Spatial ecology of a top-order marine predator,

the tiger shark (Galeocerdo cuvier)

by

Luciana Cerqueira Ferreira

BSc Univ. Federal do Rio Grande, MSc Univ. Federal de Pernambuco

This thesis is presented for the degree of

Doctor of Philosophy of

The University of Western Australia

Faculty of Science

School of Animal Biology

2017

iii

Declaration of authorship

I declare that this thesis is my own composition, all sources have been acknowledged and my

contribution is clearly identified in the thesis. This thesis has been substantially completed

during the course of enrolment at the University of Western Australia and has not previously

been accepted for a degree at any tertiary education institution. I confirm that for any work in

this thesis that has been co-published with other authors, I have the permission of all co-authors

to include this work in my thesis, and include a signed declaration.

Luciana Cerqueira Ferreira

Date: 12/05/2017

v

Abstract

Top-order predators are large consumers that occupy the highest trophic level of food-

webs. Through both consumptive and behavioural impacts on prey, these animals play

important roles in the structure, stability and resilience of communities and ecosystems.

In marine environments, top-order predators are often highly mobile and can display

large-scale (1000s km) seasonal migrations. This poses a major challenge for

conservation of these animals, because common approaches, such as the establishment

of marine protected areas, rarely encompass the scale and extent of their movement

patterns. Furthermore, broad-scale migrations make it likely that top-order predators

will encounter multiple human threats across their range and as a consequence,

populations of many species have undergone rapid declines. Identification of the key

environmental drivers of movement patterns and habitat use by top-order predators is

thus urgently required in order to understand the scale and consequences of

anthropogenic threats that these sharks now face. Here, I address these issues for the

tiger shark (Galeocerdo cuvier), a keystone top-order consumer in tropical and warm-

temperate marine ecosystems.

In Chapter 2, I reviewed the conservation status of tiger shark according to the

guidelines for the International Union for the Conservation of Nature (IUCN) Red List.

I summarised information available on its biology, ecology and threats to populations,

and indicated gaps in the ecological knowledge of the species. This assessment formally

updated the status of the species for this species as “Near Threatened” IUCN Red List.

In Chapter 3, I described and analyse spatial patterns in movement of tiger sharks along

the coastline of Western Australia as derived from satellite telemetry. Using a Brownian

Bridge movement kernel method to analyse tracks, I showed that the extensive

movements of tiger sharks (thousands of km) can encompass tropical and cool-

temperate environments across international maritime boundaries that offer varying

levels of fisheries protection for the species. Individual variability was high with respect

to levels of residency, space use and environmental preferences in each climate zone.

In order to describe spatial variability in diet and trophic ecology of tiger sharks, I used

the analysis of stable isotopes from multiple tissues (dermis, muscle, blood) of sharks

collected from both the western and eastern coasts of Australia (Chapter 4). This

continental-scale analysis of isotopic signatures demonstrated that the species resides

vi

near the top of the food chain, but is a generalist with a very broad diet. I showed that

tiger sharks explored multiple resources throughout their distribution that led to changes

in the position of the species within the food chain in different regions. Finally, in

Chapter 5, I compiled and analysed a global dataset of tiger shark tracks across multiple

ocean basins to identify the key environmental drivers of movement and habitat use at

the scale of the range of the entire species. I applied Biased Random Bridge methods to

generate monthly utilization distributions for all individual sharks and overcome issues

of data quality and sparseness, and then applied generalised additive mixed-models to

identify the key drivers of migratory movements and large-scale patterns of space

utilisation. Sea surface temperature and water depth were important predictors of space

utilization for all sizes and sexes of sharks and at all locations, but patterns varied

greatly at both temporal and spatial scales. Furthermore, these environmental predictors

explained only small amounts of variance in habitat utilisation. Geographically

weighted regression was used to identify areas within the species range where

environmental variables failed to explain space utilisation, allowing the generation of

alternative hypotheses to explain patterns of habitat use and the spatial targeting of

future research.

Combined, these findings demonstrated the value of using large datasets pooled across

studies and locations to provide insights into the spatial ecology of wide-ranging marine

predators. My analyses showed that tiger sharks are cosmopolitan and generalist

predators that demonstrate high levels of intra-specific variation in their use of space,

movement and trophic ecology. This plasticity in spatial and trophic ecology is possibly

a key element of their success as a top-order marine predator and might provide some

resilience to human threats. However, it also highlights the difficulties involved in

establishing effective conservation strategies in order to manage and protect the species.

vii

Acknowledgements

A sincere and heartfelt thank you goes to my supervisors Dr Mark Meekan, Dr Michele

Thums and Prof Jessica Meeuwig, for their support and guidance. Prof Jessica thanks

for giving me the opportunity to pursue this work as part of your team and sharing you

insights into marine conservation. Mark, thank you for your indispensable mentorship,

inputs and edits to my work, excitement about my results, motivational chats about

science and life as scientist, incentive to share my science and to attend as many

conferences as I could, and for pushing me to “finish that paper!”. Michele, I cannot

begin to thank the incredible help and guidance you have given me throughout this

process. Thanks for sharing your expertise in quantitative modelling, animal and

movement ecology, R and stats, but most importantly for the endless support and

sincere friendship. Your supervision has helped me develop into the scientist I am today

and I deeply appreciate the opportunities you and Mark have given me.

Many collaborators and co-authors have had a great input in this thesis. I am

enormously grateful for the help and contribution of Dr Ben Radford in developing the

analytical approach for my last chapter, his inputs and unending enthusiasm about my

research, which was a much needed motivation during data analysis. I am grateful to Dr

Becky Fisher for always finding time to help me with my many R and statistics

questions. I am also grateful to my collaborator and co-authors Dr Rory McAuley, Dr

Michael Heithaus, Dr Fabio Hazin, Dr John Stevens, Dr Neil Hammerschlag, Dr Adam

Barnett, Dr Bonnie Holmes, Dr André Afonso, Dr Kátya Abrantes, Dr Brad Wetherbee,

Dr Mahmood Shivji, Dr Robert Nowicki, Dr Jeremy Vaudo, Dr Derek Burkholder and

Dr Julian Pepperell, for providing tracking data and samples, facilitating access to lab

equipment and valuable suggestions for my papers. Thanks to Dr Michele Thums, Dr

Mark Meekan, Dr Ana Sequeira, Dr Carlos Duarte, Dr Graeme Hays, and all

participants of the Marine Megafauna Analytical Program. Thanks also to Dr Colin

Simpfendorfer and Dr Michelle Heupel, for the support in the preparation of the IUCN

assessment of tiger sharks, facilitating my attendance in the Australian Shark Specialist

Group (SSG) workshop, for providing ideas and samples, and always finding time to

chat about my PhD. My most sincere gratitude goes to the Graduate Research

Coordinators in the School of Animal Biology Prof Leigh Simmons and Prof Joseph

Tomkins for your wise advice and support during challenging moments in the PhD.

viii

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

institutions that have provided funding and field assistance in obtaining data for this

research. My gratitude goes to the Conselho Nacional de Desenvolvimento Científico e

Tecnológico for my PhD scholarship, and the UWA Oceans Institute and Australia

Institute of Marine Science for providing a top-up scholarship. I am also grateful to the

Royal Zoological Society of New South Wales for providing funding through the Paddy

Pallin Science Grant and to Save Our Seas, the Department of Economic Development

and Tourism of KwaZulu-Natal South Africa, the American Elasmobranch Society, the

Postgraduate Student Association at UWA, the Graduate Research Office (UWA) and

the Australian Institute of Marine Science for travel support. Thanks to OCEARCH, in

special to Chris Fischer and Fernanda Ubatuba, for the field work support and tags, and

the hard-working crew that made the WA Ningaloo Expedition a unique experience.

Receiver data was sourced though the Australian Animal Tracking and Monitoring

Systems (AATAMs). The WA Department of Fisheries provided tracking data,

laboratory space and technical support for some of my analysis.

Thanks to my dear friends and fellow marine geeks Bev, Joyce, Phillipa (aka Pipi),

Tom, Gabe, Milly, Dani, Al, Conrad, Charli, Mike, Sammy and Emily. Thank for the

fun times and the many chats about our research that would have bored anyone outside

the “PhD world”. Pipi, Milly and Sammy, I am truly grateful for all your help with the

formatting of this thesis, you are rock stars! Bev, my fieldwork buddy and coffee addict,

going through this process with you made it so much easier and fun. Joyce, thank you

for always trying to keep me levelled-head and providing sensible advice when I needed

and being such a great company during the final months of PhD. Pipi, thanks for

making my days so much more fun, for the daily doses of uncontrollable laughter and

for helping me get through the craziness of the final stages of thesis writing. You are an

awesome office mate and very dear friend. I will never forget the great moments I had

living in the Subi house with my two big brothers, Tom and Gabe. Thanks for being

awesome housemates and true friends.

Many thanks to all my non-marine-scientist friends Laura, Paddy, Lily, Tash, Phil,

Renee, Siobhan, Nick, Jackson, Mike, Faye and Robin, for always reminding me that

life is not always about sharks. Laura, huge thanks for your support and friendship, and

for all the hilarious moments, blue parties and home-cooked meals. To my oldest group

of amazing friends Marina, Caio, João, Cris and Dai, I’ve known you for a life-time and

no matter where my crazy profession took me you have been the most loyal of friends.

ix

Thanks for always understanding (sometimes a bit reluctantly) that I could not always

be present, for your unconditional friendship and always being there through all the

good and rough times.

Profound thanks to everyone at the Perth AIMS office for your support, assistance and

encouragement. Olwyn, I am immensely thankful for your constant support and for

always making sure I was ok and that things were running smoothly with the PhD, and

for helping to fix them when they weren’t. Kim Brooks, thanks for your unique sense of

humour, for bringing me on the whale shark trip and honest friendship. Most sincere

thanks to Louise, Kat and Dr James Gilmore for the constant support, entertaining

conversations, and always taking the time to check if I was OK when the PhD was

stressful.

Finally, an unmeasurable thank you to my family, my mom Lucia, my dad Marcos, my

sister Dea and my brother-in-law Rafa (and obviously all the furry family members).

None of this would be possible without your love and support. You have always

allowed me to dream big and reminded me that you believe I can reach all my goals,

even when it seemed too hard. Thanks for encouraging me to pursue my dreams despite

having to endure the “saudades” and long distance. I hope I have made you proud. I am

also thankful for all the support my grandparents have given me, in particular my

grandpa Hiram. “Vô”, you helped me develop the thirst for knowledge that has driven

me to seek a career in science, and I will miss you forever.

xi

Publication from this thesis

Chapter 2

Ferreira LC, Simpfendorfer CA. In review. Galeocerdo cuvier, Tiger shark. The IUCN

Red List of Threatened Species.

Chapter 3

Ferreira LC, Thums M, Meeuwig JJ, Vianna GMS, Stevens J, McAuley R, Meekan MG

(2015) Crossing latitudes—long-distance tracking of an apex predator. Plos One, 10(2):

e0116916.

Chapter 4

Ferreira LC, Thums M, Heithaus MR, Barnett A, Abrantes K, Holmes B, Zamora LM,

Frisch AJ, Pepperell J, Burkholder D, Vaudo J, Nowicki R, Meeuwig J, Meekan MG. In

Review. The trophic role of a large marine predator, the tiger shark Galeocerdo cuvier.

Scientific Reports.

Chapter 5

Ferreira LC et al. (In preparation) Scale and habitat-dependent movement behaviour of

a large marine predator, the tiger shark Galeocerdo cuvier. Ecology.

xiii

Statement of candidate contributions

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

general introduction and general discussion sections. These papers were developed by

my own ideas and hypotheses with inputs from my supervisors.

Fieldwork for Chapter 4 and 5 was supported by the Paddy Pallin Science Grant (Royal

Zoological Society of NSW), The University of Western Australia, the Australian

Institute of Marine Science and OCEARCH. Tuition stipend was provided by the

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil) and a

RCA Top-up scholarship by The UWA Oceans Institute and Australian Institute of

Marine Science (AIMS).

Chapter 2 was facilitated by Dr Colin Simpfendorfer (James Cook University) and the

IUCN Shark Specialist Group (SSG) that supported travel to attend the SSG Australian

Regional Workshop. Existing tracking data for Chapter 3 was provided by Dr Mark

Meekan (Australian Institute of Marine Science), Dr Rory McAuley (Department of

Fisheries, Government of Western Australia) and Dr John Stevens (CSIRO,

Commonwealth Scientific and Industrial Research Organisation).

I collected samples at Ningaloo Reef for Chapter 4. Samples from other locations were

obtained from Dr Adam Barnett (James Cook University), Dr Bonnie Holmes

(University of Queensland), Dr Julian Pepperell (Pepperell Research) and the

Queensland Shark Control Program. Dr Michael Heithaus (Florida International

University) provided existing isotope data for Shark Bay. I deployed tags at Ningaloo

Reef for Chapter 5 and existing tag data was provided by the by Dr Mark Meekan, Dr

Rory McAuley, Dr John Stevens, Dr Neil Hammerschlag (University of Miami), Dr

Fabio Hazin (Universidade Federal Rural de Pernambuco), Dr Richard Fitzpatrick

(Biopixel) and Dr Michael Heithaus.

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

written by me with feedback from Prof Jessica Meeuwig (University of Western

Australia, Chapters 1-3, 6), Dr Mark Meekan (all chapters), Dr Michele Thums (all

chapters), Dr Colin Simpfendorfer (Chapter 2), Dr Gabriel Vianna (Rare Brasil, Chapter

3), Dr Rory McAuley (Chapter 3), Dr John Stevens (Chapter 3), and Dr Ben Radford

(AIMS, Chapter 5)

xv

Co-author authorisation

By signing below, co-authors agree to the listed publication being included in the

candidate’s thesis and acknowledge that the candidate is the primary author, i.e.

contributed greater than 50% of the content and was primarily responsible for the

planning, execution and preparation of the work for publication.

Publication title: Crossing latitudes—long-distance tracking of an apex predator.

Dr. Michele Thums

(Australian Institute of Marine Science)

Prof Jessica Meeuwig

(University of Western Australia)

Dr Gabriel Vianna

(Rare Brasil)

Dr John Stevens

(CSIRO Marine and Atmospheric

Research)

Dr Rory McAuley

(Western Australia Department of

Fisheries)

Dr Mark Meekan

(Australian Institute of Marine Science)

Publication title: The trophic role of a large marine predator, the tiger shark

Galeocerdo cuvier (In Review)

Dr. Mark Meekan, signed in name of co-authors

(Australian Institute of Marine Science)

xvii

Table of Contents

Declaration of authorship ................................................................................................ iii

Abstract…………………… ....................................................................... …………….v

Acknowledgements ........................................................................................................ vii

Publication from this thesis ............................................................................................. xi

Statement of candidate contributions ............................................................................ xiii

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

Table of Contents ......................................................................................................... xvii

List of Figures ............................................................................................................... xxi

List of Tables................................................................................................................ xxv

Chapter 1 General Introduction ................................................................................. 27

1.1 Top-order predators .................................................................................. 27

1.2 Movement ecology ................................................................................... 30

1.3 Challenges for the conservation of large marine predators ...................... 35

1.4 The tiger shark .......................................................................................... 37

1.5 Aims .......................................................................................................... 43

1.6 Thesis outline ............................................................................................ 43

Chapter 2 Review of tiger shark ecology and conservation status ............................ 45

2.1 Introduction ............................................................................................... 45

2.2 Distribution ............................................................................................... 45

2.3 Population trends ...................................................................................... 46

2.4 Life-history and ecology ........................................................................... 51

2.5 Threats ...................................................................................................... 53

2.5.1 Shark Fisheries .......................................................................................... 54

2.5.2 Non-shark commercial fisheries ............................................................... 55

2.5.3 Recreational fisheries ................................................................................ 56

2.5.4 Artisanal and IUU Fisheries ..................................................................... 56

2.5.5 Shark Control Programs ............................................................................ 57

2.6 Assessment Rationale ............................................................................... 58

Chapter 3 Crossing latitudes - long-distance tracking of an apex predator ............... 61

3.1 Abstract ..................................................................................................... 61

3.2 Introduction ............................................................................................... 61

xviii

3.3 Material and Methods ............................................................................... 64

3.4 Results ....................................................................................................... 67

3.5 Discussion ................................................................................................. 74

3.6 Acknowledgments .................................................................................... 78

3.7 Supporting information ............................................................................. 79

Chapter 4 Diet and trophic role of a large marine predator, the tiger shark

Galeocerdo cuvier .................................................................................... 81

4.1 Abstract ..................................................................................................... 81

4.2 Introduction ............................................................................................... 82

4.3 Methods .................................................................................................... 84

4.3.1 Sample collection ...................................................................................... 84

4.3.2 Data analysis ............................................................................................. 87

4.4 Results ....................................................................................................... 91

4.4.1 Comparisons of stable isotopic signatures among locations .................... 91

4.4.2 Comparisons of isotopic signatures within locations ............................... 93

4.4.3 Comparison of tissues among locations ................................................... 96

4.4.4 Diet stability ............................................................................................ 102

4.5 Discussion ............................................................................................... 103

4.6 Acknowledgements ................................................................................. 109

4.7 Supporting information ........................................................................... 110

Chapter 5 Scale and habitat-dependent movement behaviour of a large marine

predator, the tiger shark Galeocerdo cuvier ........................................... 111

5.1 Abstract ................................................................................................... 111

5.2 Introduction ............................................................................................. 112

5.3 Methods .................................................................................................. 114

5.3.1 Telemetry ................................................................................................ 114

5.3.2 Environmental data ................................................................................. 116

5.3.3 Identification of areas of high use .......................................................... 116

5.3.4 Identification of migratory behaviour ..................................................... 117

5.3.5 Modelling movement behaviour ............................................................. 118

5.3.6 Assessment of spatial variability in model predictions .......................... 121

5.4 Results ..................................................................................................... 122

5.4.1 Drivers of high use of habitats ................................................................ 126

5.4.2 Drivers of migration ............................................................................... 127

5.4.3 Assessment of spatial variability in model predictions .......................... 131

5.5 Discussion ............................................................................................... 134

5.6 Acknowledgements ................................................................................. 138

xix

5.7 Supporting information ........................................................................... 139

Chapter 6 General Discussion ................................................................................. 145

6.1 Variability in movement behaviour ........................................................ 145

6.2 Drivers of movement and habitat use ..................................................... 146

6.3 Low predictability of models .................................................................. 147

6.4 Implications for conservation ................................................................. 151

6.5 Future directions ..................................................................................... 153

6.6 Concluding remarks ................................................................................ 155

Bibliography… ............................................................................................................. 157

Appendix A… .............................................................................................................. 191

Appendix B… .............................................................................................................. 192

Appendix C… .............................................................................................................. 193

xxi

List of Figures

Figure 1.1 The tiger shark. ............................................................................................ 38

Figure 2.1 Distribution range of tiger sharks (blue). ..................................................... 46

Figure 2.2 Relative abundance (calculated as an index of standardised catch) for

the Shark Bottom Longline Observer Program (SBLOP, blue

diamonds) and longline survey of the Northeast Fisheries Science

Centre (NEFSC, green triangles) and SEFSC Longline (MS longline,

red squares). Figure produced by J. Carlson (pers. comm.). ..................... 48

Figure 2.3 CPUE (number of sharks gear-1

day-1

) by year and size (small < 3 m

and large > 3 m) of sharks for drumlines (A) and nets (B) by the

QSCP between 1993 and 2010. Figure extracted from Holmes et al.

(2012). ....................................................................................................... 49

Figure 2.4 Locations of the Queensland Shark Control Program with details of

gear (1993-2010), from Holmes et al. (2012). .......................................... 50

Figure 2.5 Catch per unit of effort of tiger sharks by the New South Wales Shark

Meshing Program from 1950-2010 modified from Reid et al. (2011).

Dashed line indicates Loess estimates. ..................................................... 51

Figure 2.6 Annual catch per unit of effort of tiger sharks in nets from the

KwaZulu-Natal beach protection program between 1978 and 2003.

Dash line represents the slope of a linear regression. Figure adapted

form Dudley & Simpfendorfer (2006). ..................................................... 51

Figure 2.7 Summary table of the criteria (A - E) used to evaluate Red List status

(IUCN 2012). ............................................................................................ 60

Figure 3.1 Movement patterns of tiger sharks in Western Australia. Maps show

location uplinks of 8 tiger sharks. Triangles indicate tagging location

and grey polygons indicate Commonwealth Marine Reserves. ................ 68

Figure 3.2 Home range of tiger sharks. The utilisation distribution calculated

using the Brownian Bridge kernel method. Black line represents the

50% Brownian Bridge home range distribution. ...................................... 69

Figure 3.3 Time spent in each temperature bin. Plots show the percentage of time

spent within the specified temperature bins for each tiger shark (ID

number in the top right hand corner of each plot). ................................... 71

Figure 3.4 . Latitudinal variation of temperature profiles for Shark 5. Percentage

of time spent in each temperature bin at each region of Western

Australia for Shark 5. ................................................................................ 72

Figure 3.5 Time spent in each depth bin. Percentage of time spent in each depth

bin for two tiger sharks tagged with SPLASH tags. ................................. 72

Figure 3.6 Vertical profiles of water temperature. Plots show temperature profiles

recorded by Argo floats of the north (17.6°S 117.6°E) and south

(35.78°S 119.5°E) sections of the WA coast. Vertical lines represent

the minimum temperature reported by the shark’s satellite

transmitter and horizontal lines represent the estimated diving depth. ..... 72

xxii

Figure 3.7 Generalised linear model for Shark 5. Generalised linear model (GLM)

predicted probabilities (solid line) of a shark being in a 25%

utilisation distribution in relation to (a) water depth, (b) water

temperature in the north and (c) in the south. Dotted lines show the

standard error. ........................................................................................... 73

Figure 4.1 Map showing the study locations across Australia and sampling sites

within each region in colour coded triangles. Circles represent

relative and total sample size for each dataset. GBR = Great Barrier

Reef, QSCP = Queensland Shark Control Program, NSW = New

South Wales. Map was created with ArcGis 10.3

(http://www.esri.com/). ............................................................................. 85

Figure 4.2. Mean and standard deviation stable isotopic composition of each

tissue for each of the locations sampled (SB= Shark Bay, GBR =

Great Barrier Reef, NSW =New South Wales, QSCP = Queensland

Shark Control Program, SA = South Africa) for A) slow tissues

(muscle, dermis, fin) and B) multiple tissues (muscle, dermis, fin,

red blood cells, plasma), for sites where multiple tissues were

collected in this study. Data from GBR1, Reunion Island and South

Africa obtained from published studies (Hussey et al. 2015b; Frisch

et al. 2016; Trystram et al. 2016) ............................................................. 92

Figure 4.3. Mean isotope values (± SD) of slow turnover tissues (muscle, dermis,

fin) of tiger sharks, other predators, consumers and primary

producers at Ningaloo Reef, Shark Bay, Great Barrier Reef and New

South Wales. Community data for each location was obtained from

Davenport & Bax (2002), Arthur et al. (2008), Revill et al. (2009)

Vaudo & Heithaus (2011), Speed et al. (2012), Wyatt et al. (2012),

Thomson et al. (2012), Heithaus et al. (2013), Frisch et al. (2014,

2016), Marcus, unpublished data. Red = sharks, orange = rays,

blue/golden = teleosts, purple = turtles, magenta = penguins, grey =

seals, maroon = cephalopods, light green = producers, dark green =

zooplankton. .............................................................................................. 95

Figure 4.4. Muscle. Standard ellipse areas for analysis of muscle tissue corrected

for sample size (SEAc) (A) and Bayesian Standard ellipse areas

(SEAb) (B), representing the 50, 75 and 95% credible intervals in

decreasing order of box size, with SEAb mode indicated by a black

circle and SEAc by a triangle. Partial dependence plots of the

relationship between δ13

C and the explanatory variables in the top-

ranked model, total length (C) latitude and sex and their interaction

(D). Points represent residuals and shaded areas show the 95%

confidence intervals. ................................................................................. 97

Figure 4.5. Dermis. Standard ellipse areas for analysis of dermal tissue corrected

for sample size (SEAc) (A) and Bayesian Standard ellipse areas

(SEAb) (B), representing the 50, 75 and 95% credible intervals in

decreasing order of box size, with SEAb mode indicated by a black

circle and SEAc by a triangle. Partial dependence plots of the

relationship between δ13

C (C) and δ15

N (D), and the explanatory

variables in the top-ranked models; total length (C) and location (D).

Points represent residuals and shaded areas show the 95%

confidence intervals. ................................................................................. 98

xxiii

Figure 4.6. Red Blood Cells. Standard ellipse areas for analysis of red blood cells

corrected for sample size (SEAc) (A) and Bayesian Standard ellipse

areas (SEAb) (B), representing the 50, 75 and 95% credible

intervals in decreasing order of box size, with SEAb mode indicated

by a black circle and SEAc by a triangle. Partial dependence plots of

the relationship between δ13

C and the explanatory variables in the

top-ranked model; total length (C) and location (D). Points represent

residuals and shaded areas show the 95% confidence intervals. ............ 100

Figure 4.7. Plasma. Standard ellipse areas for analysis of plasma corrected for

sample size (SEAc) (A) and Bayesian Standard ellipse areas

(SEAb)(B), representing the 50, 75 and 95% credible intervals in

decreasing order of box size, with SEAb mode indicated by a black

circle and SEAc by a triangle. Partial dependence plots of the

relationship between δ13

C and the explanatory variables in the top-

ranked model; total length and location. Points represent and shaded

areas show the 95% confidence intervals respectively. .......................... 102

Figure 4.8. Relationship between δ13

C values of paired tissues of fast (plasma)

and intermediate (red blood cells) turnover rates (A). Individuals

with a difference between tissues (calculated from resident sharks)

larger than mean differences Δ13

C are highlighted in filled symbols.

Open symbols represent individuals in steady state (stable). Solid

line represents the relationship between paired tissues and hashed

lines enclose the area within ± mean Δ13

C. Partial dependence plots

for the relationship between the stability of diet (probability of

steady state) and the explanatory variable in the top-ranked model;

location (B). Numbers indicate sample sizes. ......................................... 103

Figure 5.1 Telemetry data included in the analyses, colour coded by individual

shark tag ID, from Hawaii (Pacific Ocean) (top left), North Atlantic

(top right), South Atlantic (left), Great Barrier Reef, Australia

(Pacific Ocean) (middle) and Indian Ocean, Australia (bottom right). .. 120

Figure 5.2 Utilisation densities combined and averaged for each month for each

grid cell for all individuals in each regional dataset and filtered to the

0.75UD. Colour scale represents the average utilisation of each cell

and grey contours indicate the 500 m isobath. ........................................ 123

Figure 5.3 Conditional plots of female utilisation density relative to the

explanatory variables in the top ranked mixed model for each region

from the suite of models used to explain the response variable. Solid

curves are the model fit and shaded areas indicate the 95%

confidence intervals. Tick marks on the x-axis indicate the

distribution of observations. Plots a, c, e, g are fits for bathymetry for

North Atlantic, Indian Ocean, Pacific (Great Barrier Reef) and South

Atlantic, respectively; b, d, f, h are fits for sea surface temperature

for North Atlantic, Indian Ocean, Pacific Ocean (Hawaii) and South

Atlantic, respectively; i-j are fits for standard deviation of elevation

and aspect in the North Atlantic. ............................................................ 130

Figure 5.4 Conditional plots of male utilisation density relative to the explanatory

variables in the top ranked mixed model from the suite of models

used to explain the response variable. Solid curves are the model fit

and shaded areas indicate the 95% confidence intervals. Tick marks

on the x-axis indicate the distribution of observations. .......................... 131

xxiv

Figure 5.5 Conditional plots of probability of migration relative to the

explanatory variables in the top ranked mixed model from the suite

of models used to explain the response variable. Solid curves are the

model fit and shaded areas indicate the 95% confidence intervals.

Tick marks on the x-axis indicate the distribution of observations. ....... 132

Figure 5.6 GWR-derived local coefficient estimates for the relationship between

predicted and observed values from the top ranked mixed model

explaining the drivers of habitat use for females. A-B = North

Atlantic Ocean and B is zoomed in on the box in A, C-D = Indian

Ocean and D is zoomed in on the box in C, E = Pacific Ocean

around Hawaii, F = Great Barrier Reef, G = South Atlantic. Grey

isolines represent bathymetry. Red and orange show areas where

observed UD was higher than predicted (see detail in B and D) and

blue and green show area where observed UD was lower than

predicted. ................................................................................................ 133

Figure 5.7 GWR-derived local coefficient estimates for the relationship between

predicted and observed values from the top ranked mixed model

explaining the drivers of habitat use for males. A-B = North Atlantic

Ocean, B = Indian Ocean, C = Great Barrier Reef, D = South

Atlantic. Grey isolines represent bathymetry. Red and orange show

areas where observed UD was higher than predicted and blue and

green show area where observed UD was lower than predicted. ........... 134

Figure S 3.1. Sea Surface Temperature map (smoothed OISST data) for 16th May

2009 with location uplinks for Shark 5. .................................................... 79

Figure S 4.1. Location of acoustic receivers (grey and black circles) around

Ningaloo Reef, Australia. Black circles represent receivers where

tiger sharks were detected. Star indicates tagging location. ................... 110

Figure S 5.1 Representative example of a tiger shark track (black points) analysed

with Biased Random Bridges showing both residency (points within

the 0.75 UD (red lines) and migration (points outside the 0.75 UD). .... 143

Figure S 5.2 Figure extracted from: A) (Bouchet 2015) indicating hotspots in fish

abundance off the coast of Western Australia, B)Figure re extracted

from (Double et al. 2014) showing areas of high occupancy by

pygmy blue whales off the coast of Western Australia. ......................... 143

xxv

List of Tables

Table 1.1 Range and drivers of movement patterns displayed by large epipelagic

top-order sharks and teleosts. .................................................................... 34

Table 1.2 Tiger shark tagging programs with the number of tags deployed, sex

ratio of animals tagged, size range of sharks tagged and information

provided by telemetry devices. ................................................................. 41

Table 2.1 Summary of long-term trends of catch rates of tiger sharks based on

regional datasets. ....................................................................................... 47

Table 2.2 Average CPUE(number of sharks gear-1

day-1 x1000) for drumlines

from the QSCP for 1993 and 2010 from Holmes et al. (2012) and

projected CPUE for three generation lengths (Projection 1 =

generation length of 17.5 years, Projection 2 = generation length of

22.5 years). ................................................................................................ 49

Table 3.1. Details of satellite transmitter deployments on tiger sharks. ....................... 66

Table 3.2 Ranked Generalised Linear Models with bootstrap sampling of the

probability of a shark being in a 25% utilisation distribution

explained by bathymetry (Depth) and region of the coast (Region). ........ 70

Table 3.3 Ranked Generalised Linear Models with bootstrap sampling of the

probability of a shark being in a 25% utilisation distribution

explained by sea surface temperature (Temperature) at the north and

south coast separately. .............................................................................. 70

Table 4.1. Mean and standard deviation (SD) stable isotopic composition of tiger

sharks sampled in Queensland (QSCP), New South Wales (NSW)

the Great Barrier Reef (GBR) and Ningaloo and Shark Bay for each

sex (F = female, M= male). δ15

N and δ13

C given in parts per mill

(‰). N= number of samples, TL = mean total length (cm) ± SD. ........... 86

Table 4.2. Isotopic niche area described by Standard Ellipse Area SEAc (‰),

Bayesian SEAb and parameters; N= number of samples, TA = total

area of the convex hull, CD = mean centroid distance and standard

deviation, E = eccentricity, θ = angle of ellipse and CI = confidence

interval. F = Females, M = Males, QLD = Queensland Shark Control

Program and GBR, GBR = Great Barrier Reef and NSW = New

South Wales. ............................................................................................. 94

Table 4.3. Ranked generalised linear models of δ13

C and δ15

N for each tissue. The

top six models for each response are shown; values in bold indicate

the top ranked model (or the most parsimonious model). Degrees of

freedom (df), Sample corrected Akaike’s Information Criterion

(AICc), change in AICc relative to the model with the lowest AICc

value (ΔAICc), relative AICc weight (wAICc) and percent deviance

explained (DE%). Lat = latitude, TL = total length (cm). ........................ 99

xxvi

Table 4.4. Ranked binomial generalised linear models of stability of diet for

sharks (stable with the local food web), using paired tissue values of

δ13

C from red blood cells (RBC)-plasma comparisons. All models

are shown, values in bold indicate the most parsimonious model.

