 1, Burrows Reef; 2, VLF Bay; 3, NW Murion; 4, Seal Island; 5, Hartenbos river mouth.

In both Mossel Bay and Western Australia, the exact locations of deployments were chosen

randomly, and each ReMoRA was dropped at least 500 m apart to ensure each treatment

remained independent. Each system was deployed in pairs: treatment plus control, which

was considered collectively as a trial. After each deployment, lasting approximately 90

minutes, the ReMoRAs were retrieved and redeployed at a different site following the

retrieval of the video footage. Each treatment was rotated between all sites throughout a

day’s testing, to control for any effects of testing location and time of day.

4.3.6. Video analysis

Only the 60 minutes of treatment time on the video footage was analysed. In addition to

manually reviewing the videos, we also used the open source software MotionMeerkat

(Weinstein, 2014) to capture the candidate motion events of interest, thus decreasing a

potential observer effect. The software EventMeasure (SeaGIS Pty. Ltd.) was used to identify

and quantify individuals, and assess the different behaviours. The great white sharks in South

Africa were each identified by individual markings, scars and dorsal fin profiles (Gubili et al.,

2009). Since no such obvious markings could be identified in the reef sharks in Western

Australia, individuals could not be discriminated. The data acquired from the video analysis

82

consisted of species, individual shark IDs (for great white sharks), time of arrival, total time

the shark was present in the field of view of the cameras (on screen), number of interactions,

and notable behaviours. Note that even a shark swimming by, present in the field of view,

was accounted as an ‘interaction’, although it did not factually ‘interact’ with the ReMoRA.

Observed behaviours for each interaction were classified into one of six categories, similarly

as in Chapter 3: 1) ‘pass’ (shark in the field of view but did not make contact with the

ReMoRA), 2) ‘touch rig’ (shark touched any part of the ReMoRA), 3) ‘bump rig’ (shark

touched ReMoRA elsewhere than the bait bag or canister, with snout), 4) ‘bump bait’ (shark

touched the bait bag or canister with snout), 5) ‘taste bait’ (shark touched the bait canister

with an open mouth), and 6) ‘bite’ (shark was observed to make full contact with the bait

banister in the form of a bite). We scored these behaviours in a progression of interactivity

from 1-6, the lowest interaction being a ‘pass’ (scoring a 1) and the highest a ‘bite’ (scoring a

6). We treated this score as a measurement of the ‘inquisitiveness’ of the sharks towards the

ReMoRA.

4.3.7. Data analysis

The datasets for both the field trips to Exmouth (AU) and Mossel Bay (ZA) were analysed

differently due to species-specific requirements. A total of four drops were removed, each

containing an outlier interaction with individual sharks spending a large amount of time on

screen (values that exceeded 50 min; mean value = 20.1±1.7 min). The results presented

below do not include these outliers.

For both datasets, the effect of the sound treatments was determined using mixed model

analyses performed in R (R Development Core Team, 2015) with the packages ‘lme4’ (Bates

et al., 2015) and ‘glmmADMB’ (Fournier et al., 2012; Skaug et al., 2012). The treatment was

set as a fixed factor and the date, field site, time of the day (morning, midday, afternoon),

trials (matching treatment and control) were random factors. For the AU dataset, species and

the presence or absence of tuna heads on the bait bag, were added as random factors. For

the ZA dataset, shark identity (ID) and the number of previous interactions with the

ReMoRA (‘experience’) were added as random factors.

To determine if the presence or absence of sharks on the footage was defined by treatments,

we performed a binomial generalised linear mixed model (GLMM). To investigate if there

were differences in the number of interactions per treatment, we started with a Poisson

general linear model (GLM) and obtained non-linear residual patterns and overdispersions.

We then fitted a Negative Binomial GLM and verified levels of independence. The same

model was used to test the scores. In some cases, transformation of the time data was needed

83

(see Table 4-3 and Table 4-4 for details) to achieve linearity of the residuals. To determine

the difference between treatments, a multiple comparison for parametric models was

performed using the R package ‘multcomp’ (Hothorn et al., 2008). All statistical plots

presented in this study were designed with R package ‘ggplot2’ (Wickham, 2009).

4.4. Results

4.4.1. Reef sharks

The sound treatments presented to seven different species of reef sharks revealed that both

the Artificial and the Orca sounds significantly affected the number of sharks observed in

the area, all having a lower probability of being observed in the vicinity of the ReMoRA

(Table 4-3, Figure 4-6). Overall, we recorded 533 interactions of sharks in the footage, and

almost 90% of these were ‘passes’ (Table 4-2). Both sound treatments resulted in a lower

number of interactions compared to the control, (Figure 4-7 A). We obtained a total of 7.45

minutes video footage showing the presence of sharks near the sound sources (out of a total

of 67 hours of footage analysed). The sharks spent significantly less time in the vicinity of

the speaker when the Orca sound was playing, while their behaviour was not altered by the

Artificial Sound (Table 4-3, Figure 4-7 C).

Different species responded differently as the time spent in the vicinity of the speaker was

significantly affected by the factor ‘species’ (Likelihood ratio test, χ2 = 113.53, df = 6,

p<0.01). Overall, seven different species of sharks appeared on our videos, and 82 sharks

were unidentified to species level (depicted as ‘sp’). The time of arrival of the first sharks on

the footage for each drop was significantly higher for the treatment Artificial Sound (i.e. the

first sharks arrived later on camera, (Table 4-3). Again, there was a strong effect of the factor

‘species’ (Likelihood ratio test, χ2 = 17.455, df = 6, p<0.01). When we transformed the

different behaviours into scores and tested the total scores in a model, there was a significant

decrease in the total score with both treatments (Orca and Artificial Sound) (Table 4-3,

Figure 4-7 C). Here too, we found a difference of scoring between different species (i.e.

‘species’ was found to be a significant factor if placed as a fixed factor: likelihood ratio test,

χ2 = 12.572, df = 6, p<0.05). For this dataset on reef sharks, we could not test the differences

in the number of tuna heads taken by the sharks, due to the low sample size of tuna heads

taken: for seven heads taken overall, five were taken on the controls, and only one each on

both treatments (Orca and Artificial Sound).

4.4.2. White sharks

Only great white sharks (Carcharodon carcharias) were encountered in South Africa. The two

sound treatments did not affect the likelihood of observing a shark on camera (Table 4-4,

84

Figure 4-6). Overall, we recorded 593 interactions, with ‘passes’ representing 45% of those

(Table 4-2). The number of interactions was not influenced by either of the sound treatments

compared to the control (Table 4-4, Figure 4-7 A). We obtained a total of 42.35 minutes of

footage showing white sharks in the vicinity of the sound sources. However, the sharks spent

significantly less time near the speaker when the Artificial Sound was playing, while the Orca

sound had no effect (Table 4-4, Figure 4-7 B). It is important to note that the time in the

area (as defined by the ability to observe white sharks on the video footage) was also

significantly affected by the factor ‘IDs’ (Likelihood ratio test, χ2 = 142.56, df = 38,

p<0.01). Overall, we identified 38 individual white sharks. The time of arrival of the first

white shark for each drop was not different between treatments (Table 4-4). However, there

was a strong effect of the IDs (Likelihood ratio test, χ2 = 147.99, df = 38, p<0.01). When

we transformed the different behaviours into scores and tested the total scores in a model,

there was no significant effect of the treatments (Table 4-4, Figure 4-7 C). However, the IDs

were found to be a significant factor when considered as a fixed factor (Likelihood ratio test,

χ2 = 123.65, df = 38, p<0.01). We tested if the total time on screen changed significantly

with repeated encounters with the equipment, and found that ‘experience’ was significant as

a fixed factor interacting with ‘time on screen’ (Likelihood ratio test, χ2 =13.428, df = 3,

p<0.01). The factor ‘experience’ significantly affected the treatment Artificial Sound (Table

4-4, Figure 4-8 B), where most experienced sharks spent comparatively less time in the area

when the Artificial Sound was playing with respect to the control. There was no effect on

the scores when the factor ‘experience’ was considered as a fixed factor (Likelihood ratio

test, χ2 =37.5, df = 3, p=0.54) (Figure 4-8 A).

85

Table 4-2 Table summarising all data recorded from the reef sharks in Exmouth, Australia (in blue), and from the white sharks in Mossel Bay, South Africa (in pink). Numbers in brackets are standard errors.

Parameters All Control Orca

Sound Artificial Sound

All Control Orca

Sound Artificial Sound

N˚ of drops 67 32 16 18 101 44 24 33

N˚ of interactions (total) 533 434 48 51 593 318 96 179

N˚ of passes 478 384 46 48 271 146 39 86

N˚ of touches 12 12 0 0 32 24 2 6

N˚ of bumps rig 9 7 0 2 11 7 2 2

N˚ of bumps bait 7 6 1 0 187 98 34 55

N˚ of taste bait 18 17 1 0 74 38 13 23

N˚ of bites 9 8 0 1 18 5 6 7

Total time on screen (min)

8.3 5.4 2.2 0.7 45.3 24.9 8.3 12.1

Mean time on screen (s)

15.4 (0.6)

15.7 (0.6)

11.5 (0.5)

17.4 (1.7)

4.3 (0.1)

4.5 (0.1)

4.5 (0.2)

3.9 (0.1)

Mean time of arrival (min)

23.7 (2.8)

26.3 (3.2)

14.4 (6.0)

24.1 (6.6)

47.9 (4.7)

46.7 (7.0)

66.9 (7.1)

41.3 (8.6)

Mean total score 15.5 (3.0)

20.5 (4.5)

8.1 (3.1)

6.0 (1.8)

25.3 (2.6)

27.3 (3.8)

32.1 (9.7)

20.1 (2.8)

Figure 4-6 Proportions of presence and absence of sharks conditional on treatments for reef sharks (left) and great white sharks (right). Black stars indicate significance.

86

Table 4-3 Results of mixed models for reef sharks. The treatments are considered as fixed effects: Control, Orca Sound, Artificial Sound. The upper table shows the results for the binomial models. The lower table shows the results of the Gaussian models, and the multiple comparisons for the mixed models.

Stars indicate significance: *: p≤0.05, **: p≤0.01, ***: p≤0.001. The sign indicates a transformation of data.

MODEL RESPONSE RANDOM EFFECTS

FACTOR ESTIMATE ± SE

DF WALD’S Z P VALUE

BINOMIAL GLMM Presence/ Absence of

sharks

Area, time of the

day, date

Intercept 2.92 ± 1.48 1 1.98 0.04 *

Treatment Orca -2.39 ± 0.80 -2.97 < 0.01 **

Treatment Artificial Sound -3.17 ± 1.02 -3.10 < 0.01 **

NEGATIVE BINOMIAL

GLMM

Total number of

interactions

Area, time of the

day, date

Intercept 1.67 ± 0.78 1 2.16 0.03 *

Treatment Orca -1.39 ± 0.59 -2.35 0.02 *

Treatment Artificial Sound -1.36 ± 0.58 -2.34 0.02 *

NEGATIVE BINOMIAL

GLMM

Score

(Inquisitiveness)

Area, date, species Intercept 3.14 ± 0.18 1 17.3 < 0.01 **

Treatment Orca -1.041 ± 0.45 -2.3 0.02 *

Treatment Artificial Sound -1.35 ± 0.39 -3.4 <0.01 **

MODEL RESPONSE RANDOM EFFECTS

FACTOR ESTIMATE ± SE

COMPARISONS DF P VALUE

GAUSSIAN GLMM Time on screen

(log)

Area, date, time of

the day, species

Intercept -0.73 ± 0.10 Orca vs Control 3 <0.01 **

Treatment Orca -0.23 ± 0.05 Artificial Sound vs Control 0.9

Treatment Artificial Sound 0.002 ± 0.04 Artificial Sound vs Orca <0.01 **

GAUSSIAN GLMM Time of arrival Area, date, time of

the day, species

Intercept 36.72 ± 5.37 Orca vs Control 3 0.18

Treatment Orca 8.41 ± 6.09 Artificial Sound vs Control 0.01 *

Treatment Artificial Sound 14.92 ± 6.16 Artificial Sound vs Orca 0.41

87

Table 4-4 Results of mixed models for great white sharks. The treatments are considered as fixed effects: Control, Orca Sound, and Artificial Sound. The upper table shows the results for the binomial models. The lower table shows the results of the Gaussian models, and the multiple comparisons for the mixed models.

Stars indicate significance: *: p≤0.05, **: p≤0.01, ***: p≤0.001. The sign indicates a transformation of data. The sign × indicates an interaction between terms.