Sample corrected Akaike’s Information Criterion (AICc), change in

AICc relative to the model with the lowest AICc value (ΔAICc),

relative AICc weight (wAICc) and percent deviance explained

(DE%). TL= total length (cm). ............................................................... 103

Table 5.1 Description of all environmental, temporal and biological predictors for

the analysis of monthly utilisation distribution. ..................................... 124

Table 5.2 Predictors used to model migratory movements of tiger sharks. ................ 124

Table 5.3 Summary tracking datasets including information of tagging location,

number of tags deployed, track duration and size range of sharks

tagged. Mean values and SD are presented for total length (TL) and

track duration. ......................................................................................... 125

Table 5.4 The top ranked generalised additive mixed models from the suite of

models used to explain monthly utilisation distributions of female

and male tiger sharks. SST = sea surface temperature, Std = standard

deviation of elevation. Shown are the percent deviance explained

(%DE), Akaike’s information criterion (AIC), and AIC weight

(wAIC). ................................................................................................... 127

Table 5.5 The top three ranked generalised additive mixed models with 1000

bootstraps sampling for the suite of models used to explain the

probability of migration of tiger sharks. Shown for each model are

the median, 25th

and 75th

quantile Akaike’s information criterion

corrected for small samples (AICc), AICc weight (wAICc) and the

adjusted R-squared (Adj R2). The top ranked model is in bold. ............. 129

Table S 4.1 Details of tiger sharks tagged at Ningaloo Reef in April-May 2015,

their duration of detection (until last receiver download) by receivers

stations at Ningaloo and their behavioural classification (resident or

non-resident). .......................................................................................... 110

Table S 5.1 Details of each of the deployments of telemetry devices on tiger

sharks. ..................................................................................................... 139

27

Chapter 1 General Introduction

Top-order predators play keystone roles in the structure, stability and resilience of

communities and ecosystems (Mills et al. 1993) by both consuming prey (Tegner &

Levin 1983; Werner et al. 1983; Estes & Duggins 1995; Creel & Christianson 2008)

and influencing their behaviour (Lima & Dill 1990; Schmitz et al. 1997; Lima 1998;

Kie 1999; Estes et al. 2001; Wirsing & Ripple 2011). Such predators are often capable

of moving long distances (100-1000s km; Harestad & Bunnel 1979; Hays & Scott

2013), creating linkages between multiple ecosystems (McCauley et al. 2012). This

movement also brings them into contact with multiple human threats such as habitat

loss and fragmentation, pollution and overexploitation (Ritchie & Johnson 2009; Heupel

et al. 2014). As a result, top-order predators have been disproportionately and

negatively affected by anthropogenic impacts in both terrestrial and marine

environments. The removal of top-order predators can result in major shifts in the

structure and function of communities, reducing the resilience of ecosystems to human

impacts and climate change (Heithaus et al. 2008a, 2014; Beschta & Ripple 2009; Estes

et al. 2011; Ruppert et al. 2013; Atwood et al. 2015).

In this chapter, I examine the role of top-order predators in the trophic ecology of

ecosystems and show why the study of movement ecology is essential for the

development of an understanding of the ecology of these species. I describe the current

research challenges for these highly mobile predators and outline the rationale for my

choice of study species for this thesis.

1.1 Top-order predators

The terms “apex” and “top-order predator” encompass carnivores that occupy the

highest level of the trophic ladder (Estes et al. 2001; Wallach et al. 2015). These

animals are often large-bodied and specialized hunters (Ritchie & Johnson 2009), invest

in K-selected reproductive strategies and parental care, have large territories and display

complex social behaviours (Wallach et al. 2015). In terrestrial systems, carnivorous

mammals typically occupy these roles. Tigers (Panthera tigris), for example, are large,

solitary, territorial carnivores that are specialized forest-edge hunters of large ungulates

and preferentially predate on wild boar (Sus scrofa) and sambar deer (Rusa unicolor)

(Smith et al. 1989; Hayward et al. 2012). The species displays high investment in a low

28

numbers of offspring, with litters of females averaging only 1 - 5 cubs that are

dependent on their mother for up to two years (Sankhala 1967; Singh et al. 2013).

Carnivorous mammals can also act as top-order predators in some marine ecosystems.

In the Antarctic, leopard seals (Hydrurga leptonyx) are strongly territorial, solitary

predators whose predation on Adelie penguins (Pygoscelis adeliae) is a significant

structuring force of penguin colonies (Ainley et al. 2005). However, in marine

environments, top-order roles are more often occupied by fishes, notably sharks,

billfishes and tunas. These taxa differ from their mammalian counterparts by having

greater reproductive output (for example, the broadcast spawning of many hundreds of

thousands of tiny, pelagic eggs in tunas) and having little or no investment in parental

care. They also exhibit ontogenetic shifts in diet, habitat use and behaviour that

complicate their classification as top-order predators throughout their life cycle. For

example, white sharks (Carcharodon carcharias) use coastal habitats and have a diet of

fish as juveniles and are susceptible to predation by other species (e.g. orca, Orcinus

orca; Pyle et al. 1999). As adults, they target marine mammals and use offshore habitats

(Bruce et al. 2006; Estrada et al. 2006; Weng et al. 2007b; a; Bruce & Bradford 2008;

Carlisle et al. 2012; Hoyos-Padilla et al. 2016). Thus, the size of predatory fishes has a

strong influence both on their prey and on whether they have a role as a top-order

carnivore or lower, secondary-order consumer within a food chain. For many large (>3

m length) sharks such as the white (Carcharodon carcharias), tiger (Galeocerdo cuvier)

and hammerhead (Sphyrna sp.), only the adult life history stages can be classified as

truly top-order predators (Heupel et al. 2014).

Many top-order predators preferentially target specific types of prey (Hayward et al.

2011, 2012; Lyngdoh et al. 2014), although feeding strategies vary both among and

within species and can range from specialist to generalist. Dietary specialization is often

strongly associated with the availability of resources (Cherel et al. 2007; Woo et al.

2008). Populations of a species that have a generalist diet may be composed of multiple,

generalist individuals or of sub-groups or even individuals, each of which is specialized

on different types of prey (or some combination of the two options) (Van Valen 1965;

Bolnick et al. 2002; Bearhop et al. 2004; Urton & Hobson 2005). For example, orcas

are described as a generalist predator, hunting a diverse range of marine prey including

fishes, marine mammals and even large sharks, however, different populations exhibit

high levels of specialization on particular types of prey that reflect their availability in

29

the environment (Hoelzel 1991; Pauly et al. 1998b; Pyle et al. 1999; Saulitis et al. 2000;

Ford et al. 2010).

Irrespective of their degree of specialisation, predators regulate the structure,

composition and diversity of prey communities directly by consumption and indirectly

through evoking predator risk effects in prey. Risk effects include shifts in the types of

habitat used by prey to reduce the likelihood of predation, altering the location of

foraging to reduce detection and/or facilitate evasion of predators and increased

investment in vigilance. These behaviours are costly to prey, since time spent avoiding

predators will result in a reduction in activities that enhance fitness such as foraging,

territory defence or reproduction (Brown et al. 1999; Ripple & Beschta 2004; Wirsing

& Ripple 2011). Behavioural responses to risk effects have been recorded and described

in a variety of terrestrial systems (Holmes 1991; Schmitz et al. 1997; Lima 1998;

Brown et al. 1999; Kie 1999; Sinclair et al. 2003; Creel & Christianson 2008; Thaker et

al. 2011), such as the effects of grey wolves (Canis lupus) on the grazing rate, habitat

selection and group dynamics of elk (Cervus elaphus) (Ripple & Beschta 2004; Creel et

al. 2005; Fortin et al. 2005) and the behavioural responses of ungulates under the threat

of lions (Panthera leo) in the African savanna (Sinclair & Arcese 1995; Valeix et al.

2009a,b; Thaker et al. 2010; Creel et al. 2014). Risk effects have been the subject of a

number of studies in aquatic communities (Tegner & Levin 1983; Werner et al. 1983;

Lima & Dill 1990; Lima 1998). However, there is relatively little information available

about the role of large top-order teleosts and sharks in creating risk effects in marine

environments, with the exception of the well-documented behavioural responses of

large herbivores such as turtles and dugongs to the threat of predation by tiger sharks in

seagrass habitats (Wirsing et al. 2007a,b, Heithaus et al. 2007b, 2009).

Although predator risk effects are exhibited by prey in both terrestrial and marine

ecosystems (Wirsing & Ripple 2011), the physical properties of marine environments

have some unique elements that can structure these interactions, such as three-

dimensional distributions of species, greater spatial structure of populations and

“openness” of the environment, stronger and more complex trophic relationships, and

the presence of pronounced ontogenetic shifts in trophic relationships (Carr et al. 2003).

Additionally, top-order predators in nearly all terrestrial ecosystems (with perhaps the

exception of the Arctic) are now constrained to only a fraction of their historical range

due to habitat loss and indiscriminate hunting as a result of the encroachment of humans

and livestock (see review by Ripple et al. 2014). Despite markedly reduced abundances,

30

in marine systems many top-order predators still occupy much of their original ranges,

although the extent and impacts of anthropogenic threats in these ecosystems are

growing rapidly (McCauley et al. 2015). Conservation and management strategies for

these wide-ranging and highly mobile species in marine ecosystems need to

accommodate their complex life-histories, the dynamics of oceanic habitats and their

spatio-temporal overlap with various human threats (Hooker et al. 2011).

1.2 Movement ecology

Movement is a change in the location of an individual in space over time (Nathan et al.

2008) and is a major component of some part of the life history of almost every

organism (Holyoak et al. 2008). Movement has implications for the individual,

populations, communities, ecosystems and, ultimately, evolution (Turchin 1991;

Webster et al. 2002; Dingle & Drake 2007). The decision to move is related to the

organism’s internal state (physiology, locomotory and navigation capabilities; Nathan et

al. 2008), but is influenced by external environmental factors such as the landscape,

weather, physical processes, resource availability, and other organisms that can act as

competitors, predators or mates. Movement can thus be considered as the result of an

interaction between environmental and internal factors and is driven by ecological

processes at various spatial and temporal scales (Crist et al. 1992; Nathan et al. 2008).

Animal movement can be classified into a wide range of modes depending on the

definition of the goals, motivation or temporal dynamics of the processes(e.g. foraging,

dispersion, nomadic, migration), but most frequently is connected to the search for and

use of resources (Dingle & Drake 2007; Nathan et al. 2008; Sims et al. 2008).

Resources in environments are spatially heterogeneous, resulting in unequal or patchy

distributions that drive the dispersion and foraging behaviours of organisms (Turchin

1991; Johnson et al. 1992). Moreover, this patchiness is argued to have a hierarchical

spatial structure, where a patch at a larger spatial scale has nested, internal structures of

heterogeneity at increasingly finer scales (Kotliar & Wiens 1990). For example, small

pelagic fishes, the prey of many larger marine predators, occur in high density schools

at small scales (m - km) that are aggregated into patches associated with mesoscale

oceanographic features at intermediate scales (10 - 100s km), which occur on migration,

feeding or spawning grounds at large (100 - 1000s km) spatial scales (Fauchald 1999;

Fauchald & Tveraa 2006). As a result of these patchy distributions of prey, in the

absence of knowledge of the distribution of resources, predators must optimize their

31

movement in order to maximize the probability of encountering food (Fauchald 1999;

Fauchald & Tveraa 2003; Bartumeus et al. 2005). According to theories of optimal

search strategies, animals will display more directed, straight-line movements in areas

with a low density of resources, whereas in areas of high density of prey they will slow

down and increase the frequency of turning angles to maximize the probability of prey

encounter (Kareiva & Odell 1987). Without previous knowledge of the distribution of

resources, predators may adapt their search and movement patterns to optimise prey

encounter (Sims et al. 2008). A specialised type of random walk known as Lévy walks

(Viswanathan et al. 2000; Bartumeus et al. 2005; Sims et al. 2008) appears to describe

probabilistic search patterns of some species consisting of numerous discrete steps that

are linked by rarer longer steps, which are repeated across multiple scales. However,

much controversy exists around the validity of Lévy foraging hypothesis to describe

optimal search in comparison with cue-driven Brownian-type motion (Benhamou &

Collet 2015), where animals would move in a straight-line random direction until prey

is found, and then switching to a smaller steps described by a Brownian walk

(Benhamou 2007).

Environmental conditions and habitat characteristics create much of the patchiness

within the marine environment. This occurs through the influence on movement of

currents and wind (Weimerskirch et al. 2000; Fritz et al. 2003; Chapman et al. 2011);

variation in optimal ranges of salinity, temperature and dissolved oxygen (Jonsen et al.

2007; Heupel & Simpfendorfer 2008; Espinoza et al. 2011; Bestley et al. 2013) and/or

by physical features that may have an aggregating function such as thermal fronts,

upwelling, eddies, canyons and shelf breaks (Klimley & Butler 1988; Royer et al. 2004;

Croll et al. 2005; Cotte et al. 2007; Doniol-Valcroze et al. 2007; Scales et al. 2014;

Bouchet 2015; Bouchet et al. 2015; Queiroz et al. 2016). In turn, this patchiness also

affects the movements of predators (Humphries et al. 2010). For example, the thermal

structure of the water column shapes the vertical distribution of the sardine (Sardinops

sagax) and as a result it influences the vertical movements of their predators, the Pacific

bluefin tuna (Thunnus orientalis) (Kitagawa et al. 2004, 2007). Relationships between

such environmental drivers and movement of animals is largely scale dependent, both

spatial and temporally (Guinet et al. 2001; Pinaud & Weimerskirch 2005; Fryxell et al.

2008; Avgar et al. 2013). For instance, at a large scale (1000s km) the habitat use of

yellow-nose albatrosses (Thalassarche carteri) is related to bathymetry (a proxy for

ocean and shelf habitats) and sea surface temperatures (SST) associated with subtropical

32

waters, but at mesoscales (10 – 100s km) habitat use is associated with chlorophyll-a

and sea-surface height anomalies that are indicative of mesoscale oceanic features such

as eddies (Pinaud & Weimerskirch 2005).

The patchy nature of marine environments constrains the movements of animals. The

area within which animals move continuously for feeding, resting and reproduction is

termed the home range (Burt 1943). As an statistical concept, home range is define as a

fixed percentage (commonly 95%, 50%, 25%) of the confidence region obtained from

the relative frequency distribution of an animal’s location, usually two dimensional,

over time (Worton 1987). However, as noted above, how much an individual utilises

specific areas within its home range will vary temporally, and according to resources

available and habitat quality (Benhamou 2011; Benhamou & Riotte-Lambert 2012).

Areas of high use will be related to accessibility of food, but must also offer

environmental conditions that are within an optimal range. For example, dusky sharks

(Carcharhinus obscurus) in the Gulf of Mexico, although highly mobile, spend the

majority of their time within waters off the continental shelf where water temperatures

are between 24-28°C (Hoffmayer et al. 2014).

Seasonal events such as migration may break the stability of an animal’s use of space

(Bunnefeld et al. 2011). Similar to ‘animal movement’, the term “migration” is used to

describe a large array of movement types, from vertical movements in the water column

to seasonal largescale movements of several species, and to describe movements from

individual to population levels (Dingle & Drake 2007; Nathan et al. 2008). However, it

generally refers to largescale movements occurring as a result of seasonal variability in

the distribution of resources (Alerstam et al. 2003; Dingle & Drake 2007) or spatial

segregation between breeding and non-breeding areas (Webster et al 2002). More than

12% of vertebrates make long-distance (100-1000s km) migrations throughout the

continents and oceans, however, in general these are more common in swimming and

flying vertebrates as a result of the lower cost of locomotion (Robinson et al. 2009). In

marine systems environmental conditions have been identified as major element of

migratory movements. For instance, migration of juvenile bluefin tuna (Thunnus

thynnus) to the waters of the northern California coast during spring is associated with

warmer ocean temperatures and reduced wind stress, which also results in higher prey

availability (Kitagawa et al. 2007).

33

Large marine predators, such as epipelagic sharks and tunas, are found throughout the

world’s oceans and considerable information is available on the horizontal movements

of some of the most wide-ranging species (Table 1.1). These top-order predators display

a mix of resident behaviours interspersed with extensive pelagic movements.

Environmental variables, such as sea surface temperature (SST) and chlorophyll-a, have

been identified as some of the major drivers of movement patterns for these large

predators (Block et al. 2005, 2011; Queiroz et al. 2016). However, for some of these

species, drivers have only been defined for a limited region within their distribution

range, and the motivation behind large scale movements (foraging, breeding) has yet to

be determined. Defining environmental and habitat preferences of mobile predators

during residency and along migration pathways allows key habitats within their

distribution to be identified (Cooke 2008; Block et al. 2011). This information is also a

key step for ecosystem-based fisheries management (Pikitch et al. 2004) such as

dynamic ocean management (Hobday et al. 2014; Maxwell et al. 2015), where

management adapts to the spatial and temporal dynamics of the ocean based on the

integration of real-time biological, oceanographic and socio-economic data (Maxwell et

al. 2015). The identification of preferred environmental conditions and association with

oceanographic features (i.e. mesoscale features such as upwelling areas, fronts and

eddies) could be used to inform fisheries and enhance dynamic management actions for

highly mobile species (Hobday & Hartog 2014; Lewison et al. 2015).

34

Table 1.1 Range and drivers of movement patterns displayed by large epipelagic top-order sharks and teleosts.

Species Common

name

Maximum

size (cm)

Range of movements Drivers of movements References

Thunnus thynnus bluefin tuna > 450 Seasonal residency, migration to

breeding grounds, longitudinal and

transatlantic movement to foraging

grounds

SST**, chlorophyll-a,

body size, population

(Lutcavage et al. 2000; Block et al.

2005, 2011; Walli et al. 2009;

Galuardi et al. 2010; Galuardi &

Lutcavage 2012)

Carcharodon carcharias white shark > 600 Residency and site fidelity to coastal

areas, philopatry, long-distance pelagic

movements, trans-oceanic migration

foraging, mating and

breeding (hypothesised),

SST**

(Boustany et al. 2002; Bruce et al.

2006; Weng et al. 2007b,a, Domeier &

Nasby-Lucas 2008, 2013; Bonfil et al.

2010; Jorgensen et al. 2010; Block et

al. 2011)

Galeocerdo cuvier tiger shark > 500 Residency and restricted movements in

coastal habitats, oceanic islands and

around coral reefs, seasonal migrations

and residency, long-distance latitudinal

and pelagic movements

SST, bathymetry, season,

chlorophyll-a

(Holland et al. 1999; Heithaus et al.

2007c; Meyer et al. 2009;

Hammerschlag et al. 2012; Hazin et al.

2013; Papastamatiou et al. 2013;

Werry et al. 2014; Holmes et al. 2014;

Ferreira et al. 2015; Lea et al. 2015)

Isurus oxyrinchus mako shark 400 Extensive offshore movements and site

fidelity to slope and shelf edge, seasonal

latitudinal movements,

SST, SST gradients,

chlorophyll-a

(Musyl et al. 2011; Block et al. 2011;

Rogers et al. 2015; Queiroz et al.

2016; Vaudo et al. 2016)

Carcharhinus

longimanus

oceanic

whitetip

350 Long-distance pelagic movements, some

indication of philopatry, trans-equatorial

migration*

SST** (Kohler et al. 1998; Musyl et al. 2011;

Carlson & Gulak 2012; Howey-Jordan

et al. 2013)

Sphyrna lewini great

hammerhead

< 350 Residency and site fidelity in continental

shelfs and islands, occasional offshore

movements

SST gradients, water

temperature, currents

(Klimley 1993; Bessudo et al. 2011;

Hammerschlag et al. 2011b; Ketchum

et al. 2014a,b; Queiroz et al. 2016)

Prionace glauca blue shark 320 Overwintering around Gulf Stream and

Sargasso Sea (central North Atlantic),

long-distance pelagic movements, trans-

oceanic migrations*

SST, foraging (Kohler et al. 2002; Queiroz et al.

2005, 2016; da Silva et al. 2010;

Campana et al. 2011; Block et al.

2011; Vandeperre et al. 2014)

* Information obtained from tag-recapture data.

** Driver only identify for the North Pacific by Block et al. 2011a

SST = sea surface temperature

35

1.3 Challenges for the conservation of large marine predators

Recent analyses show that body size is one of the strongest predictors of the risk of

human-driven extinction in the modern oceans, with large-bodied animals suffering the

most elevated levels of threat (Payne et al. 2016) because they are preferentially

targeted by human hunting (Pauly et al. 1998a; Pauly & Palomares 2005). Top-order

marine predators are typically large animals and this trait, combined with their life

history strategies and wide-ranging movements has led to declines in many species so

that they are now classified as Vulnerable, Threatened or Endangered by the

International Union for the Conservation of Nature (IUCN) Red list guidelines (IUCN

2012).

The mobility of marine predators such as large sharks presents one of the greatest

challenges for conservation, as is the case for many other species (Shuter et al. 2011;

Runge et al. 2014; Campana 2016). For some species, nomadic movement behaviours

might be predominant, in which home ranges, and breeding timing and areas are not

predictable (Dingle & Drake 2007). Nomadic or migratory behaviour brings these

animals into contact with multiple threats and at the same time, it hinders our ability to

assess impacts on their populations (Shuter et al. 2011; Runge et al. 2014; McCauley et

al. 2015). Mobile species are extremely vulnerable when large portions of a population

migrate to restricted breeding or wintering locations, as is the case for many taxa

including fishes, reptiles and pelagic birds (Webster et al. 2002) or when migrations

traverse socio-political boundaries that have varying levels of protection as occurs for

many sharks and fishes (Martin et al. 2007; Berger et al. 2008; Hooker et al. 2011;

Dallimer & Strange 2015). For terrestrial species, a common response to ensure the

conservation of mobile species has been a focus on the protection of migration corridors

(Berger 2004; Berger et al. 2008), but for marine predators, particularly in the open

ocean, such essential habitats are much harder to identify and delineate.

Significant efforts have been directed towards the protection of “hotspots” of

biodiversity through the designation of Marine Protected Areas (MPAs) (Mittermeier et

al. 1998; Médail & Quézel 1999; Myers et al. 2000; Hughes et al. 2002; Roberts et al.

2002) and these have become a key strategy for ecosystem-based conservation and

management (Hooker et al. 2011; Abecasis et al. 2014; Lascelles et al. 2014; O’Leary

et al. 2016). In recent years, the mean size of MPAs has increased greatly, however

most are still too small relative to the scale of movements and home ranges of many

36

top-order predators such as sharks to ensure the protection of all life history stages

(Heupel et al. 2015; McCauley et al. 2015). In such species, individuals can be spread

over many thousands of kilometres, which prevents protection of the entire population

(Hooker et al. 1999; Chapman et al. 2005; Game et al. 2009; Hays & Scott 2013). If

MPAs are to be an effective strategy for the conservation of large predators, they must

target areas of higher vulnerability such as mating and foraging grounds, migration

corridors, or nursery areas (Heupel et al. 2007; Lascelles et al. 2014; Pendoley et al.

2014; Hays et al. 2014). Dynamic ocean management has also indicated great potential

benefits to managing highly mobile species or processes (Lewison et al. 2015; Maxwell

et al. 2015; Dunn et al. 2016). Real-time tracking has been suggested as a useful tool for

several migratory species (Hobday & Hartmann 2006; Hobday & Hartog 2014; Hazen

et al. 2016). For example, a management tool “WhaleWatch” has been developed for

blue whales (Balaenoptera musculus) in the California Current by combining tracking

data with distribution models and the collection of environmental data to develop near-

real-time habitat prediction tool (Hazen et al. 2016). Optimisation of these strategy

requires a clear understanding of the relationships between movement patterns,

environmental drivers and habitat preferences of these animals (Bailey & Thompson

2009; Game et al. 2009; Hooker et al. 2011; Abecasis et al. 2014). For many species,

particularly large sharks, we still lack much of this data.

One of the key issues hampering our understanding of the relationship between

movement patterns and environmental drivers of marine predators has been limitations

on the numbers and components of populations animals tracked in tagging programs.

Over the last three decades, satellite tags have provided unprecedented insights into the

movement behaviours of marine predators and their relationships to environmental cues

(Biuw et al. 2007; Block et al. 2011; Schlaff et al. 2014; Hussey et al. 2015a).

However, due to the expense of this technology and the logistics involved in capturing

animals for tagging (Hussey et al. 2015a) most studies of large marine predators rarely

tag more than a few dozen individuals, unless the species is targeted by fisheries, as is

the case for tunas (Block et al. 2005). Moreover, the ontogenetic stage and sex of

animals tagged is rarely representative of the entire population due to differential

accessibility of different demographic components during sampling. This has limited

our understanding of patterns across species and populations. However, the combination

of information from multiple studies of the same species, or from studies of multiple

species within a single region, has begun to overcome this issue, allowing

37

generalisations of spatial patterns of habitat use and drivers of movement on a

population level and even for entire communities. For example, in the Pacific Ocean,

the Tagging of Pacific Predators (TOPP) program compiled tracking data for 23 species

and was able to describe how various taxa including tunas, sharks, cetaceans, seabirds

and marine turtles were associated with the California Current Large Marine Ecosystem,

where seasonal movements were associated with changes in ocean temperature and

measures of productivity (Block et al. 2011). Similar studies in other marine ecosystems

are urgently required, given that many of the threats facing marine systems such as

climate change and ocean acidification are global in scope (Hoegh-Guldberg & Bruno

2010; Hazen et al. 2013; Jones & Cheung 2015). Due to the importance of

environmental cues for migratory patterns, climate change is predicted to affect the

distribution and abundance of marine fishes worldwide (Perry et al. 2005; Jones &

Cheung 2015). However, most of our knowledge about the effects of climate change on

migratory patterns of animals is related to birds, with large gaps in our knowledge of

long distance movement and potential human impact in other groups (Robinson et al.

2009). Detailed information about the environmental drivers of movement is necessary

to understand the impacts of projected climate changes, predict what areas and which

species are at greater risk, and to plan conservation strategies.

1.4 The tiger shark

Tiger sharks (Galeocerdo cuvier) (Figure 1.1) are distributed worldwide in most of the

tropical, subtropical and temperate habitats of oceanic and coastal ecosystems (Randall

1992). They are categorised as a top-order predator in tropical marine ecosystems and

can exert strong structuring forces in prey communities through predator risk effects.

For example, in the shallow seagrass habitats of Western Australia, green turtles

(Chelonia mydas), dugongs (Dugong dugon) and cormorants (Phalacrocorax varius)

often select habitats that are less profitable for foraging, but reduce the chance of

interactions with tiger sharks at times when these predators are abundant (Heithaus

2005; Wirsing et al. 2007a,b, Heithaus et al. 2007b, 2008b). The impact of tiger sharks

on the behaviour of prey species in these seagrass communities is well established and

is frequently cited as an example of risk effects in marine predator-prey interactions

(Heithaus et al. 2008b; Wirsing & Ripple 2011). However, tiger sharks exhibit a

generalist diet (Stevens & McLoughlin 1991; Simpfendorfer 1992; Lowe et al. 1996;

Simpfendorfer et al. 2001) and also often feed at lower trophic levels in food chains or

38

scavenge on dead prey. Despite their importance as top-order predators in some

habitats, such as the seagrass system described above, this role may not be duplicated

across all the marine environments they traverse. For example, analyses of

specialization of diet and trophic niche using stable isotopes have shown that tiger

sharks are indeed true generalists (Matich et al. 2011; Trystram et al. 2016) and

although they occupy apex levels of seagrass and reef-associated food webs (Heithaus et

al. 2013; Frisch et al. 2016), the species feeds at the lowest trophic level within the

assemblage of large sharks off coastal reefs in South Africa (Hussey et al. 2015b). Such

conflicting results imply that the movement patterns, habitat use, diet and trophic role of

tiger sharks are intertwined and not necessarily consistent throughout the species range.

To disentangle these factors, and others such as sex and ontogenetic stage that might

also influence the role of these sharks (MacNeil et al. 2005; Borrell et al. 2011; Carlisle

et al. 2014; Munroe et al. 2015), comprehensive data sets are required that document

patterns of movement, habitat use and diet across multiple spatial and temporal scales.

Figure 1.1 The tiger shark.

Tiger sharks have a global distribution and display high utilisation of coastal systems

(Heithaus et al. 2002). As a result, a number of research programs have investigated

movement patterns and habitat use of tiger sharks throughout most of the species range

(Table 1.2). These sharks have been tagged with a wide range of tags (acoustic, PAT,

SPOT, accelerometers) and both vertical and horizontal movements have been

described. Typically, tagging studies of tiger sharks show great individual variability in

the use of space (Holland et al. 1999; Heithaus et al. 2007c; Fitzpatrick et al. 2012;

Hammerschlag et al. 2012; Hazin et al. 2013; Papastamatiou et al. 2013; Holmes et al.

2014; Lea et al. 2015). Sharks tagged in seagrass habitats off Shark Bay, Western

Australia, exhibited either restricted movements near the area where they were tagged,

remaining within the coastal embayment and surrounds, or moved to oceanic waters

39

(Heithaus et al. 2007c). The same pattern of individual variation in residency and

migration has been recorded for tiger sharks in waters off Hawaii and the eastern coast

of the United States (Hammerschlag et al. 2012; Papastamatiou et al. 2013). Some

individuals also displayed large seasonal migrations (1000s km) in the North Atlantic

and off both the east (Holmes et al. 2014; Lea et al. 2015) and west coasts (Ferreira et

al. 2015) of Australia. However, these studies tagged mostly female sharks and many

satellite tag deployments were short, typically in the order of only a few weeks or

months. Longer durations of tagging have been achieved using acoustic tags, however

detection rates are typically low (Meyer et al. 2009, 2016; Papastamatiou et al. 2010;

Werry et al. 2014). Furthermore, only two studies have modelled tracking information

in order to define the drivers of movement and residency for tiger sharks (Fitzpatrick et

al. 2012; Papastamatiou et al. 2013). Although tiger sharks have been tagged in many

sites across their distribution range and in different habitats, the explanation for the

female bias in most datasets is still unresolved. This bias can be a result of the habitats

being sampled (warm, coastal habitats); however, Lea et al. (2015) described the

seasonal use of coral reef systems in the Caribbean by male tiger sharks

Since most studies have tagged females, little is also known about critical habitats and

movement patterns of the other components of the population, particularly males,

juveniles and neonates (Driggers et al. 2008). Moreover, contrasting evidence of

ontogenetic habitat shifts have been reported, with sub-adult sharks believed to forage

over large areas in some regions (Meyer et al. 2009) but showing limited movement in

others (Werry et al. 2014; Afonso & Hazin 2015). Although most sharks have a K-

selected life history (Stevens et al. 2000; Frisk et al. 2001; Dulvy et al. 2014a), tiger

sharks produce litters of up to 80 pups (Stevens & McLoughlin 1991; Simpfendorfer

1992; Whitney & Crow 2007). In contrast, other large predatory sharks often produce a

limited number of embryos, usually less than 10 (Gilmore 1993), such as white sharks

(litter sizes of only 2-10 pups; Francis 1996) and the grey nurse or sand tiger shark

(Carcharias taurus), which produces only two embryos (Pollard et al. 1996). Tiger

sharks also display extreme rapid growth in their early life history stages (Branstetter et

al. 1987; Natanson et al. 1999; Wintner & Dudley 2000; Afonso et al. 2012), doubling

their size at birth in the first year of life. These biological traits suggest that tiger sharks

might have higher population growth rates and be more resilient to fishing pressure than

most sharks that are top-order predators (Simpfendorfer 2009). However, they are a key

target in coastal shark control programs (Dudley & Simpfendorfer 2006; Green et al.

40

2009; Simpfendorfer et al. 2010; Reid et al. 2011) and components of catches in many

artisanal and unregulated fisheries (Field et al. 2009; Moir Clark et al. 2015). At present

the limited information available on trends in population sizes suggests that abundances

are declining, leading to tiger sharks being classified as “Near Threatened” by the IUCN

(Chapter 2).

The progress made by tracking studies that have pooled information from different

sampling sites across an ecosystem (Costa et al. 2010a; Block et al. 2011; Raymond et

al. 2015) or species range (Biuw et al. 2007; Hindell et al. 2016) demonstrate a means

to move towards a better understanding of key aspects of the ecology of marine

predators. These studies show the value of large datasets in defining environmental

preferences and critical habitats for populations and communities in areas such as the

California Current Large Marine Ecosystem in the North Pacific Ocean (Block et al.

2011) and the Antarctic continental shelf, an important foraging ground for southern

elephant seals (Mirounga leonina) (Hindell et al. 2016). For tiger sharks, the pooling of

key data sets could advance our understanding of the movement and feeding ecology of

the species: firstly, tracking studies over locations distributed across the species range

and composed of different parts of the populations (males and females sub-adults etc.);

and secondly, dietary information across a range of habitats and environments. The

latter will allow a better understanding of the role of food availability as one of the

major biological drivers of movement patterns.

41

Table 1.2 Tiger shark tagging programs with the number of tags deployed, sex ratio of animals tagged, size range of sharks tagged and information provided by

telemetry devices.

Tagging

program

Ocean

basin

N

Tags Tag type

Sex

ratio

(F:M)

Total

length

(cm)

Track

duration

(days)

Distance

(km)

Temperature

range (°C) Data collected Aims Reference

Hawaii Pacific 140 Acoustic,

PSAT, SPOT,

accelerometers

2.1:1a 99 - 464 <1-

464b,161

c

1000s 5 - 30 Presence, location,

depth,

temperature,

swimming

direction, stroke

frequency, video,

acceleration,

speed

Diel movements,

horizontal and vertical

movement, depth-

temperature profile, yo-

yo diving, foraging

behaviour, residency,

utilization distribution

and migration (and

drivers), site fidelity

(Tricas et al.

1981; Holland et

al. 1999; Meyer

et al. 2009, 2010,

Papastamatiou et

al. 2010, 2013;

Nakamura et al.

2011)

East

Australia

Pacific 17 PSAT, SPOT 5.7:1 150 - 350 6 - 408 15791d 6-29.5 Location, depth,

temperature, light-

level (geolocation)

Depth - temperature

profile, horizontal

movements, habitat use,

residency

Holmes et al.

2014

Raine

Island

Pacific 9 SPOT 3.5:1 288 - 350 16 - 356 - <12 - >30 Location,

temperature, depth

Area-restricted search,

horizontal movements,

relation with turtle

nesting season

Fitzpatrick et al.

2012

Coral Sea Pacific 34 PSAT, SPOT,

Acoustic

3.4:1 154 - 390 1 - 255b;

210c

1141 5.6 - 32.2 Presence, depth,

temperature,

location

Site fidelity, migration,

diving, habitat use,

depth- temperature

profile

Werry et al. 2014

Florida

and

Bahamas

North

Atlantic

25 SPOT 11.5:1 184 - 403 26 - 297 3500 - location Horizontal movements,

residency, activity space,

impacts of provisioning

ecotourism

Hammerschlag et

al. 2012

Challenger

Bank

North

Atlantic

24 SPOT 0.2:1 173 -369 < 45 -

>1000

42996 18 -26e location Migration, seasonal

horizontal movements,

Lea et al. 2015

42

philopatry, straightness

of movement

U.S.

Virgin

Islands,

Bermuda

North

Atlantic

14 PSAT 0.5:1 210 - 305 9 - 184 1354 6.3 - 29.1 Depth,

temperature, light-

level (geolocation)

Horizontal and vertical

movements, depth-

temperature profile,

depth behavioural type

Vaudo et al. 2014

Recife South

Atlantic

21 PSAT,

miniPAT,

Acoustic

1.6:1 120 - 295 6 - 159 1156f 4 - 25 Depth,

temperature, light-

level (geolocation)

release survival, vertical

and horizontal

movements patterns,

shark bite mitigation

Hazin et al.

2013a; Afonso &

Hazin 2014, 2015

Ningaloo

Reef

Indian 8 SPOT,

SPLASH

4.5:1 145 - 333g 7 - 517 > 4000 6-33 Location, depth,

temperature

Residency and drivers,

home range area, large-

scale movements

Ferreira et al.

2015

Shark Bay Indian 57 SPOT,

acoustic,

crittercam

1.7:1 230 - 401 <1 - 99 505h - Presence, video,

depth, location,

Site fidelity, horizontal

and vertical movements,

habitat use, foraging

behaviour

Heithaus 2001a,

Heithaus et al.

2001, Heithaus,

Dill, et al. 2002,

Heithaus,

Wirsing, et al.

2007 a Sex ratio of reported numbers, sex ratio of acoustic tagged sharks was up to 6.3:1 (Papastamatiou et al. 2013)

b Acoustic tag

c Satellite tag

d track length (km)

e Remote sensed sea surface temperature

f Minimum distance travelled

g Fork length (cm)

h A movement of 8000 km was registered by a single location

43

1.5 Aims

The overall goal of my thesis is to describe the movement ecology of tiger sharks

(Galeocerdo cuvier) and identify the drivers of movement patterns and habitat use

across a range of spatial and temporal scales. My research aims to:

1) Outline the current understanding of the biology and population trends, and assess

the conservation status of the species.

2) Describe large-scale movements, residency and environmental preferences of tiger

sharks along the coastline of Western Australia.

3) Identify spatial patterns of diet and trophic ecology of different sexes and sizes of

tiger sharks in Australia. Describe the variability and stability in diets in relation to local

food-webs at different temporal scales.

4) Describe the movement patterns and habitat use of tiger sharks at a global scale and

use novel statistical approaches to identify the potential physical and biological factors

that drive these patterns.

In line with these aims, my thesis is organised into an introductory literature review,

four research chapters, and the findings are drawn together in a final discussion chapter.

1.6 Thesis outline

In Chapter 2 I review the global literature on the distribution, population, ecology and

threats to tiger shark populations. I then use this information to re-assess the

conservation status of the species. This review followed the category guidelines of the

International Union for Conservation of Nature (IUCN) and will update the IUCN Red

List assessment for tiger sharks.

Chapter 3 describes depth and temperature preferences associated with movements and

residency of tiger sharks along the coastline of Western Australia. Movement-based

home range analyses were applied to satellite tracking data to define areas of residency

and space used by individual sharks. Mixed models were then fitted to the relationship

between residency and environmental variables (depth and sea surface temperature). In

this chapter, I show that, as top-order predators, tiger sharks are able to move extensive

44

distances, linking tropical and temperate ecosystems and that their environmental

preferences vary among different regions (north/south) of the coast of Western

Australia.

An important motivation behind predator movement is the distribution of prey.

Knowledge of spatial changes in diet is likely to be key in understanding the function of

habitats used by marine predators. In Chapter 4, I use stable isotope analysis to

characterise diet and trophic ecology of tiger sharks throughout Australia and identify

factors driving spatial and temporal variation in isotopic signatures of tissues. I discuss

diet in the context of the differential use of multiple habitats and food webs by tiger

sharks across their range in northern Australia.

In Chapter 5, I compile and analyse a global dataset of tiger shark tracks across the

Atlantic, Indian and Pacific oceans to search for and identify environmental drivers of

movement and habitat use for tiger sharks at the scale of the entire range of the species.

Using a novel analytical approach, I examine how the influence of environmental

factors varies across different ocean basins and between sexes, and identify areas where

environmental variables did not explain habitat use. These areas can then be targeted for

future study.

I integrate and synthesise the findings of my thesis in Chapter 6. I discuss how insights

into the movement, habitat use and diet of tiger sharks across multiple spatial and

temporal scales can fill some of the gaps in our knowledge on the ecology of the

species. Using this information, I examine the role of the species as a top-order

predator, how the behaviours and traits of the species compare to other top-order

predators and the implications for the resilience of tiger sharks in marine environments.

Finally, I evaluate the benefit of better understanding of the drivers of movement

ecology of marine predators for conservation planning.

45

Chapter 2 Review of tiger shark ecology and

conservation status

2.1 Introduction

Globally, shark populations have shown accelerated declines due to overfishing and

habitat degradation (Dulvy et al. 2008; Ward-Paige et al. 2010; McCauley et al. 2015;

Campana 2016), with one quarter of shark and ray species now classified as

“Threatened” by the International Union for Conservation of Nature (IUCN) Red List

(Dulvy et al. 2014a). The IUCN Red List assessments are regarded as reliable tools to

determine the conservation status of species because they use a comprehensive and

scientifically rigorous approach to estimate extinction risk (Rodrigues et al. 2006). The

goal of the assessment is to provide information and analyses on the status, trends and

threats to species in order to inform conservation actions. Current assessments of sharks

have been undertaken during regional Red List workshops coordinated by the IUCN

Shark Specialist Group (SSG) in the past 5 - 10 years (Dulvy et al. 2014a), with the last

assessment for tiger sharks occurring in 2009 (Simpfendorfer 2009). In this chapter, I

update the IUCN assessment for tiger sharks, reviewing their current conservation status

and indicating the research needs for the future using the guidelines of the IUCN Red

List (IUCN 2012), with some adaptations appropriate for a thesis chapter.

2.2 Distribution

Tiger sharks (Galeocerdo cuvier) have a worldwide distribution in tropical and warm

temperate oceans, ranging between the latitudes of approximately 40°N - 36°S (Figure

2.1). Compagno (1984) and Randall (1992) describe the distribution across ocean basins

as follows:

- In the western Atlantic, they range from the USA (from Cape Cod, Massachusetts) to

Uruguay, including the Gulf of Mexico, Bermuda and islands of the Caribbean. In the

eastern Atlantic, they are found along the West African coast, from Morocco to Angola,

including the Canary Islands and at isolated islands such as Fernando de Noronha in the

South Atlantic.

- Tiger sharks are also reported from locations throughout the Pacific Ocean. In the

eastern Pacific they range from southern California to Peru, including the Galapagos

46

and Revillagigedo Islands. In the western Pacific they occur off the coasts of Japan, east

China, Australia and northern New Zealand; as well as in the western, central Pacific in

Palau, east to Solomon, Marshall and Hawaiian Islands, French Polynesia and other

isolated atolls.

- In the Indian Ocean, tiger sharks are found all along the east coast of Africa, including

remote localities such as Réunion Island and the British Indian Ocean Territory, north to

the Red Sea, and throughout the tropical Indian Ocean off the coasts of Pakistan, India,

Sri Lanka, Thailand, Vietnam, Southern China, the Indo-Pacific region and most of

western Australia.

Tiger sharks also appear seasonally in cool temperate waters, most likely following

warmer currents with reports from the United Kingdom (Compagno 1984) and Iceland

(Matsumoto et al. 2005), the southern coasts of New South Wales and Western

Australia (Pepperell 1992; Holmes et al. 2014; Ferreira et al. 2015) and South Africa

(Dicken & Hosking 2009).

Figure 2.1 Distribution range of tiger sharks (blue).

2.3 Population trends

Long-term trends of catch rates of tiger sharks are only available from limited sources

including regional shark control programs and fisheries observer and fisheries-

independent longline survey datasets for the US east coast and Gulf of Mexico ( Table

2.1). Tiger sharks are not usually targeted by large commercial fisheries, but occur as

by-catch in low numbers. As a result, captures are often not recorded and there is a lack

47

of adequate data for stock assessment, definition of population structure and

documentation of trends in demography of the species.

Table 2.1 Summary of long-term trends of catch rates of tiger sharks based on regional

datasets.

In the northwest Atlantic, the bottom longline fishery for sharks is active along the east

coast of the US and Gulf of Mexico (Gulak et al. 2013), and the Northeast Fisheries

Science Centre (NEFSC) conducts fisheries-independent surveys of large and small

coastal sharks in waters off Florida to the Mid-Atlantic (http://nefsc.noaa.gov/nefsc/

Narragansett/sharks/survey.html). Data from NEFSC surveys conducted from 1960 -

2000 showed an upward trend in the relative abundance of tiger sharks, with an increase

of 6.7% per year since the 1980s. Because relative abundance is calculated as an index

of actual or absolute abundance based on standardised catches (Blackhart et al. 2006),

these trends reflect changes in CPUE (catch per unit effort) rather than being a

consequence of an increase in effort. Similarly, the Shark Bottom Longline Observer

Program (SBLOP) also showed an increase in relative abundance of the species of 8%

per year between 1994 and 2013. However, the Pelagic Observer Program from the

Southeast Fisheries Science Centre (SEFSC) showed no change in abundance for the

same period (Figure 2.2) (J. Carlson, pers. comm.) and previous estimates based on the

standardised CPUE time series of logbook data for the U.S. pelagic longline fleets in the

northwest Atlantic suggested a decline of 61 - 65% in abundance of tiger sharks (Baum

et al. 2003). These estimates of declines might be biased due to analytical issues

(Burgess et al. 2005)

Region Pop trend Period Source Metric used

NW Atlantic Increasing 1994-2013 Northeast Fisheries Science

Centre

Relative

abundance

Queensland Declining 1993-2010 Queensland Shark Control

Program

CPUE

(projection)

New South Wales Declining 1998-2007 NSW Shark Meshing Program CPUE

South Africa Increasing 1978-2003 KwaZulu-Natal Beach

Protection Program

CPUE

48

Figure 2.2 Relative abundance (calculated as an index of standardised catch) for the Shark

Bottom Longline Observer Program (SBLOP, blue diamonds) and longline survey of the

Northeast Fisheries Science Centre (NEFSC, green triangles) and SEFSC Longline (MS

longline, red squares). Figure produced by J. Carlson (pers. comm.).

In Australia, the Queensland Shark Control Program (QSCP) has used a combination of

nets and drumlines adjacent to popular swimming beaches to target large sharks since

1962 (Paterson 1990). Catch per unit effort (defined as sharks net−1

day−1

or sharks

drumline−1

day−1

) of tiger sharks showed an overall declining trend for the QSCP

between 1993 and 2010 (Figure 2.3), with the most significant decline in the southern

region (Holmes et al. 2012). A separate analysis for nine beaches in Cairns and

Townsville, northern Queensland (Figure 2.4), showed that annual, standardised catch

rates (annual catch was modelled including effort by beach in all models to account for

changes in effort over time and generate an index equivalent to CPUE) increased up

until the 1980s and then decreased to approximately 66% of their original level in the

2000s (Simpfendorfer et al. 2010). Based on the CPUE for 1993 and 2010 from Holmes

et al. (2012), and a generation length of 17.5 to 22.5 years for tiger sharks (Natanson et

al. 1999; Holmes et al. 2015), a prediction of population change within three

generations was calculated according to the rules for estimating reductions by the Red

List guidelines (IUCN 2012). Generation length was calculated according to the

formula: Age at first maturity + (Maximum age - Age at first maturity/2). The

prediction considered CPUE values from 1993 and 2010 as “Past” population sizes

assuming a regression with an exponential rate of decline. The prediction showed that

49

tiger shark CPUE will undergo a decline of 90 - 96% for drumlines and of 94-99% for

nets over the next 30-45 years in relation to the CPUE for the QSCP in 1993 (Figure

2.3). These predictions should nevertheless be treated with caution, as they do not

account for fluctuations in CPUE or demographic characteristics of the tiger shark

population.

Figure 2.3 CPUE (number of sharks gear-1

day-1

) by year and size (small < 3 m and large > 3 m)

of sharks for drumlines (A) and nets (B) by the QSCP between 1993 and 2010. Figure extracted

from Holmes et al. (2012).

Table 2.2 Average CPUE(number of sharks gear-1

day-1 x1000) for drumlines from the QSCP

for 1993 and 2010 from Holmes et al. (2012) and projected CPUE for three generation lengths

(Projection 1 = generation length of 17.5 years, Projection 2 = generation length of 22.5 years).

Population Past 1 (1993) Past 2 (2010) Projection 1 (2044) Projection 2 (2060)

QSCP Drumlines 0.631 0.430 0.044 0.015

QSCP Nets 0.244 0.187 0.010 0.003

50

Figure 2.4 Locations of the Queensland Shark Control Program with details of gear (1993-

2010), from Holmes et al. (2012).

In southeast Australia, the New South Wales Shark Meshing Program (SMP) has

operated since 1937 (Reid et al. 2011), with marked fluctuations in tiger shark CPUE

seen in the first four decades of the program (Figure 2.5). The overall CPUE has not

changed significantly between 1950 and 2010 (R2 = 0.048, p = 0.0937, Reid et al.

2011), although a downward trend in CPUE was observed in the last two decades

(Figure 2.5). The size-frequency distribution of tiger sharks caught by the program has

changed over 60 years (1998 - 2007) with a significant reduction in the modal size of

tiger sharks from 3.5 to 3.0 m. The apparent decline in CPUE over the last 20 years

combined with the decrease in the proportion of large individuals suggests changes in

the population structure, and raises concern on impacts the program is having on the

tiger shark population off New South Wales (Reid et al. 2011).

In contrast, the KwaZulu-Natal beach protection program in South Africa has deployed

large-mesh gillnets off popular swimming beaches since 1952, and has found an annual

increase of 3% in the CPUE of tiger sharks between 1978 - 2003 (Dudley &

Simpfendorfer 2006) (Figure 2.6).

51

Figure 2.5 Catch per unit of effort of tiger sharks by the New South Wales Shark Meshing

Program from 1950-2010 modified from Reid et al. (2011). Dashed line indicates Loess

estimates.

Figure 2.6 Annual catch per unit of effort of tiger sharks in nets from the KwaZulu-Natal beach

protection program between 1978 and 2003. Dash line represents the slope of a linear

regression. Figure adapted form Dudley & Simpfendorfer (2006).

2.4 Life-history and ecology

Tiger sharks occur throughout tropical and subtropical waters on continental shelves,

coral reefs, offshore islands and atolls. Tiger sharks are encountered from very shallow

waters (Heithaus et al. 2002) to open ocean areas (Polovina & Lau 1993; Meyer et al.

2009; Hammerschlag et al. 2012; Werry et al. 2014; Holmes et al. 2014; Ferreira et al.

2015; Lea et al. 2015; Domingo et al. 2016; Queiroz et al. 2016). Vertical movements

have been recorded to depths of up to 1,136 m (Werry et al. 2014); however they

52

typically spend approximately 90% of time within the first 50 - 100 m of the water

column (Meyer et al. 2010; Fitzpatrick et al. 2012; Vaudo et al. 2014; Afonso & Hazin

2015).

Relative to other requiem sharks, family Carcharhinidae, tiger sharks show higher

intrinsic rates of population increase based on life history parameters (Dudley &

Simpfendorfer 2006). Size at birth has been estimated to be 50 - 90 cm (Stevens &

McLoughlin 1991; Simpfendorfer 1992; Winter & Dudley 2000; Whitney & Crow

2007) and growth rates during early life stages are thought to be high (36 - 118.4 cm yr-

1) (Branstetter et al. 1987; Natanson et al. 1999; Kneebone et al. 2008; Afonso et al.

2012). In comparison, the grey nurse shark (Carcharias taurus) is estimated to grow

between 14.5 - 18.5 cm per year in early life stages (Goldman et al. 2006). Maximum

total length attained by tiger sharks is estimated to be 6 m but individuals larger than 5

m are rarely seen (Compagno 1984; Randall 1992). The maximum age and generation

length are estimated to be 27 - 33 years and 17 - 22.5 years respectively. Tiger sharks

reach maturity between 5 - 13 years (Branstetter et al. 1987; Smith et al. 1998;

Natanson et al. 1999; Wintner & Dudley 2000; Meyer et al. 2014; Holmes et al. 2014),

with females maturing at sizes (total lengths) between 274 - 345 cm and males around

250 - 305 cm (Compagno 1984; Stevens 1984; Stevens & McLoughlin 1991; Randall

1992; Simpfendorfer 1992; Wintner & Dudley 2000; Whitney & Crow 2007; B.

Holmes, in press) although estimates of size and age at maturity vary greatly among

locations. The high level of separation between tiger shark in the Indo-Pacific and

western Atlantic (Bernard et al. 2016) suggests that hat these dissimilarities in estimates

of size and age at maturity may be due to differential population parameters among

ocean basins.

Several studies have reported aspects of tiger shark reproduction, but the complete

reproductive cycle has only been described for Hawaii (Whitney & Crow 2007). In this

location, the cycle occurs triennially, with mating thought to occur in the boreal winter

between January and February and females storing sperm in the oviducal gland while

oocytes develop. Ovulation occurs in June to July with pupping during the boreal

autumn between late September and early October of the following year after a 15 - 16

month gestation (Whitney & Crow 2007). Conflicting information on the seasonality of

reproduction is available for other parts of the world. For example, in Australia, a

female was sighted bearing fresh mating wounds during the austral autumn in early May

(LC. Ferreira, pers. obs.) and pupping is reported to occur during the austral summer

53

(Stevens & McLoughlin 1991; Simpfendorfer 1992). The number of embryos in a litter

ranges from 3 - 82 and litters have on average 26 - 33 embryos (Stevens & McLoughlin

1991; Simpfendorfer 1992; Whitney & Crow 2007). Little is also known about pupping

areas for the species. In the North Atlantic tiger sharks do not appear to use specific

areas for pupping that can be defined as nurseries, because parturition takes place over

an extensive area of shelf waters <100 m deep between the Gulf of Mexico and the

northwest Atlantic, with higher abundances of neonates recorded between the

longitudes of 30° - 33°N (Driggers et al. 2008). No information on pupping areas exists

for the Indian or Pacific Oceans and rates of juvenile mortality remain unknown.

Telemetry data show that tiger shark movements vary greatly among individuals, with

some individuals displaying elevated levels of residency in discrete areas and others

displaying extensive movements of thousands of kilometres between tropical and

temperate ecosystems, as well as seasonal migrations to open oceanic waters (Kohler et

al. 1998; Hammerschlag et al. 2012; Papastamatiou et al. 2013; Werry et al. 2014;

Holmes et al. 2014; Ferreira et al. 2015; Lea et al. 2015). Although many tracking

programs exist globally, the likely drivers of movements (e.g. prey abundance,

temperature, bathymetry, productivity, ocean currents, etc.) remain mostly unknown. In

Western Australia, residency of tiger sharks is associated with shallow depths and water

temperatures above 23°C (Ferreira et al. 2015), and in Hawaii inter-island movements

are associated with season (late summer/early spring), sea surface temperatures of 23° -

26°C and chlorophyll-a concentrations above 0.12 mg m-3

(Papastamatiou et al. 2013).

Variability in movement behaviour may be due to partial migrations, where only part of

the population performs large-scale movements while the remainder shows more

resident behaviour (Papastamatiou et al. 2013). Tiger sharks also seem to show

ontogenetic changes in habitat use and range of movement, with smaller individuals

restricting their movements to coastal areas (Werry et al. 2014; Afonso & Hazin 2015).

2.5 Threats

Tiger sharks are caught as secondary target species in multi-species shark fisheries and

are also retained as bycatch in commercial, small-scale and artisanal fisheries where

they are valued for their high quality flesh, fins, skin, liver oil and cartilage. The fins of

tiger sharks are a common component of the Hong Kong fin trade (Clarke et al. 2006).

The species has been increasingly exploited by fisheries since the 1950s largely due to

the increasing demand for shark fins (Ward-Paige et al. 2010). Tiger sharks are also

54

subject to illegal, unreported and unregulated (IUU) fishing operations and are regularly

caught in recreational fisheries and targeted by shark control programs in Australia

(Paterson 1990; Reid et al. 2011), South Africa (Dudley & Simpfendorfer 2006; Cliff &

Dudley 2011), and since 2014 at Réunion Island.

2.5.1 Shark Fisheries

Catches of tiger sharks in multi-species shark fisheries have been documented in a

number of regions including the western Atlantic (Hoey & Casey 1986; Berkeley &

Campos 1988; Bonfil 1994; Morgan et al. 2009; Carlson et al. 2012), Brazil (Bonfil

1994), Australia (Stevens et al. 1982; Lyle et al. 1984; Macbeth et al. 2009; Tillett et al.

2012), Papua New Guinea (Kumoru 2003), Peru (Gonzalez-Pestana et al. 2014),

Taiwan (Bonfil 1994), India (Bineesh et al. 2014) and Saudi Arabia (Spaet & Berumen

2015). Tiger sharks are also a common, non-target component of the US east coast/Gulf

of Mexico commercial shark bottom longline fishery that targets large coastal species

(Carcharhinus plumbeus and Carcharhinus limbatus), where the species accounts for 8-

36% of the catch (Morgan et al. 2009; Carlson et al. 2012). Tiger shark mortality rate is

estimated at 30.9% with only 14% of the catch being landed and the remaining used as

bait or discarded. Of sharks caught alive (69%) almost half are released upon capture

(Morgan et al. 2009). Most of the captured individuals are juveniles and sub-adults as

the average size of individuals caught by this fishery is 113.9 cm for females and 112.1

cm for males, with sizes ranging from 40 - 336 cm (J. Carlson pers. comm.).

Tiger sharks are relatively common in the Indonesian shark fishery and contributed

5.2% of the total biomass of sharks in catches between 2001 and 2006 (White 2007). In

Australia, tiger sharks are also targeted by commercial shark fisheries in northern New

South Wales and in Western Australia. Tiger sharks compose 3.12 t and 5.9% of total

catch of the Ocean Trap and Line (OTL) Fishery in northern New South Wales.

However, catches by this fishery are probably smaller than landings from recreational

fisheries in that same area (Park 2007; Macbeth et al. 2009). In the Western Australia

Tropical Shark Fishery, tiger sharks were caught as a secondary target species and

annual catches averaged approximately 41 t between 2000 and 2004. However, this

fishery was closed in late 2005 due to indication of overfishing the target species,

sandbar sharks (Carcharhinus plumbeus) (Department of Fisheries 2005; Department of

Fisheries Western Australia 2006). Tiger sharks are also caught in the seamount gillnet

55

and longline fishery off the west coast of India but species-specific catches are not

recorded (Bineesh et al. 2014).

2.5.2 Non-shark commercial fisheries

Tiger sharks (along with many other sharks) are taken as bycatch in a variety of

commercial fisheries that target large teleosts such as tuna and swordfish (ICCAT

2014). They are typically caught in small numbers compared to pelagic sharks that are

considered to be of high value, and, as a result, catch is often not reported or only

recorded in very coarse resolution. Catch of tiger sharks is reported for datasets that

encompass complete ocean basins (International Commission for the Conservation of

Atlantic Tunas, ICCAT) or for national longline pelagic fisheries (US longline) and for

fisheries that act within the exclusive economic zone of a country (Panama, Australia).

Tiger sharks are occasionally caught in the international tuna longline fishery in the

Atlantic Ocean regulated by ICCAT, where an average of 16 t was landed per year

between 1982 and 2013 (ICCAT database). Although the overall catch (in tons as effort

data is not available for the computation of CPUE) of tiger sharks is low, it has shown

an upward trend in the last decade, driven mostly by increased catch from US, Brazil

and Netherlands (ICCAT database). Between 2007 and 2013, annual landings by tuna

longline boats regulated by the ICCAT averaged 54 t (ICCAT database, P. de Bruyn,

pers. comm.). Tiger sharks also represented less than 10% of the total bycatch of all

shark species in each of the management zones of the U.S. pelagic longline fishery

(Mandelman et al. 2008). In the Panama longline fishery, tiger sharks represented 1.6%

of the estimated catch of 109,500 t of various sharks since the mid-1980s (Harper et al.

2014). In the central and western Pacific, tiger sharks are caught in low numbers (0.025

sharks per set of longline; P. Williams, pers. comm.) by tuna longline boats from most

countries fishing in that region (PNG, Australia, Kiribati, New Caledonia, Hawaii,

Solomon Islands, Fiji, Vanuatu, Federated States of Micronesia, Tonga, French

Polynesia, Palau). Tiger sharks are bycatch (27 t in 2012) in the Offshore Net and Line

Fishery of northern Australia (Lyle et al. 1984; Northern Territory Department of

Primary Industry and Fisheries 2012) and the species is also taken in the Southern and

Western Demersal Gillnet and Demersal Longline Fishery in Western Australia, with a

catch of 112 t reported in 2005/2006 (McAuley & Leary 2008). Tiger sharks also

constitute bycatch in squid, fish and crustacean trawl fisheries, although normally in

small numbers and there are few records of landings for these fisheries. The Australian

Commonwealth Trawl Sector reported a total catch of tiger sharks of 4.7 t from 2004 -

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2011. Tiger sharks are caught occasionally in the longline fisheries of Costa Rica

(CPUE = 0.006 shark hook-1

×1000, Dapp et al. 2013), Mozambique (Sousa 2012) and

Saudi Arabia (<1% of all sharks, Spaet & Berumen 2015) and by purse seiners in the

Indian Ocean (Chassot et al. 2014) with no data available on catches of tiger sharks for

most regions.