MODEL RESPONSE RANDOM EFFECTS FACTOR ESTIMATE

± SE

DF WALD’S Z P VALUE

BINOMIAL GLMM Presence/ Absence of

sharks

Area, time of the day,

date

Intercept 2.70 ± 0.30 1 0 1

Treatment Orca -0.69 ± 0.53 -1.31 0.19

Treatment Artificial Sound -0.31 ± 0.46 -0.66 0.51

NEGATIVE BINOMIAL

GLMM

Total number of

interactions

Area, time of the day,

date

Intercept 2.09 ± 0.35 1 5.8 <0.01 **

Treatment Orca -0.67 ± 0.60 -1.11 0.27

Treatment Artificial Sound -0.46 ± 0.55 -0.85 0.40

NEGATIVE BINOMIAL

GLMM

Score

(Inquisitiveness)

Area, date, ID,

experience

Intercept 0.96± 0.05 1 16.49 <0.01 **

Treatment Orca 0.04 ± 0.39 0.39 0.69

Treatment Artificial Sound -0.29 ± 0.36 -0.36 0.72

MODEL RESPONSE RANDOM

EFFECTS

FACTOR ESTIMATE ± SE COMPARISONS DF P VALUE

GAUSSIAN GLMM Time on screen

Area, date, ID,

experience

Intercept 0.07 ± 0.01 Orca vs Control 3 0.46

Treatment Orca -0.005 ± 0.007 Artificial Sound vs Control <0.01 **

Treatment Artificial Sound -0.02 ± 0.005 Artificial Sound vs Orca 0.15

Time on screen

× Experience

Area, date, ID,

experience

Intercept

Treatment Orca

Treatment Artificial Sound

Experience

Treatment Orca : Experience

Treatment DS : Experience

0.069 ± 0.007

0.001 ± 0.008

-0.015 ± 0.006

0.007 ± 0.0002

-0.0008 ± 0.0004

0.0003 ±0.0003

Exp:Ctrl vs Exp:Orca

Exp:Ctrl vs Exp:DigitSnd

Exp:Orca vs Exp:DigitSnd

3 0.79

0.05 *

0.84

WALD’S Z

GAUSSIAN GLMM Time of arrival

(√)

Area, date, time of the

day, ID

Intercept 1.42 ± 1.12 1.27 3 0.20

Treatment Orca -0.61 ± 0.91 -0.67 0.50

Treatment Artificial Sound -2.03 ± 1.18 -1.722 0.08

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Figure 4-7 Boxplots representing the data for reef sharks (blue, left column) and for the great white sharks (pink, right column). (A) Number of interactions conditional on treatments; (B) Time that individuals interacted during each treatment; (C) Total score of inquisitiveness for the sharks for each treatment. Bold horizontal line represents the median. A black star labels the significant effects (see text for details).

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Figure 4-8 Scatter plots representing the development of scores (A) and time on screen (B) through experience of great white sharks (C. carcharias). The regression lines for each treatment (function ‘linear model’) are presented and are surrounded by a 95% confidence band.

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4.5. Discussion

In this study, we investigated whether two sound stimuli altered the behaviour of sharks in

the wild, using a baited downward facing midwater stereo-video system (ReMoRA) rigged

with underwater speakers. We played a pseudo-natural orca sound and an artificial sound to

different species of reef sharks (Order: Carcharhiniformes) in Exmouth, Western Australia

and to great white sharks (Carcharodon carcharias) in Mossel Bay, South Africa. The reef sharks

were found to behave differently with both treatments compared to the control, with less

sharks approaching the bait/apparatus and less interactions overall. There was also a decrease

in behavioural scores (‘inquisitiveness’), as the sharks exhibited lower scores behaviours (e.g.

‘pass’ rather than ‘bite’) when the sounds were presented. In addition, the Orca sound

decreased the overall time that the reef sharks were in the area (on camera), and the Artificial

Sound delayed the time of appearance. Conversely, the presented sounds did not appear to

affect the behaviour of the great white sharks. Only the time of arrival was lowered with the

Artificial Sound treatment. These results suggest a difference in species’ sensitivity and/or

reactivity to auditory stimuli under the conditions tested.

4.5.1. Sound as an aversive stimulus

We confirmed that a chaotic artificial sound can repel some species of sharks and alter their

behaviour (Myrberg et al., 1978). The typical soundscape of a shark is comprised of the

ambient sea noise, with both abiotic (wind, waves, etc.) and biotic (sounds made by marine

organisms: mammals, fish, invertebrates) components (Cato, 1976; McCauley, 2001; Krause,

2008; Erbe et al., 2015). The sounds perceptible to sharks (below 1 kHz) would mostly

include continuous and/or rhythmic sounds, such as waves and bubbles, hydrodynamic flow

of fish schools, and some fish calls. An arrhythmic and chaotic sound (such as our artificial

sound), with quick variations of intensities and frequencies, would represent an atypical and

unfamiliar acoustic signal. This unnatural cue may trigger aversion in some species of sharks.

We also observed an effect of the killer whales’ calls on the behaviour of reef sharks in

Western Australia. However, the behaviour of great white sharks was not altered by the

presence of the orca sounds. Although we cannot eliminate the possibility that great white

sharks are insensitive to these calls, this lack of reaction may also be due to the specific

signature of the tested orca calls. Killer whales are known to have pod-specific calling

behaviour and repertoire (Barrett, 2000), and even within-pod-specific call types (Miller &

Bain, 2000). The white sharks of Mossel Bay in South Africa may not be reactive to our killer

whale playbacks, whose signatures came from a pod in South Australia, although it is

currently unknown whether white sharks are sensitive to regionally-specific orca calls. As the

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white sharks examined in this study would not have been exposed to this repertoire before,

calls from a geographically distinct orca population may not be biologically relevant to our

study animals. The situation might be different for the reef shark population that we studied

in Western Australia. Australian killer whales are suspected to migrate long distances, moving

in both South Australian and Western Australian waters (Riggs, D. personal

communications). Therefore, it is possible that the sharks encountered during our testing in

Western Australia could have experienced their repertoire, which would explain their

aversive response in this study. There is however no published information on the type of

prey targeted by these killer whales in Western Australia, although killer whales have been

observed following sharks (Riggs, D. personal communications), which suggested hunting

behaviour. Unfortunately, quality recordings of killer whales recorded while they are hunting

are rare, and we only had one recording from South Australia to test in this study. Finally,

the size of the sharks could also have affected the outcome of the experiments. Reef sharks

encountered in Western Australia were, on average, smaller (about 2 – 2.5 m length) than the

great white sharks (about 4 m) in South Africa. Large adult great white sharks may predate

on killer whale calves (Weller, 2002; Estrada et al., 2006) and thus may be attracted to calls,

rather than deterred. Future studies should play back recordings from local pods of killer

whales, for which we are sure that they predate on sharks, to the local population of sharks.

These findings raise the question of sound discrimination ability: are certain species of sharks

able to discriminate between these calls, and only recognise the resident predatory

populations from other killer whale calls? A study by Deecke et al. (2002) showed that

harbour seals were capable of discriminating between calls coming from different killer whale

populations and responded appropriately by showing anti-predator behaviour only after

mammal-eating killer whale calls. Based on current research, it is impossible to predict if and

how sharks are able to both discriminate intraspecific calls and only respond aversively to

distinct populations. Although this study revealed a lack of any response by white sharks to

an orca call from South Australia, we cannot conclude that white sharks are completely

insensitive to killer whale calls until we test them using calls from a resident or overlapping

killer whale population. Therefore, we emphasise the importance of using local recordings

for playbacks when testing natural cues as attractants or deterrents.

4.5.2. Interspecific effects of sound

Reef sharks spent relatively less time in the vicinity of the sounds presented than the white

sharks, and were also less likely to directly interact with the ReMoRAs and the bait bag. We

also found interspecific differences in behavioural responses between the reef sharks tested:

the ‘species’ factor was a strong predictor of time spent in the area, time of arrival and the

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total behavioural scores. For example, we observed sicklefin lemon sharks (Negaprion

acutidens) biting down on the bait and not releasing for a few seconds, while other species,

like the sandbar shark (Carcharhinus plumbeus) or the scalloped hammerhead (Sphyrna lewini)

would never touch the bait. Overall, the white sharks (Carcharodon carcharias) proved to be

the most inquisitive of all the species encountered, having the most interactions with the bait

and obtaining the highest behavioural scores.

These differences are not surprising, as sharks occupy different ecological niches and present

a large array of life history traits. Body size, habitat, mobility, diet and mode of reproduction

are examples of factors that are highly variable in sharks (Compagno, 1990). In this study,

the response to the apparatus and bait, as well as the playback speaker and type of sound,

may be partially shaped by the combination of those life history traits. Specifically, the

reaction to sound may be related to the characteristic of the soundscape niche occupied by

different species of sharks. The habitat and its acoustics, mode of locomotion and diet are

potential factors that could shape the soundscape niche occupied by different species. As an

example, we would expect reef sharks to live in a relatively ‘complex’ soundscape, defined

by the ambient sounds of the reef, characterised by invertebrates and fish sounds (Simpson

et al., 2007; Radford et al., 2014). Additionally, sharks have been shown to possess a large

repertoire of complex behaviours and social displays (Bres, 1993; Guttridge et al., 2009a;

Jacoby et al., 2012).

The relationship between the behaviour of sharks and their physical, biological and sensory

environments is multifaceted. However, one way to investigate these links is by analysing

their brains. As a group, sharks have developed a large brain size relative to body mass,

comparable to those of many birds and mammals (Northcutt, 1977; Yopak et al., 2007), with

high levels of interspecific variability in central (Yopak, 2012) and peripheral (reviewed by

Collin, 2012) sensory organisation. Many of the Western Australian species examined here

(e.g. Carcharhinus sp., Sphyrna sp., Negaprion sp.) possess a relatively large telencephalon, a region

of the brain that has been linked to enhanced cognitive abilities and both social and spatial

complexity (Yopak et al., 2007; Yopak, 2012) and relatively large optic tecta, a region of the

brain associated with vision and visual processing (Yopak and Lisney, 2012). Thus, their brain

organization appears to reflect the requirements for living in a spatially complex habitat

(Yopak, 2012). In contrast, white sharks (C. carcharias) possess a relatively small telencephalon

(Demski and Northcutt, 1990; Yopak et al., 2007) and a relatively large optic tectum (Yopak

& Lisney, 2012), with a correspondingly large eye (Lisney & Collin, 2007), and relatively large

olfactory bulbs (Yopak et al., 2014), and are assumed to rely heavily on vision and olfaction.

These differences have led to a number of predictions about the variation in the relative

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importance of different sensory systems within and across species (Lisney et al., 2007; Yopak

et al., 2007; Lisney et al., 2008; Yopak, 2012; Yopak & Lisney, 2012; Yopak et al., 2015).

However, very little work has been done on the auditory centres in the brain of sharks

(Northcutt, 1978; Bullock & Corwin, 1979; Corwin & Northcutt, 1982), where the terminal

fields of the acousticolateralis nerve (VIIIth cranial nerve) have not been traced within the

central nervous system (CNS) in many species. It is consequently difficult to make

assumptions regarding the relative importance of hearing in certain species or even groups

of species from brain size and organisation alone. Nevertheless, we assert that a response to

a sensory stimulus is likely heavily influenced by the neuroecology of each species, where a

combination of morphological, environmental and phylogenetic factors will ultimately be

important in determining sensitivity to acoustic stimuli (Collin, 2012).

There is also a high degree of variation in inner ear morphologies across cartilaginous fishes

(Corwin, 1978; 1989; Evangelista et al., 2010; Mills et al., 2011), where behavioural studies

have shown that different species possess different sensitivities and acoustic thresholds

(Myrberg, 2001; Casper, 2006). Consequently, the differences in behavioural responses of

each species to sound documented in this study are not surprising. Although it has been

shown that neither phylogeny nor feeding strategy solely accounts for the diversity of the

elasmobranch inner ear (Evangelista et al., 2010), a wide combination of eco-morphological,

developmental, and phylogenetic factors are also likely driving these interspecific differences.

As illustrated in this study, some species likely use different strategies to respond to a sensory

stimulus, relying on more than one sense to assess threat, and the presence of food or

conspecifics. Gardiner et al. (2014) demonstrated significant interspecific differences in

sharks’ hunting strategies, where some species hierarchically switched sensory modalities

when hunting prey. Similarly, in this study, we observed a shift in reactivity to an unnatural

acoustic stimulus in the presence of prey (bait), which may suggest a corresponding shift in

sensory strategy, similar to that found by Gardiner et al. (2014). The lack of understanding

of many aspects of shark behaviour, combined with the poor knowledge of the auditory

pathways and centres within the CNS, hinder the comparative analysis of reactions to

auditory stimuli by sharks. Additional studies on the characteristics of sounds and the reactive

physiology of shark hearing would help us to understand the exact contribution of the sound

structures to the active response of sharks.