2.5.3 Recreational fisheries

Tiger sharks are a common target of recreational fisheries in the United States (east

coast), Australia, South Africa and in the Gulf of Mexico. Approximately 96% of the

catch in US waters are released alive and many shark fishing tournaments have become

catch and release. Survival rates of tiger sharks are assumed to be high in catch-and-

release activities based on blood chemistry analyses (Gallagher et al. 2014). In the game

fishery off the southeast coast of Australia (New South Wales Gamefish Tournament),

tiger sharks comprised 10% of catches from 1961 to 1990 with captured individuals

ranging in mass from 21 to 560 kg (Pepperell 1992). Between 1993 - 2005, tiger shark

catches in that game fishery were approximately 8 t year-1

, comprising approximately 1-

5% of the total catch for all species and 10-30% of all sharks in that period (Park 2007).

The largest shark caught in this tournament weighed 626 kg in 1939. A decline in

CPUE from approximately 1.2 to 0.5 shark vessel-day-1

has been recorded in New

South Wales game fishing tournaments between 1993/94 and 2004/05, even though

fishers still target tiger sharks during the tournaments; thus suggesting a decline in the

population (Park 2007). Tiger sharks are probably caught by recreational fishermen in

many countries, and the tournaments documented above and recreational fishing is

likely to account for significant mortality of populations in coastal waters of other

countries, although catches and potential impacts remain unmonitored.

2.5.4 Artisanal and IUU Fisheries

Artisanal fisheries and IUU fisheries are also likely to be catching tiger sharks, however

information about landings from these fisheries is scarce as they remain mostly

unmonitored, not allowing further analysis of catch rates and trends. Tiger sharks are

commonly caught in artisanal fisheries in the tropics and subtropics, including in

Mexico ( Cartamil et al. 2011), Panama (Harper et al. 2014), Brazil (Bornatowski et al.

2014; Pimenta et al. 2014) and African countries ( Food and Agriculture Organization

of the United Nations 2014) but information on catch rates is extremely limited. In

57

Bangladesh, landings of tiger sharks of 4.48 t represent an average 1.36% of the total

shark catch (Jit et al. 2014). Although gear limitations probably preclude the capture of

large individuals in artisanal fisheries, catches are often not reported and it is not

possible to fully quantify the impact that these fisheries may have on the population

status of tiger sharks.

Tiger sharks compose 19% of the total shark biomass and 7.4% of total catch in

numbers from Indonesian and Taiwanese IUU fishing vessels (Marshall 2011). There is

also evidence that tiger sharks have been overfished by illegal Indonesian fishing boats

at Ashmore, Cartier and Scott Reefs in northern Australian waters. This area had been

targeted by Indonesian fishermen since the 1800s but fishing was banned in 1988 at

Ashmore Reef and 2000 at Cartier Reef (Meekan et al. 2006). A study using baited

remote cameras showed an absence of tiger sharks at reefs historically fished despite the

18 and 6 years of protection, respectively, whereas they were recorded as present at

nearby atolls that had been always protected from fishing (Meekan et al. 2006).

2.5.5 Shark Control Programs

Shark control programs are established on the basis that by reducing the abundance of

large shark species, human safety will be increased. The evidence as to whether such

programs reduce the probability of shark/human interactions is inconclusive. In

Australia, the Queensland Shark Control Program (QSCP) captured 4,757 tiger sharks

between 1993 and 2010 (Holmes et al. 2012) and the species represented approximately

10 to 30% of total catch in the northern locations of the QSCP between 1964 - 2007

(Simpfendorfer et al. 2010). This pattern is most likely due to the replacement of nets

with drumlines, which are particularly effective in catching tiger sharks (Simpfendorfer

et al. 2010). In southeast Australia, tiger sharks were commonly captured in low

numbers by the New South Wales (NSW) Shark Meshing Program with a total of 352

sharks caught between 1950 and 2008, representing approximately 10% of the common

shark species caught by the program in each of its locations (Reid et al. 2011). The

NSW program also showed marked fluctuations in CPUE of tiger sharks, with peaks

occurring approximately every 10 years (Green et al. 2009; Reid et al. 2011). In South

Africa, the KwaZulu-Natal beach protection program has caught an average of 48.7 (±

13.3 SD) tiger sharks annually from 1978 and 2003, which represents a catch rate much

lower when compared to dusky sharks Carcharhinus obscurus (232.7 ± 113.3) and sand

tiger sharks Carcharhinus taurus (194.4 ± 101.8) (Dudley & Simpfendorfer 2006).

58

Since 1989, all sharks caught alive by the KwaZulu-Natal program are released;

however, mortality of tiger sharks in nets is 27% of captures (Dudley & Simpfendorfer

2006).

2.6 Assessment Rationale

In summary, tiger sharks are a large, highly mobile top order predator with a worldwide

distribution throughout the world’s tropical and warm temperate oceans. Growth and

reproductive rates are known for some areas within the species distribution range and

are comparatively higher than rates for other large sharks. These biological

characteristics suggest that tiger sharks can support some levels of exploitation.

However, the triennial reproductive cycle suggested for the species could greatly reduce

its ability to recover from fishing pressure. Moreover, we still lack information on

natural mortality for the species, especially in terms of juvenile survivorship. Tiger

sharks are caught by several commercial, recreational and artisanal fisheries as a target

species or as bycatch. However, records on the actual catch of tiger sharks by many

fisheries globally are still mostly under-reported or unknown. The species is also one of

the major components in catches from shark control programs that target large shark

species in Australia, South Africa and Reunion Island.

My assessment of the tiger shark remains as “Near Threatened”. The species is close to

being classified as Threatened under criterion A3bd (Figure 2.6, IUCN 2012). The basis

for this assessment is: projected declines in CPUE for eastern Australia that indicated

potential significant reduction in the population in that region. However, tiger sharks

have a wide-ranging distribution, and although they are caught in a variety of fisheries

that represent a major threat for the species, population estimates for the species are

scarce. Biological traits of tiger sharks such as rapid growth rates, relatively high

reproductive output, moderate rates of intrinsic population increase and low estimated

fishing mortality indicates resilience to fishing but also creates vulnerabilities,

particularly regarding the three year reproductive cycle and the lack of information on

juvenile mortality. Information on stock assessment, population structure and overall

population trends for the tiger sharks are still lacking for much of the species

distribution and available trends in CPUE that could be used for this assessment were

based on three long-term datasets (north-eastern Australia, South Africa and the

Northwest Atlantic). The dataset from the Queensland Shark Control Program in

Australia is the longest available for the species and allowed projections of trends in

59

CPUE to be calculated, which suggested a marked decrease of around 95% in CPUE

over the length of three generations. However, these projections should be interpreted

with caution, as they are based on regression relationships that do not account for

demographic metrics or the fluctuation in the catch rate and considering that the time

series used in the predictions is relatively short.

Evidence of regional declines is compounded by the continuing exploitation of tiger

sharks by commercial, recreational and unregulated fisheries. Catches of the species

remain unrecorded by many fisheries and the low numbers of sharks caught by

regulated, commercial fisheries do not allow for population trends to be calculated at

this time, despite raising concern over population trajectories. Projections from the

KwaZulu-Natal beach protection program in South Africa suggest an increase in

populations over a period of 25 years. The program releases sharks that are caught alive,

which might have contributed to the recorded increase in CPUE. Although the rate of

increase in South Africa is lower than the projected decline seen in Queensland, it

provides estimates of population trends from standardised catches and suggests that the

meshing program is not impacting the tiger shark population of southern Africa. In the

northwest Atlantic, increases of 6 - 8% per year in relative abundance (index of absolute

abundance based on standardised catch) were also recorded by the Southeast Fisheries

Science Centre and the Northeast Fisheries Science Centre (NOAA) for the bottom

longline shark fishery and fisheries independent longline surveys that are active

throughout the Gulf of Mexico and US eastern coastline. However, this rate of increase

should be treated with caution, since the time series of data has a much shorter temporal

range than data from Queensland.

It is recommended that further research is required to accurately assess exploitation

rates by fisheries and to define population size, structure and trends throughout the

species range. It is also recommended that the trends from shark control programs in

Australia should continue to be monitored in relation to the Red List criteria (A3bd) and

the projections made by this assessment. Continuing declines in Queensland and New

South Wales may possibly determine if these populations have attained a level of threat

that would recommend the update of the status assessment to “Vulnerable”. Recent

evidence based on global genetics of tiger sharks indicated separation between the

populations in the western Atlantic and Indo-Pacific (Bernard et al. 2016). Based on

these results, it is also recommended that tiger shark populations from different ocean

basins and regions should be assessed separately by the IUCN. Regional assessments

60

could consider CPUE trends reported from the northwest Atlantic and South Africa

separately from the trend in Australia and will potentially better reflect the threat level,

population trends and conservation status for each population of tiger sharks.

Figure 2.7 Summary table of the criteria (A - E) used to evaluate Red List status (IUCN 2012).

61

Chapter 3 Crossing latitudes - long-distance

tracking of an apex predator

3.1 Abstract

Tiger sharks (Galeocerdo cuvier) are apex predators occurring in most tropical and

warm temperate marine ecosystems, but we know relatively little of their patterns of

residency and movement over large spatial and temporal scales. We deployed satellite

tags on eleven tiger sharks off the north-western coast of Western Australia and used the

Brownian Bridge kernel method to calculate home ranges and analyse movement

behaviour. One individual recorded one of the largest geographical ranges of movement

ever reported for the species, travelling over 4000 km during 517 days of monitoring.

Tags on the remainder of the sharks reported for shorter periods (7 - 191 days). Most of

these sharks had restricted movements and long-term (30 - 188 days) residency in

coastal waters in the vicinity of the area where they were tagged. Core home range areas

of sharks varied greatly from 1166.9 to 634,944 km2. Tiger sharks spent most of their

time in water temperatures between 23° - 26°C but experienced temperatures ranging

from 6°C to 33°C. One shark displayed seasonal movements among three distinct home

range cores spread along most of the coast of Western Australia and generalized linear

models showed that this individual had different patterns of temperature and depth

occupancy in each region of the coast, with the highest probability of residency

occurring in the shallowest areas of the coast with water temperatures above 23°C.

These results suggest that tiger sharks can migrate over very large distances and across

latitudes ranging from tropical to the cool temperate waters. Such extensive long-term

movements may be a key element influencing the connectivity of populations within

and among ocean basins.

3.2 Introduction

Throughout human history, top-order predators have been disproportionately and

negatively affected by anthropogenic activities, both directly through human behaviour

such as hunting and indirectly by alteration of habitat and depletion of their food

resources (Jackson et al. 2001; Sydeman et al. 2006; Beschta & Ripple 2009; Estes et

al. 2011). We now know that the ecological impacts of our elimination of top-order

predators can be severe, leading to shifts in the composition and resilience of

62

ecosystems through processes such as mesopredator release and trophic cascades (Pace

et al. 1999; Heithaus et al. 2008a; Estes et al. 2011). Globally, the removal of sharks

due to fishing has accelerated rapidly in recent decades, with the result that many

species are now threatened with or vulnerable to extinction in many regions (Baum et

al. 2003; Dulvy et al. 2008, 2014a; Field et al. 2009; Ward-Paige et al. 2010; Ruppert et

al. 2013). There is a growing recognition of the importance of sharks as keystone

species in the structuring of marine ecosystems through their influence on species

composition, biomass and the trophic roles of prey assemblages (Estes et al. 2001;

Johnson et al. 2007; Heithaus et al. 2010; Ruppert et al. 2013). However, we remain

largely unaware of some of the most basic aspects of the ecology of many species,

including movement patterns and habitat requirements. As top-order predators, sharks

tend to have large body sizes (Heupel et al. 2014) and thus generally require large areas

in which to forage. For this reason, they are likely to undergo long distance (10 - 1000s

km) movements that could bring them into contact with multiple habitats, ecosystems

and anthropogenic threats (Harestad & Bunnel 1979; McCauley et al. 2012; Heupel et

al. 2014).

Tiger sharks (Galeocerdo cuvier) are one of the largest sharks, growing to over 5 m

(Compagno 1984; Randall 1992; Holmes et al. 2012) and the species is both an apex

predator and scavenger that occurs in most tropical and warm-temperate marine

ecosystems. They feed on a wide array of prey (Grudger 1949; Clark & von Schmidt

1965; Stevens 1984; Stevens & McLoughlin 1991; Randall 1992; Simpfendorfer 1992;

Lowe et al. 1996; Heithaus 2001; Simpfendorfer et al. 2001), on which they exert both

lethal and behavioural risk effects (Heithaus et al. 2007b). Globally, anthropogenic

threats to populations of tiger sharks include commercial fisheries (Polovina & Lau

1993; Bonfil 1997; Castillo-Geniz et al. 1998; Baum et al. 2003; Handley 2010) and

illegal, unreported and unregulated (IUU) fishing (Field et al. 2009). Moreover, they

have been targeted by shark control programs as a species potentially dangerous to

humans (Paterson 1990; Wetherbee et al. 1994; Dudley 1997; Dudley & Simpfendorfer

2006; Hazin et al. 2008; Green et al. 2009) and evidence of declines in populations of

tiger sharks have been reported by beach meshing programs in some areas of Australia

(Reid et al. 2011; Holmes et al. 2012). However, while the species is classified as “Near

Threatened” by the International Union for the Conservation of Nature (IUCN) due to

evidence of declines in some populations (Simpfendorfer 2009), broad-scale trends in

abundances are still unknown.

63

Given the anthropogenic threats to these apex predators, information on their movement

behaviour, particularly over large spatial and temporal scales (100s - 1000s of km,

months to years) is essential for the development of appropriate management and

conservation strategies (Sims 2010; Block et al. 2011; Papastamatiou & Lowe 2012;

Drymon et al. 2013). To date, most information on the horizontal movements of tiger

sharks is available from SPOT and PAT satellite tracking studies conducted at relatively

small temporal scales (<1 year; e.g. (Heithaus et al. 2007c; Meyer et al. 2010;

Fitzpatrick et al. 2012; Hazin et al. 2013; Werry et al. 2014)). The information provided

by SPOT transmitters is usually limited by the relatively short intervals that sharks

spend on the surface, which results in low numbers of location estimates, obtained at

irregular intervals and typically with low spatial resolution (Heithaus et al. 2007c;

Fitzpatrick et al. 2012). Additionally, physical damage, biofouling and premature

shedding of tracking devices are also widely reported (Kerstetter et al. 2004; Heithaus et

al. 2007c; Hays et al. 2007; Meyer et al. 2010), resulting in short and sparse location

data sets. For PAT tags in particular, deployment periods are commonly much shorter

than programmed due to tag damage and biofouling that may cause premature release of

the tag. Longer-term tracking studies have used passive acoustic telemetry to monitor

movements of tiger sharks (e.g. (Holland et al. 1999; Meyer et al. 2009; Papastamatiou

et al. 2013; Werry et al. 2014)), but these are limited by the detection range of receivers

and the number and scale of receiver arrays (Heupel et al. 2006). Overall, these studies

have shown high individual variability in movement patterns of tiger sharks, with some

degree of residency in particular habitats interspersed with occasional forays into the

open ocean (Heithaus et al. 2007c; Meyer et al. 2010; Werry et al. 2014).

Due to the short duration of most tracking datasets it is unclear whether the wider

movements across scales of 1000s of km, typical of other large sharks such as basking

(Cetorhinus maximus), white (Carcharodon carcharias) and whale sharks (Rhincodon

typus) (Boustany et al. 2002; Bonfil et al. 2005; Bruce et al. 2006; Wilson et al. 2007;

Skomal et al. 2009; Farmer & Wilson 2011; Domeier & Nasby-Lucas 2013) might also

exist for tiger sharks. Our study reports on the results of the deployment of eleven

satellite transmitters on tiger sharks, one of which produced one of the longest duration

tracks ever recorded for the species (517 days). In combination with the results from the

other deployments, we examined horizontal and vertical movements of tiger sharks and

quantified habitats of high and low use. We hypothesise that tiger sharks display a mix

64

of both restricted and transient movement, and that residency patterns will be driven by

water temperature and bathymetry.

3.3 Material and Methods

Ethics statement

This project was conducted under permit number SF6104, WA Fisheries permit 2007–

30–32, and ethics approvals A07035 (Charles Darwin University Ethics Committee)

and DPIW 7/2007–08.

Tiger sharks were caught using longlines off Ningaloo Reef, Western Australia, in June

2007, August 2008 and May-June 2010. Between 118 and 350 hooks were set at

approximately 10 m intervals along one to five demersal longlines deployed on the

seaward side of the reef each day. Longlines were usually deployed at dawn, with a few

deployments at dusk. Short soak times of between 2.2 h to 5.2 h were used to maximise

the survival rates of captured sharks. One shark (Shark 7) was caught by rod and reel

between longline sets. Hooked sharks were brought on deck or restrained in a stretcher

at the stern of the vessel, measured and sexed. Eleven tiger sharks were instrumented

with fin-mounted satellite-linked transmitters (SPOT4, SPOT5 or SPLASH tags,

Wildlife Computers, Redmond, Washington, USA); however, three did not report any

data after they were deployed (Table 3.1). All reporting transmitters relayed satellite

positions via the ARGOS satellite network and time-at-temperature histograms in 14

user-defined temperature ranges from 0°C to 60° (± 0.2°C). Two SPLASH tags (Table

3.1) also relayed summaries of time-at-depth with user-defined depth ranges of 0-600 m

and 0-800 m (± 0.5 m).

Position estimates were provided by ARGOS with an associated error (Location Class

(LC) of 3 (<250 m), 2 (250–500 m), 1 (500–1500 m), 0 (>1500 m), A and B (not

specified), www.argos-system.org). A small amount (0.5%) of location points were

excluded or substituted by the secondary location estimate reported by ARGOS because

they were obviously erroneous, i.e. they were well beyond the bounds of possible

distances the shark could have travelled based on both earlier and later location

estimates for the track. An analysis of the travel speed found that these erroneous

locations required a travel speed of >1000 km per day. More advanced filtering methods

were attempted, such as a Bayesian state-space model; however, the models did not

converge, probably due to the sparseness of the data.

65

We calculated the home range of tiger sharks using the Brownian Bridge kernel method

using the adehabitatHR package in R software V2.15.3 (Calenge 2011; R Core Team

2013). This method takes into account not only the shark locations, but also the path

travelled by the animal between successive locations (Bullard 1991; Horne et al. 2007)

by applying a conditional random walk to model the expected path between locations.

Two smoothing parameters were set: sig1, which controlled the width of the “bridge"

connecting successive positions (this is the Brownian Bridge motion variance parameter

- BMV); and sig2, which was related to the imprecision of the positions. Values of sig1

were chosen using the function liker that implemented the maximum likelihood

approach developed by Horne (Horne et al. 2007) and sig2 was set at 1.9 km, the

median ARGOS location error across pooled position classes (Costa et al. 2010b).

Tracks from Sharks 1 and 2 were removed from the analysis due to their very short

length (7 and 14 days respectively). Because of the possibility of effects of tagging on

behaviour, we assumed that the first two weeks might not be representative of the

shark’s natural behaviour (Afonso & Hazin 2014). Location estimates from Sharks 3

and 8 were particularly sparse (0.18 and 0.04 locations per day respectively), and Shark

3 had a very high Brownian Bridge motion variance parameter compared to the scale of

the shark’s movements (sig1 = 822.5). Consequently, these tracks were not included in

further analyses of home range.

The proportion of observations in each temperature and depth bin was calculated from

time-at-temperature and time-at-depth histograms provided by the tag to determine

thermal and depth range of all sharks. For one shark (Shark 5), time-at-temperature

histograms associated with latitude/longitude information were used to assess variation

in the proportion of time at temperature. Water temperature profiles were constructed

from data downloaded from IMOS floats (IMOS, http://imos.aodn.org.au/imos/) located

in the vicinity of position uplinks within a week of the time the uplinks were recorded

(South coast of WA: 35.378°S, 119.531°E; North coast of WA: 17.604°S, 117.657°E).

Maximum possible diving depth of Shark 5 was estimated by comparing the minimum

temperature registered by the shark’s tag with profiles of water temperature from IMOS

floats in the same region where the shark was resident. The maximum depth of descent

was assumed to be the greatest depth in the water temperature profile where the

minimum temperatures reported by the tag and those of the water temperature profile

were the same. For the track with multiple home range cores (Shark 5), movement

patterns were categorised as within and outside the 25% utilisation distribution. we then

66

used generalised linear models with a binomial distribution and a logit link function to

assess the relationship between the probability of the shark being in a home range core

and water temperature, bathymetry and region of the WA coast (north - latitude < 24°S

and south - latitude >24°S). We were not able to fit all three explanatory variables in

one model as there was no overlap of the temperature ranges between the two regions.

Consequently, we fitted a model to examine the probability of being in a home range

core in relation to bathymetry and region and two separate models (using the data from

north and south coasts, respectively) to examine the probability of the shark being in a

home range core in relation to sea surface temperature. To address the autocorrelation

present in the data we used a matched-block bootstrap sampling for all models with

replacement procedure (Carlstein et al. 1998; Politis & White 2004) that resampled

blocks of data randomly and then recombined them in a random order, creating a

bootstrapped dataset that minimized the effect of autocorrelation (Carlstein et al. 1998;

Politis & White 2004; Patton et al. 2009). Model fitting was applied to 100

bootstrapped samples and model selection used the sample-corrected Akaike’s

information criterion (AICc), AICc weight (wAICc), and percent deviance explained

(%DE) (Burnham & Anderson 2004; Vianna et al. 2013). Bathymetry data with a grid

resolution of 2’ from ETOPO1 database hosted by the NOAA was obtained by the R

software package marmap (Pante & Simon-Bouhet 2013). Daily Sea Surface

Temperature was obtained through the daily Optimum Interpolation Sea Surface

Temperature (OISST) analysis (Reynolds et al. 2007) on a 0.25 degree

latitude/longitude grid from NOAA's National Climatic Data Centre

(ftp://eclipse.ncdc.noaa.gov/pub/OI-daily-v2/NetCDF).

Table 3.1. Details of satellite transmitter deployments on tiger sharks.

ID Fork

length

(cm)

Sex Date

deployed

Location Type Duration of

transmission

(days)

Locations day-1

(mean ± SD)

1 145 F 19/06/2007 23.32°S 113.71°E SPOT 7 0.75 (±1.16)

2 276 F 21/06/2007 22.97°S 113.76°E SPLASH 14 0.92 (±1.50)

3 179 F 17/08/2008 21.86°S 113.96°E SPOT 105 0.18 (±0.43)

4 214 F 19/08/2008 22.38°S 113.73°E SPOT 70 0.54 (±0.83)

5 222 F 19/08/2008 22.42°S 113.71°E SPOT 517 0.43 (±0.95)

6 333 M 27/05/2010 23.07°S 113.70°E SPOT 191 1.86 (±1.66)

7 224 M 2/06/2010 21.58°S 114.52°E SPOT 38 0.60 (±0.72)

8 240 F 30/05/2010 22.27°S 113.72°E SPLASH 154 0.04 (±0.19)

9 155 F 19/06/2007 23.35°S 113.72°E SPOT Did not report

10 252 F 21/06/2007 22.97°S 113.76°E SPLASH Did not report

11 254 F 20/08/2008 22.66°S 113.60°E SPLASH Did not report

67

3.4 Results

The time that data was received from the tagged sharks varied greatly among

individuals (7 to 517 days; Table 3.1). 49% of locations were from ARGOS Location

Class B, 20% were from Location Class A and only 31% of locations were within

Location Classes 3-0. Four satellite transmitters provided data for less than 100 days but

even the longest deployments had long periods of no transmission. For example, Shark

5 had a period of 118 days between May and September 2009 when no location data

were transmitted, while Shark 8 did not transmit data between July and October 2010.

Transmitters deployed on three sharks (1, 2 and 7) provided data for only 7, 14 and 38

days respectively. During this time they occupied waters with average depths of 42.2 m

(± 26.8 m SD) and their movements were restricted to the vicinity of where they were

initially tagged (Figure 3.1a) on the shelf. Three sharks (3, 4 and 6; Figure 3.1b-c)

stayed within shelf waters that averaged 117.5 m deep (± 113.0 m SD). Shark 6’s

movements were restricted to a relatively small area (1166.9 km2) off the Ningaloo Reef

for six months (Figure 3.1c). Shark 8 moved 303.4 km from the point of first capture

and tagging into waters over 5000 m deep, where the last data transmission was

received (Figure 3.1b). There were six position estimates in this area and the data were

very limited for the duration of tracking (154 days between tagging and the last uplink).

One shark (Shark 5) ranged over 4000 km and apart from one period of approximately

three and a half months, provided relatively frequent transmissions over 517 days of

monitoring (Figure 3.1d).

68

Figure 3.1 Movement patterns of tiger sharks in Western Australia. Maps show location uplinks

of 8 tiger sharks. Triangles indicate tagging location and grey polygons indicate Commonwealth

Marine Reserves.

The kernel utilisation distributions of all sharks indicated movement between coastal

regions and islands or atolls off the north coast of Western Australia, with predominant

use of coastal waters (Figure 3.2). The 50% kernel utilisation distributions varied greatly

in area from 1166.9 km2 to 634,944 km

2 among sharks (Figure 3.2). Overall, a total of

56.7% of all locations received from the satellite transmitters were within the

Commonwealth Marine Reserve network. All sharks had some locations inside marine

reserves and six of the sharks had 50% or more locations inside reserves (Figure 3.1).

69

Figure 3.2 Home range of tiger sharks. The utilisation distribution calculated using the

Brownian Bridge kernel method. Black line represents the 50% Brownian Bridge home range

distribution.

Shark 5, a 222 cm female, was monitored for 517 days and ranged from 10.4°S to

35.8°S of latitude and from 113.0°E to 124.1°E of longitude (Figure 3.1d). The

transmitter was deployed on this shark at Ningaloo Reef and the shark then moved

along the 500 m bathymetric contour to the Rowley Shoals and Kimberley region. It

then made a path to Sumba Island, Indonesia, and returned, crossing ocean depths of 5

km and covering a distance of more than 1000 km in 2 weeks. In December 2008 the

shark moved south, traveling to waters off Jurien Bay and Perth between January-

February 2009. Between April and May 2009 the shark rounded Cape Leeuwin with

transmissions clustering off Albany. After a period of no transmissions (118 days), the

tag then started to transmit again in September when the shark moved towards the north,

returning again to Perth/Jurien Bay in January 2010. The shark had three distinct areas

of 50% Brownian Bridge kernel utilisation that included a large area off the north coast

of Western Australia, another off Jurien Bay and Perth and a smaller one off the coast of

Albany (Figure 3.2).

70

Table 3.2 Ranked Generalised Linear Models with bootstrap sampling of the probability of a shark being in a 25% utilisation distribution explained by

bathymetry (Depth) and region of the coast (Region).

Maximum log-likelihood (LL), degrees of freedom (df), Akaike’s information criterion corrected for small samples (AICc), AICc weight (wAICc), the %

deviation explained (%DE), lower quantile LCI (25th quantile) (25%) for LL.25, wAICc.25, AICc.25 and %DE.25 and upper quantile (75

th quantile) for

LL.75, wAICc.75, AICc.75 and %DE.75.

Table 3.3 Ranked Generalised Linear Models with bootstrap sampling of the probability of a shark being in a 25% utilisation distribution explained by

sea surface temperature (Temperature) at the north and south coast separately.

Maximum log-likelihood (LL), degrees of freedom (df), Akaike’s information criterion corrected for small samples (AICc), AICc weight (wAICc), and

the % deviation explained (%DE), lower quantile LCI (25th quantile) (25%) for LL.25, wAICc.25, AICc.25 and %DE.25 and upper quantile (75

th

quantile) for LL.75, wAICc.75, AICc.75 and %DE.75.

Model LL df AICc wAICc %DE LL.25 LL.75 wAICc.25 wAICc.75 AICc.25 AICc.75 %DE.25 %DE.75

Home ˜ Depth -82.456 2 168.967 0.508 34.49 -124.637 -101.77 0.001 0.317 207.595 253.328 5.75E+00 20.347

Home ˜ Depth +

Region -82.010 3 170.131 0.284 34.84 -122.153 -97.006 0.167 0.5143 200.125 250.417 6.85E+00 23.756

Home ˜

Depth*Region -81.283 4 170.751 0.208 35.42 -117.994 -95.781 0.221 0.649 199.749 244.173 9.53E+00 25.453

Home ˜ Region -125.803 2 255.661 0.000 0.05 -134.792 -118.812 0 0.001 241.678 273.639 2.98E-01 4.687

Home ˜ 1 (Null) -125.863 1 253.744 0.000 0.00 -137.028 -124.8 0 0 251.619 276.075 -1.91E-14 0

Model LL df AICc wAICc %DE LL.25 LL.75 wAICc.25 wAICc.75 AICc.25 AICc.75 %DE.25 %DE.75

North

Home ˜ Temperature -94.845 2 193.756 0.999 9.67 -101.645 -80.5813 0.748725 1 165.2297 207.3572 0 0

Home ˜ 1 (Null) -111.514 1 212.024 0.000 0.00 -111.514 -91.553 0 0.251 185.128 225.05 2.184 25.802

South

Home ˜ Temperature -16.535 2 37.337 1 44.54 -

13.75475 0 1 1 4.267 31.7765 0 0

Home ˜ 1 (Null) -29.812 1 61.711 0.000 0.00 -32.895 -29.812 0 0 61.711 67.877 30.254 57.026

71

Figure 3.3 Time spent in each temperature bin. Plots show the percentage of time spent within

the specified temperature bins for each tiger shark (ID number in the top right hand corner of

each plot).

Temperatures experienced by sharks ranged from 6°C to 33°C. Overall, tiger sharks

spent most of their time in temperatures between 23°-26°C (Figure 3.3). The shark with

the widest latitudinal range of movements (Shark 5) also experienced the greatest range

of temperatures (from 10°-33°C), but spent approximately 78% of time in temperatures

between 23°-29°C. It also experienced different ranges of temperature in each home

range (Figure 3.4). Off the Kimberley and Pilbara coasts (10°-24°S of latitude) the

72

temperature range experienced by the shark was much greater (10°-33°C) than when the

shark visited the south coast off Albany (35°S of latitude; 18°-23°C).

Figure 3.4 . Latitudinal variation of temperature profiles for Shark 5. Percentage of time spent

in each temperature bin at each region of Western Australia for Shark 5.

Figure 3.5 Time spent in each depth bin. Percentage of time spent in each depth bin for two

tiger sharks tagged with SPLASH tags.

Figure 3.6 Vertical profiles of water temperature. Plots show temperature profiles recorded by

Argo floats of the north (17.6°S 117.6°E) and south (35.78°S 119.5°E) sections of the WA

coast. Vertical lines represent the minimum temperature reported by the shark’s satellite

transmitter and horizontal lines represent the estimated diving depth.

73

Time-at-depth histograms of the two sharks with SPLASH tags showed that these

individuals differed greatly in depth range (Figure 3.5). The first (Shark 2) did not dive

deeper than 150 m, with 27% of observations between the surface and 5 m and 97% in

water depths up to 75 m. Locations for this shark were all close to the coast in areas

around the 100 m isobath. The second (Shark 8), which moved to pelagic waters

experienced a greater range of depths. Though this shark spent 57% of time between the

surface and 10 m, it had a maximum recorded depth of 400 m, with approximately 20%

of time spent at 150 m.

Water temperatures reported by Shark 5, which was tracked for 517 days, were used to

estimate diving depths. Analyses of the vertical profiles of water temperature in each

region where location data were recorded indicated that the shark was diving up to 380

m in tropical latitudes, but not descending below 100 m in temperate latitudes (Figure

3.6). Sea surface temperatures were consistent with a strong Leeuwin Current that

supplied a warm mass of water around the Cape Leeuwin and Albany coasts with

temperatures of 19°-23°C during the time locations were registered in this region

(Figure S 3.1). Here, the shark spent 82% of time in waters between 21-23°C (Figure

3.4), indicating that it remained relatively close to the surface.

Figure 3.7 Generalised linear model for Shark 5. Generalised linear model (GLM) predicted

probabilities (solid line) of a shark being in a 25% utilisation distribution in relation to (a) water

depth, (b) water temperature in the north and (c) in the south. Dotted lines show the standard

error.

74

The highest ranking model describing whether the shark remained in a 25% home range

core (i.e. remained resident and did not switch to transit behaviour), included water

depth only, and explained 34.5% of the deviance in the data set (Table 3.2). There was a

negative relationship between the probability of the shark being in a 25% home range

core and water depth, meaning the shark was more likely to be resident (in a home

range core) in the shallower areas and less likely to remain resident in deeper waters

(Figure 3.7). Even though the tiger shark crossed areas over 5000 m deep while

migrating, it had low probability of being in a home range core in deeper water (> 1000

m). Temperature explained 9.7% of the deviance in the probability of Shark 5 being in a

home range core on the north coast and 44.5% in the south (Table 3.3). In both regions,

the shark had a greater than 80% chance of being in a home range core in temperatures

of 23-24 °C, suggesting that this was a preferred temperature in both regions (Figure

3.7).

3.5 Discussion

Tiger sharks are typically considered to be residents of tropical and warm temperate

habitats (Randall 1992), but individuals appear on a seasonal basis in temperate or cool

temperate waters (Last & Stevens 1994). The most extreme examples of this pattern are

the occasional records of tiger shark catches in the waters of countries such as Iceland

(Matsumoto et al. 2005) and far off the south coast of New South Wales in Australia

(Pepperell 1992). Such occurrences seem to be related to the influx of warm waters

brought by western boundary currents such as the Gulf Stream and the East Australian

Current (Schmitz & McCartney 1993; Reid 1994; Cresswell 2000). Similarly, in

Western Australia the Leeuwin Current flows southward bringing warm, low salinity

water along the shelf into the cool temperate environments off Albany (35°S) (Smith et

al. 1991). In our study, one shark moved into temperate waters twice, both times during

the summer months of January-February. This implies that such behaviour is most

likely to be the result of directed and seasonal migrations, rather than simply a

haphazard event. Other evidence to support this idea of regular southward movements

of tiger sharks during the austral summer comes from data from beach protection

programs, which show that tiger sharks are more common off the coast of New South

Wales, a temperate region in the southeast of Australia, during summer (Krogh 1994;

Reid et al. 2011), when the warm East Australian Current flows southward. A recent

tagging study found that a tiger shark moved to warmer waters off Queensland during

winter and went south to 37°S latitude during the austral summer (Holmes et al. 2014).

75

Similarly, the abundance of tiger sharks in the Aliwal Shoals off South Africa increases

during summer months (Dicken & Hosking 2009).

The shark we tracked for 517 days had home range core areas in both the tropics (15°-

20°S) and the cool-temperate coast off Albany (35°S). The movement of this shark to

temperate waters off Perth occurred twice in consecutive years, both times during

January. Overall, this track was the longest and covered one of the greatest range of

latitudes recorded by satellite telemetry for the species. However, such movements are

not entirely without precedent. For example, a fisheries tagging study recorded tiger

sharks travelling more than 3,400 km in the Atlantic (Kohler et al. 1998) and recent

satellite tagging studies have shown tiger sharks swimming distances of over 1000 km

in the South Pacific (Werry et al. 2014; Holmes et al. 2014), and as far as 3500 km from

the tagging site in the North Atlantic (Hammerschlag et al. 2012). A study by Heithaus

et al. (2007b) recorded a potential movement of a tiger shark of 8000 km from the point

where it was tagged in Shark Bay, WA to the coast of South Africa. However, this

result was inferred from a single low-quality position fix that could not confirm if the

tag was still attached to the shark or determine if the position estimate was simply an

artefact of the position estimation algorithm. In our study, associated water temperature

data reported by the tag and multiple position fixes showed that the track of the shark

was both reliable and that the tag remained deployed on the shark. In terms of

geographic scale, the movements of this tiger shark were comparable to those of white

sharks (Carcharodon carcharias), which in certain regions appear to display seasonal

residency at cool temperate coastal locations, mostly in areas with high abundances of

pinnipeds (Bruce 1992; Klimley et al. 2001a; Domeier & Nasby-Lucas 2007; Johnson

et al. 2009), interspersed with long oceanic migrations into the tropics (Boustany et al.

2002; Bonfil et al. 2005; Bruce et al. 2006; Domeier & Nasby-Lucas 2008).

The durations of residency and distances of migration of tiger sharks recorded by our

study appear to be at least partly related to the length of tag deployment. When tags

reported for a short time, movements of sharks tended to be restricted to one home

range core within a relatively small area. However, longer-term tag deployments

revealed a very different pattern, where periods of residency were interspersed with

long distance (100 -1000s km) movements that appeared both directed and predictable.

However, some individuals appeared highly resident despite relatively long tagging

records (up to 6 months) suggesting that two forms of movement may be present. In our

study, home range cores (averaging 4,474.1 km2, excluding Shark 5) were consistent

76

with earlier work that has shown movement patterns of tiger sharks off the east coast of

Australia (Holmes et al. 2014) and the Florida coast (Hammerschlag et al. 2012).

Similar to our results, most other studies of tiger sharks have also shown that the degree

of residency varies greatly among individuals. Fitzpatrick et al. (2012) found that some

sharks remained in the area of Raine Island on the Great Barrier Reef throughout the

year, while others ventured into the Coral Sea and to the Torres Strait and northern

Great Barrier Reef. Werry et al (Werry et al. 2014) found that tiger sharks could reside

year-round at the atolls of the Chesterfield Reefs, with other sharks venturing over 1000

km into the open ocean. Meyer et al. (2010) found that some tiger sharks were resident

on the French Frigate Shoals of Hawaii throughout the year, while others were recorded

at this locality only when fledging seabirds were available as a food source during

summer. It has been suggested that variations in movement behaviour of tiger sharks

might be due to partial migrations, where only a part of the population would be

transient and perform large scale movements while the other part would show resident

behaviour (Papastamatiou et al. 2013; Werry et al. 2014). Although our study was

comprised of a small sample size, recent tagging studies in Florida, Bahamas, Australia

and New Caledonia have consistently demonstrated that a few individuals can move

over large distances while most of the remainder sharks show more restricted residency

patterns (Hammerschlag et al. 2012; Werry et al. 2014; Holmes et al. 2014). Analyses

involving larger datasets are needed to understand the characteristics of individuals that

show these partial and/or complete migrations, to determine their prevalence and to

identify the drivers that lead to differential patterns of movement.

Despite the number of satellite and acoustic telemetry studies now describing the

horizontal movements of tiger sharks (approximately 10 to date) it is difficult to

determine if there are consistent patterns in the life stage or sex of individuals that are

likely to remain resident or migrate. Meyer et al. (2009) suggested that juveniles were

more likely to display broad-scale patterns of movement than other components of the

population, a pattern also recorded in Northeast Brazil (Hazin et al. 2013). The

individual that we recorded moving the greatest distance on the WA coast was also a

sub-adult (female). In contrast, Papastamatiou et al. (2013) found that adult females

were the most likely to migrate among the islands of Hawaiian Archipelago, a pattern

they attributed to the movement of females to pupping sites. However, these researchers

mostly relied on records generated from acoustic tracking and a receiver array in coastal

waters, limiting the possibility of recording movements of sharks into the open ocean.

77

The tagged tiger sharks showed preferences for temperatures between 23° and 26°C,

consistent with surface waters off Ningaloo Reef (Taylor & Pearce 1999), although the

sharks experienced temperatures as low as 6°C. These lower temperatures suggest that

sharks were descending below the thermocline (± 100-200 m depths at Ningaloo Reef;

Rousseaux et al. 2012). Data from a SPLASH tag deployed on Shark 8 recorded

occasional transits to depths of 400 m, although time-at-depth histograms showed a

preference by this shark for shallow waters (65% of time in depths < 50 m) within the

mixed layer of the water column, similar to patterns reported by other studies (Holland

et al. 1999; Meyer et al. 2010; Nakamura et al. 2011; Fitzpatrick et al. 2012; Hazin et

al. 2013; Werry et al. 2014; Vaudo et al. 2014). For five sharks, location estimates at

Ningaloo Reef with depths <15 m suggested that some sharks spent time in the shallow

lagoon.

Shark 5 experienced a wide range of temperatures within a home range in the tropics,

suggesting that it used the water column to depths of 380 m, well below the thermocline

in this region (Rousseaux et al. 2012). When the shark moved to the south coast off

Albany, it remained in waters above 100 m deep, implying that its vertical movements

may have been constrained by water temperatures when in the cooler waters at more

southern latitudes.

The long-term residency of tiger sharks in limited areas implies that they may have

strong structuring effects on those ecosystems. A study conducted for 15 years

(Heithaus et al. 2012) found that the presence of tiger sharks at Shark Bay had major

influences on the behaviour, movement and feeding of prey such as dolphins, turtles and

dugongs, but beyond this largely seagrass habitat, we have little idea of the role of these

predators in environments such as coral or temperate reef systems. In addition to

quantifying the residency patterns of tiger sharks, our study showed large-scale

movements that have implications for conservation, since this behaviour may take them

across management and national boundaries. In our study, more than half of the

locations provided by satellite transmitters were within the Commonwealth Marine

Reserves network, although our results also support the suggestion that such regional

scale management zones are likely to only provide temporary protection for some parts

of the population or life history stages (Hooker et al. 1999; Chapman et al. 2005;

Heupel et al. 2014; Werry et al. 2014). The movement of sharks from Australia to

Indonesian waters also shows that conservation of these animals will also depend on

international cooperation to mitigate anthropogenic threats to the resilience of the

78

species, such as the large IUU shark fishery in Australia’s northern waters and small-

scale shark fishing industry in Indonesia to the north (Field et al. 2009).

Future research efforts will be aided by technological developments of satellite

transmitters and attachment methods. One such development, the FastlocTM

GPS,

already exists, however is unavailable on fin-mounted devices. Fastloc tags are capable

of acquiring the data required for a location fix in a much shorter period of time and

with greater location accuracy than other types of satellite tags, improving both the

frequency of location estimates and the accuracy of position fixes. Ultimately, there are

two key goals that must be achieved in order to obtain a better understanding of the

movement ecology of tiger sharks: firstly, improved attachment techniques for tags that

allow both frequent uploads of position data and long deployments (> 1 year) and

secondly, tagging of a larger number of animals and a wider range of life history stages

of both sexes. Our study shows that these advances will be necessary to gain a better

appreciation of the role of these animals in the ecology of marine ecosystems.

3.6 Acknowledgments

We acknowledge the Conselho Nacional de Desenvolvimento Científico e Tecnológico,

Brazil.

79

3.7 Supporting information

Figure S 3.1. Sea Surface Temperature map (smoothed OISST data) for 16th May 2009 with

location uplinks for Shark 5.

81

Chapter 4 Diet and trophic role of a large

marine predator, the tiger shark

Galeocerdo cuvier

4.1 Abstract

Stable isotope analysis of tissues reveals diet, trophic niche and food-web interactions

of animals over a range of temporal scales. Multiple tissues (muscle, fin, dermis, blood)

of tiger sharks were sampled at Ningaloo Reef and Shark Bay in Western Australia and

from the Great Barrier Reef (GBR), southern Queensland and the central New South

Wales (NSW) coasts in eastern Australia. Stable isotope analysis was used to

investigate the effects of location, size and sex of sharks on diet, trophic ecology and

role among these locations. Sharks sampled in Shark Bay and on the GBR had a diet

that was based on seagrass food chains, whereas sharks at Ningaloo Reef had diet that

was transitional between pelagic and seagrass chains. Sharks collected in NSW had a

diet based on pelagic food chains with an isotopic signature consistent with earlier

studies of the species at Réunion Island and in South Africa. Tiger sharks were located

at the top of the food-chain at Shark Bay and on the Great Barrier Reef (GBR);

however, these predators did not occupy apex roles at Ningaloo Reef or in New South

Wales. Bayesian standard ellipses were used to assess niche occupancy and generalised

linear models were used to test the effect of shark size, location and sex on δ13

C and

δ15

N values derived from the results of isotope analyses of each tissue. Analysis of

muscle samples suggested that niche width of female sharks in Queensland was broader

than that of males in the same locality and the niche widths of both sexes in NSW. The

diet of tiger sharks became more enriched in 13

C with increasing body size and towards

tropical latitudes consistent with a diet shifted towards large marine herbivores.

Analysis of other tissues (dermis, red blood cells and plasma) also showed a distinct

trophic niche of sharks at Ningaloo Reef, where they exhibited lower levels of δ13

C

values than at Shark Bay or on the GBR. Diets of sharks at Ningaloo were more stable

in the short term (weeks) than at Shark Bay or on the GBR. The large amount of spatial

variation in δC13

values indicated that diet and trophic role seems to be context and

habitat dependent, and reflects variations in food-web complexity, the availability of

resources and body size of tiger sharks.

82

4.2 Introduction

Marine megafauna such as large sharks, cetaceans, billfishes and tunas typically occupy

upper trophic levels in marine food webs. Although their direct predation on - and

induced anti-predator behaviours (risk effects) in - their potential prey can structure

marine ecosystems (Heithaus et al. 2010; Wirsing & Ripple 2011; Heupel et al. 2014),

it is often difficult to quantify their trophic interactions. First, top-order predators are a

relatively rare component of food-webs and documenting their feeding behaviours

within a three-dimensional environment presents some unique logistic obstacles that

still challenge available technology (Hays et al. 2016). Second, the prey targeted by

many top-order species may not be consistent in space and time. Most predators can

switch among potential prey taxa depending on context and availability in order to

optimise energy gain from foraging behaviour (Beck et al. 2007; Thums et al. 2011).

Difficulties involved in the direct observation of feeding behaviour of marine

megafauna have led to the use of alternative techniques to provide insights into the

process of foraging. One of the most common of these is the analysis of stable isotopes

of carbon and nitrogen of consumers’ tissues, which can provide information on diet

(Hobson & Clark 1992; Hussey et al. 2011), trophic niche (Bearhop et al. 2004;

Newsome et al. 2007), trophic interactions among different species (Churchill 2015;

Kiszka et al. 2015), and migratory movements (Hobson 1999; Phillips et al. 2009).

Ratios of nitrogen isotopes (15

N/14

N = δ15

N) indicate the trophic position of a predator

in the food web and the δ15

N composition of its prey, whereas ratios of carbon (13

C/12

C

= δ13

C) indicate the basal source of carbon in the food chain (Post 2002). Together, δ15

N

and δ13

C can provide a general understanding of the structure of a food web (Vander

Zanden & Rasmussen 2001).

In marine ecosystems, values of δ13

C in primary producers are indicative of

environmental (inshore to offshore) and carbon source (benthic to pelagic) gradients

(Hobson et al. 1994; Clementz & Koch 2001). However, for highly mobile and slow-

growing predators such as some large sharks, the interpretation of stable isotopic values

must consider the time taken to incorporate isotopes into different tissues (Hobson &

Clark 1992; Malpica-Cruz et al. 2012). Blood plasma and liver have rapid turnover rates

due to their high catabolic rate and represent a short-term (days to weeks) integration of

a predator’s diet (Meyer et al. 2010). In contrast, muscle, red blood cells, cartilage and

fin have slower turnover rates and provide long-term (months to years) integration of

83

dietary information (Matich et al. 2010; Malpica-Cruz et al. 2012). This means that the

isotopic composition measured from different tissues within the same individual can

resolve dietary information at different time scales (MacNeil et al. 2005; Madigan et al.

2015) and thus assess individual strategies of dietary specialisation (Matich et al. 2011).

Because isotopic signatures integrate diet over time and space (Carlisle et al. 2012),

changes in composition in different tissues of mobile marine predators can “track” their

use of isotopically distinct environments at different time scales (Hobson 1999). This

attribute has been used successfully to define shifts in diets of marine megafauna that

relate to changes in use of habitats (Hatase et al. 2002; Cherel et al. 2007).

Here, stable isotope analyses were used to examine the trophic role of tiger sharks

(Galeocerdo cuvier). These apex predators are highly mobile, large-bodied sharks

distributed globally in tropical, sub-tropical and temperate regions (Randall 1992;

Domingo et al. 2016). Based on their diet, size and lack of predators, adult tiger sharks

(>3 m) are classified as apex predators (Heupel et al. 2014). In at least some contexts,

they play an important role in structuring the environments they inhabit (Heithaus et al.

2002; Burkholder et al. 2013). Tiger sharks have a diverse diet, feeding on prey from

many trophic levels including invertebrates, teleosts, sea snakes, large marine

herbivores such as sea turtles and sirenians, seabirds and other (Stevens & McLoughlin

1991; Simpfendorfer 1992; Lowe et al. 1996; Heithaus 2001; Simpfendorfer et al. 2001;

Trystram et al. 2016). The species also exhibits high individual variability in movement

patterns with some evidence of ontogenetic expansion in habitat utilisation (Werry et al.

2014; Holmes et al. 2014; Afonso & Hazin 2015; Ferreira et al. 2015; Lea et al. 2015).

It is likely, therefore, that their role in structuring marine ecosystems will vary both in

space and with ontogenetic stage. Sampling over broad spatial and temporal scales and

multiple habitats could allow incorporation of stable isotope data into studies of the

spatial ecology (movement patterns, space use and habitat preferences) of these

predators, and provide novel insights into both the motivations behind patterns (prey

preference and foraging locations) and the role of these sharks within the various marine

habitats that their movements encompass.

In this study, we compare the stable isotope composition of multiple tissues of tiger

sharks collected at locations spread across their range in Australian waters. Sampling

encompassed tropical and warm-temperate environments to provide insights into the

trophic ecology and niche of the species across multiple habitats. This study also sought

84

evidence for the influence of biological (size, sex) and spatial (latitude, location) factors

that could influence the stable isotopic composition of tiger sharks and how that

relationship changed at both short (days to weeks) and long (months to years) time

scales by analysing tissues with different turnover rates.

4.3 Methods

4.3.1 Sample collection

Samples were collected under permit numbers: 2563 (WA Department of Fisheries),

SF010311 (WA Department of Parks and Wildlife); G14/36467.1 and G14/37133.1

(Great Barrier Reef Marine Park Authority); 100541, 165491 and 56095 (NSW

Department of Primary Industries and Fisheries); QS2009/GS001, QS2010/MAN26 and

QS2010/GS059 (QLD Department of Environment and Resource Management). All

methods and experimental protocols were performed in accordance with approved

guidelines by Animal Ethics Committees from the University of Western Australia

(RA/3/100/1209), James Cook University (A1974) and University of Queensland

(CMS/300/08/DPI/SEAWORLD and CMS/326/11/DPI).

Tiger sharks were sampled from Ningaloo Reef and Shark Bay in Western Australia,

the Great Barrier Reef (GBR), and the coast of Queensland and deeper continental shelf

waters (> 100 m) of New South Wales (NSW) (Figure 4.1). Two different data sets

were available from Queensland, one composed of muscle samples collected between

2005 and 2014 from sharks captured by the Queensland Shark Control Program (QSCP)

between latitudes of 28°S and 10°S (hereafter the QSCP dataset), and another of

multiple tissues (muscle, dermis, blood) from individual sharks collected at different

locations throughout the Great Barrier Reef (hereafter the GBR dataset). The majority

of the individuals sampled in the QSCP were captured in coastal waters of southern

Queensland, below the latitude of the southern extremity of the GBR. QSCP and GBR

data sets were not pooled due to the different analytical methods applied to each (see

below).

Sample collection was a collaborative effort across multiple research programs in

Australia and as a consequence, the number of samples for each location, sex, types of

tissues collected and sampling methods varied among locations. For the sampling at

Ningaloo Reef and the GBR, tiger sharks were caught using drumlines inside the reef

lagoon and led onto a submerged platform. The platform was raised clear of the water

85

and the shark was restrained by a tail rope. A hose with a continuous flow of

oxygenated seawater was inserted in the mouth of the animal. The shark was then

measured and sexed and fitted with electronic tags prior to release (described below). A

small sample (3 g) of white dorsal muscle and dermis were collected with a scalpel or

an 8-mm diameter tissue punch, about 5 cm lateral to the first dorsal fin. Blood samples

of 5 ml were collected with an 18-gauge needle from the caudal vein. The blood was

separated into plasma and red blood cells with a portable centrifuge (6500 rpm) and all

samples were immediately frozen in either liquid nitrogen (-80°C) or -20°C freezers

after collection. Dermal denticles were removed from dermal samples prior to analysis.

Stable isotope data from sharks captured in Shark Bay were collected by a multi-year

program (Heithaus et al. 2012, 2013) and added to the dataset. For field and sampling

procedures for sharks captured in Shark Bay see Heithaus (2001) and Wirsing et al.

(2006). For sharks caught by the QSCP, muscle samples were collected from the

musculature adjacent to frozen vertebrae. In NSW, shark muscle was collected from the

dorsal region next to the first dorsal fin at recreational game-fishing competitions and

frozen immediately for storage. Sampling procedures for Queensland and NSW are

described by Holmes et al. (2015).

Figure 4.1 Map showing the study locations across Australia and sampling sites within each

region in colour coded triangles. Circles represent relative and total sample size for each dataset.

GBR = Great Barrier Reef, QSCP = Queensland Shark Control Program, NSW = New South

Wales. Map was created with ArcGis 10.3 (http://www.esri.com/).

86

Table 4.1. Mean and standard deviation (SD) stable isotopic composition of tiger sharks sampled in Queensland (QSCP), New South Wales (NSW) the Great

Barrier Reef (GBR) and Ningaloo and Shark Bay for each sex (F = female, M= male). δ15

N and δ13

C given in parts per mill (‰). N= number of samples, TL = mean

total length (cm) ± SD.

* n=1

Location Sex N TL Muscle Dermis Fin Red Blood Cells Plasma

δ15

N δ13

C δ15

N δ13

C δ15

N δ13

C δ15

N δ13

C δ15

N δ13

C

QSCP F 36 207 ± 84.8 12.4 ± 1.0 -16.8 ± 0.7

M 12 161 ± 37.3 12.2 ± 1.1 -17.4 ± 0.5

NSW F 17 322 ± 50.9 12.6 ± 0.8 -18.2 ± 0.6

M 25 345 ± 39.5 12.3 ± 0.8 -18.3 ± 0.8

GBR F 14 296 ± 48.3 11.6 ± 0.9 -14.2 ± 1.6 11.3 ± 0.5 -12.0 ± 1.3 11.0 ± 0.3 -13.5± 1.9 11.5 ± 1.1 -13.4 ± 1.9

M 2 232 ± 235 12.8 ± 0.7 -16.2 ± 0.1 11.9 ± 0.2 -13.8 ± 0.1 11.9 ± 0.5 -15.2 ± 1.1 12.3 ± 1.3 -15.7 ± 0.0

Ningaloo F 20 348 ± 46.3 12.0 ± 0.4 -13.9 ± 1.4 11.6 ± 0.3 -16.6 ± 1.6 11.0 ± 0.5 -16.7 ± 1.2

Shark Bay F 124 280 ± 59.2 11.8 ± 0.7 -16.6 ± 1.3 11.4 ± 0.5 -13.2 ± 1.6 11.6 ± 0.9 -15. ± 1.7

M 23 268 ± 52.6 12.0 ± 0.6 -11.9 ± 1.4 11.0* -13.1* 12.0 ± 1.9 -15.3 ± 1.7

87

Samples from Ningaloo Reef, the GBR, the QSCP and NSW were analysed by the

Western Australian Biogeochemistry Centre at the University of Western Australia.

Samples were freeze dried for 48 h and homogenised using a Mixer Mill MM 200 with

6.4-mm ball bearings. Samples were analysed using a continuous flow system in a Delta

V Plus mass spectrometer coupled to a Thermo Flush 1112 via Conflo IV (Thermo-

Finnigan/Germany). Stable isotope ratios were expressed as δ13

C and δ15

N and reported

in parts per mil (‰) relative to the standard reference Vienna Pee Dee Belemnite

(VPDB) for δ13

C and atmospheric N2 for δ15

N. Samples were standardised against

primary analytical standards from the International Atomic Energy Agency, Vienna

(δ13

C: NBS22, USGS24, NBS19, LSVEC; δ15

N: N1, N2, USGS32 and laboratory

standards). The analytical precision (standard deviation of mean values) of both carbon

and nitrogen isotopes was 0.1‰. Samples had mean C:N ratios of 2.5 ± 0.5 (mean ±

SD) for samples analysed at the Western Australian Biogeochemistry Centre, and

because elasmobranchs are reported to have low lipid content with relatively small

changes to δ13

C values after lipid extraction, we did not correct δ13

C values for the

effects of lipids (Post et al. 2007; Hussey et al. 2010; Vaudo & Heithaus 2011; Speed et

al. 2012; Heithaus et al. 2013). Samples from Shark Bay were analysed at Yale

University and Florida International University (Matich et al. 2011; Heithaus et al.

2013) with analytical precision of 0.10 - 0.19‰ for δ13

C and 0.02 - 0.08‰ for δ15

N

(Heithaus et al. 2013) and showed low C:N ratios (2.45 ± 0.39).

4.3.2 Data analysis

Stable isotopic composition

In order to assess variations in the isotopic signature of tiger sharks among widely

distributed locations, isotopic signatures of muscle samples previously described by

studies in South Africa (Hussey et al. 2015), the Great Barrier Reef (Frisch et al. 2016)

and Reunion Island (Trystram et al. 2016) were plotted against the average values of

δ15

N and δ13

C of slow and intermediate tissues (dermis, muscle, and fin) from this

study. Dermis is likely to have faster turnover rates than muscle (Carlisle et al. 2012; Li

et al. 2016a), however, in the absence of muscle samples at Ningaloo, dermis was

considered as the longer-term representation of diet. Dermis is a poorly vascularised

structural tissue that may reflect longer-term (months) signals in diet (Zamora et al.

2016) when compared to tissues that are metabolically active such as blood (Kim et al.

2012). To assess the trophic position of tiger sharks in each ecosystem, the stable

88

isotope composition of each tissue (muscle, dermis, red blood cells and plasma) for all

sharks at each location was also plotted against that of taxa from different trophic

positions from each location (Ningaloo Reef, Shark Bay, GBR and NSW), with values

extracted from published literature. Additional isotope data from muscle tissue of four

tiger sharks sampled on the GBR (Lizard Island and southern GBR) between 2013 and

2014, and analysed at James Cook University, were combined with isotopic values from

the literature for the comparison with the values obtained in the present study. As earlier

studies were analysed in different laboratories with varying sample handling and

processing protocols (Bessey & Vanderklift 2014), these comparisons were made

visually and no statistical analysis was attempted.

Isotopic niche

Isotopic niche space occupied by tiger sharks in each location in Australia was

measured by calculating standard ellipse areas corrected for sample size (SEAc), the

SEAc parameters of eccentricity (E) and theta (θ), the Bayesian estimation of SEA

(SEAb) and the overlap between paired groups as a probability index with values

between 1 and 0 (Jackson et al. 2011). The Bayesian approach used vague and non-

informative priors and was built with 2 Markov chains with 20,000 steps per chain, a

discarded burn-in of 1,000 iterations and a thinning interval of 10. This produced a

range of SEAb with 95% credible intervals that allowed for direct probabilistic

interpretation of pairwise comparisons between groups that could be interpreted as the

probability of the Bayesian ellipse area of one group being larger than another (Jackson

et al. 2011; Reid et al. 2016). The total area (TA) occupied was calculated as the areas

of the convex hull that incorporated all individuals and represents a measure of niche

width and reflects the isotopic diversity of a group (Layman et al. 2007; Vaudo &

Heithaus 2011). The mean distance to centroid (CD) was calculated as the distance in

isotopic space of each individual to the mean of all individuals and represented a

measure of the average trophic diversity within a group (Vaudo & Heithaus 2011). As

eccentricity (E) was related to the variance on the x (δ13

C) and y (δ15

N) axes and to the

circularity of the SEAc, high values of E were representative of ellipses that were

stretched either in the x or y-axes. Theta was a measure of the inclination of the ellipse

and is reported here in radians but was converted to the angular range between -90 to

90, with positive and negative values indicating the direction of inclination of standard

ellipses (Jackson et al. 2011; Reid et al. 2016). Values for each tissue were analysed

separately using the R packages siar (Parnell & Jackson 2013) and SIBER (Jackson et

89

al. 2016). Sexes were considered as separate groups within a region for datasets that

contained a representative number of samples (n > 10) for both male and female sharks.

Spatial patterns of isotopic composition of multiple tissues

Generalised linear models (GLMs) with a Gaussian distribution were used to assess if

δ13

C and δ15

N varied spatially (by location or latitude), and in relation to sex and size of

sharks and at different temporal scales by using tissues with different turnover rates.

Data from each tissue (muscle, dermis, red blood cells and plasma) was modelled

separately using both δ13

C and δ15

N as response variables and a set of explanatory

variables that were dependent on the data available for each tissue. The set of predictors

varied among tissues and was selected based on datasets available (e.g. sex was only

used as predictor if the number of males was sufficient, whereas season was only

included in the analysis of datasets of muscle). For muscle, the stable isotope

composition was analysed with regard to the latitude, season (austral summer, autumn,

winter and spring), sex, total length (TL, in cm) and interaction terms between sex and

latitude, and sex and TL. For dermis, stable isotopes were assessed against the

categorical variable (location) rather than the continuous variable (latitude), due to

sampling locations on the west and east coast of Australia having similar latitudes. Total

length and an interaction term between location and TL were also included. For red

blood cells and plasma, variables analysed included location, TL, and the interaction

terms between TL and location. Fin samples were only available from Shark Bay and,

therefore, these were not included in this analysis. All potential combinations of these

predictor variables were fit with the package MUMIn (Barton 2013). Model selection

was done by ranking the models by Akaike’s Information Criterion corrected for sample

size (AICc) and the AICc weight (wAICc) which varies from 0 (no support) to 1

(complete support). When multiple models were ranked equally (models within 2 AICc

units), the most parsimonious model was selected (the model containing the lowest

number of explanatory variables). Goodness of fit was assessed by the percentage

deviance explained (%DE). Conditional plots for each variable in the top-ranked model

were plotted using the R package ggplot2 and visreg (Breheny & Burchett 2016).