4.5.3. Intraspecific effects of sound

Our findings not only suggest that species respond differently to auditory stimuli, but also

that behavioural reactions to acoustic stimuli vary between individuals. Individual white

sharks showed significant differences in the time spent around the rig and the scores of

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behaviours exhibited. These results are very similar to those of Chapter 3, where four white

sharks displayed significantly different behaviours than the other individuals towards the

artificial sound and strobe lights presented. Although we could not identify individual reef

sharks in this study, we would predict similar results for these species, as their orders are

closely related (Lamniformes and Carcharhiniformes) and they share a similar neuroecology

(Yopak et al., 2007). Intraspecific differences could be influenced by a range of factors,

including sex, size, different life stages, group dynamics and social hierarchy. Size classes

have been shown to be important to many species with different behaviours attributed to a

size-dependant hierarchy within a group (review by Jacoby et al., 2012). Similarly, sexual

segregation is a common characteristic of shark populations (reviewed by Wearmouth &

Sims, 2008) and although the work so far has focused on spatial differences, behavioural

differences are predictable. Ontogenetic changes in the relative size of various sensory brain

regions have also been found in sharks, where for example, juveniles appeared to rely more

on vision and adults more on olfaction in seven species of elasmobranchs (Lisney et al.,

2007). It has been suggested that these changes reflect shifts in habitat throughout ontogeny

(Lisney et al., 2007; Yopak 2012), although further work is required. All these factors suggest

that the response behaviour of individuals of the same species can vary towards a sound

stimulus.

The behavioural variation between individual sharks could also be explained by recently

emerging evidence for different ‘personality traits’ in sharks. ‘Personalities’, usually defined

as a repeatable behaviour across time and contexts, have been largely demonstrated in other

animal groups (Gosling, 2001; Sih et al., 2004). A recent study by Jacoby et al. (2014) has

indicated that in a social grouping of the same species, such as the small spotted catshark

(Scyliorhinus canicula), there are individual preferences for aggregation. Two major personality

types can also be recognised in teleost fishes: bold (proactive/‘fight-flight’/active coping)

and shy (reactive/’non aggressive’/passive coping) (reviewed by Budaev & Brown, 2011; &

Castanheira et al., 2013). In the present study, we observed traits associated with these

extreme behaviours in some cases, where some individuals of the same species would stay

close to the bait, aggressively mouthing it for a few seconds, while other individuals would

not even make contact, but would only circle the underwater camera system. However, the

majority of the sharks exhibited behaviours in between those two extremes of responding

proactively versus reactively by maintaining a distance from the sensory stimuli. Should it

exist empirically in cartilaginous fishes, ‘personality’ across individuals would likely fall along

a gradient of behaviours between two extremes, but this area of research is a complex and

controversial issue in the field of behavioural ecology (Bell, 2007; Feeney et al., 2012) and

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much more work is required. In any case, these findings highlight the difficulty of studying

the effect of a sensory stimulus on a large group such as sharks, without taking into account

species differences, sexual hierarchies, and the potential for individuality.

4.5.4. Tolerance

Tolerance is defined as ‘the intensity of disturbance that an individual tolerates without

responding in a defined way’ (Nisbet, 2000). It is important not to confuse it with habituation,

which involves a reduction in response over time as individuals learn that there are neither

adverse nor beneficial consequences of the occurrence of the stimulus/disturbance (Bejder

et al., 2009). Tolerance can lead to habituation in the long term. Habituation of marine

organisms towards acoustic deterrents has been reported previously (Tixier et al., 2015) and

it has been revealed that sharks possess the ability to learn and habituate (Myrberg et al.,

1969; Nelson & Johnson, 1972; Guttridge et al., 2009b). Furthermore, habituation appears

after repeated exposure to stimuli, especially to those of non-biological origin (Sokolov, 1963;

Sanford et al., 1992), like our artificial sound. However, it seems that certain sound stimuli

are less likely to result in habituation than others and habituation does not always occur (Neo

et al., 2015). In this study, we measured the level of tolerance that the individual great white

sharks showed towards a stimulus after a few encounters (‘experience’), by observing the

change in the time they spent in the vicinity of the speaker or the change in behaviours

towards the apparatus and bait (scores). Habituation was not estimated, as it would require

sequential measures taken from the same individuals over a long period of time (Bejder et

al., 2009).

Contrary to our expectations, we did not observe any tolerance to the sensory stimuli over

time. In fact, we observed that experienced great white sharks (C. carcharias) spent

comparatively less time in the area around the rig with the artificial sound playing than with

the control treatment. One possible explanation is that, after an initial inspection, some

individuals refrained from further investigation due to the potential that the stimulus could

be aversive. This process could lead to sensitisation, where the animals learn that a repeated

or ongoing stimulus has significant consequences for them (Bejder et al., 2009). Each of our

tests only lasts for an hour, and over the course of 12 days, which is not enough time to

explore the potential temporal aspects of habituation and/or sensitisation as long term

processes. Nevertheless, our artificial sound was especially designed to have a constantly

changing factor in both the frequency and time domain, which would theoretically reduce a

potential tolerance and habituation effect. In harbour porpoises, the habituation effects to

acoustic deterrents have been reduced when different signal waveforms are produced in

random order by a single alarm (Kastelein et al., 2000). Additionally, it has been shown that

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the combination of different signal modes and targeting more than one sensory modality at

a time can reduce habituation effects (Biedenweg et al., 2011). For instance, in large

mammalian predators, such as wolves and bears, habituation to an acoustic deterrent is

diminished by adding a visual deterrent (Smith et al., 2000). A combination of multiple,

aversive multi-modal signals would represent a complex cue, which could reduce or negate

habituation.

4.5.5. Challenges of playback studies and limitations

One limitation of this study is that only one Orca sound and one Artificial Sound were

presented, which may have resulted in pseudoreplication, due to non-independent replicates

and a small sample size of the sounds tested (Hurlbert, 1984; McGregor et al., 1992). This is

a common problem in playback experiments (Kroodsma et al., 2001). Due to time and

financial constraints, our sample size was limited and it was thus decided to only test one

sound per class (class 1: Artificial Sound; class 2: killer whale calls). Ideally, we would have

liked to test multiple sounds of the same class and individually test their effects on these

different species of sharks. However, we believe that we decreased the pseudoreplication

effect with the way sounds were sampled. For the Orca sound and for the Artificial Sound,

we sampled different killer whale calls and created a segment of unique artificial soundwaves,

respectively, obtaining a 15-minute track completely free of repetition. Any sharks swimming

in and out of the ‘sonified’ zone during these 15 minutes would be fully ‘naïve’ to the sound

presented. Nevertheless, by repeating this track four times, and by repeating these treatments

at every drop, using the same speaker system, we cannot rule out pseudoreplication: we

therefore cannot make assumptions that these particular sounds are quantitatively more or

less aversive to the sharks. However, our experiments were designed to examine a relative

response to an ‘active acoustic stimulus’ compared to the ambient noise, thus representing a

binomial response rather than a preference study.

It was not possible to individually recognise every shark (i.e. for the reef sharks), which

presented another limitation to this study. As our results on great whites critically

demonstrated, intraspecific variation may have a major effect on the outcome (see also

Chapter 3). Not every shark will respond in the same way to a stimulus, and generalisations

must remain conservative. This issue is already well known in the field of impact assessment

on wildlife: Bejder et al. (2009) have stated that behavioural responses to human disturbance

are complex, influenced by a range of factors and are best understood through a framework

based on individual decision making. We suggest that the assessment of animal responses to

repellent stimuli should follow the same principles. The sequence of individual tolerance,

experience, or condition will eventually dictate a behaviour towards a certain stimulus (Gill

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et al., 2001; Bejder et al., 2009). Although we gained insights into the individual behaviours

of white sharks in South Africa, our testing in Western Australia did not allow us to

investigate further than the species-level.

Baited underwater video cameras like the ReMoRA system used in this study are now widely

used in the assessments of fish biodiversity and population density, but also provide insight

into the behaviour of fish (Mallet & Pelletier, 2014). However, the presence of bait to attract

fishes to the field of view of the cameras may bias the behaviour of the animals in focus. For

example, recent studies have demonstrated that agonistic behaviours from conspecifics and

other species may discourage some animals from approaching the baited apparatus and

negatively bias detection rates (Dunlop et al., 2015). In this study, the motivational state of

the sharks attracted to the baited ReMoRA may be raised in comparison to a non-baited

encounter. The change of behaviour with sounds presented here may have been stronger

without the bait incentive, which probably kept the sharks motivated to target the ReMoRA.

However, baited remote cameras remain one of the only non-invasive methods to study and

monitor apex predators like sharks that are typically cryptic and difficult to observe in the

wild.

4.5.6. Predictions on shark sensitivity to anthropogenic noise

In a conservation context, the fact that reef sharks changed their behaviour to a fairly low

sound pressure level (SPLrms = 150 dB re 1uPa at 2 m from source) is worrying. Most

anthropogenic sources (not only high intensity sources like seismic air guns, pile driving and

sonar, but also background noise like shipping) have much higher levels (Popper & Hastings,

2009a). For example, McCauley et al. (2003) explored the effect of an airgun on pink snapper

(Pagrus auratus), with an airgun which had a source level at 1m of 203.6 dB re 1 µPa (SPLrms).

The fish exposure to such a stimulus caused significant damage to the hair cells of the inner

ear. Here, we have shown that a low intensity sound played with a small underwater speaker

is able to significantly modify reef shark behaviour. The frequency sensitivity of sharks

overlaps with the range of anthropogenic noise, most of it lying in the low frequency range

(< 2000 Hz) (Popper & Hastings, 2009a). Coastal resident sharks, like most species referred

as ‘reef sharks’ in this study, are not migratory species and are usually living in restricted

territories, typically less than 100 km2 (Speed et al., 2010; Chapman et al., 2015). With such a

limited home range, it is probably harder for those species to swim away from noisy areas

and relocate to a non-disturbed habitat, unlike migratory species like most marine mammals.

Considering the lack of knowledge of hearing physiology and behaviour in sharks (Casper et

al., 2012), we consider that there is an urgent need for more studies on the impact of

anthropogenic noise on sharks, rays and skates.

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4.5.7. Future of acoustic shark mitigation devices

Although acoustic deterrents have proven successful against the bycatch of some marine

organisms, such as cetaceans, pinnipeds and bony fishes (Noatch & Suski, 2012; Dawson et

al., 2013; Goetz & Janik, 2013), this study shows that further work is required to assess their

efficiency on sharks. Species-specific and individual differences documented in behaviour,

ecology, and the peripheral and central nervous systems (Bres, 1993; Wetherbee et al., 2012),

(Pikitch et al., 2008), (Yopak et al., 2007) suggest that one acoustic stimulus will unlikely deter

all species of shark equally and in the same manner. In fact, targeting more than one sensory

modality at a time might be a better strategy. Combinations of sounds, lights and bubbles,

for example, which target the auditory, visual and lateral line systems of fishes, respectively,

have proven successful in various applications (Lambert et al., 1997; Welton et al., 2002;

Maes et al., 2004; Taylor et al., 2005). Our previous study (Chapter 3) also confirmed an

enhanced effect of the combination of an artificial sound and strobe lights in deterring

sharks, compared to when the strobe lights were presented alone. As suggested by Hart and

Collin (2015), a multisensory approach would be significantly beneficial when developing or

deploying shark mitigation technology.

There is also a substantial technical challenge in developing underwater acoustic repellents.

The transducer must be large enough to produce low frequency components and have

enough energy and particle motion to spread several metres in order to affect the sharks’

auditory system, which, at present, is financially and technically challenging. With these

constraints in mind, we are still far from an individual acoustic repellent device that the public

would be able to use in their daily aquatic activities or a mitigation device that can be attached

to nets or longlines. However, confined areas like beaches could potentially by surrounded

by repellent sounds and thus reduce the risk of shark-human interactions and/or bycatch. In

this context, the effect of such a sound on other marine organisms present in the area would

need to be carefully addressed. Nevertheless, in conjunction with underwater acoustic

technological advancement, there may be a possibility of developing static, long-term

management strategies for mitigation, especially if combined with another stimulus as a

multimodal system.

4.5.8. Conclusion

In summary, the present study suggests that auditory stimuli can modify the behaviour of

wild sharks, although the technical and practical aspects of producing underwater sound in

the context of shark deterrence/mitigation is still challenging. We suggest a multisensory

strategy, where sound could be combined with other stimuli to target multiple sensory

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modalities at once. An approach on individual species and/or individuals of one species is

advised when undertaking research on the effectiveness of sensory deterrents in the future.

This study also highlights the importance of exploring the potential effect of anthropogenic

noise on sharks, and our current lack of knowledge. We encourage more studies on the

fundamental biology of shark hearing and the impact of sounds on shark populations.

4.5.9. Acknowledgements

This research was funded by the Western Australian State Government Applied Research

Program to NSH and SPC. The acoustic gear was supported by a Sea World Research

Foundation grant to SPC, RDM, NSH and LC. Invaluable field assistance was provided in

both South Africa and Western Australia by the UWA team: Ryan Kempster, Caroline Kerr,

Laura Ryan, Carl Schmidt and Channing Egeberg. We also thank Laura Ryan and Caroline

Kerr for assistance with the sound calibration fieldwork. We are very grateful to Dave and

Jennene Riggs who shared their killer whale recordings, and also to John Totterdell for advice

and information on killer whales. We would like to thank Warren Newman as our skipper in

Exmouth, Western Australia. We are extremely thankful to Enrico Gennari and his team at

Oceans Research, who welcomed us in Mossel Bay and offered all the facilities and help

required to perform our field research. We also would like to warmly thank all the volunteers

that helped us during field work, especially: Denisse Fierro, Sherrie Wilson, Rachel Loud in

Exmouth; Lauren Peel, Jack Hollins, Dylan Iron, Danielle Goodreau, Olivia Seeger and Mike

Barron in Mossel Bay. We are grateful to the UWA Centre for Marine Futures, led by Prof.