Diet Stability

Tiger sharks at Ningaloo Reef were also tagged internally with a V16TP or V16

acoustic transmitters (Vemco Ltd., Canada) (Table S 4.1). Upon capture, sharks were

restrained and rolled to expose their ventral side. A 4 cm incision was made along the

90

centre line of the abdomen, an acoustic tag was inserted and the incision was closed

with three stiches in the muscle and skin using surgical sutures. Sharks tagged with an

acoustic transmitter were monitored by receiver arrays around Ningaloo Reef (Figure S

4.1) deployed by the Australian Animal Tracking and Monitoring System. This tracking

allowed sharks that were resident at Ningaloo Reef to be identified so that their isotopic

composition could be used as a baseline value for analyses. An earlier study at Ningaloo

Reef demonstrated that tiger sharks either resided in the areas where they were tagged

for up to 6 months or departed and ranged 1000s of km along the coast of Western

Australia (Ferreira et al. 2015). Consequently, we assumed that individuals that

remained resident after tagging and tissue sampling were likely to have been resident at

Ningaloo Reef prior to capture. As receiver downloads were sporadic during the

duration of the study, sharks were defined as residents if they were detected by the

receiver arrays for at least five successive months after tagging and prior to the last

download of receivers (Table S 4.1), again consistent with residency times recorded by

earlier studies (Ferreira et al. 2015). As blood has an isotopic turnover rate of months

(Logan & Lutcavage 2010), the signature of this tissue was used as a baseline for

resident sharks.

Stability of diet was assessed based on differences in δ13

C between paired tissues (red

blood cells and plasma) for the locations where multiple tissues were collected from the

same sharks (Ningaloo Reef, Shark Bay and GBR). Plasma has a faster isotopic

turnover rate than red blood cells (a half-life of 32 vs. 60 days, Kim et al. 2012) than red

blood cells (~60 days, Kim et al. 2012). The contrasting turnover rates of these tissues

allowed for comparison of isotopic values within an individual, giving an insight into

the temporal stability of diet (Matich et al. 2011; Malpica-Cruz et al. 2013). Preliminary

analysis of this dataset and previous studies of tiger sharks in Shark Bay showed greater

inter-tissue differences in δ13

C than in δ15

N (Matich et al. 2010, 2011), therefore, this

analysis only considered δ13

C values. Linear regression was fitted to the paired values

of δ13

C for sharks from Ningaloo Reef, Shark Bay and the GBR in order to describe the

consistency of differences in δ13

C between tissues and to quantify the inter-tissue

relationship, as a measure of tissue discrimination differences (Matich et al. 2010;

Matich & Heithaus 2014; Davis et al. 2015). As paired tissue discrimination shows

stable relationships in other species including seabirds, mako (Isurus oxyrinchus) and

white sharks (Carcharodon carcharias) (Quillfeldt et al. 2008; Malpica-Cruz et al.

2013), it can be used to compare stability of diet in samples from various locations by

91

identifying individuals that are not in a steady state. AIC values were compared for the

regression of δ13

C between red blood cells and plasma against a null model. If there was

strong support for the regression model and the R2 value was high (>0.8), the

relationship was considered significant and stable. Stable relationships between Δδ13

C

values of paired tissues suggest a stable discrimination of differences among locations

and validate use of the fitted line of the regression and δ13

C differences to investigate

dietary stability in individual sharks. Since only a relationship with strong support was

used among different locations, mean values of the paired tissue differences of resident

sharks at Ningaloo Reef were used as a proxy for the δ13

C for discrimination between

red blood cells and plasma (Δδ13

C). Paired tissue differences for each individual

(Δδ13

C) were then plotted over the fitted line and interval (± mean Δδ13

C) around it.

Data points that plotted more than Δδ13

C above the fitted line indicated plasma values

with higher δ13

C values than predicted by the model and data points that plotted less

than Δδ13

C below the fitted line indicated plasma values with lower δ13

C than predicted

by the model (Matich & Heithaus 2014; Davis et al. 2015). Values of plasma outside the

± mean Δδ13

C interval area around the fitted line, therefore, indicated a recent shift in

diet. Values within the ± mean Δδ13

C interval around the fitted line were considered as

“stable” and were assigned a value of 1. Values that were either higher or lower than the

± mean Δδ13

C interval were considered “non-stable” and assigned a value of 0.

Generalised linear models with a binomial distribution were used to test the probability

of sharks having a stable diet in relation to total length and location. All potential

combinations of the predictor variables (location, total length and an interaction term

with total length and location) were used and model selection was as described

previously.

4.4 Results

4.4.1 Comparisons of stable isotopic signatures among

locations

A comparison of slow-turnover tissues (dermis, fin and muscle) shows a spread of mean

isotopic signatures that indicated variation in the diet of tiger sharks across both

latitudes and shelf positions (inshore/offshore habitats) (Figure 4.2a). ). Most of the

variation occurred along the δ13

C axis. Individuals from Shark Bay had higher δ13

C,

whereas sharks from the QSCP, NSW and Reunion Island had lower δ13

C. Sharks

sampled on the GBR had intermediate δ13

C values and were very similar to those of

92

muscle tissue collected and analysed by other studies in the same locality (Frisch et al.

2016; Figure 4.2a). However, δ13

C values of dermis from GBR sharks were high and

overlapped with those of Shark Bay individuals. There was limited variation in mean

δ15

N values of sharks among localities, with the exception of tiger sharks in South

Africa, which had higher δ15

N values (Figure 4.2a).

Figure 4.2. Mean and standard deviation stable isotopic composition of each tissue for each of

the locations sampled (SB= Shark Bay, GBR = Great Barrier Reef, NSW =New South Wales,

QSCP = Queensland Shark Control Program, SA = South Africa) for A) slow tissues (muscle,

dermis, fin) and B) multiple tissues (muscle, dermis, fin, red blood cells, plasma), for sites

where multiple tissues were collected in this study. Data from GBR1, Reunion Island and South

Africa obtained from published studies (Hussey et al. 2015b; Frisch et al. 2016; Trystram et al.

2016)

The collection of multiple tissues from the same animals in some localities allowed the

isotopic composition of both fast (blood, plasma) and slow (muscle, dermis and fin)

turnover tissues to be compared (Figure 4.2b). Once again, there was greater variation in

δ13

C than δ15

N values. Fast tissues tended to have lower δ13

C compared to slow

turnover tissues collected at the same locality. At Ningaloo, red blood cells and plasma

had lower δ13C compared to dermis (Figure 4.2b), whereas at Shark Bay, plasma had a

lower δ13

C compared to red blood cells and blood cells had lower δ13

C compared to fin

(Figure 4.2b). Analyses of the GBR samples revealed a somewhat different pattern, with

blood and plasma having lower δ13

C compared to dermis, but muscle samples having

similar δ13

C values to these fast turnover tissues (Figure 4.2b).

93

4.4.2 Comparisons of isotopic signatures within

locations

Stable isotopic composition of tiger sharks was plotted with those of other fishes, sharks

and taxa from differing trophic levels at each location (Figure 4.3). Only values for slow

turnover tissues of sharks were included in these plots since signatures for other

vertebrates were largely derived from the analysis of muscle (Figure 4.3Only values for

slow turnover tissues muscle and dermis) of sharks were included in these plots since

stable isotopic compositions for other vertebrates were largely derived from the analysis

of muscle. At Ningaloo, tiger sharks were identified as upper-order predators, but did

not display the highest average δ15

N values in the shark community. Two species of reef

sharks (grey reef, Carcharhinus amblyrhynchos and blacktip reef, Carcharhinus

melanopterus) had higher δ15

N (Speed et al. 2012) in comparison to tiger sharks,

implying a higher trophic position and had higher δ13

C (Figure 4.3a). In Shark Bay,

tiger shark muscle had higher δ15

N than all other sharks at this location (Vaudo &

Heithaus 2011; Heithaus et al. 2013) (Figure 4.3b). On the GBR, tiger sharks had higher

δ15

N compared to other sharks at this location, with the exception of C. obscurus, the

dusky shark. This pattern was consistent across tissue types (dermis and muscle) of

sharks sampled on the GBR (Figure 4.3c). Muscle tissue from sharks collected by the

QSCP displayed similar δ15

N values as dermis and muscle tissue from animals collected

on the GBR, but had lower δ13

C, suggesting a diet based on a more pelagic food chain

for sharks sampled in the QSCP than on the GBR (Figure 4.3c). The isotopic

composition of muscle from tiger sharks collected on the NSW coast was also

consistent with a diet based on pelagic food chains or different basal sources of carbon.

In contrast to both the GBR and Shark Bay, the stable isotopic composition of

Australian fur-seals (Arctocephalus pusillus doriferus) (Davenport & Bax 2002) and a

number of large pelagic fishes including tunas and mako sharks (Isurus oxyrinchus)

(Revill et al. 2009) had higher δ15

N than those of tiger sharks, suggesting that these

species feed at higher trophic levels. The isotopic composition of tiger shark tissues in

this location was similar to those of billfishes (Figure 4.3d).

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Table 4.2. Isotopic niche area described by Standard Ellipse Area SEAc (‰), Bayesian SEAb

and parameters; N= number of samples, TA = total area of the convex hull, CD = mean centroid

distance and standard deviation, E = eccentricity, θ = angle of ellipse and CI = confidence

interval. F = Females, M = Males, QLD = Queensland Shark Control Program and GBR, GBR

= Great Barrier Reef and NSW = New South Wales.

Location N SEAc SEAb TA CD ± SD E θ 95% CI

Muscle

F NSW 18 1.16 1.36 4.52 0.55 ± 0.23 0.74 60.6 0.85 - 2.26

M NSW 25 2.06 1.83 7.26 0.74 ± 0.27 0.68 51.5 1.17 - 2.71

F QLD 45 3.79 4.23 15.32 1.34 ± 0.17 0.88 -28.9 3.14 - 6.68

M QLD 14 2.38 1.01 5.25 0.84 ± 0.25 0.77 -72.8 0.57 - 1.82

Dermis

Ningaloo 20 1.61 1.7 5.04 0.22 ± 0.09 0.97 -6.4 1.00 - 2.51

GBR 1.42 1.54 23.33 1.21 ± 0.73 0.98 -18.9 0.89 - 2.70

Red Blood Cells

Ningaloo 17 1.78 1.57 5.05 0.80 ± 0.69 0.98 2.7 0.99 - 2.69

Shark Bay 25 2.48 2.33 7.01 1.02 ± 0.93 0.95 -9.2 1.55 - 3. 47

GBR 14 2.58 2.33 5.61 0.89 ± 0.46 0.98 -7.4 1.30 - 4.08

Plasma

Ningaloo 19 1.85 1.66 5.04 1.39 ± 0.69 0.93 -6.6 1.06 - 2.70

Shark Bay 45 5.65 5.48 23.33 0.93 ± 0.43 0.79 -3.6 3.97 - 7.38

GBR 14 6.72 5.93 15.07 1.09 ± 0.24 0.86 -12.9 3.30 - 10.40

95

Figure 4.3. Mean isotope values (± SD) of slow turnover tissues (muscle, dermis, fin) of tiger sharks, other predators, consumers and primary producers at Ningaloo

Reef, Shark Bay, Great Barrier Reef and New South Wales. Community data for each location was obtained from Davenport & Bax (2002), Arthur et al. (2008),

Revill et al. (2009) Vaudo & Heithaus (2011), Speed et al. (2012), Wyatt et al. (2012), Thomson et al. (2012), Heithaus et al. (2013), Frisch et al. (2014, 2016),

Marcus, unpublished data. Red = sharks, orange = rays, blue/golden = teleosts, purple = turtles, magenta = penguins, grey = seals, maroon = cephalopods, light

green = producers, dark green = zooplankton.

96

4.4.3 Comparison of tissues among locations

Muscle

Differences in isotopic niches were assessed separately for each sex. For this analysis,

muscle samples from the QSCP and the GBR were pooled (QLD; Figure 4.4 Female

tiger sharks in QLD had a larger niche and higher trophic diversity than all other

combinations of sex and location, based on the distribution of SEAb and TA (Table 4.2;

Figure 4.4a-b). The SEAc parameter for females from QLD showed that the ellipse for

this group was stretched with dispersion both on the δ13

C axis and the δ15

N axis

(E=0.88, θ= -28.87°; Figure 3a, Table 1). In contrast, male sharks in QLD waters had a

greater dispersion over the δ15

N axis (E=0.77, θ= -72.85°) than the δ13

C axis as did both

males and females from NSW (E=0.68 and 0.74, θ= 51.5 and 60.6, respectively). Sharks

in NSW waters had a large overlap between sexes (0.68) and little difference in niche

area (probability = 0.29). Females and males in QLD displayed high niche overlap

(0.60), but little overlap with tiger sharks from NSW waters. Females in NSW showed

the lowest trophic diversity and total area, despite having a larger SEAb than males in

QLD (Table 4.2).

The generalised linear model that included latitude, sex, total length and the interaction

between latitude and sex had the highest support (wAICc = 0.76) and explained 65.3%

of the deviance in the response (Table 4.3). This model indicated a shift to higher δ13

C

from smaller to larger sharks and towards lower latitudes for both sexes, trends that

were more pronounced for females than males (Figure 4.4c-d In the analysis of the δ15

N

data set, the four top-ranked models were all within two AICc points of the most

parsimonious model (i.e. with the least number of parameters) including latitude only.

The model indicated lower δ15

N values towards lower latitudes, but explained only

7.6% of the deviance in the response.

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Figure 4.4. Muscle. Standard ellipse areas for analysis of muscle tissue corrected for sample

size (SEAc) (A) and Bayesian Standard ellipse areas (SEAb) (B), representing the 50, 75 and

95% credible intervals in decreasing order of box size, with SEAb mode indicated by a black

circle and SEAc by a triangle. Partial dependence plots of the relationship between δ13

C and the

explanatory variables in the top-ranked model, total length (C) latitude and sex and their

interaction (D). Points represent residuals and shaded areas show the 95% confidence intervals.

Dermis

Dermis was only collected from sharks captured at Ningaloo Reef and the GBR

and data from both sexes were pooled for analysis due to low numbers of males in

samples (n=2). Samples from sharks on the GBR had greater dispersion over the δ15

N

axis (θ Ningaloo Reef = -6.4, θ GBR=-18.90) than those at Ningaloo (Figure 4.5a-b).

Although the size of the SEAb area was similar in both locations, niches were mostly

distinct with sharks in GBR showing a larger TA and trophic diversity (Figure 4.5a,

Table 4.2) with only 0.2 probability of overlap. Of the three models selected to describe

variation in δ13

C values, the most parsimonious included only total length (wAICc =

0.28) and explained 28% of the deviance (Table 4.3). Model predictions indicated a

shift to lower δ13

C with increasing total length (TL) of sharks (Figure 4.5c). For δ15

N

data sets, the three top-ranked models were within two AICc points, with the most

parsimonious model including location only and explaining 32% of the deviance.

98

Dermis tissue of sharks at Ningaloo Reef had higher δ15

N than those of sharks from the

GBR (Figure 4.5d).

Figure 4.5. Dermis. Standard ellipse areas for analysis of dermal tissue corrected for sample

size (SEAc) (A) and Bayesian Standard ellipse areas (SEAb) (B), representing the 50, 75 and

95% credible intervals in decreasing order of box size, with SEAb mode indicated by a black

circle and SEAc by a triangle. Partial dependence plots of the relationship between δ13

C (C) and

δ15

N (D), and the explanatory variables in the top-ranked models; total length (C) and location

(D). Points represent residuals and shaded areas show the 95% confidence intervals.

99

Table 4.3. Ranked generalised linear models of δ13

C and δ15

N for each tissue. The top six

models for each response are shown; values in bold indicate the top ranked model (or the most

parsimonious model). Degrees of freedom (df), Sample corrected Akaike’s Information

Criterion (AICc), change in AICc relative to the model with the lowest AICc value (ΔAICc),

relative AICc weight (wAICc) and percent deviance explained (DE%). Lat = latitude, TL = total

length (cm).

Response Model df AICc Δ AICc wAICc %DE

Muscle

δ13

C lat + sex+ TL+TL × lat 6 237.9 0.00 0.76 65.29

δ13

C lat + sex + TL 5 241.4 3.57 0.13 63.01

δ13

C lat + sex + lat × sex 5 243.5 5.61 0.05 62.18

δ13

C lat + sex + TL + sex × TL 6 243.7 5.81 0.04 63.03

δ13

C lat + season + sex + TL 8 246.6 8.69 0.01 63.77

δ13

C lat + TL 4 247 9.13 0.00 59.74

δ15

N lat 3 250.5 0.00 0.34 7.65

δ15

N lat + sex + lat × sex 5 251.9 1.38 0.17 10.66

δ15

N lat + sex 4 252.6 2.10 0.12 7.74

δ15

N lat + TL 4 252.7 2.15 0.12 7.69

δ15

N lat + sex + TL 6 254.2 3.62 0.05 10.71

δ15

N lat + sex + TL + lat × sex 5 254.8 4.31 0.04 7.77

Dermis

δ13

C location + TL 4 121.8 0.00 0.42 34.85

δ13

C TL 3 122.7 0.86 0.28 27.93

δ13

C location 3 123.8 1.96 0.16 25.54

δ13

C location + TL + TL × location 5 124.0 2.22 0.14 35.89

δ15

N location 3 49.5 0.00 0.40 31.82

δ15

N location + TL + TL × location 5 49.8 0.23 0.36 41.34

δ15

N location + TL 4 50.8 1.23 0.21 34.42

δ15

N TL 3 55.3 5.76 0.02 19.22

Red blood cells

δ13

C location + TL 5 212.9 0.00 0.86 55.62

δ13

C location+ TL + TL × location 7 216.6 3.73 0.13 56.72

δ13

C location 4 222.3 9.37 0.01 45.23

δ13

C TL 3 230.7 17.84 0.00 33.58

δ15

N location 4 78.6 0.00 0.72 15.93

δ15

N Location + TL 5 81.0 2.41 0.22 15.93

δ15

N TL 3 84.5 5.84 0.04 2.75

δ15

N location + TL + TL × location 7 85.9 7.25 0.02 16.37

Plasma

δ13

C location + TL 5 297.2 0.00 0.83 33.38

δ13

C location+ TL + TL × location 7 300.8 3.69 0.13 34.29

δ13

C location 4 303.3 6.16 0.04 25.76

δ13

C TL 3 307.2 10.06 0.00 19.70

δ15

N location 4 218.2 0.00 0.66 8.66

δ15

N location + TL 5 220.5 2.26 0.21 8.69

δ15

N TL 3 222.2 3.95 0.09 1.13

δ15

N location +TL + TL × location 7 224.3 6.04 0.03 9.83

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Red Blood Cells

Red blood cells were obtained from female sharks at Ningaloo and from both

males and females at Shark Bay and on the GBR. Samples from different sexes were

pooled within locations due to the very low numbers of males (3 in total). Plots of

isotopic niches of sharks from all locations had ellipses stretched across the x-axis with

high variation in δ13

C values (Figure 4.6a, Table 4.2). Sharks at Ningaloo Reef had a

more distinct, smaller and less diverse niche than in Shark Bay and on the GBR based

on SEAb, TA and CD (probability=0.86 and 0.85, respectively), who were more similar

to each other (Figure 4.6a-b). In the analysis of the δ13

C data set there was one top-

ranked model, which included total length and location and explained 56% of the

deviance (Table 4.3 Red blood cells had lower δ13

C with increasing size and higher δ13

C

at Shark Bay and on the GBR than at Ningaloo (Figure 4.6c-d). The top-ranked model

for the δ15

N dataset only included location and explained 16% of the deviance,

indicating marginally higher δ15

N at Ningaloo (δ15

N = 11.6 ±0.1‰) in comparison to

individuals from Shark Bay (δ15

N = 11.4 ±0.1‰) and the GBR (δ15

N = 11.11 ±0.12‰).

Figure 4.6. Red Blood Cells. Standard ellipse areas for analysis of red blood cells corrected for

sample size (SEAc) (A) and Bayesian Standard ellipse areas (SEAb) (B), representing the 50,

75 and 95% credible intervals in decreasing order of box size, with SEAb mode indicated by a

black circle and SEAc by a triangle. Partial dependence plots of the relationship between δ13

C

and the explanatory variables in the top-ranked model; total length (C) and location (D). Points

represent residuals and shaded areas show the 95% confidence intervals.

101

Plasma

Samples used for determination of niche area were collected from female sharks at

Ningaloo Reef and from both sexes at Shark Bay and the GBR. Again, data were pooled

for analysis between sexes within locations due to limited sample sizes. Sharks from the

GBR and Shark Bay had significantly greater SEAb and TA areas than those at

Ningaloo Reef (probability=1 for both). However, sharks at Ningaloo Reef showed

higher trophic diversity than in Shark Bay and on the GBR based on CD (Table 4.2). At

all locations the dispersion of stable isotopic composition occurred on both axes (θ= -

6.6 Ningaloo, θ= -3.6 Shark Bay, θ= -13.9 GBR, Table 4.2, Figure 4.7a-b) and no niche

overlap was evident in samples from Ningaloo Reef and the GBR, but there was an

overlap of niche area with individuals from Shark Bay (0.30). The model including

location and total length had majority support (wAICc = 0.83) and explained 33% of the

deviance in the response (Table 4.3). The model predictions showed lower δ13

C with

increasing body length, with the lowest δ13

C value recorded for sharks at Ningaloo Reef

(Figure 4.7c-d). For δ15

N values, the top-ranked model only included location and had

the majority of support (wAICc = 0.66), but explained only 9% of the deviance in the

data set.

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Figure 4.7. Plasma. Standard ellipse areas for analysis of plasma corrected for sample size

(SEAc) (A) and Bayesian Standard ellipse areas (SEAb)(B), representing the 50, 75 and 95%

credible intervals in decreasing order of box size, with SEAb mode indicated by a black circle

and SEAc by a triangle. Partial dependence plots of the relationship between δ13

C and the

explanatory variables in the top-ranked model; total length and location. Points represent and

shaded areas show the 95% confidence intervals respectively.

4.4.4 Diet stability

Six of the 18 tiger sharks tagged at Ningaloo Reef were considered to be

residents as they were detected by receiver arrays at Ningaloo Reef (Table S 4.1for five

or more months after tagging and tissue sampling. A mean Δδ13

C value of -1.26 ‰ for

the difference between red blood cell and plasma of resident sharks was considered as a

threshold for the comparison of paired tissues that defined sharks as having stable diets.

Due to a lack of tagging data from Shark Bay and the GBR in this study, the threshold

calculated for Ningaloo was used in analyses for three locations.

Linear regressions between δ13

C values from red blood cells and plasma from the same

individuals had strong support when compared to a null model (wAICc =1, 0.82 %DE)

and the fitted line was then used for the identification of stability of diet (Figure 4.8a).

The most parsimonious binomial generalised linear model (wAICc =0.33, Table 4.4) to

explain the probability of diet stability over one or two months only included location

and explained 16% of the deviance. The predictions from this model showed that sharks

103

at Ningaloo had a higher probability of having a stable diet than those sampled on the

GBR and at Shark Bay (Figure 4.8b).

Table 4.4. Ranked binomial generalised linear models of stability of diet for sharks (stable with

the local food web), using paired tissue values of δ13

C from red blood cells (RBC)-plasma

comparisons. All models are shown, values in bold indicate the most parsimonious model.

Sample corrected Akaike’s Information Criterion (AICc), change in AICc relative to the model

with the lowest AICc value (ΔAICc), relative AICc weight (wAICc) and percent deviance

explained (DE%). TL= total length (cm).

Response Model df AICc Δ AICc wAICc %DE

Stability location 3 74.8 0.00 0.61 9.87

Stability Location + TL 4 76.2 1.40 0.30 11.10

Stability TL 2 79.9 5.11 0.05 0.17

Stability Location + TL + TL × location 6 80.1 5.28 0.04 12.51

Figure 4.8. Relationship between δ13

C values of paired tissues of fast (plasma) and intermediate

(red blood cells) turnover rates (A). Individuals with a difference between tissues (calculated

from resident sharks) larger than mean differences Δ13

C are highlighted in filled symbols. Open

symbols represent individuals in steady state (stable). Solid line represents the relationship

between paired tissues and hashed lines enclose the area within ± mean Δ13

C. Partial

dependence plots for the relationship between the stability of diet (probability of steady state)

and the explanatory variable in the top-ranked model; location (B). Numbers indicate sample

sizes.

4.5 Discussion

Our analysis of stable isotopes suggested that the functional role of tiger sharks varied

among different marine habitats within the range of the species distribution along the

tropical and temperate coasts of Australia. Typically, there was less variation in δ15

N

than δ13

C values in slow turnover tissues (muscle, dermis and fin), indicating that the

species remains close to the top of food webs at the body sizes we sampled, but that the

104

ultimate sources of nutrition varied among localities and were dependent on body size,

habitat and food-web composition.

Sharks sampled in the enclosed seagrass habitats of Shark Bay on the west coast and the

large, shallow lagoon of the GBR showed the strongest evidence of long-term diets

based in coastal-associated food-webs originating with seagrass. Dugong and turtles are

likely prey in these localities (Heithaus 2001; Heithaus et al. 2013). At higher latitudes,

such as the coast of NSW, mean stable isotopic composition indicated a diet more

closely linked to pelagic food chains. However, isotopic composition also appeared to

be affected by an interaction between latitude and oceanographic setting. Most of the

samples collected by QSCP were obtained from beaches south of the GBR, which are

exposed to prevailing oceanic swells, upwelling and open waters across the shelf (Oke

& Middleton 2000). Stable isotopic compositions in this locality were similar to NSW,

where equivalent oceanographic conditions prevail (Tranter et al. 1986; Suthers et al.

2011), , but were also similar to samples from Reunion Island, an exposed and isolated

island in the Indian Ocean that does not feature an extensive shelf or abundant seagrass

(Trystram et al. 2016). In NSW, sharks were collected in deeper, pelagic waters at the

edge of continental shelf during game-fishing competitions. These results suggest that

lower δ13

C was not just a function of latitude but also the oceanographic setting and

proximity of pelagic food chains. When plotted in the isotopic space, values of δ13

C of

sharks sampled at Ningaloo Reef were between pelagic and seagrass food chains, as

might be expected given that this location occurs at the narrowest point of the

continental shelf along the Australian coast and so is strongly influenced by the

Leeuwin Current, seasonal upwelling and the oceanographic conditions of the open

ocean (Taylor & Pearce 1999; Harris et al. 2005; Rousseaux et al. 2012). Although

seagrass beds exist at Ningaloo Reef in the shallow lagoon that extends one to two

kilometres behind the reef crest to the shore (Cassata & Collins 2008), these are

relatively small in area compared to those in the inter-reefal lagoon of the GBR or at

Shark Bay, where they are a dominant type of benthic habitat (Walker et al. 1988;

Wachenfeld et al. 1998; Carruthers et al. 2002).

The average δ15

N for tiger sharks in the present study differed greatly from those

calculated by Hussey et al. (2015b), who obtained samples from sharks caught in bather

protection programs along beaches off the Natal coast of South Africa. Hussey et al.

(2015b) reported δ13

C values for tiger sharks that were consistent with samples from the

exposed sub-tropical coastlines of the present study (the QSCP and NSW datasets), but

105

δ15

N values were higher relative to all sampling locations in Australia. It is possible that

the difference seen in South Africa may have been a reflection of differences in isotopic

baselines and composition of the food-web, nutrient inputs or and/or sample treatment

between these locations (Spies et al. 1989; Graham et al. 2010; Li et al. 2016b)

Tiger sharks are a wide-ranging species and tracking studies show that these animals

move between tropical and temperate environments and between coastal habitats and

the open ocean off the continental shelf. Our results suggest that the role of tiger sharks

within food webs and their classification as an apex predator varies as they move across

these environmental gradients. Tiger sharks occupied the apex position of the food-

webs in seagrass habitats and shallow lagoons of Shark Bay and the GBR, where nearly

all other shark species for which isotopic data were available had lower δ15

N values. In

contrast, at Ningaloo Reef, other, smaller sharks such as grey reef (Carcharhinus

amblyrhynchos; adult size 1.9 m total length, Wetherbee et al. 1997) and blacktip reef

(C. melanopterus; adult size 1.8 m total length, Compagno 1984; Last & Stevens 1994),

had higher δ15

N (Speed et al. 2012) than tiger sharks. These differences may reflect the

feeding by reef sharks on mesopredator teleosts whose productivity is driven by

complex coral reef food chains, whereas the diet of tiger sharks can also include species

such as herbivorous teleosts, turtles and sirenians, which feed at a more basal level of

the food web. As a result of the large home range areas of tiger sharks off the northwest

coast of Australia (Ferreira et al. 2015), it is also possible that sharks were feeding in

different habitats or offshore areas (i.e. pelagic food-webs) with a lower stable isotopic

baseline (Sherwood & Rose 2005) which could explain the differences between the

stable isotopic composition of tiger sharks and the more resident reef shark community

at Ningaloo Reef (Speed et al. 2016).

In NSW, large oceanic fishes such as tunas and other sharks including makos (Isurus

oxyrinchus and I. paucus) (Revill et al. 2009) had higher δ15

N values than tiger sharks,

suggesting that they fed at higher trophic levels, which is supported by the high

proportion of other predatory teleosts reported in the stomach contents of these species.

This was also the case for Australian fur-seals (Arctocephalus pusillus doriferus), which

feeds on the continental shelf (Kirkwood et al. 2006). A lower trophic level for tiger

sharks has also been reported in the coastal waters of warm temperate environments in

southern Africa, where tiger sharks occupy the lowest trophic position within the

community of large sharks (Hussey et al. 2015b). Our results suggest that when tiger

sharks move into pelagic and offshore habitats where other large teleost and sharks are

106

present, these predators will occupy higher trophic levels than tiger sharks by having

more specialised diets largely focused on fishes that are tertiary consumers or higher.

Because no community isotopic data was available for the southern Queensland coast, it

was not possible to determine the extent to which tiger sharks occupied the highest

trophic level in these environments. However, the similarity of oceanographic

conditions in this locality to the coast of NSW and the migrations of tiger sharks along

much of the eastern coast of Australia suggests (Holmes et al. 2014) that they are likely

to have similar trophic roles in both locations.

The position of tiger sharks in a food chain may also reflect sampling bias of other

predators within these systems. For example, Vaudo & Heithaus 2011 and Heithaus et

al. 2013 comprehensively sampled the fauna of elasmobranchs and dolphins in shallow

banks and sandflats habitats of Shark Bay and these shallow habitats might not be

suitable for many large pelagic fishes such as tunas. The classification of tiger sharks as

the highest trophic level predators in these habitats thus seems appropriate because all

other species that might act in this role had been sampled. At Ningaloo Reef, sampling

of the shark and predatory fish community was limited to reef environments, so that the

larger pelagic species that might occupy higher trophic positions such as tunas could not

be included in plots. With the exception of Shark Bay, where samples were collected

over 6 years, there was also a lack of temporal context for sampling in Ningaloo and

GBR localities. It may be possible that the diet and role of tiger sharks could vary across

both seasons and years, as is the case with other marine predators (MacNeil et al. 2005;

Cherel et al. 2007; Carlisle et al. 2012), although such variation may be limited, given

the fact that the composition of samples in terms of δ15

N values was very similar across

all locations and that the mean stable isotopic composition from muscle tissues of tiger

sharks analysed by earlier studies on the GBR (Frisch et al. 2016) were nearly identical

to those presented here (Figure 4.3c).