Jessica Meeuwig, and her team including David Tickler, Lloyd Groves and Phil Bouchet, for

sharing their knowledge of open water video camera systems, which permitted the

development of the ReMoRA. Finally, we would like to thank Prof. Tim Langlois and his

team at the UWA Marine Ecology Group.

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101

Chapter 5

Examining the mistaken identity hypothesis: do sharks mistake the acoustic

signatures of humans for pinnipeds?

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5.1. Abstract

It has been hypothesised that negative interactions between sharks and humans occur as a

result of “mistaken identity,” where the shark misidentifies a human as potential prey. This

hypothesis asserts that humans may produce similar sensory cues to a shark’s natural prey,

such as a seal or a sea lion for the great white shark Carcharodon carcharias. This chapter tests

this hypothesis by comparing the acoustic cues produced by both a human and a shark prey

item, a pinniped, in the water. The acoustic signatures of a human swimmer, a human

paddling on a surfboard, and an Australian seal lion, recorded in a large pool enclosure, were

compared in terms of both frequency and time domains. We found spectral and temporal

differences between the sound signatures of the Australian sea lion, the swimmer, and the

surfer, and predict that the sharks mostly known for interacting with humans, like the great

white shark C. carcharias or the tiger shark Galeocerdo cuvier, would likely be able to discriminate

between the sound of its prey and of a human under these test conditions. These preliminary

results suggest that sound may not be a determinant cue contributing to a hypothesised

mistaken identity in the case of a negative shark encounter. Other sensory signals, such as

visual or olfactory cues, may prove to be more similar and ultimately support the mistaken

identity hypothesis but further research is needed.

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5.2. Introduction

Negative interactions between sharks and humans occur worldwide and typically involve just

a few species, such as the white shark (Carcharodon carcharias), the oceanic whitetip shark

(Carcharhinus longimanus), the bull shark (Carcharhinus leucas) and the tiger shark (Galeocerdo

cuvier) (Davies, 1960; Schultz et al., 1961; Baldridge, 1973; Compagno, 1987; Cliff, 1991;

Levine, 1996; McCosker & Lea, 1996; West, 1996; 2011). Although rare, they have

devastating consequences for the victims, which can then lead to heightened appeals for

lethal shark mitigation programs (e.g. shark nets, drum lines, ‘catch-and-kill’) by local

communities. Previous efforts to prevent shark bites have focused on the use of repellent

strategies such as the presentation of a strong electric field or the use of chemicals (reviewed

by Hart & Collin, 2015). Many of these repellents have been developed through trial-and-

error rather than from theoretical principles and have been found to be only partly successful

in repelling the species of sharks involved in negative interactions. What is missing—and yet

crucial to the development of reliable shark repellents—is an understanding of the sensory

cues contributing to the initiation of a shark-human interaction.

It has been hypothesised that negative interactions between sharks and humans occur as a

result of “mistaken identity,” where the shark misidentifies a human as their usual prey.

Tricas and McCosker (1984) noted the resemblance between surfers lying on their surfboards

and the silhouette of pinnipeds at the surface of the water, thus creating a sensory scenario,

where the surfer may be predisposed to attacks. Even if a particular object (human or

surfboard) may not resemble a normal prey item exactly, the stimulus characteristics may be

sufficient to trigger a predatory reaction (Burgess et al., 2010). The observation of natural

predatory events of the white shark (Carcharodon carcharias), one of the species most often

associated with shark bites (Baldridge, 1996), on pinnipeds (seals and sea lions) have been

used to interpret attack patterns on humans (Klimley et al., 1992; 1996). C. carcharias often

retreats following the first attack on the prey, allowing the prey to lapse into shock or bleed

extensively, before returning to feed on its body (Tricas & McCosker, 1984; Tricas, 1985;

Klimley et al., 1992; 1996). Conversely, humans involved in a shark interaction are often just

seized and released or lacerated on an extremity, in a one-off interaction (Caldicott et al.,

2001). This suggests that, although the initial strike might be motivated, sharks are not

primed to consume human tissue, a finding that is consistent with the mistaken identity

hypothesis.

Prey capture and feeding mechanisms of sharks have been extensively studied (reviewed by

Motta & Huber, 2012). However, we know much less about the neurosensory mechanisms

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behind the detection, identification and the approach to prey. Sharks will most likely integrate

information from all their sensory modalities to initiate any prey capture behaviour(Gardiner

et al., 2012). Gardiner et al. (2014) examined the ability of three species of shark (the blacktip,

Carcharhinus limbatus; bonnethead, Sphyrna tiburo; and nurse shark, Ginglymostoma cirratum) to

capture live prey after selective blocking of the visual, olfactory, lateral line, and

electrosensory systems. Although the species studied varied in their multimodal integration

of these cues, olfactory and visual cues were thought to be essential to these species for

discriminating a food item, whereas non-visual cues (lateral line and electroreception) were

thought to direct the strike and time jaw movements (Gardiner et al., 2014). Although the

acoustic cues were not tested in that particular study, however it has been shown that sharks

are also attracted by prey sounds (Nelson & Gruber, 1963; Myrberg et al., 1972), and may

integrate acoustic information along with other sensory cues when they locate and consume

prey items.

The hearing capabilities of sharks are poorly understood. Their frequency sensitivity is

known to range between 40 and 1000 Hz, and they are thought to be sensitive only to the

particle motion component of the sound (i.e. not the pressure component), thereby limiting

their sensitivity to the near field of a sound source (reviewed by Myrberg, 2001). However,

audible thresholds, or audiograms, of only a handful of species of elasmobranchs have been

estimated so far (reviewed by Ladich & Fay, 2013, see also Chapter 2) and almost nothing is

known about the function of hearing in sharks. This is probably because sharks do not

produce sounds and do not communicate acoustically, making it hard to identify meaningful

stimuli for this taxa. It is also difficult to control and measure the sound field in experimental

tanks and study sharks in a controlled environment (Parvulescu, 1964; 1967; Gardiner et al.,

2014; Gray et al., 2016; Rogers et al., 2016). Morphological variation in inner ear morphology

was discovered in a study comparing 17 species of elasmobranchs (Evangelista et al., 2010),

suggesting some physiological and functional diversity. In Chapters 3 and 4 of this thesis, we

demonstrated the interspecific behavioural responses of sharks to sounds in the field and in

captivity and suggested how the combination of life history traits and the characteristics of

the soundscape niche might shape the responses. However, a paucity of knowledge makes it

difficult to make precise predictions on the importance of sound and acoustic cues in locating

and consuming prey items for the species of sharks of concern to humans.

Given the panel of sensory cues that many sharks seem to simultaneously process (Gardiner

et al., 2014), it is hard to understand how some species would mistake a human for a prey

item such as a pinniped. The prey discrimination process in elasmobranchs, and how these

acute senses integrate to form a prey ‘sensory image’ is unknown (Tillett et al., 2008; Heithaus

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& Vaudo, 2012). If the mistaken identity hypothesis is correct, one or more cues emitted by

humans during water activities must resemble cues emitted by pinnipeds or other primary

prey items. Given the simultaneous processing of signals from their peripheral sense organs,

which of these cues are contributing to a human resembling a pinniped in sensory space?

In this study, we tested one aspect of the mistaken identity hypothesis by examining the

auditory cues emitted by one typical prey item of the great white shark (C. carcharias), the

Australian sea lion (Neophoca cinerea). We compared these cues with the acoustic signature of

a human (1) swimming and (2) paddling on a surfboard.

5.3. Methods

5.3.1. Ethics statement

This study was carried out with the approval of The University of Western Australia Animal

Ethics Committee (Application RA/3/100/1193) and Human Ethics Committee

(Application RA/4/1/7316), the Taronga Conservation Society Australia Animal Ethics

Committee (Application 4A/12/14), and in strict accordance with the guidelines of the

Australian Code of Practice for the Care and Use of Animals for Scientific Purposes (8th

Edition, 2013).

5.3.2. Study sites and animals

We recorded the sound produced underwater by an Australian sea lion (N. cinerea), a human

swimming (freestyle) and a human on a surfboard, paddling at the surface. The work took

place at Taronga Zoo in Sydney, Australia, in a pinniped pool enclosure of 50,000 L (3 m

deep, approximate dimensions 4 m x 5 m) in March 2015 (Figure 5-1 A, F). Only one specific

behaviour, the ‘porpoising’ of a mature female N. cinerea (48 kg, length nose-tail 1260 mm),

‘Nala’, was recorded in the enclosure, at different distances from two hydrophones (details

below). The sea lion was executing this behaviour in response to orders given by a

professional trainer. However, this behaviour is known to happen naturally in the wild in

Australian sea lions (Marlow, 1975; Smith & Litchfield, 2010). Porpoising, or leaping, is

generally defined as the high-speed surface piercing motion of pinnipeds, dolphins and other

marine species (Au & Weihs, 1980). Pinnipeds likely select movement tactics to minimise

risks of shark predation events (Laroche et al., 2008). For example, Cape fur seals

Arctocephalus pusillus pusillus have been shown to perform evasive manoeuvring in shark

infested waters (De Vos et al., 2015), defined as a fast zigzag swim at the surface combined

with high porpoising. For this reason, we focused on the acoustic signature of the porpoising

behaviour of sea lions for this study. As most negative shark-human encounters are

documented with surfers and swimmers (Levine, 1996; McCosker & Lea, 1996; West, 1996),

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we focused on only two activities on the water’s surface for the human acoustic signatures

i.e. (1) one human swimmer (female) executing a front crawl (freestyle) and (2) one human

swimmer (female) hand-paddling on a surfboard (length 5’4’’), in the same enclosure. Both

activities were performed along the same path, passing two hydrophones (see below) located

in the centre of the pool (Figure 5-1 C). The human acoustic signatures were then compared

with that of a pinniped porpoising.

Figure 5-1 Recording setup at Taronga zoo (not to scale). (A) Seal pool enclosure 50,000 L. (B) Laptop and data acquisition device. (C) Pair of hydrophones. (D) Pair of cameras placed underwater (mounted on metal bar). (E) Land camera with LED flashlight (see text for details). (F) Australian sea lion Neophoca cinerea.

5.3.3. Recordings

Sounds were recorded with two HTI 90U hydrophones (High Tech, Inc.), with specifications

that dictated that responses were linear from 2 Hz – 20 kHz. The recordings were made with

a data acquisition device (NI USB 6353, National Instruments) linked to a laptop (Figure 5-1

B). System gain was estimated with a white noise calibrator. The hydrophones were placed

approximately in the middle of the pool enclosure, suspended from a rope and positioned at

a depth of a few centimetres from the surface (Figure 5-1 C). To decrease background noise,

the system pumps of the pool enclosures were turned off and no other animals were allowed

to enter the pool. For all sampling days, background noise was recorded as a baseline

reference.

All behaviours were also video-recorded with HD GoPro Hero3 video cameras. Two

cameras were mounted on a metal bar facing upwards, and placed underwater at the bottom

of the pool (depth 3 m), immediately under the hydrophones (Figure 5-1 D). Another camera

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was placed on land, next to the enclosure (Figure 5-1 E). A white LED was installed in the

field of view of one of the video cameras to allow for synchronisation: the LED was

automatically turned on when recording started and then flashed every second until the end

of the recording. All video cameras were also synchronised together using the clapperboard

method, where the shutting of the clapstick could be identified easily on separate visual

tracks.

5.3.4. Signal processing

All signal processing was performed with custom-made codes in MATLAB (The

MathWorks, Inc., 2015). Firstly, the recordings were “de-noised” with a least mean squares

(LMS) adaptive filter for noise cancellation. Adaptive filters have the ability to adjust their

impulse response to filter out the correlated signal in the input (Chhikara & Singh, 2012;

Sireesha et al., 2013). Thus, they require little a priori knowledge of the signal and noise

characteristics. The de-noised signals were then synchronised with the videos in order to

select the desired behaviours and remove the unwanted signals (e.g. reward fish hitting the

water or bucket noise). The sound files were then divided into signals of interests (animal

porpoise stroke, human crawl stroke, human paddling stroke) and processed individually. A

stroke was defined as a single ‘splash’ signal. A Butterworth low pass filter (< 3000 Hz) was

used to remove all frequencies above 3000 Hz, which are theoretically irrelevant since the

highest frequency detected by all species of elasmobranchs examined thus far with

audiometry techniques was found to be around 1500 Hz (Ladich & Fay, 2013). Phase

components were checked for distortion. Signals were then analysed in both the frequency

and time domains.