There was some evidence that size affected the diets of tiger sharks, although patterns

were inconsistent among tissues. Muscle, a slow turnover tissue, provided the largest

data set available for analysis and showed higher δ13

C with increasing size (total length)

suggesting a diet increasingly shifted towards prey from seagrass habitats. This is likely

due to larger sharks having a greater capability to attack and subdue large marine

herbivores such as turtles and dugong than smaller, younger sharks. It is also consistent

with the results of studies of the stomach contents of tiger sharks that show a change in

diet towards larger prey with increasing size (Simpfendorfer 1992; Lowe et al. 1996;

107

Heithaus 2001). There was also a shift to higher δ13

C at lower latitudes where these

large herbivores such as turtles and dugongs are more abundant. In addition, plots of

isotopic niches calculated from muscle tissues and generalised linear models suggested

that there was some effect of sex on diet. Female sharks had a larger trophic niche

(SEAb, TA and CD) and showed greater variability in δ13

C values, a pattern that may be

due to high physiological and energetic demands associated with reproductive cycles

(Sulikowski et al. 2016) which may require a more adaptable and opportunistic diet

than male sharks. However, analysis of dermis tissue revealed an opposite pattern to

muscle, with decreasing δ13

C values with increasing body size. This result must be

treated with caution because sample sizes of dermis were low (n = 34) and the range in

total lengths of the animals from which they were obtained were truncated compared to

analyses of muscle. Furthermore, with the exception of a single individual, dermis

samples of the largest sharks (>350 cm TL) were only collected from Ningaloo Reef

and these clustered at one end of the plot, thus may have strongly weighted the

relationship with size. Plots of the niche areas for all tissues did, however, separate

samples from Ningaloo Reef from those collected on the GBR, suggesting a diet shifted

towards more planktonic food webs at the former locality.

Analysis of fast-turnover tissues (red blood cells and plasma) also displayed a decrease

in δ13

C values with size, contrasting with results for muscle. In this case the shift in the

relationship with size could represent a seasonal or short term (months) component to

the diet that differed between smaller and larger sharks. Given that blood samples were

only taken within a time window of a few months of the year, even where sampling

extended over multiple years (as was the case at Shark Bay), it is difficult to determine

how often this trend with size was likely to occur. Notably, the effect of location evident

from the analysis of muscle tissue was also present in these tissues, with the trophic

niches of sharks from the GBR and Shark Bay largely overlapping and sharks at

Ningaloo having a mostly separated trophic niche with lower δ13

C values. The analysis

of plasma suggested that sharks sampled in Shark Bay and the GBR had much wider

trophic niches than those at Ningaloo, consistent with the stability analysis that also

suggested that diets of sharks at Ningaloo were more stable in short term (weeks-

months) than those of sharks on the GBR and at Shark Bay. The low probability of

sharks showing a stable diet in Shark Bay and the GBR suggests high variability in diet

across these short temporal scales and supports the idea that tissues with slower rates of

turnover (muscle, dermis, and fin) may provide a better picture of the overall diet of

108

tiger sharks. Nevertheless, results from the analysis of diet stability should be

interpreted with caution as residency was defined on the basis tracking data that was

only collected from sharks at Ningaloo Reef.

Overall, the local environment appeared to be a better determinant of the diet of tiger

sharks than latitude, despite the ability of the species to migrate long distances from the

tropics to cool temperate environments (Holmes et al. 2014; Ferreira et al. 2015).

Sharks sampled at locations that were separated by <300 km such as Ningaloo and

Shark Bay displayed very distinct stable isotopic compositions, which is unexpected

considering the movement patterns of these sharks between those locations (Heithaus et

al. 2007c; Ferreira et al. 2015). In contrast, stable isotopic composition of sharks

sampled from the mid-NSW coast were similar to those collected in central and

southern QLD, around 1000 km to the north, which may reflect seasonal movements

along that coastline (Holmes et al. 2014). The isotopic composition of sharks in more

temperate and pelagic habitats in Australia were also similar to sharks sampled at

Reunion Island (Trystram et al. 2016), an isolated atoll in the far western Indian Ocean.

Environment also appeared to determine the degree to which the species acted as an

apex predator, although in some localities (notably Ningaloo Reef and southern

Queensland) more comprehensive sampling of other elements of the fauna of pelagic

consumers is required to accurately place tiger sharks within the trophic pyramid.

The description of the species as a generalist predator (Simpfendorfer 1992; Lowe et al.

1996; Simpfendorfer et al. 2001; Matich et al. 2011; Trystram et al. 2016) 39

appears

justified with stable isotopic compositions being habitat-dependent and ranging from

seagrass (Shark Bay) to planktonic food chains (NSW coast) or a mixture of both

(Ningaloo Reef). Given that individuals tagged at Shark Bay and Ningaloo have

dispersed across the north-western coast of Australia (Heithaus et al. 2007c; Ferreira et

al. 2015) and sharks tagged in NSW have dispersed to the tropical Queensland coast

(Holmes et al. 2014), it appears that the degree of specialisation by an individual shark

on prey at any one time is likely to be context-dependent and reflect both resource

availability and to some extent, body size. Although tiger sharks at Ningaloo Reef

showed greater trophic diversity based on tissues with fast turnover (e.g. plasma), they

also had a greater stability of diet than sharks in Shark Bay and on the GBR. This might

suggest that tiger sharks at Ningaloo Reef have wider diet than those in Shark Bay and

on the GBR, but are more likely to remain within the same habitat, at least over short

time frames (weeks - months).

109

Our results emphasise the flexibility of the trophic ecology and role of tiger sharks

throughout their distribution range in Australia. Generalist predators such as tiger sharks

are able to explore multiple habitats and food-webs, likely adapting foraging strategies

to specific prey in different habitats. This generalist behaviour may both drive or be

driven by scale- and habitat-dependent space use by the species. However, separating

the cause and consequence of the relationship between movement and trophic ecology

for large, highly mobile, top-level predators is still challenging (Hays et al. 2016). An

important next step to better understand the relation between movement strategies and

feeding behaviour of these sharks is offered through the combination of diet analysis

and the information collected by high resolution tags (Fastloc GPS, camera tags and

accelerometers) that could provide a more comprehensive picture of animal movement

behaviours and patterns of prey search, and how they change in different habitats and

over time, so that can then be matched against animal trophic ecology.

4.6 Acknowledgements

Funding for this work was provided by the Royal Zoological Society of New South

Wales through the Paddy Pallin Science Grant, the University of Western Australia, the

Australian Institute of Marine Science, and the Conselho Nacional de Desenvolvimento

Científico e Tecnológico (CNPq, Brazil). The authors also acknowledge Chris Fischer

and the crew of the MV OCEARCH for the aid with logistics and field work, the

Australian Animal Tracking and Monitoring System (AATAMS) for providing receiver

data, and the Queensland Shark Control Program and the New South Wales

Gamefishing Association for providing samples.

110

4.7 Supporting information

Table S 4.1 Details of tiger sharks tagged at Ningaloo Reef in April-May 2015, their duration of

detection (until last receiver download) by receivers stations at Ningaloo and their behavioural

classification (resident or non-resident).

Shark ID Sex TL (cm) Monitoring

(months) Behaviour

1 F 373 3 Non-resident

2 F 319 1 Non-resident

3 F 372 2 Non-resident

4 F 391 1 Non-resident

5 F 407 2 Non-resident

6 F 285 6 Resident

7 F 363 0 Non-resident

8 F 304 5 Resident

9 F 390 6 Resident

10 F 390 3 Non-resident

11 F 303 3 Non-resident

12 F 405 0 Non-resident

13 F 322 5 Resident

14 F 352 0 Non-resident

15 F 268 5 Resident

16 F 290 0 Non-resident

17 F 393 1 Non-resident

18 F 282 6 Resident

Figure S 4.1. Location of acoustic receivers (grey and black circles) around Ningaloo Reef,

Australia. Black circles represent receivers where tiger sharks were detected. Star indicates

tagging location.

111

Chapter 5 Scale and habitat-dependent

movement behaviour of a large

marine predator, the tiger shark

Galeocerdo cuvier

5.1 Abstract

Tiger sharks are top-order marine predators found throughout the world’s tropical and

temperate oceans. Although many tracking datasets are available for the species, we still

lack information on the major processes that structure movement patterns and habitat

use at large spatial scales. This study combined multiple tracking datasets (108 tracks)

from locations in the Atlantic, Pacific and Indian oceans in order to define the major

environmental predictors of movement and space use of tiger sharks throughout most of

the distribution range of the species. Movement-based kernel methods and generalised

additive mixed models were applied to define predictors of utilisation distributions

(UD), and migratory movements. Geographically weighted regression (GWR) was then

used to test for spatial non-stationarity in the relationship between values predicted from

the models and those generated by kernel methods. Bathymetry and sea surface

temperature (SST) were identified as the major drivers of utilisation distributions and

migration patterns of tiger sharks of both sexes in different ocean basins, however, the

strength of the relationship with these drivers varied greatly among regions and the

explanatory power was relatively low (15% DE). This was due to spatial non-

stationarity, showing that the relationships changed across space. Although this may

have been due to missing biological covariates in models, notably food availability,

spatial resolution of both response and explanatory variables appear to be the main

driver. The combined results of additive mixed models and GWR show that the

relationships between sharks and the environment occurred at finer scales on the shelf

than in the open ocean, suggesting that movement patterns were both scale- and habitat-

dependent. This implies that the physical processes that aggregate food occur at

different spatial scales in each of these habitats and the analysis indicates the locations

where further research might reveal these underlying patterns.

112

5.2 Introduction

Large fishes and sharks, reptiles, cetaceans and pinnipeds (marine megafauna) tend to

act as top-order consumers and commonly occupy keystone roles in the trophic ecology

of marine ecosystems (Estes et al. 2016). The relative rarity of these top-order species in

food chains and their typically slow life history traits make them vulnerable to growing

anthropogenic threats (climate change, over-harvesting, pollution, habitat loss etc.) now

present throughout the world’s oceans (Stevens et al. 2000; Schipper et al. 2008; Hazen

et al. 2013; Worm et al. 2013; McCauley et al. 2015). As a consequence, the abundance

of many species has declined, so that they are now classified as Vulnerable, Threatened

or Endangered by the International Union for the Conservation of Nature (IUCN)

(Dulvy et al. 2014b).

Despite the importance of marine megafauna, management and conservation strategies

for this group are complicated by a lack of understanding of movement patterns

(Hooker et al. 2011; Hays et al. 2016). Although we know that most can range over

relatively large distances (100–1000s km), many of the key drivers of these behaviours

have not yet been quantified. In part, this is due to the difficulty of monitoring

movement patterns of such animals in the open ocean. Satellite-linked tags offer an

effective means to track marine megafauna (Hussey et al. 2015a; Hays et al. 2016),

however this technology is expensive and researchers must usually deal with significant

logistic hurdles in order to attach these tags to animals. As a result, most studies tend to

deploy relatively few tags, so that low sample sizes preclude the population or species-

level analyses of movement patterns necessary for the evaluation and optimisation of

conservation strategies and recovery plans (Hays et al. 2016).

Recent collaborative initiatives that have facilitated data sharing (e.g. TOPP, CLIOTOP,

Marine Megafauna Movement Synthesis Group, MEOP) have now enabled researchers

to overcome some of these issues (Costa et al. 2010a; Block et al. 2011; Hindell et al.

2016). For example, the pooling of the results of tagging of southern elephant seals

(Mirounga leonina) among studies has provided high-resolution information about the

physical environment occupied by the species throughout the entire Southern Ocean

(Biuw et al. 2007; Hindell et al. 2016). Nevertheless, such studies remain exceptions

and limitations on our ability to collect replicated, multi-year tracking data still impedes

a comprehensive understanding of the long-term movement behaviour and migration

patterns of many species of megafauna (Lea et al. 2015; Hays et al. 2016).

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The tiger shark (Galeocerdo cuvier) is a large marine predator that typifies these

problems. In addition to the difficulties of deploying satellite tags, these sharks spend

limited time at the surface, restricting the opportunities for tags to broadcast location

data to satellites. Consequently, location estimates along tracks tend to be both sparse

and patchy, even when long (months–years) deployments of tags are achieved (Pinaud

2008; Meyer et al. 2009; Hammerschlag et al. 2012; Papastamatiou et al. 2013; Werry

et al. 2014; Ferreira et al. 2015; Lea et al. 2015). This complicates the analysis of

movement patterns, information that is urgently required given that there is increasing

concern over the conservation status and management of the species due to

unsustainable demand for shark products in some markets, compounded by high rates of

bycatch of sharks in many fisheries (Pauly et al. 2005; Lewison et al. 2014; Dulvy et al.

2014b). Such threats and uncertainty in population trajectories have led to designation

of the species as “Near Threatened” by the IUCN (Ferreira et al. in review).

Here, these issues were addressed using two approaches. Firstly, the results of a large

number of tracking studies of tiger sharks were pooled to form a large, global data set so

that general patterns in movement behaviour could be identified across the entire range

of the species. Secondly, a novel combination of statistical approaches was applied to

identify the drivers of movement patterns and to disentangle the biological and physical

factors likely to structure this process. Although the focus of the work was on tiger

sharks, these techniques are applicable to a wide range of marine megafauna, given that

sparse data with low sample sizes are common issues for most tracking studies. Firstly,

the Biased Random Bridge (BRB) kernel method (Benhamou 2011) was used to

estimate monthly utilisation distributions (UD) from the tracks of each shark. This

method differs from traditional kernel approaches in that it incorporates movement

trajectories between locations and aspects of the landscape, in addition to accounting for

the temporally auto-correlated nature of animal trajectories (Benhamou 2011).

Utilisation distributions created by BRB analyses were then used as response variables

in generalised additive mixed models that examined the influence of a variety of

physical variables (sea surface temperature (SST), depth, current direction etc.) in

driving migration and high use of particular areas. Finally, geographically weighted

regression (GWR) (Fotheringham et al. 1998) was used to identify non-stationarity in

the relationships between these variables, showing areas where there was higher or

lower utilisation than predicted by the model. This approach is a new concept for

marine species, but has been used for studies of species distribution and occurrence in

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the terrestrial realm (Foody 2004; Osborne et al. 2007; Powney et al. 2010; Eiserhardt

et al. 2011).

Tiger sharks are an ideal model species for this work for a number of reasons. They are

a large (up to 5 m total length) and mobile (Heithaus et al. 2007c; Meyer et al. 2009;

Hammerschlag et al. 2012; Hazin et al. 2013; Werry et al. 2014; Ferreira et al. 2015;

Lea et al. 2015) predator present throughout the world’s tropical and warm temperate

oceans (Compagno 1984; Randall 1992). As top-order predators, they are iconic and

charismatic and have been the subject of a number of tracking studies world-wide,

although these tend to have limited sample sizes or have been restricted in spatial and/or

temporal scope (Meyer et al. 2009; Hazin et al. 2013; Werry et al. 2014; Holmes et al.

2014; Ferreira et al. 2015). Sampling by these studies also tends to be biased towards

female sharks (but see Lea et al. 2015), possibly reflecting easier accessibility of this

sex for tagging. However, the number and diversity of localities where tagging has

occurred offers the opportunity for pooled data sets to provide a global picture of

movement behaviours of these predatory sharks. We combine the results of tracking

studies from eight localities across the Indian, Pacific and Atlantic oceans and using this

pooled data set, we seek to identify the key physical and biological drivers of movement

patterns at global scales. We hypothesise that the drivers of high use of areas and

migration will be different, thus we identify these components in the data and analyse

them separately as response variables against a range of environmental variables and

deployment location. We also hypothesise that the variability of movement patterns

reported previously for this species might be related to sex and ontogeny and thus we

examine the importance of total length (as a proxy for age) and sex as explanatory

variables in the models.

5.3 Methods

5.3.1 Telemetry

Telemetry data from 130 adult and sub-adult tiger sharks of both sexes, tagged at

Ningaloo Reef (13 sharks), Shark Bay (9), Great Barrier Reef (12), USA (Florida and

the Bahamas - 71), Hawaii (3) and Brazil (Recife - 18; Fernando de Noronha Island - 4)

between 2008 and 2015 were combined to create a global database (Figure 5.1). Several

types of telemetry tags were used, including ARGOS satellite transmitting tags (SAT;

mostly Wildlife Computers SPOT and SPLASH), and Pop-up archival tags (PAT;

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mostly Wildlife Computers PSAT Mk10 and miniPAT; see Table S1). Sharks were

caught by drumlines, longlines and rod-and-reel and tags were attached to the first

dorsal fin using either corrodible bolts or by a 2.0 mm polyamide monofilament line

passed through a hole in the anterior region of the fin. All procedures were approved by

the Animal Ethics Committees of the organisations that led the field work. For more

details on these methods see Heithaus et al. (2007c), Fitzpatrick et al. (2012),

Hammerschlag et al. (2012), Hazin et al. (2013), Ferreira et al. (2015).

Location estimates were obtained from either the Argos satellite network (SPOT and

SPLASH) or via light-based geolocation (PAT) (Hill & Braun 2001). For SAT tags, all

Argos Location Classes (3, 2, 1, 0, A, B, Z) were included in the analysis. Raw

locations from Argos were filtered according to Ferreira et al. (2015), with extreme

location estimates (e.g. points requiring movement rate of > 1000 km a day) either

removed or replaced by the secondary locations reported by Argos. Tracks shorter than

30 days or with very sparse locations (e.g. 5 location estimates in >100 days) were not

included in the analysis. This gave a total sample size of 105 individual tiger shark

tracks for analysis. We attempted to use the BSAM package in R (Jonsen et al. 2016) to

fit state-space models to account for location error, however these models did not

converge due to sparse location data.

Location estimates for tiger sharks tagged with PAT tags were calculated from light

level data recorded and stored on board the tag. The data was relayed via the Argos

Network after detachment of the tag from the shark or downloaded from the archive

when the tag was recovered. Locations were estimated from light data using the

software WC-GPE: Global Position Estimator Program (Wildlife Computers Inc.). The

most probable track was reconstructed using the TrackIt state-space model with a

Kalman filter (Nielsen & Sibert 2007) in R (R Core Team 2013). Sea surface

temperature (SST) was applied to the model to improve the accuracy of location

estimates and reduce confidence intervals (Lam et al. 2010). However, the majority of

tiger sharks tracked with PAT tags remained within the equatorial Atlantic Ocean (Fig

1) where SSTs are relatively uniform and cloud cover is high. This led to model under-

performance and convergence (Nielsen & Sibert 2007) so that SST corrections were not

incorporated in some tracks.

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5.3.2 Environmental data

Data for SST and ocean currents (direction and magnitude) were obtained using the

Marine Geospatial Ecology Tools (MGET) for ArcGis10.3 (Roberts et al. 2010). Daily

and monthly average SSTs were generated by the Moderate Resolution Imaging

Spectroradiometer (MODIS), Aqua satellite Level 3 with a 4 km resolution. Daily data

on direction and magnitude of ocean currents was derived from the Hybrid Coordinate

Ocean Model with a 1/12 degree resolution. Bathymetry data was obtained from the

General Bathymetry Chart of the Oceans Gebco15 database in a 30 arc-second

resolution grid (http://www.gebco.net). A suite of bathymetric covariates were

calculated from this data that have been previously associated with distributions of

marine fauna (Bouchet et al. 2015; Yates et al. 2016) and these were used as a proxy for

habitat complexity. Bathymetric predictors were calculated in ArcGis 10.3 using digital

terrain analysis with fixed window sizes (Holmes et al. 2008) and a resolution of 1 km

to match the bathymetry dataset (Table 5.1).

5.3.3 Identification of areas of high use

In order to identify areas of high use through time, utilisation distributions were

calculated monthly across a 4 km square grid for each shark using the Biased Random

Bridge (BRB) kernel method (monthly UD values were calculated to account for the

dynamic nature of some covariates (e.g. SST). Similar to the Brownian Bridge kernel

method (Bullard 1991), the time spent between successive locations and the correlation

between locations is taken into account in the calculation of utilisation distributions.

However, the BRB approach also includes a component of advection in the trajectory so

that movement from one location to the next is considered to be biased towards the next

location. This is an important distinction, because successive locations in the track of an

animal are typically auto-correlated, where the next is bound both to the previous

starting location and the capability of an animal for locomotion within the given time

period (Papworth et al. 2012). The advection component of the analysis affects the

orientation and shape of the bridge, with stronger advection generating longer and

narrower bridges (Benhamou 2011). An upper time limit between successive locations

is also used as a threshold value in the kernel bridging computation to ensure that steps

longer than this maximum are not included in the analysis. Here, a maximum threshold

of 5 days was imposed, based on the average rate of transmission from the tiger shark

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tags, as it was assumed that steps longer than this would result in unrealistic estimations

of utilisation distributions.

The BRB method also includes a component related to the uncertainty of locations and

the mean values for the track of the error associated with each ARGOS Location Class

(LC 3 = 270 m, LC 2= 540 m, LC 1 = 1330 m, LC 0 = 5179 m, A = 8072 m, B = 11484

m) were used for this analysis (Hays et al. 2001a; Hazel 2009). For geolocations,

location uncertainty was estimated from the variance of location estimates provided by

the TrackIT model. All analyses for the BRB used the adehabitatHR package (Calenge

2011) in the software R (R Core Team 2013).

The monthly utilisation distributions were then converted into point data, where each

point contained the UD value for each 4 km grid cell, and the values were normalised to

values between 0 and 1, so that the gridded UD value for each cell became the

probability of shark use of a grid cell. Utilisation distribution is a similar concept to that

of home range (Powell 2000), where home range cores of an animal typically range

from 25% to 95% with the lower values containing the highest density of points.

However, as a result of the normalisation, utilisation distribution values here were

represented as the inverse of home range (i.e. 0.75 UD represented the 25% HR core,

whereas 0.25 UD represented the 75% HR core). This produced distributions of values,

more useful as a response variable in subsequent models; for example a 25% HR core

corresponds to a distribution of values between 0.75-1 UD. The larger the value of the

UD for a cell in the grid, the greater the use of that cell by the shark. Normalised values

of monthly utilisation distributions for males were log-transformed to improve

distribution of the model residuals.

In order to identify general areas of high use for tiger sharks in each ocean basin, these

normalised gridded monthly utilisation distribution of values between 0.75-1 UD for all

sharks in each region were also averaged, so that each grid cell represented the average

UD of all sharks that utilised that cell.

5.3.4 Identification of migratory behaviour

In order to examine the environmental drivers of shark migratory behaviour, periods of

migration within tracks were identified, as long, directional, relatively straight,

extensive movement (Dingle & Drake 2007; Lea et al. 2015), as opposed to residency,

which was defined as a cluster of locations in a defined area (Figure S 5.1). To

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quantitatively identify these behaviours, utilisation distributions were calculated for the

entire track of each shark using BRB. Tracks were then interpolated to a single location

estimate per day with the R package crawl (Johnson 2014) in order to match the

resolution of the environmental data. Gaps in the data longer than 20 days were not

interpolated to avoid unrealistic interpolated paths (Block et al. 2011; Queiroz et al.

2016). The interpolated track from each individual shark was then overlaid on the 0.75

utilisation distribution indicating a high degree of residency (Ferreira et al. 2015). All

locations inside the 0.75 UD were categorised as resident and assigned a value of 0 and

locations outside the 0.75 UD categorised as migratory and assigned a value of 1

(Figure S 5.1). Resident was defined as the behaviour associated with high-use areas,

whereas migrant is the straight-line behaviour associated with large-scale movement.

5.3.5 Modelling movement behaviour

Habitat use

As tagging was skewed towards females (82% of tagged individuals) UD was analysed

separately for each sex to avoid over-estimating parameters for males. Due to the large

size of the dataset for females (4,211,047 data points), the normalised UD dataset was

filtered by removing values below 0.25 UD. These values represented the grids with

very low probability of use that were present in large numbers as a result of the shape of

a Brownian Bridge (Bullard 1991), but provided only low explanatory power of the

drivers of residency. The dataset for females was also analysed separately for each

region: North Atlantic, South Atlantic, Indian and Pacific oceans (Hawaii and Great

Barrier Reef were analysed separately) due to differences in the range of some of the

environmental variables among regions. Utilisation density (normalised for females,

normalised and log-transformed for males) was used as the dependent variable in a suite

of generalised additive mixed models with a Gamma distribution with an inverse link

function, with individual shark as the random effect using the R function bam (gam for

large datasets) from the R package mgcv (Wood 2016). A set of biological, temporal

and environmental variables was assembled to determine the important predictors of

high use of habitat by tiger sharks (Table 5.1). Exploratory analyses identified any

collinearity within the set of bathymetric covariates and only one was selected for use in

the models when they were highly correlated (Pearson’s r > 0.8). Models with all

possible combinations of selected predictor variables were fitted then compared and

ranked according to Akaike’s Information Criterion (AIC), and by the AIC weight

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(wAIC). The wAIC varies from 0 (no support) to 1 (complete support). The amount of

variance (percentage deviance) in the response variable explained by each of the

candidate models was used as a measure of goodness-of-fit to the data (Anderson &

Burnham 2002; Burnham & Anderson 2004). We considered all models within 2 AIC

units of the top-ranked model, using the principal of parsimony to select the final

model. Conditional plots of UD relative to each explanatory variable in the top-ranked

models were generated using the package visreg (Breheny & Burchett 2016).

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Figure 5.1 Telemetry data included in the analyses, colour coded by individual shark tag ID, from Hawaii (Pacific Ocean) (top left), North Atlantic (top right), South

Atlantic (left), Great Barrier Reef, Australia (Pacific Ocean) (middle) and Indian Ocean, Australia (bottom right).

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Migration

The drivers of migration were also examined using generalised additive mixed models

with a binomial distribution, using the package gamm4 (Wood 2016). The probability of

migration (migrant = 1, resident = 0) was the dependent term in the models with a set of

environmental predictors (Table 5.2) and a random effect of individual shark. Because

direction of ocean current was a circular variable, it was modelled with a cyclic cubic

regression spline, whereas a regular cubic regression spline with shrinkage was applied

to the other (non-circular) covariates. The number of predictors in each model was set to

two or less and basis dimension “k” to five to avoid over-fitting. Autocorrelation was

addressed by using a matched-block bootstrap sampling with replacement procedure

(Carlstein et al. 1998; Patton et al. 2009). Model fitting was applied to 1000

bootstrapped samples and model selection was as described above, but with upper and

lower quartiles also presented.

5.3.6 Assessment of spatial variability in model

predictions

Geographically Weighted Regression (GWR) is a tool used to explore spatially varying

relationships between variables, termed spatial non-stationarity (Fotheringham et al.

1998). A global regression model applied to spatial data will estimate a single set of

parameters for the relationship between the response and explanatory variables.

However, the presence of spatial non-stationarity alters the relationship between

variables in space, limiting the descriptive and predictive power of a model (Foody

2004). Non-stationarity might occur due to methodological issues (model

misspecification, missing variables and/or their use at inappropriate temporal or spatial

scales), spatially auto-correlated data and, in this case, potential differences in

movement behaviour of animals among locations. Geographically Weighted Regression

accounts for these issues, allowing parameters to be estimated locally by fitting a

regression equation to any point in space to explore local spatial relationships.

Consequently, it allows relationships to vary over space so that localised model

parameters are estimated around each regression point. Geographical weighting is

implemented on neighbouring observations according to a spatial kernel function that is

then used in the localised model. The weighting of observations decreases as the

distance from the centre of the kernel increases and the area or number of neighbouring

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observations around the centre of the kernel used in the weighting function is controlled

by the bandwidth, which ultimately defines the scale of the analysis (Foody 2004).

Geographically Weighted Regression has been used as a tool to explore localised

variation in the relationships of variables used in a global model such as a logistic

regression, generalised linear models or generalised additive models (Foody 2004;

Osborne et al. 2007; Windle et al. 2009; Miller & Hanham 2011). However, in contrast

to previous studies, GWR was used here to identify the presence of non-stationarity in

the relationship between the predicted values of UD (from the top-ranked models)

against the observed value of UD. The ‘global model’ was thus a regression between

predicted and observed values from each top-ranked model in the analysis of drivers of

high habitat use. Effectively, the analysis determined if the environmental drivers

identified in top-ranked models adequately explained areas of high use and if this

relationship varied spatially. A Gaussian kernel was used in the geographical weighting

so that all data points contributed to the regression, but points spatially distant from the

centre of the kernel were inversely weighted according to a Gaussian curve. An adaptive

bandwidth was also used in the weighting function (e.g. an adaptive distance of the

kernel bandwidth) that ensured that sufficient local information was used for each local

model in an irregular sampling configuration. Automated optimal bandwidth selection

was determined by minimising AIC. The presence of spatial non-stationarity was

evaluated by the range in the local parameters of GWR coefficients where large

variations of values and the presence of highly positive or negative clusters suggested

non-stationarity. All GWR analyses were done in ArcGIS 10.3 software.

5.4 Results

The telemetry dataset provided a total of 14,357 tracking days of tiger sharks with

average track duration of 136 ± 141 days for females and 85 ± 76 days for males

(Figure 5.1, Table 5.3). Females were the dominant sex tagged in all regions (82.3% of

all tracks) and the majority of male tiger sharks in the sample (82%) were tagged in the

Atlantic Ocean. Female sharks ranged in total length from 120 - 407 cm (average 289 ±

68 cm) whereas males ranged from 130 - 395 cm (average 231 ± 72 cm). Sharks tagged

in the South Atlantic appeared to be smaller (average TL of 186 ± 54 cm) than in the

Indian (mean 314 ± 55 cm), North Atlantic (mean 288 ± 68 cm) and Pacific (319 ± 52

cm) oceans (Table 5.3).

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Figure 5.2 Utilisation densities combined and averaged for each month for each grid cell for all

individuals in each regional dataset and filtered to the 0.75UD. Colour scale represents the

average utilisation of each cell and grey contours indicate the 500 m isobath.

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Table 5.1 Description of all environmental, temporal and biological predictors for the analysis of monthly utilisation distribution.

Table 5.2 Predictors used to model migratory movements of tiger sharks.