5.3.5. Frequency domain analysis

As each signal of interest was typically composed of a few pulses (Figure 5-2), we chose to

focus on the loudest pulse of each signal, in order to keep the data independent without

losing spectral information (Figure 5-2). Power spectral densities (PSD) were built for every

pulse and the following parameters were calculated: peak frequency, 3dB bandwidth of signal

and a weighted mean frequency. Figure 5-3 shows those different measures used as examples

of PSD analysed. The weighted mean frequency 𝑓 ̅̅ ̅ was calculated instead of an arithmetic

mean in order to find the central tendency of the spectrum, rather than the true mean. It was

calculated as follows, with w = spectrum of amplitudes and f = spectrum of frequencies.

𝑓̅ =∑ 𝑤𝑖

4𝑓𝑖𝑖

∑ 𝑤𝑖4

𝑖

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Figure 5-2 Examples of acoustic signatures for (A) a human crawling stroke, (B) a human paddling stroke and (C) an Australian sea lion porpoising stroke. The waveforms, amplitude spectra, and spectrograms are shown for each type of stroke. Amplitude spectrum and spectrograms were generated from the whole window (2 s) of the waveform.

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5.3.6. Time domain analysis

The temporal signature of each signal was investigated by measuring the duration of each

stroke and the duration between strokes (inter-stroke duration) (Figure 5-3). In order to

measure the duration of the signal unambiguously, we used the equivalent energy function

to define each selected stroke, as described in McCauley et al. (2000). This calculation

included the background noise preceding the signal, the signal itself, and background noise

following the signal. From this cumulative equivalent energy curve, the 5% and 95% points

of total cumulative energy were defined as the start and the end times of the signal of interest,

respectively (Ridgway et al., 1997; McCauley et al., 2000). The inter-stroke duration was

calculated by taking the time between consecutive 5% points of cumulative energy.

Figure 5-3 Parameters measured in this study, shown here in examples from different recordings of the human crawling stroke. Time domain parameters include stroke and inter-stroke duration (A) calculated on the waveform. Frequency domain parameters include peak and weighted mean frequency (B) and 3dB bandwidth (C) calculated from the power spectral density (PSD).

5.3.7. Statistical analysis

Statistical analyses were performed using the R software (R Development Core Team, 2015)

with the package ‘lme4’ (Bates et al., 2015). A linear mixed-effect model was fitted with the

signal (‘porpoising’, ’crawling’, and ’paddling’) as the independent variable and the recording

file number (i.e. temporal sequence of sound recordings) as a random factor. The

contribution of each model term was judged by comparing equivalent models that did or did

not contain the term in question, but contained all other terms. All terms of the final model

made a statistically significant contribution to that model at the 5% level. Statistical

significance was judged using a simple permutation approach applied to the full model. This

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non-parametric approach allowed us to avoid making assumptions about the underlying

distribution of these data. The only assumptions were that observations were independent

and identically distributed under the null hypothesis. To statistically test for differences

amongst signals, the dependent variables were randomly permuted 10,000 times

5.4. Results

5.4.1. Frequency domain analysis

Overall, we collected 13 files containing the recordings of three different signals: the

Australian sea lion porpoising (10 files), the human crawling (2 files) and the human paddling

on a surfboard (1 file). We selected and analysed the pulses of highest intensity in strokes

recorded for each of the three signals. There were 123 pulses selected in total: 38 human

crawling strokes (‘crawling’), 28 human paddling strokes (‘paddling’) and 57 Australian sea

lion porpoising strokes (‘porpoising’). The peak frequencies and the weighted mean

frequencies of the human strokes (crawling and paddling) were both significantly lower than

the sea lion porpoising (Table 5-1, Table 5-2, Figure 5-4 A and B). However, no differences

were observed for the 3dB bandwidth between the three signals (Table 5-1, Table 5-2, Figure

5-4 C).

Table 5-1 Results of the mixed model for the parameters: peak frequency, weighted mean frequency, 3dB bandwidth, duration of strokes and inter-stroke duration. The signals (human crawling, human paddling, sea lion porpoising) are considered as fixed effects. Stars indicate significance: *: p≤0.05, **: p≤0.01.

Response Effects df F p

Peak frequency Signal 2 7.50 0.01*

Comparison Estimate SE Z p

paddling – crawling -58.78 467.70 -0.13 0.9

porpoising – crawling 1034.12 322.91 3.20 <0.01**

porpoising –paddling 1092.81 401.74 3.72 <0.01**

Weighted mean frequency

Signal 2 14.70 <0.01**

Comparison Estimate SE Z p

paddling – crawling -80.42 591.36 -0.13 0.78

porpoising – crawling 1018.71 391.16 2.60 <0.01**

porpoising – paddling 1099.13 507.42 2.16 0.03*

3dB bandwidth Signal 2 3.27 0.56

Duration of strokes Signal 2 4.85 0.9

Inter-stroke duration

Signal 2 677.57 <0.01**

Comparison Estimate SE Z p

paddling – crawling 0.24 0.07 3.37 <0.01**

porpoising – crawling 1.23 0.05 21.35 <0.01**

porpoising – paddling 0.98 0.07 13.62 <0.01**

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Figure 5-4 Boxplots showing the differences for the frequency domain analysis: (A) peak frequency, (B) weighted mean frequency, and (C) 3dB bandwidth; and the results for the time domain analysis: (D) duration of strokes and (E) inter-stroke duration. Statistical significance of the factor’s contribution to the model is shown with a star.

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Table 5-2 Summary of means for parameters calculated (peak frequency, weighted mean frequency, 3dB bandwidth, stroke duration and inter-stroke duration) for the human crawling strokes (red), the human paddling stroke (orange) and the Australian sea lion porpoising stroke (blue). SD = standard deviation. N= sample size.

Peak frequency

(Hz)

Weighted mean

frequency (Hz)

3dB bandwidth

(Hz)

Stroke duration

(s)

Inter-stroke duration (s)

n mean SD mean SD mean SD mean SD n mean SD

crawling 38 287 393 292 387 118 49 0.25 0.17 143 0.22 0.11

paddling 28 343 139 354 133 125 40 0.34 0.23 49 0.45 0.19

porpoising 57 1386 935 1366 255 152 87 0.57 0.19 40 1.44 0.34

5.4.2. Time domain analysis

There were no significant differences between the duration of the strokes for the paddling,

crawling or porpoising (Table 5-1, Table 5-2, Figure 5-4 D), although we observed a tendency

for the sea lion porpoising strokes to last approximately twice as long as the human strokes

(Table 5-2, Figure 5-4 D). Conversely, the duration between strokes was significantly

different depending on the signals: the longest inter-stroke duration was found for the

Australian sea lion porpoising and the smallest inter-stroke duration was found for the

human swimming (‘crawling’, Table 5-2, Table 5-1, Figure 5-4 E). The crawling swim was

performed with about 4.5 strokes per second (hand and feet movements), the paddling stroke

on the surfboard averaged at 3 strokes per second and the Australian sea lion took 1.4

seconds (on average) between each porpoising stroke (Table 5-2).

5.5. Discussion

We investigated whether the underwater acoustic signature of an Australian sea lion

performing porpoising strokes was similar to the acoustic signatures of a human paddling on

a surfboard or executing a front crawl. Our results show that the sea lion porpoising strokes

are spectrally and temporally different from both the paddling and crawling strokes (Figure

5-4). The differences are also visually evident on the waveforms, amplitude spectra and

spectrograms of the examples pictured in Figure 5-2.

The spectral analysis showed that the peak and weighted mean frequencies produced by an

Australian sea lion porpoising stroke were higher than the human strokes, whether they were

from paddling on a surfboard or executing a front crawl. The frequencies of the sea lion

porpoising strokes also covered a wider frequency range than the others, suggesting a large

variability in the frequency spectrum of single porpoising strokes. Temporally, the human

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inter-stroke duration (representing the ‘tempo’ of the activity) was shorter than the sea lion

porpoising strokes.

5.5.1. The question of mistaken identity

Our results revealed large differences in the acoustic signatures of the Australian sea lion

versus a human paddling on a surfboard or performing a front crawl. There are two different,

but non-mutually exclusive ways, to interpret these results in the context of the mistaken

identity hypothesis.

Firstly, the outcome of this study suggests that the hypothesis, if correct, is not driven by

acoustic cues. Elasmobranchs, like all vertebrates, are thought to possess the ability to

discriminate between frequency and time domains (Popper & Fay, 1993). Frequency

discrimination has been demonstrated in a number of fish species (Jacobs & Tavolga, 1968;

Fay, 1970; Marvit & Crawford, 2000; Simpson et al., 2008), although the mechanism is not

completely resolved (reviewed by Popper et al., 2003). Nelson (1967) used approach-

avoidance and escape conditioning experiments with lemon sharks, Negaprion brevirostris, to

demonstrate frequency discrimination at the level of 0.5 octave (40 Hz vs 60 Hz). Hence,

predatory species, like the white shark (Carcharodon carcharias), may likely differentiate the

acoustic signature of a human paddling or crawling in the water versus a pinniped, at least

for the Australian sea lion (Neophoca cinerea). Therefore, other sensory modalities might play

a larger, if not a crucial, role in the perhaps mistaken approach towards a non-natural prey

object. Vision has been suggested to be the principal sense in directing sharks to food at mid-

range from the prey (< 3 m), although this is dependent on water visibility (Eibl-Eibesfeldt

& Hass, 1959; Strong, 1996; Gardiner et al., 2014) and the relative importance of vision to

other senses, which varies substantially between species (Yopak et al., 2007; Yopak, 2012;

Yopak & Lisney, 2012). Studies have confirmed visually-mediated behaviours for the white

shark (C. carcharias) as well, showing that this species takes advantage of the backlighting and

low-light conditions (dawn and dusk) to hide from their intended prey, the Cape fur seal

(Arctocephalus pusillus pusillus), and ambush from depth (Martin & Hammerschlag, 2012). In

addition, it has been suggested that white sharks approach their prey by positioning the sun

directly behind them, possibly to increase visual detection and prey identification (Huveneers

et al., 2015), thus emphasising the importance of vision for this species. Swimmers and

surfers paddling on their boards visually resemble pinnipeds and a white shark may be

predisposed to an attack event if they are already in a hunting mode (Tricas & McCosker,

1984). In comparison, electrosensitivity and mechanoreception (lateral line) are thought to

act at closer range (<20 cm) from the prey (reviews by Collin, 2012; Gardiner et al., 2012),

and bioelectric fields seemed to be significantly important only in the last stages of the

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hunting event in the bonnethead (S. tiburo), blacktip (C. limbatus) and nurse (G. cirratum)

sharks studied by Gardiner et al. (2014). Even if the human bioelectric fields and/or

hydrodynamic cues were similar to the signals emitted by a prey object, they would probably

not represent the initial source of motivation for a strike from a great white shark, for

example. Conversely, olfaction is thought to play an important role in the early detection

phase of prey hunting, as chemical signals (odours) can travel far in the aquatic environment

(Hueter et al., 2004; Meredith & Kajiura, 2010; Collin, 2012; Gardiner et al., 2014). There is,

however, no indication as to whether the chemical compounds emitted by a human being in

the water would resemble the scent of pinnipeds or other prey and initiate an interaction. We

hypothesise that vision and/or olfaction might be the leading sensory modalities in the case

of a mistaken identity situation.

Secondly, it is plausible that the acoustic signatures of a human swimming or paddling on a

surfboard, although different from the acoustic signature of pinnipeds, might resemble other

potential prey items, or may simply be novel, and thus may be generating a sensory cue that

is attractive to certain species of shark. The low frequencies emitted by the human recordings

(between 200 – 400 Hz) overlap the range of shark auditory sensitivity, which peaks between

200 and 600 Hz for most species studied (Popper & Fay, 1977; Corwin, 1981a; 1989;

Myrberg, 2001; Casper & Mann, 2006; 2007a; 2009; Casper et al., 2012). The low frequency

impact sounds of the arms and legs of humans at the surface of the water could be similar

to the sound produced by injured fish, showing a low frequency spectral signature and a

pulse-like temporal signature (Nelson & Gruber, 1963). Early studies suggest that the sound

of wounded fish acts to attract some species of large sharks (Nelson & Gruber, 1963; Nelson

et al., 1969; Nelson & Johnson, 1976), although this requires further investigation in C.

carcharias. More recently, Radford and Montgomery (2016) showed that diving gannets

produce distinctive acoustic signatures that could potentially be detected by a range of pelagic

predators, including sharks. In addition, some sharks could be attracted to unknown, novel

stimuli. In Chapter 3, we highlighted the behavioural differences observed in the captive Port

Jackson (Heterodontus portusjacksoni) and epaulette (Hemiscyllium ocellatum) sharks, and the great

white shark (C. carcharias), in response to an artificial sound and a strobe light. We suggested

that large species from a high trophic level, like C. carcharias, may exhibit ‘inquisitive’

behaviours and be attracted by novel stimuli and objects. A great white shark might thus

recognise the sound of a human swimming or paddling on a surfboard as a novel stimulus

significant enough to trigger a closer inspection.

The distance from which the sharks could detect the strokes would depend on a number of

factors: hearing threshold of a given shark species, strength of the acoustic signal, type of

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sound source, the underwater acoustics of the area (e.g. shallow or deep, coral or sand

substrate), and competing sound sources (anthropogenic or natural) that could mask the

signal. Understanding the relationship between these variables is critical to make assumptions

and predictions regarding the orientation behaviour of sharks towards an acoustic cue.

However, both the lack of knowledge of shark auditory sensitivity and the nature of

underwater sound propagation makes any prediction and generalisation unrealistic at this

stage. The splash created by a stroke at the surface of the water will be affected by reflection

and refraction (water-air interface) in the shallow water-column and scattering from the

bubbles created by the drop of the body part in the water (Urick, 1979; Rogers & Cox, 1988).

Sharks, devoid of air cavities or a gas organ such as the swim bladder, are only sensitive to

the particle motion of the sound, and therefore, will have a limited maximum detection range

of a few metres, depending on the frequencies (Kalmijn, 1988). To add to the difficulty, no

audiograms are available for most species of sharks involved in negative interactions with

humans, such as the white shark (Carcharodon carcharias), the oceanic whitetip shark

(Carcharhinus longimanus) and the tiger shark (Galeocerdo cuvier). We highlight the need for basic

sensory information of these species to understand their behaviour towards their prey and

their interactions with human beings.

5.5.2. Limitations

The outcomes of this study are limited by the fact that only one individual of one species,

the Australian sea lion, Neophoca cinerea, was considered. Similarly, we recorded only one

individual human performing the paddling and front crawling strokes. In particular, variation

between the acoustic signatures of different species of pinnipeds porpoising behaviour might

exist. There are numerous species of pinnipeds known to be hunted by large sharks like great

whites and tiger sharks (reviewed by Wetherbee et al., 2012), all with different biomechanics

(mass and swimming speeds) (Fish, 2010). However, ultimately, the sound of an object (here,

a pinniped), dropping from the air into the water has the distinct characteristic of the gas-to-

liquid entry sound (‘splash’), typically composed of three phases: an impact (usually a

broadband signal), a quiet interval, and finally bubble oscillations (with lower frequencies)

(Franz, 1959; Sun & Li, 2013). Therefore, the frequency spectrum of the splash, independent

of the species, may not be highly variable amongst pinnipeds. Yet, the temporal scale of the

signal and the length of the signal may change with differences in swimming speed and angle

entry (notably the ‘quiet’ interval will shift) (Sun & Li, 2013). In addition, we expect the stroke

rates of different pinniped species to vary depending on body size, as small pinnipeds will

swim with a higher stroke rate than larger ones (Sato et al., 2007). Similarly, different

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swimmers or paddlers, of different sexes and swimming abilities, will likely emit similar

frequencies but at different rates (faster or slower stroke rates).

The fact that our animal was a captive Australian sea lion also represents a limitation. The

porpoising strokes that we observed and recorded may demonstrate differences compared

with wild individuals. Although little is known of the extent of variation in behaviour between

captive and free-living Australian sea lions (Smith & Litchfield, 2010), their limited access to

large areas of water, the lack of opportunities for foraging and the lack of predators, could

influence their porpoising behaviour. However, as for humans, we expect the temporal

structure (stroke rate) to vary rather than the frequency spectrum (essentially a splash).

Finally, the tank underwater acoustics may have affected our recordings (Parvulescu, 1964;

1967; Gray et al., 2016; Rogers et al., 2016). Ideally, sound signatures should be recorded in

open water to benefit from a relatively simple acoustic field. However, the practical

difficulties of encountering these animals in the wild, while collecting usable recordings, were

limiting. To control for the sound field affecting the recordings, it would have beneficial to

repeat the experiment in different tanks. We emphasise the need to test more individuals

from different species, preferably in the field.

5.6. Conclusions

In conclusion, we have shown that the acoustic signature of an Australian sea lion is spectrally

and temporally different from the signature of a human paddling on a surfboard and

swimming with a front crawl within an enclosure. Based on these differences, we predict that

a great white shark would likely be able to discriminate between the sound of its preferred

prey item and that of a human. These preliminary results suggest that the sound is likely not

a contributing factor to mistaken identity, often stated as an explanation for negative shark-

human interactions. Other sensory modalities, like vision, might still prove the hypothesis to

be valid. However, the sound frequencies created by human activities in the water overlaps

with the peak hearing sensitivity of elasmobranchs, and might represent an attractive cue to

certain species of sharks.

5.7. Acknowledgements

This research was funded by the Western Australian State Government Applied Research

Program (ARP) scheme to NSH and SPC. We would like to thank Laura Ryan for her

generous help and commitment to perform the human signatures recorded in this study. We

are grateful to the Taronga Zoo authorities for allowing us to work with their animals in their

facilities, especially Dr David Slip from the Australian Marine Mammals Research Centre,

the animal keepers and trainers Ryan Tate, Lindsay Wright, and all their team for their

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invaluable assistance. We are also immensely grateful to Dr Jan Hemmi for assistance with

signal processing and analysis.

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119

Chapter 6

General discussion

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This thesis explores the acoustic world of sharks and has identified the behavioural responses

of a number of elasmobranchs species to acoustic signals (and other sensory cues) by using

a novel technique to observe sharks in the wild. The thesis also outlines a methodology for

examining the hearing sensitivity of elasmobranchs, while adhering to best practice for

animal welfare, and explores whether audition plays a role in triggering a negative interaction

between sharks and humans.

The hearing abilities of elasmobranchs, including the detection of sound, frequency

discrimination, sound source localisation, and auditory processing at the level of both the

inner ear and the auditory centres in the brain, are poorly understood. This paucity of

information also extends to how elasmobranchs respond to sound in the wild, how this

affects their behaviour (feeding, social interactions, reproductive strategies) and how this

might change during development and over their, often long, life history. However, the fields

of bioacoustics and acoustic technology have undergone rapid change over the past 60 years,

resulting in quite sophisticated methods to monitor, interpret and model how sound

propagates in the ocean. Further, the acoustic landscape within the ocean has also changed.

From around the 1950s, there has been a rapid increase in the use of mechanical propulsion

vessels in the shipping industry. Other types of human-made (anthropogenic) noise have also

increased via pile driving, seismic surveys, explosions, and sonars. We believe we have a

responsibility to understand the relationship between the ocean’s aquatic inhabitants and

their acoustic environment - their soundscape.

A number of pioneering studies in the 1970s and 1980s focused heavily on the hearing

abilities of elasmobranchs, but there has been little research performed since then. Only the

work of Casper and Mann (2006; 2007a; 2007b; 2009) has explored the hearing sensitivity of

selected elasmobranchs, but still a large knowledge gap remains. This thesis aims to fill some

of these voids and create a new research framework around advancing the field with respect

to animal welfare, the impacts of sound on behaviour, and providing a platform for studies

that incorporate both laboratory-based experiments and field-based studies. Specifically, this

study sought to answer three questions:

Is there scope to revise the current electrophysiological techniques available to

study hearing abilities in elasmobranchs, to improve both the validity of the data

and the welfare of the animal?

Can sound (and other sensory cues) be considered as a means to repel sharks

from specific areas?

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Does the sound produced by humans in the water potentially resemble that of a

typical shark prey object, and thus serve as an attractant?

In this last chapter, we will synthesise our findings, highlight the contributions and

implications of our results to the field and, most importantly, issue recommendations for

future research.

6.1. Implications and future directions

6.1.1. Auditory evoked potential audiometry: criticisms and recommendations

Investigating sensitivity and frequency range sets the limits of sound detection and is an

essential component to understand the hearing function of any species. Electrophysiological

thresholds are easily established with auditory evoked potentials (AEPs), which is a non-

invasive and rapid technique to study hearing in vertebrates. AEPs have therefore been used

extensively to study hearing and reveal both intra- and inter-specific differences in a range of

aquatic species. Hearing sensitivities of more than 100 species of fishes (primarily teleosts)

have been investigated with the AEP technique (Ladich & Fay, 2013). However, the

technique has recently been under review and often criticised. When constructing

audiograms, AEPs often provide different thresholds compared to behavioural conditioning

techniques (Ladich & Fay, 2013; Sisneros et al., 2016). In addition, reproducibility is often an

issue as there appears to be large differences in AEP audiograms produced for the same

species of fish, but in different laboratories (Ladich & Fay, 2013).

Behavioural audiograms arguably represent a more realistic estimation of the hearing

thresholds of an animal, as they measure responses resulting from the integration of the

entire auditory scene. Results from this approach are often recommended when audiograms

are necessary for applied outcomes, such as the effect of anthropogenic noise on a specific

species (Hawkins & Popper, 2014; Hawkins et al., 2015). However, behavioural audiograms

require conditioning techniques coupled with psychophysical methods (reviewed by Fay,

1988), requiring long training periods and testing trials, which are obviously time-consuming

and often not possible. Some animals are unresponsive to training and thus cannot be

investigated using conditioning methods. Moreover, discrepancies between behavioural

thresholds generated in different laboratories on the same species suggest that the problem

is not specific to AEPs and may originate from a lack of standardisation.

Since Kenyon et al. (1998) produced the first protocol for AEPs in bony fishes, modifications

naturally arose as researchers diversified the species and questions investigated. In terrestrial

vertebrates, including for human assessments, more rigorous methodologies and procedures

have been developed to standardise the stimulus types and the methods for estimating

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thresholds (Vidler & Parker, 2004; Ramsier & Dominy, 2009). The same recommendation

for standardisation has been recently suggested by Sisneros et al. (2016) for AEPs in aquatic

animals (fishes and marine mammals). The results of the second chapter of this thesis

strongly support such an initiative. We obtained an audiogram for the brownbanded bamboo

shark (Chiloscyllium punctatum), which was different from an audiogram of the same species

from other studies (Casper & Mann, 2007b). We also discussed the impact of a restrained,

but unanaesthetised, animal on the stability and validity of AEP recordings, and showed that

the use of anaesthesia does not have a detrimental effect on the AEP recordings in C.

punctatum, but addresses major animal welfare issues. Overall, our experience with AEPs

suggests that a number of issues should be addressed to make full use of this technique. The

following is a synthesis of the elements, which we believe should be standardised:

a) Vibration source

Different sources have been used to produce vibrations and evoke auditory

potentials. The most common sources used in fish hearing studies include

monopoles (e.g. underwater or air speaker), dipoles (e.g. plastic ball driven by a

mechanical shaker) and three-dimensional shakers. Large differences between

audiograms of the same species generated from different sources have been observed

in elasmobranchs (Casper & Mann, 2007a; 2007b). The sound fields created by these

sources present large discrepancies. Monopoles have essentially omnidirectional

radiation, while dipoles are directional along the axis of motion (Kalmijn, 1988).

Shaker tables can provide whole body acceleration independently in three directions,

and thus only produce particle motion without the pressure normally generated by a

sound (Casper & Mann, 2007b). Those sources will potentially evoke responses from

specific areas of the inner ear, notably due to the polarisation of the hair cells.

Different vibratory sources might also provoke stimuli for both lateral line and inner

ear sensory channels (Braun & Coombs, 2010; Dailey & Braun, 2011). Therefore, the

source used to generate AEPs should be carefully selected depending on the aim of

the study. Although, dipole stimuli more closely represent biological sounds that

fishes would detect in the wild (Rogers & Cox, 1988), they are not as commonly used

in hearing studies as monopole stimuli. Therefore, the audiograms generated with

dipole sources are not readily comparable with most published datasets.

b) Stimuli presented

Most AEP studies in bony fishes estimate sensitivity of the auditory system using

short (e.g. 10-50 ms) tone bursts or click stimuli (Kenyon et al., 1998). The

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behavioural thresholds, however, are typically obtained using pure tones of longer

duration (e.g. 300-1000 ms), with a long rise/fall time. This approach prevents signal

distortion by the speaker (spectral splatter), but auditory potentials cannot be evoked

with such long stimuli. AEPs are evoked by the ‘changes in the sound’, such as the

amplitude difference between the onset and offset of a tone (Burkard et al., 2007)

and requires short duration and rapidly repeated stimuli. Therefore, differences are

expected between behavioural conditioning and AEP techniques and any

extrapolation of AEP thresholds back to relevant signal levels in nature is considered

inadequate (Ladich & Fay, 2013; Sisneros et al., 2016). However, it is not well known

how the type of stimulus used for AEPs affects the estimation of thresholds. The

short signals most commonly used include clicks or tone bursts of different duration

and presented at different rates. The number of cycles in a tone burst is usually

adjusted at each test frequency to obtain the best compromise between the rapid rise

to steady level and the duration of the signal (Kenyon et al., 1998; Burkard et al.,

2007). Blackman or Hanning windows are typically used on the tone bursts to try to

reduce spectral sidelobes and provide ramped onsets and decays. It is unknown how

changes in the characteristics of these stimuli (duration, onset/decay duration,

windowing) affect the threshold in the resultant audiogram. In marine mammals, it

is known that some stimuli are more effective at evoking auditory potentials in certain

species than others (Supin, 2012). It is possible that the same applies for fishes.

Therefore, one should carefully consider the stimuli to be presented, depending on

the particular study question, and repeat the experiment using other methodologies

if a comparison is required.

c) Speaker and fish position

Ladich and Wysocki (2009) demonstrated that speaker choice (underwater or air

speaker) and fish position (close to the surface or deeper in the water column) were

not the primary reasons for the reporting of different AEP thresholds within the

same species (i.e. goldfish). However, for elasmobranchs and other fishes sensitive

solely to particle motion, the setup must be calibrated for particle motion levels in

addition to sound pressure. Great care should be exercised while measuring particle

motion since, once again, only a few techniques have been described (reviewed by

Nedelec et al., 2016), and no comparisons have been made to determine how these

measurements compare.

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d) Temperature

Temperature is known to affect physiological processes and has reportedly altered

AEPs thresholds in some bony fishes, especially the eurythermal species (i.e. those

tolerating a wide range of temperatures) (Mann et al., 2009; Wysocki et al., 2009b;

Papes & Ladich, 2011; Maiditsch & Ladich, 2014). The situation would also be true

for most elasmobranchs, although it may be different for some sharks that have

evolved the ability to retain metabolic heat within the circulatory system, e.g.

members of the Lamnidae family, such as the mako shark Isurus oxyrynchus and the

white shark Carcharodon carcharias (Bernal et al., 2012; reviews by Morrison et al.,

2015). The hearing abilities should always be tested at the known average temperature

for the species and this temperature should be always referenced for future studies

on the same species.

e) Anaesthesia and immobilisation

Although the results presented here should be validated with further species, based

on our study (Chapter 2), we suggest that a low dose of anaesthesia, at a level

sufficient to induce a stage III.1 (levels of anaesthesia as described in Brown, 1993)

is used in every AEP experiment to standardise the quality of the data acquired.

Immersion anaesthesia with constant maintenance will produce the most stable

recordings, while ensuring the welfare of the fish.

f) Threshold estimation

The estimation of auditory thresholds is probably one of the most important factors

to standardise in any future AEP studies. As suggested recently by Sisneros et al.

(2016), future studies should use objective AEP threshold determination techniques.

Most laboratories (including ours, see Chapter 2) are tempted to develop their own

threshold criteria. Although this is a valid approach for comparative studies within

the laboratory, it becomes problematic when the aim is to determine absolute

thresholds. Xiao and Braun (Xiao & Braun, 2008) have developed an objective

method of threshold determination based on a comparison between AEP amplitude

and controlled residual noise using a signal detection theory approach to set specific

threshold criteria. We suggest that such a methodology is used in all future AEP

audiometry in bony and cartilaginous fishes.

g) Acoustic field and experimental tank

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Following on from the review of Parvulescu (1967), Rogers et al. (2016) noted the

difficulty of assessing the acoustic field, while carrying out hearing experiments in

small tanks. An alternative proposed is the use of a traveling-wave tube, allowing

both sound pressure and particle motion to be individually specified (Parvulescu,

1964; Rogers et al., 2016), similar to the one used in Chapter 2. While some

investigators have used such a tube for AEPs in bony fishes (Hawkins & MacLennan,

1976; Martin & Rogers, 2008), it has never been used in a study on elasmobranchs.

h) Intraspecific differences: size, life stages and sex

Although size does not seem to account for variability in goldfish hearing sensitivity

(Ladich & Wysocki, 2009), ontogenetic and size changes in the auditory sensitivity

have notably been demonstrated in other species of bony fishes, e.g. members of the

damselfish family (Pomacentridae) (Kenyon, 1996), members of the catfish order

(Siluriformes) (Lechner & Ladich, 2008; Lechner et al., 2010), and the zebrafish

(Danio rerio) (Wang et al., 2015). Sex-specific differences in hearing sensitivity have

only been rarely reported, e.g. in the goby (Neogobius melanostomus) (Zeyl et al., 2013),

with most species studied revealing no difference between sexes (Ladich, 1999;

Maruska et al., 2007; Schulz-Mirbach et al., 2011; Hadjiaghai & Ladich, 2015).

Depending on the question, controlling for size, sex and life history differences may

thus be important. If comparing different species, one should target animals of the

same sex and same stage of development. The number of individuals tested should

be sufficient to establish reasonable confidence in the quality of the measurements.

Standardising the evoked potential audiometry will undoubtedly improve the effectiveness

of this method as an indicator of the sensitivity of the auditory system in specific species. We

believe that this is a valuable approach to assist other hearing studies and, if adhered to,

would expand the number of comparative studies appreciably.

To be able to fully extrapolate on our results presented in Chapter 2, we plan to add AEP

measurements from the goldfish (Carassius auratus). If the results are consistent, the argument

of using a low dose of anaesthesia in future AEPs (in both bony and cartilaginous fishes) will

be confirmed and strengthened. We will also test the same species with higher dosage of

anaesthesia, to explore the dosage-dependent effect(s) of anaesthesia on AEP amplitude and

latency. Such a process is widely known in humans, where AEPs are actually used as a tool

to characterise the depth of anaesthesia in surgery (Plourde, 2006).

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6.1.2. Using sound as a mitigation strategy

In Chapters 3 and 4, we investigated the use of sounds as sensory cues to mitigate shark

presence around fisheries equipment or areas populated by humans. We observed the

species-specific responses of sharks to sounds in the wild and in captivity. Our results

highlight the difficulty of targeting such a large taxonomic group (i.e. elasmobranchs), which

comprise more than c. 1200 species with various body forms, lifestyles, habitat and behaviour

(Compagno, 1990; Last, 2007; White & Last, 2012). Similarly and as discussed in Chapter 5,

the integration of sensory modalities used for the detection of any object in the water (e.g.

prey item or human) also differ between species (Gardiner et al., 2014). Therefore, despite

attempts to exploit the sensory systems of elasmobranchs for more than 70 years, no

deterrent has proved effective for all species and situations (reviewed by Hart & Collin,

2015). Our results strongly support the presence of inter- and intraspecific differences in

individual responses to a specific sensory cue presented (i.e. a sound). While only a few

species of sharks are involved in negative human-shark interactions, the bycatch (incidental

catch) concerns almost all species, with an emphasis on coastal species (Dulvy et al., 2014).

A multisensory approach, as suggested in Chapter 3, is likely the most effective way to deter

those animals, which use all their senses to catch prey. A species- and fishery-specific

approach would also be the most realistic to reduce bycatch (Jordan et al., 2013).

In Chapter 3, the addition of the sound treatment to the strobe light appeared to enhance

the aversive effect of the sensory cues on the great white shark C. carcharias. The blacktip

shark (Carcharhinus limbatus), the bonnethead (Sphyrna tiburo), and the nurse shark

(Ginglymostoma cirratum) have shown to process multiple sensory cues simultaneously and

alternate between different sensory signals to capture a prey item (Gardiner et al., 2014).

Some sharks may thus be capable of processing a task (e.g. capturing prey), even when the

optimal sensory cues are unavailable, by switching to alternate sensory modalities. Such

plastic feeding behaviour is not unique to elasmobranchs, and multisensory approaches have

been suggested and employed in a range of vertebrate taxa to repel the animals more

efficiently in order to protect resources from depredating wildlife (Beauchamp, 1995). For

example, combined visual, aural and chemical stimuli have been proven more effective than

individual component stimuli to repel red-winged blackbirds (Agelaius phoeniceus) from rice

fields (Avery & Mason, 1997). In brown trout (Salmo trutta), avoidance was enhanced when

light was added to the repelling effect of abrupt accelerations of water flow velocity (Vowles

& Kemp, 2012). A combination of sounds and an illuminated human effigy were found to

effectively frighten the white-tailed deer (Odocoileus virginianus) from browsing in crops areas

(Beringer et al., 2003). Multiple sensory cues can increase the repellent effect at different

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levels, not only affecting several sensory systems at once, but also improving the detectability,

discriminability, and memorability of the aversive stimuli (Avery, 1995). A repellent strategy

targeting numerous sensory systems together might thus prove to be more effective in

repelling various species of elasmobranchs in diverse scenarios, for both human protection

and bycatch reduction.

6.1.3. Anthropogenic noise

The sound stimuli presented in the wild (Chapter 3) resulted in a behavioural response from

seven species of benthopelagic reef sharks: sicklefin lemon sharks (Negaprion acutidens),

bronze whalers (Carcharhinus brachyurus), grey reef sharks (Carcharhinus amblyrhynchos), dusky

sharks (Carcharhinus obscurus), sandbar sharks (Carcharhinus plumbeus), scalloped hammerhead

sharks (Sphyrna lewini), and zebra sharks (Stegostoma fasciatum). The reef sharks were less

inclined to approach the bait/apparatus and to interact when the playback of an artificially-

made sound or an orca call was presented. Although the primary aim of the study was to

investigate the ability of sound to repel sharks, these results raise concerns on the effect of

anthropogenic noise on elasmobranchs. Anthropogenic sound is a relatively recent addition

to the marine soundscape, driven by a range of sources of noise, such as shipping, pile

driving, seismic surveys, explosions, sonar, deep-sea mining activities, dredging and motor-

powered recreational and commercial crafts (Hildebrand, 2009). The sounds produced by

these activities have been found to elicit behavioural reactions and shifts in many marine

species, changes in whole populations of organisms and even physical injury (Nowacek et

al., 2007; Popper & Hastings, 2009b; Slabbekoorn et al., 2010). The level of noise in the sea

has been linked to the global economy (Frisk, 2012), whereby shipping constitutes 90% of

the method of trade between different countries (IMO 2016), and it is certain to continue to

increase as the ocean becomes more industrialised. Most research efforts have been focused

on how these disturbances affect large iconic marine mammals, such as dolphins and whales,

by describing their auditory systems and studying the impacts of noise on their movement

patterns, social interactions, communication systems and feeding behaviour (Slabbekoorn et

al., 2010; Ketten, 2014). More recently, the impacts of anthropogenic noise on invertebrates

(Morley et al., 2014) and bony fishes (Popper et al., 2014; Hawkins et al., 2015) have been

investigated. Studies involving marine mammals and bony fishes have revealed that

anthropogenic noise can cause auditory masking and can lead to inner ear damage, changes

in individual and social behaviours, altered metabolism, reduced population recruitment, and

can subsequently affect the health and service functions of marine ecosystems (Peng et al.,

2015). Although the literature still has large knowledge gaps and extrapolation from species

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to species is not possible, these studies recognise that various types of anthropogenic noise

can affect a variety of marine taxa (Williams et al., 2015).

To our knowledge, there have been no studies that have addressed the effects of

anthropogenic noise on elasmobranchs. There are, however, some concerning facts, which

suggest a potential vulnerability of this taxa to noise pollution. The hearing sensitivity of all

elasmobranch species tested so far ranges from 40 to 1500 Hz (reviews by Myrberg, 2001;

Ladich & Fay, 2013). This range overlaps dangerously with the frequency of sounds

produced by human-generated sources and therefore the risks cannot be under estimated

(Casper et al., 2012). Figure 6-1 shows the frequency range of some anthropogenic sound

sources, and the frequency hearing range of all the species of elasmobranchs investigated

thus far. Unfortunately, anthropogenic noise is usually expressed in sound pressure levels

rather than particle motion, which makes it difficult to precisely overlap the particle motion

audiograms of elasmobranchs with the sound profiles of anthropogenic sources. Our

observations from Chapter 4, where we found a small underwater speaker was sufficient to

disrupt the behaviour of reef sharks, is equally concerning. We emphasise the critical and

timely need to investigate the impacts of changing levels of anthropogenic noise on this

taxon.

Recommendations have been issued to guide future research that would advance knowledge

on the effects of man-made sounds on fishes (including elasmobranchs) (Normandeau

Associates, Inc, 2012; Popper et al., 2014; Williams et al., 2015). The first priority is to

measure the hearing abilities of a wider range of species using audiometry. With less than ten

species of elasmobranchs investigated, there is a need for supplementary information

acquired using a standardised AEP technique and/or behavioural tests. To enhance our

understanding of the acoustic ecology of this taxon, species from different phylogenetic and

ecological niches should be selected for audiometry. More specifically, the species of interest

for industry managers and regulators should be identified and investigated (Hawkins et al.,

2015).

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Figure 6-1 Hearing range of elasmobranch species for which an audiogram has been published (in blue) and frequency range for some examples of anthropogenic noise sources (in orange). The vertical dashed line demarcates the typical frequency range of most anthropogenic noise sources present in the ocean (10 – 1000 Hz). Sources: 1 - (Casper & Mann, 2006); 2 - (Banner, 1967); 3 - Chapter 2; 4 - (Casper & Mann, 2007b); 5 - (Casper & Mann, 2007a); 6 - (Kritzler & Wood, 1961); 7 - (Casper & Mann, 2009); 8 - (Casper et al., 2003); 9 - (Greene & Moore, 1995); 10 - (Hildebrand, 2009)

The second priority is to investigate the possibility of hearing loss and the mechanisms of

damage, if present. While a few studies have examined the effect of noise on teleost fish

physiology and morphology (reviewed by Smith & Monroe, 2016), none have focused on

elasmobranchs. Permanent and temporary hearing loss in fishes can be due to a change in or

the death of the sensory receptors in the inner ear (hair cells) or damage to the innervating

auditory nerve fibres (Liberman, 2016). In contrast to mammals, fishes, like other non-

mammalian vertebrates, produce hair cells throughout life and regenerate new hair cells to

replace those that have been lost (reviewed by Meyers & Corwin, 2008), and elasmobranchs

are no exception (Corwin, 1981c; 1983). The recovery of auditory hair cells, as well as

auditory deficits after an injury, have been demonstrated to occur over a few weeks in some

teleosts (Scholik & Yan, 2000; Amoser & Ladich, 2003; Smith et al., 2004; 2006). However,

there seems to be large differences between species (McCauley et al., 2003) and one cannot

readily extrapolate to a whole taxa. As the inner ear of elasmobranchs is different to that of

teleosts (i.e. with the inclusion of the macula neglecta), there is a dire need to focus on this

group to investigate the mechanisms of regeneration following damage.

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Another important concern is how anthropogenic noise alters the behaviour of fishes.

Changes in behaviour appear at a lower sound level than changes in physiology and injuries.

The behavioural effects of anthropogenic noise on teleosts include impacts on orientation,

predatory behaviours, mating, territory defences (reviewed by Braun, 2015). Anthropogenic

noise can also interfere with the detection of one specific sound, a process known as masking.

Masking affects the ability to detect, discriminate and identify sounds in the presence of

anthropogenic noise (reviewed by Radford et al., 2014). Once again, all these studies have

focused on teleost species and nothing is known on the behavioural effects of noise on

elasmobranchs. Our results (see Chapter 4), combined with the work of the pioneers who

investigated the behaviour of sharks in response to sounds in the wild (Nelson & Gruber,

1963; Banner, 1968; Myrberg, 1969; Nelson et al., 1969; Banner, 1972; Nelson & Johnson,

1972; Myrberg et al., 1976; 1978) suggest that elasmobranch behaviour can be influenced by

non-natural sounds and thus may be altered by anthropogenic sources. Identifying the threats

of anthropogenic noise on the behaviour of elasmobranchs is thus another priority.

However, the investigation of the behavioural effect of sounds on elasmobranchs comes

with great challenges. Future studies should follow the recommendations issued here to

ensure the provision of valid and comparable results for effective management.

One of the major issues is the importance of providing an appropriate acoustic environment

for experiments. As seen in Chapter 2, and in the previous section on AEPs, tanks used in

hearing experiments (physiological or behavioural) are usually not appropriate, producing a

perturbed sound field from the walls and the air-water interface (Parvulescu, 1964; 1967;

Gray et al., 2016; Rogers et al., 2016). Our experiment in Chapter 3, where we presented an

artificial sound in a tank to captive sharks, may have also produced a less predictable sound

field. Although we could calibrate the particle motion received at a few specific positions

within the tank, it was impossible to predict what the animal, free-swimming in a three-

dimensional medium, receives at a particular moment and position in the tank. In such

captive settings, the responses and behaviours recorded cannot be precisely linked to the

sound field received by the animals. In addition, wild fishes in captive conditions may elicit

different behaviours than in natural settings (Oldfield, 2011; Magellan et al., 2012; Turschwell

& White, 2016), resulting in misleading results, especially when fishes have been bred and

born in captivity (Balaa & Blouin-Demers, 2011; Jackson & Braun, 2011; Benhaïm et al.,

2012). More specifically, the facility, including the holding and/or testing tanks, usually

present a unique, non-natural soundscape (e.g. water pumps and aerators) (Craven et al.,

2009; Anderson, 2013), which has been shown to affect the hearing abilities of fishes, as in

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the goldfish (C. auratus) (Stummer & Ladich, 2008; Gutscher et al., 2011), and is thus

expected to represent a confounding factor in any hearing studies.

For all these reasons, field studies, rather than laboratory-based captive studies, are

encouraged for any acoustic behavioural experiments to investigate the effect of

anthropogenic noise (Popper et al., 2014). Experiments performed in open water allow the

establishment of simple, well-controlled sound fields (Schuijf & Buwalda, 1975; Hawkins,

2014; Hawkins et al., 2014; Gray et al., 2016), and offer the best insights into real behavioural

responses from free ranging fishes (Popper et al., 2014). It is certain that field studies often

involve higher costs and are infinitely more difficult to perform than laboratory studies, as

controlling all factors in the wild is impossible. However, it is exactly because of these

complicated interwoven factors inherent in nature that investigations focussing on important

application (mitigation and policies on anthropogenic noise) need to take place in the wild.

Studies performed on caged animals (McCauley et al., 2003; Popper et al., 2007; Halvorsen

et al., 2012; 2013; Bruintjes et al., 2016) are useful to explore inner ear damage in a controlled

sound field in open water, but less so for behavioural measurements, as it is unlikely that

these animals will exhibit complex and context-dependent behaviours when confined.

Behavioural Response Studies (BRSs, also called Controlled Exposure Experiments – CEEs)

are a novel approach for studying the short-term responses of animals to specific doses of

potential stressors, like noise. In this technique, a wild animal is tagged and its behaviour is

monitored using visual observations, passive acoustics, tags, or a combination of these.

Various measurements can be recorded, such as location through time, orientation and

movement, and sometimes a direct measurement of the received sound level. This technique

has been increasingly used in marine mammals to investigate the effects of sudden

anthropogenic sounds (i.e. sonar, airgun), by tracking the behaviour of the animals before,

during and after exposure (Johnson & Tyack, 2003; Nowacek et al., 2007; Tyack, 2009; Tyack

et al., 2011; DeRuiter et al., 2013; Dunlop et al., 2013; Isojunno et al., 2016). While very

costly, BRSs offer a direct measure of the effect of sound on behaviour from wild animals

(Harris et al., 2016). Although teleost fishes are often too small to hold large tags like those

used for BRS, large elasmobranch species would more appropriately carry such devices and

are, in fact, already routinely tagged for studying various aspects of their life history,

movements and population structure (reviewed by Kohler & Turner, 2001; Hammerschlag

et al., 2011).

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6.1.4. Acoustic ecology

Soundscapes include all the natural sounds present in the environment, such as those

produced by animals or the weather as well as anthropogenic sounds, and are increasingly

being studied, largely driven by environmental concerns (Erbe et al., 2016b). The relationship

between an organism and its soundscape is the focus of acoustic ecology (Pijanowski et al.,

2011; Merchant et al., 2015). Marine acoustic ecology is a rapidly expanding field, which

dictates species-specific differences given the range of ecological niches within the marine

environment (Hastings & Au, 2012). The ultimate aim is to elucidate the relationship between

marine organisms and their acoustic environment by examining species-specific variations in

auditory abilities in the surrounding soundscape and linking these characteristics to ethology,

physiology, neurobiology, biomechanics and evolution. This thesis aims to modestly

contribute to this approach by enhancing the understanding of the ‘ecosoundscape’ of some

elasmobranchs’ underwater niche. Unfortunately, some fundamental knowledge is lacking to

contextualise acoustic processing, describe each species’ ecosoundscape and establish the

threats they face. A large number of steps are missing at anatomical, physiological, functional

and behavioural levels, to describe the acoustic ecology of sharks, rays and skates.

Although the gross anatomy of the inner ear of several shark species has been investigated

(reviewed by Myrberg, 2001), it is still unknown which area is responsible for sound detection

in elasmobranchs. The macula neglecta, unique to elasmobranchs, has been hypothesised to

be the most important organ for sound detection in the taxa (Lowenstein & Roberts, 1951;

Tester et al., 1972; Fay et al., 1974; Corwin, 1981b). However, the function of the macula

neglecta has yet to be demonstrated, as well as its functional relationship with the other

macular endorgans (the sacculus, utriculus and lagena). Although there is a high level of

interspecific variation in the inner ears of elasmobranchs (Tester et al., 1972; Corwin, 1978;

1981b; 1989; Mulligan & Gauldie, 1989; Lychakov et al., 2000; Maisey, 2001; Evangelista et

al., 2010), we still do not understand how these differences may translate to function. The

audiograms of only nine species of elasmobranchs have been generated, which is far from

enough to link physiology to anatomy, especially when the results are not all comparable (see

Chapter 2). The behaviour of sharks towards sounds has only been investigated by a few

studies (Nelson & Gruber, 1963; Banner, 1968; Myrberg, 1969; Nelson et al., 1969; Banner,

1972; Nelson & Johnson, 1972; Myrberg et al., 1976; 1978), and our research presented in

Chapter 3 is the investigation completed under controlled conditions in the laboratory. In

addition, model species should be used for experiments at all levels (morphology, physiology

and behaviour), to be able to understand the sensory modality as a whole. For example, the

lemon shark Negaprion brevirostris was used comprehensively in the 1970 and 1980s to explore

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the hearing physiology and acoustic behaviour of the species (Banner, 1967; Nelson, 1967;

Banner, 1968; 1972; Klimley & Myrberg, 1979). By gaining insights at all levels of the hearing

biology of some species, it will become possible to start building the acoustic ecology of

elasmobranchs.

Ideally, a matrix of precise and comparable elements should be measured and used to make

informed statements and/or predictions for each taxonomic group of elasmobranchs, i.e.

hair cell number, hair cell orientation pattern, relative volume of otic capsule, relative volume

of the inner ear endorgans and maculae, audiogram (frequency and intensity bandwidth) and

behavioural indices. It is currently unknown how many of these variables can be correlated

with each other, but previous studies have established that the function of hearing can be

linked with the anatomy of the inner ear, as well as its physiology (Lanford et al., 2000;

Whitfield et al., 2002; Schulz-Mirbach et al., 2014). In mammals, for example, it is believed

that the outer, middle and inner ear characteristics determine the shape of the mammalian

audiogram (Mountain et al., 2008; Coleman & Colbert, 2009). In fishes, the interspecific

variability in the number of hair cells (Platt, 1977; Popper, 1978) might be an indication of

the type of signal used (particle motion or pressure) as well as the frequency bandwidth

(Corwin, 1977). Similarly, the diversity in hair cell orientation patterns associated with hearing

have shown functional convergence across diverse species (Popper & Coombs, 1982; Popper

et al., 2003) and is thought to be linked with the ability of animals to detect sound direction

(Popper et al., 2003; Ladich & Schulz-Mirbach, 2016; Schulz-Mirbach & Ladich, 2016).

Ultimately, the acoustic ecology of the species described informs us about their vulnerability

or resilience towards anthropogenic noise in the ocean. By adding ecological information to

the matrix, investigators would be able to extend the predictions to other species of the same

group. In the future, the framework could also allow us to build predictive audiograms based

only on accurate knowledge of the inner ear structure, as has previously been done in marine

mammals (Mountain et al., 2008; Tubelli et al., 2012).

6.2. Conclusion

Sensory neuroecologists seek to understand the neural basis of adaptive behaviours and the

strategies of an organism for survival in their environmental niche. Multidisciplinary and

collaborative approaches are required to combine all the different levels explored, from

morphology to behaviour. In this thesis, we aimed to gain some insights into the acoustic

world of sharks and, in doing so, we were exposed to the challenges of such a comprehensive

approach. Information collected at one level and under specific conditions does not often

offer the possibility of extrapolation if guidelines and standardisations are not developed and

set for a common aim. Our results present the possibility of improving techniques if a more

134

global and collaborative approach is adopted. We discovered that sharks exhibit large

interspecific differences in their behaviour towards sound, which is in line with the principles

offered by the complex field of acoustic ecology. A highly comparative and integrative

approach should be used to study those differences at all levels. Finally, we hope that our

work has refocussed attention back on the poorly characterised hearing system of

elasmobranch fishes, and promoted the study of this sensory modality in the broad scheme

of marine acoustic ecology, including the investigation of the effects of anthropogenic noise

on these animals.

135

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Appendices

Appendix A

Video representing the evolving spectrum of a three-minute extract of the Artificial Sound

used and described in Chapter 3. The sound was constructed digitally with Adobe Audition

CS5.5 and the workstation Reaper v.4.62. The spectrum and video was built with a custom-

made MATLAB script (LC). To play the video, click on the image link or launch it from the

USB flash drive appended to the thesis (Appendices/A_ArtificialSound_Chapter3).

Alternatively, find it on https://youtu.be/NJfM8BFv9YA

Appendix B

Video representing the evolving spectrum of a three-minute extract of the Artificial Sound

used and described in Chapter 4. The sound was constructed digitally with Adobe Audition

CS5.5, the workstation Reaper v.4.62 and the virtual instrument Granite (New Sonic Arts

Inc.) as an audio unit (AU) instrument plug-in. The spectrum and video was built with a

custom-made MATLAB script (LC). To play the video, click on the image link or launch it

from the USB flash drive appended to the thesis (Appendices/B_ArtificialSound_Chapter4).

Alternatively, find it on https://youtu.be/6ooPxxVDE4s