Predictor Resolution Description

Depth 30-arc second Water depth (m) in relation to bathymetry extracted from Gebco15

Aspect 1 km Direction of the steepest slope in degrees (0-360°) on a 3 x 3 cell area

Slope 1 km First derivative of elevation: Average change in elevation calculated on a 3 x 3 cell area. Calculated in degrees (0-360)

Curvature 1 km Combined index of profile (hill cross-section) and plan (contour lines) curvatures

Standard Deviation 1 km Standard deviation of elevation on a 3 x 3 cell area

Plan curvature 1 km Second derivative of elevation: concavity/convexity perpendicular to the slope, calculated on a 3 x 3 cell area

Profile curvature 1 km Second derivative of elevation: concavity/convexity parallel to the slope, calculated on a 3 x 3 cell area

Hypsometric index 1 km Indicator of whether a cell is a high or low point within the local neighbourhood

Local relief (Range) 1 km Maximum minus the minimum elevation in a local neighbourhood

Sea Surface Temperature 4 km Monthly SST(°C) generated from the Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua satellite Level 3

Month 1 - 12 Month the utilisation distribution was calculated

Total length - Total length of the shark (cm)

Predictor Resolution Description

Depth 30-arc second Water depth (m) in relation to bathymetry extracted from Gebco15

Sea Surface

Temperature

4 km Daily SST (°C)extracted from the Moderate Resolution Imaging Spectroradiometer

(MODIS), Aqua satellite Level 3

Ocean current direction 1/12 degree Daily direction in degrees (0-360°) derived from the Hybrid Coordinate Ocean Model -

HYCOM

Ocean current speed 1/12 degree Daily absolute magnitude of water velocity (m/s) derived from the Hybrid Coordinate Ocean

Model - HYCOM

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Table 5.3 Summary tracking datasets including information of tagging location, number of tags deployed, track duration and size range of sharks tagged. Mean

values and SD are presented for total length (TL) and track duration.

Ocean Tagging locations Tag Types Deployment

years Sex

N

tags TL range Duration

Indian Ningaloo, Shark

Bay SPOT, SPLASH 2007-2015

F 20 312.3 ±53.5 177.95 ±143.4

M 2 395, 271 38, 191

Pacific Hawaii, Great

Barrier Reef

SPOT,

SPLASH,

Telonics ST18

2003-2013

F 13 306.3 ±59.8 129.5 ±116.6

M 2 288,292 63, 231

North

Atlantic

Florida, The

Bahamas SPOT, uCricket 2009-2015

F 60 296.1 ±58.3 141.6 ± 155.6

M 11 247.8 ±56.5 76.1 ± 57.05

South

Atlantic

Recife, Fernando

de Noronha Island

PAT-Mk10,

MiniPAT 2008-2013

F 15 198.7 ±60.3 58.8 ± 38.8

M 7 159.6 ±25.7 45.57 ±2 4.1

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Scale of movement and habitats varied greatly among individual sharks and regions.

Wider ranging tracks were recorded in the North Atlantic, the Indian Ocean and off

Hawaii, where large-scale (100s - 1000s km) movements along the coast and/or into the

open ocean were observed (Figure 5.1). Tracks from the South Atlantic were obtained

from PSAT deployments that provided only low resolution estimates of geolocation and

tracking durations that were never >120 days. This reduced the likelihood of identifying

patterns of high habitat use and migratory behaviour for sharks in this region.

In the North Atlantic, average utilisation distributions for all sharks combined showed

areas of high use around the tagging site off the Bahamas, but also in the Gulf of

Mexico, in the waters of North Carolina and around the edge of the Gulf Stream (41°N)

(Figure 5.2a-c). In Western Australia, high use areas were associated with tagging

locations (Ningaloo Reef and Shark Bay, Figure 5.2d), while in Hawaii the sharks

mostly used oceanic areas north of the islands (Figure 5.2e). In the South Atlantic, an

area of high use was identified in the equatorial region off the coast of northern Brazil

(Figure 5.2f), whereas on the Great Barrier Reef, sharks had small areas of high use

(Figure 5.2g).

5.4.1 Drivers of high use of habitats

The model including depth, SST, aspect and standard deviation of elevation had 100%

support (wAIC = 1) out of the suite of models fitted to explain utilisation distribution of

females in the North Atlantic (Table 5.4), whereas for the South Atlantic and Indian

Ocean the model with depth and SST only had 100% support (wAIC= 1) (Table 5.4).

For Hawaii, the model with SST only had majority support (wAIC = 0.93) and for the

Great Barrier Reef, the model with depth only had complete support (wAIC = 1) (Table

5.4). In the North Atlantic, females showed an elevated utilisation of oceanic waters

(>3,000 m deep) beyond the continental shelf and in temperatures below 22°C (Figure

5.3a-b). Higher values of UD were also associated with bathymetric aspects in north and

northeast directions (350° and 50°), and areas with minor deviations of elevation

(Figure 5.3i-j). In the Indian Ocean, residency was greater in water temperature

between 22°-24°C and oceanic areas around 3000 m deep (Figure 5.3c,d), with some

preference for depths around 250 m. In the Pacific Ocean around the Great Barrier Reef

(Pacific Ocean), utilisation increased with increasing depths (>1000 m Figure 5.3e). For

sharks in Hawaii, higher values of UD were related to temperatures between 24°-25°C

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(Figure 5.3f). In the South Atlantic, sharks showed both preference for open waters

>4000 m and the shallow shelf, and waters around 25°C (Figure 5.3g-h).

Table 5.4 The top ranked generalised additive mixed models from the suite of models used to

explain monthly utilisation distributions of female and male tiger sharks. SST = sea surface

temperature, Std = standard deviation of elevation. Shown are the percent deviance explained

(%DE), Akaike’s information criterion (AIC), and AIC weight (wAIC).

For males, the model including depth and sea surface temperature had complete support

(wAIC = 1) and explained 14.4% of the total deviance in the data (Table 5.4). Males

exhibited high utilisation of habitats associated with shallow depths, in addition to sea

surface temperatures of around 26°C or above 30°C (Figure 5.4), which are

representative of coastal and shelf environments.

5.4.2 Drivers of migration

Migration was identified in tracks from female sharks tagged in the North Atlantic,

Indian and Pacific oceans. As the tracks from the region of the South Atlantic showed

no evidence for this behaviour they were excluded from this analysis. Large-scale

movements by sharks to northern areas of the North Atlantic were observed during

summer, with sharks reaching the margins of the Gulf Stream, whereas movements

south to temperate regions were observed in the Indian Ocean during austral autumn

(Figure 5.5). A repeated annual migration to the south was only observed for one shark

in the Indian Ocean (T5, Table S 5.1).

Model % DE AIC wAIC

Females

North Atlantic

Depth + SST+Aspect +Std 11.9 -71047.5 1

South Atlantic

Depth+ SST 7.94 -69164.7 1

Indian Ocean

Depth + SST 14.4 -23959.6 1

Hawaii

SST

Depth

7.74

7.39

-910.2

-905.2

0.93

0.07

Great Barrier Reef

Depth 12.6 -2636.317 1

Males

Depth + SST 14.4 757422.6 1

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The top ranked model to explain the probability of tiger sharks being in migratory mode

contained the variables depth and sea surface temperature, with 68% support and

accounting for 7% of the total deviance explained (Table 5.5). The model predictions

showed a high probability of migration across the full range of environmental variables

(>0.5, Figure 5.5). Migration was more likely to occur in oceanic areas over depths

>1000 m (Figure 5.5). Increasing probability of migration was also related to water

temperatures from both tropical and subtropical-temperate regions with likelihood of

migration being highest in water temperatures above 28°C and below 22° C (Figure

5.5).

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Table 5.5 The top three ranked generalised additive mixed models with 1000 bootstraps sampling for the suite of models used to explain the probability of migration

of tiger sharks. Shown for each model are the median, 25th and 75

th quantile Akaike’s information criterion corrected for small samples (AICc), AICc weight

(wAICc) and the adjusted R-squared (Adj R2). The top ranked model is in bold.

Model AICc wAICc % DE AICc.25 AICc.75 wAICc.25 wAICc.75 Adj R2.25 Adj R

2.75

Depth + SST 2689.06 0.68 0.07 2553.99 2833.11 0.04 1.00 0.04 0.11

Depth + Current direction 2713.43 0.18 0.01 2590.20 2852.38 0 0.10 0.00 0.10

Depth + Current speed 2718.00 0.14 0.04 2591.91 2856.18 0 0.02 0.03 0.10

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Figure 5.3 Conditional plots of female utilisation density relative to the explanatory variables in

the top ranked mixed model for each region from the suite of models used to explain the

response variable. Solid curves are the model fit and shaded areas indicate the 95% confidence

intervals. Tick marks on the x-axis indicate the distribution of observations. Plots a, c, e, g are

fits for bathymetry for North Atlantic, Indian Ocean, Pacific (Great Barrier Reef) and South

Atlantic, respectively; b, d, f, h are fits for sea surface temperature for North Atlantic, Indian

Ocean, Pacific Ocean (Hawaii) and South Atlantic, respectively; i-j are fits for standard

deviation of elevation and aspect in the North Atlantic.

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5.4.3 Assessment of spatial variability in model

predictions

The local coefficient estimates produced by the GWR indicated the presence of spatial

non-stationarity in all regions for UDs of females (Figure 5.6a-g) but not for males,

with the exception of the Great Barrier Reef (Figure 5.7a-d). This was shown by the

large variation of GWR estimates for females, indicating the presence of spatial

variability in localised relationships, particularly along the east coast of USA (Figure

5.6a,b) and off the coast of Western Australia (Figure 5.6c,d). By mapping the GWR

coefficient estimates it was possible to identify areas where the observed values of UD

were much higher or lower than the values predicted by the models. For females in the

North Atlantic, a core of strongly positive GWR estimates (utilisation higher than

predicted by the top-ranked model) were identified along the Florida and North Carolina

coasts and in the Bahamas (Figure 5.6a,b). Similarly, localised areas of strongly positive

values were also seen on the northwest coasts of Western Australia around the latitudes

19°-20°S and 24°S (Figure 5.6c,d). Small areas of positive values were also found along

the Great Barrier Reef (Figure 5.6f). The presence of higher-than-predicted values

suggests that other localised factors were accounting for high use in these areas. For

females in the South Atlantic, GWR local parameters showed a particularly large

variation with a few cores of highly negative and one area with highly positive values

(Figure 5.6g).

Figure 5.4 Conditional plots of male utilisation density relative to the explanatory variables in

the top ranked mixed model from the suite of models used to explain the response variable.

Solid curves are the model fit and shaded areas indicate the 95% confidence intervals. Tick

marks on the x-axis indicate the distribution of observations.

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Figure 5.5 Conditional plots of probability of migration relative to the explanatory variables in

the top ranked mixed model from the suite of models used to explain the response variable.

Solid curves are the model fit and shaded areas indicate the 95% confidence intervals. Tick

marks on the x-axis indicate the distribution of observations.

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Figure 5.6 GWR-derived local coefficient estimates for the relationship between predicted and

observed values from the top ranked mixed model explaining the drivers of habitat use for

females. A-B = North Atlantic Ocean and B is zoomed in on the box in A, C-D = Indian Ocean

and D is zoomed in on the box in C, E = Pacific Ocean around Hawaii, F = Great Barrier Reef,

G = South Atlantic. Grey isolines represent bathymetry. Red and orange show areas where

observed UD was higher than predicted (see detail in B and D) and blue and green show area

where observed UD was lower than predicted.

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Figure 5.7 GWR-derived local coefficient estimates for the relationship between predicted and

observed values from the top ranked mixed model explaining the drivers of habitat use for

males. A-B = North Atlantic Ocean, B = Indian Ocean, C = Great Barrier Reef, D = South

Atlantic. Grey isolines represent bathymetry. Red and orange show areas where observed UD

was higher than predicted and blue and green show area where observed UD was lower than

predicted.

5.5 Discussion

Depth and sea surface temperature were identified as key physical variables structuring

patterns of both high habitat use and migration patterns of tiger sharks. However, the

relationships between these variables and shark movements were inconsistent among

regions. Because tiger sharks mostly occupy the surface layers of the water column

(Vaudo et al. 2014), water depth at a location provided an indication of the use of shelf

(depth <200 m) and oceanic waters. In the North Atlantic, many of the female sharks

tagged off Florida and the Bahamas displayed tracks that followed the Gulf Stream and

had excursions into the Atlantic Ocean basin, thousands of kilometres from the coast.

This was reflected in the results of the mixed models, which showed preferences for the

use of habitats in very deep water (5000-6000 m). In contrast, females tagged in

Australia at Ningaloo Reef and Shark Bay tended to reside in waters closer to the coast

just beyond the shelf break, so that conditional plots from the mixed models showed

preferences of tiger sharks for shallower oceanic habitats (3000 m) than in the North

135

Atlantic. On the Great Barrier Reef females were tagged at the edge of the shelf and

displayed residencies both off the edge of the shelf and in deeper water of the Coral Sea.

Similarly, relationships between utilisation distribution and water temperatures also

varied among regions. Females in the North Atlantic and Indian oceans showed a

preference for cooler waters of <20°C and 23°C, respectively, whereas females off

Hawaii and in the South Atlantic preferred temperatures between 25-26°C. In contrast,

males showed greater use of shelf waters and selected temperatures of 25-26°C or above

30°C. Depth and sea surface temperature were also identified as the main drivers of

migratory movements of female sharks, with these occurring beyond continental shelves

and in water temperatures either below 22°C, or above 26°C. No migratory movements

could be identified in the tracks of male tiger sharks. Ocean currents have been

previously suggested as a possible driver of shark migration (Hazin et al. 2013; Holmes

et al. 2014; Ferreira et al. 2015; Lea et al. 2015). However, our mixed models did not

identify current speed or direction as important predictors in the top-ranked model,

despite the fact that long-term (several months–years) tracks of sharks in both the North

Atlantic and off the coast of Western Australia showed excursions of sharks from

tropical waters into temperate regions that followed the path of principal boundary

currents (the Gulf Stream in the North Atlantic and the Leeuwin Current in the Indian

Ocean). A lack of any easily detectable influence of direction and speed of water flow in

these long-distance movements of tiger sharks might be due to animals undertaking

vertical movements during migration to take advantage of counter-current flows at

depth or for behavioural thermoregulation (Thums et al. 2012), although such behaviour

has yet to be fully investigated for the species (Nakamura et al. 2011; Iosilevskii et al.

2012). These phenomena could mask simple relationships between migration patterns

and current systems measured by remote sensing at the ocean surface.

Despite amassing the largest tracking data set yet assembled, our models explained

relatively little of the variation in patterns of high utilisation and migration of tiger

sharks. At best, physical predictors of water depth and SST explained less than 15% of

the variance in utilisation distributions for both female and male sharks. The GWR

highlighted that spatial non-stationarity existed in the relationship between utilisation

distributions and values of UD predicted by top-ranked mixed models for each ocean

basin, thereby showing that relationships vary across space, accounting for the low

explanatory power of the models (Osborne & Suárez-Seoane 2002). Overall, the GWR

showed that the models predictions were consistent with most areas of high utilisation

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by tiger sharks, reinforcing that water temperatures and depths were reasonable

predictors of spatial patterns of habitat use at least in open ocean environments. The

non-stationarity identified for females was most pronounced in the Atlantic and Indian

oceans, as evidenced by the large range in GWR coefficient estimates. The GWR

highlighted a number of areas where the observed UDs were much higher or lower than

those predicted by our models. Two areas at the shelf edge and the mid-shelf off the

coast of Western Australia, around latitudes of 19°-21°S (Figure 5.6d), had strongly

positive GWR estimates (observed values of utilisation greater than predicted by the

models). These matched hotspots of abundances of pelagic fishes documented in earlier

studies by Bouchet (2015) (Figure S 5.2). Another zone of strongly positive GWR

estimates was present off the coast from 24-25°S, in an area of high occupancy for

pygmy blue whales (Balaenoptera musculus brevicauda) (Double et al. 2014) (Figure S

5.2). The coincidence of these distributions suggest that prey availability may indeed

account the spatial non-stationarity found in this area. However, many tracks had

relatively short duration and, therefore, areas of high use indicated by GWR may also

have been a result of local residency demonstrate by sharks tracked for shorter periods.

The GWR also identified areas on the shelf on the eastern coast of the US where the

observed UD was higher than model predictions (26°-27°N and 34°-36°N). It is notable

that these areas of high use for tiger sharks were also areas of high use for other coastal

sharks. For these coastal species, residency and movement patterns were strongly

correlated with water temperatures, relationships that were documented using data

collected at relatively fine spatial scales by oceanographic buoys and loggers deployed

across the study area (Kessel et al. 2014; Kajiura & Tellman 2016). A similar approach

may be necessary to fully describe residency and movement patterns of tiger sharks in

these habitats. In the South Atlantic, the problem of non-stationarity was probably

driven by the low resolution of both tracking and environmental data. This was due to

the reconstruction of tracks from geolocations using light data, which have large errors

associated with position estimates (Lam et al. 2010). As a consequence, maps of GWR

coefficient estimates in the South Atlantic showed a very broad spread of values across

the sampling area.

For the North Atlantic and Indian Ocean data sets, it appears that the reasons for spatial

non-stationarity are also a result of scale, but in more nuanced way. The results of the

GWR suggested that SST and depth could accurately predict the use of oceanic habitats

in the North Atlantic and Indian Ocean, probably because tiger sharks in oceanic

137

habitats are operating at large and mesoscales (10s -100s km) that can be identified at

the scale of resolution of the environmental data sets provided by satellite remote

sensing. However, in shelf waters tiger shark movements and the processes driving their

utilisation patterns are likely to operate at far smaller spatial scales, where topographic

features such as canyons, banks and reefs or habitats such as estuaries and seagrass

meadows may locally enhance productivity of food chains that sustain the targets of

feeding by tiger sharks (Heithaus et al. 2002, 2007a; Morato et al. 2010; Bouchet 2015).

The environmental signals of these habitats may be very difficult to identify in the low-

resolution data provided by remote sensing.

It has been suggested that movements of tiger sharks are mainly driven by prey

distribution (Heithaus et al. 2002), but to date this variable has not been included in any

models that seek to predict movement patterns. This is largely due to the difficulties of

collecting data sets of prey availability at scales comparable to the spatial and temporal

patterns of movement of these predators and the issue of matching predator and prey

fields is recognised as a major problem in the field of ecology of marine megafauna

(Hindell 2008; Hays et al. 2016). For tiger sharks, describing the prey field is further

complicated by the fact that the species has a very broad diet and is both an top-order

predator and scavenger depending on the habitat, prey field and opportunities that are

presented within the environment (Lowe et al. 1996; Simpfendorfer et al. 2001;

Gallagher et al. 2011; Hammerschlag et al. 2016). This makes identification of potential

prey and mapping of their distributions within the environment a very difficult task.

The finding that this scale-dependent behaviour may be causing models to have low

predictive power to describe utilisation patterns is perhaps not surprising, given that

tiger sharks are recognised as a generalist predator (Lowe et al. 1996; Simpfendorfer et

al. 2001; Matich et al. 2011; Trystram et al. 2016). This scale-dependent pattern

combined with the high individual variability found in the behaviour of tiger sharks may

have been responsible for the low explanatory power of the environmental variables

tested to describe their utilisation patterns. In terrestrial systems many predators exhibit

preferential patterns of prey choice where they specialise on a specific type of prey that

inhabit particular habitats (Hayward et al. 2007, 2012; Lyngdoh et al. 2014). For

example, lions (Panthera leo) have been shown to actively and cognitively decide to

hunt preferred prey, increasing the chance of encounter by choosing to forage in habitats

with higher abundances of those prey (Hayward et al. 2011). In the marine

environment, although orcas (Orcinus orca) can be considered as a generalist species,

138

different populations of killer whales adapt their hunting and behavioural strategies to

target preferred prey in the habitat where they are most abundant (Hoelzel 1991, Saulitis

et al. 2000, Ford & Ellis 2006, Ford et al. 2010, and references within). In contrast, tiger

sharks do not display high levels of specialisation in diet and seem to shift prey

preference across their range, seasonally targeting areas with high abundances of prey

(Simpfendorfer et al. 2001; Lowe et al. 2006). These generalist predators will thus

require multiple foraging strategies to capture prey that will be pursued across multiple

habitats, resulting in scale and habitat-dependent space use.

Scale-dependent movements of marine predators are likely to be accompanied by local

adaptation of foraging strategies according to prey type and availability. Identifying

these behaviours is challenging (Austin et al. 2006), particularly when tracking data is

very patchy, as is typically the case for tiger sharks. Location estimates in tracks are

often too sparse to extract horizontal displacement metrics that can be used in

movement-based models for the identification of foraging behaviour (Fauchald &

Tveraa 2003; Jonsen et al. 2013, but see Fitzpatrick et al 2015). The use of GWR

provides an understanding of movement patterns from such data sets because it

identifies where and how the modelled relationships change across variable space and

the potential reason as to why this occurs. Here, it seems likely that tiger sharks respond

to environmental signals that occur at smaller spatial scales on the continental shelf than

the open ocean, thus indicating where fine-scale studies of the use of habitats should be

targeted. Given that sparse tracks and scale-dependent patterns of movement are a

frequent outcome of many studies of large marine predators (Bradshaw et al. 2004a;

Austin 2007; Bonfil et al. 2009; Hammerschlag et al. 2011a), the combined approach of

additive mixed models and GWR has the potential for widespread application to the

field of movement ecology.

5.6 Acknowledgements

Funding for this work was provided by Australian Institute of Marine Science, the

University of Western Australia, and the Conselho Nacional de Desenvolvimento

Científico e Tecnológico (CNPq, Brazil). The authors acknowledge Chris Fischer and

the crew of the MV OCEARCH for the aid with logistics and field work for the tracks

deployed at Ningaloo Reef in April-May 2015. The Save our Seas Foundation also

provided funding support for this work.

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5.7 Supporting information

Table S 5.1 Details of each of the deployments of telemetry devices on tiger sharks.

*TL estimated from fork length using the equation by Stevens & McLoughlin (1991)

ID Transmitter Sex Total length (cm) Tag Type Deployment date Tagging location Region Duration (days)

T1 62346 F 180.75* SPOT 19/06/07 Ningaloo Reef Indian Ocean 7

T2 62343 F 330.27* SPLASH 21/06/07 Ningaloo Reef Indian Ocean 14

T3 83858 F 219.56* SPOT 17/08/08 Ningaloo Reef Indian Ocean 105

T4 83857 F 259.51* SPOT 19/08/08 Ningaloo Reef Indian Ocean 70

T5 83859 F 268.64* SPOT 19/08/08 Ningaloo Reef Indian Ocean 517

T6 93304 M 395.34* SPOT 27/05/10 Ningaloo Reef Indian Ocean 191

T7 93302 M 270.92* SPOT 02/06/10 Ningaloo Reef Indian Ocean 38

T8 93299 F 289.18* SPLASH 30/05/10 Ningaloo Reef Indian Ocean 154

T9 146703 F 303 SPOT 24/04/15 Ningaloo Reef Indian Ocean 117

T10 146710 F 363 SPOT 25/05/15 Ningaloo Reef Indian Ocean 251

T11 146711 F 407 SPOT 24/04/15 Ningaloo Reef Indian Ocean 205

T12 146714 F 319 SPOT 23/04/15 Ningaloo Reef Indian Ocean 57

T13 149046 F 390 SPOT 27/04/15 Ningaloo Reef Indian Ocean 267

T14 105171 F 309 SPOT 21/02/11 Shark Bay Indian Ocean 90

T15 105169 F 330 SPOT 27/02/11 Shark Bay Indian Ocean 224

T16 105173 F 305 SPOT 14/03/11 Shark Bay Indian Ocean 196

T17 105168 F 317 SPOT 14/03/11 Shark Bay Indian Ocean 292

T18 105174 F 320 SPOT 17/03/11 Shark Bay Indian Ocean 99

T19 105175 F 360 SPOT 17/03/11 Shark Bay Indian Ocean 24

T20 105167 F 306 SPOT 17/03/11 Shark Bay Indian Ocean 85

T21 105172 F 362 SPOT 15/04/11 Shark Bay Indian Ocean 482

T22 105170 F 307 SPOT 24/04/11 Shark Bay Indian Ocean 303

T23 54738 F 350 SPOT4 23/11/04 Great Barrier Reef Pacific Ocean 16

T24 62849 M 288 SPOT5 8/12/06 Great Barrier Reef Pacific Ocean 63

T25 54739 F 330 SPLASH 10/12/07 Great Barrier Reef Pacific Ocean 42

T26 62848 F 295 SPLASH 13/12/07 Great Barrier Reef Pacific Ocean 42

T27 79974 M 292 SPLASH 16/12/07 Great Barrier Reef Pacific Ocean 231

T28 79973 F 300 SPLASH 26/11/06 Great Barrier Reef Pacific Ocean 20

140

T29 79972 F 296 SPLASH 10/12/07 Great Barrier Reef Pacific Ocean 209

T30 79975 F 368 Telonics ST18 21/11/03 Great Barrier Reef Pacific Ocean 120

T31 72587 F 350 SPOT 3/12/06 Great Barrier Reef Pacific Ocean 43

T32 79984 F 311 SPOT 25/03/13 Great Barrier Reef Pacific Ocean 370

T33 79985 F 331 SPOT 25/03/13 Great Barrier Reef Pacific Ocean 110

T34 79979 F 308 SPOT 1/12/13 Great Barrier Reef Pacific Ocean 136

T35 111549 F 285 SPOT 10/04/11 Hawaii Pacific Ocean 21

T36 111547 F 405 SPOT 5/09/11 Hawaii Pacific Ocean 350

T37 111543 F 380 SPOT 8/09/11 Hawaii Pacific Ocean 141

T38 34107 F 256 SPOT 6/11/09 Florida North Atlantic Ocean 206

T39 34029 F 256 SPOT 25/05/10 U.S.A North Atlantic Ocean 191

T40 34020 M 262 SPOT 26/05/10 U.S.A North Atlantic Ocean 40

T41 34021 F 250 SPOT 26/05/10 U.S.A North Atlantic Ocean 23

T42 33992 F 201 SPOT 26/05/10 U.S.A North Atlantic Ocean 33

T43 55494 F 244 SPOT 26/05/10 U.S.A North Atlantic Ocean 95

T44 55495 F 295 SPOT 09/06/10 U.S.A North Atlantic Ocean 128

T45 68477 M 201 SPOT 10/06/10 U.S.A North Atlantic Ocean 127

T46 34203 F 335 SPOT 29/10/10 U.S.A North Atlantic Ocean 46

T47 68471 F 245 SPOT 13/11/10 U.S.A North Atlantic Ocean 26

T48 105594 F 375 SPOT 29/01/11 U.S.A North Atlantic Ocean 8

T49 68529 F 325 SPOT 19/02/11 U.S.A North Atlantic Ocean 556

T50 68554 F 403 SPOT 19/02/11 U.S.A North Atlantic Ocean 184

T51 68494 F 365 SPOT 19/02/11 U.S.A North Atlantic Ocean 191

T52 68485 F 335 SPOT 19/02/11 U.S.A North Atlantic Ocean 94

T53 105599 F 340 SPOT 19/02/11 U.S.A North Atlantic Ocean 94

T54 68555 F 286 SPOT 20/02/11 U.S.A North Atlantic Ocean 254

T55 68556 F 322 SPOT 20/02/11 U.S.A North Atlantic Ocean 418

T56 68495 F 325 SPOT 20/02/11 U.S.A North Atlantic Ocean 231

T57 68496 F 280 SPOT 20/02/11 U.S.A North Atlantic Ocean 216

T58 68488 F 295 SPOT 20/02/11 U.S.A North Atlantic Ocean 830

T59 68486 F 320 SPOT 20/02/11 U.S.A North Atlantic Ocean 99

T60 105595 F 325 SPOT 20/02/11 U.S.A North Atlantic Ocean 39

T61 106660 M 206 SPOT 22/02/11 U.S.A North Atlantic Ocean 52

T62 106661 F 320 SPOT 10/04/11 U.S.A North Atlantic Ocean 59

T63 98332 F 184 SPOT 20/09/11 U.S.A North Atlantic Ocean 85

141

T64 108064 F 175 SPOT 12/11/11 U.S.A North Atlantic Ocean 382

T65 112979 F 325 SPOT 9/12/11 U.S.A North Atlantic Ocean 2

T66 112988 F 305 SPOT 12/12/11 U.S.A North Atlantic Ocean 379

T67 112981 F 332 SPOT 14/12/11 U.S.A North Atlantic Ocean 7

T68 113537 F 346 SPOT 14/12/11 U.S.A North Atlantic Ocean 392

T69 113536 F 305 SPOT 14/12/11 U.S.A North Atlantic Ocean 145

T70 113534 F 321 SPOT 15/12/11 U.S.A North Atlantic Ocean 32

T71 115907 M 200 SPOT 16/12/11 U.S.A North Atlantic Ocean 32

T72 115906 M 200 SPOT 2/02/12 U.S.A North Atlantic Ocean 40

T73 112994 M 232 SPOT 27/05/12 U.S.A North Atlantic Ocean 1

T74 112993 F 232 SPOT 22/07/12 U.S.A North Atlantic Ocean 1

T75 112987 F 296 SPOT 22/07/12 U.S.A North Atlantic Ocean 153

T76 112986 F 248 uCricket 22/07/12 U.S.A North Atlantic Ocean 323

T77 115915 F 215 uCricket 23/07/12 U.S.A North Atlantic Ocean 31

T78 112991 F 260 uCricket 04/09/12 U.S.A North Atlantic Ocean 9

T79 115912 F 297 SPOT 06/09/12 U.S.A North Atlantic Ocean 104

T80 115913 F 157 SPOT 6/09/12 U.S.A North Atlantic Ocean 135

T81 112982 F 196 uCricket 8/09/12 U.S.A North Atlantic Ocean 43

T82 119440 F 206 SPOT 12/09/12 U.S.A North Atlantic Ocean 43

T83 111551 F 180 SPOT 18/03/13 U.S.A North Atlantic Ocean 29

T84 130582 F 220 SPOT 20/04/13 U.S.A North Atlantic Ocean 30

T85 130985 F 289 SPOT 24/05/13 U.S.A North Atlantic Ocean 291

T86 129952 F 360 SPOT 1/06/13 U.S.A North Atlantic Ocean 26

T87 130583 F 368 SPOT 05/08/13 U.S.A North Atlantic Ocean 25

T88 130986 F 273 SPOT 17/10/13 U.S.A North Atlantic Ocean 26

T89 130990 F 357 SPOT 17/10/13 U.S.A North Atlantic Ocean 147

T90 132795 M 356 SPOT 17/10/13 U.S.A North Atlantic Ocean 175

T91 133719 F 378 SPOT 17/10/13 U.S.A North Atlantic Ocean 10

T92 133720 F 307 SPOT 17/10/13 U.S.A North Atlantic Ocean 174

T93 133721 F 344 SPOT 17/10/13 U.S.A North Atlantic Ocean 131

T94 133722 F 373 SPOT 17/10/13 U.S.A North Atlantic Ocean 19

T95 133723 F 294 SPOT 17/10/13 U.S.A North Atlantic Ocean 170

T96 129957 M 230 SPOT 17/10/13 U.S.A North Atlantic Ocean 65

T97 137737 F 300 SPOT 13/11/13 U.S.A North Atlantic Ocean 9

T98 134992 F 199 SPOT 8/02/14 U.S.A North Atlantic Ocean 40

142

T99 134993 M 242 SPOT 21/03/14 U.S.A North Atlantic Ocean 41

T100 137335 F 366 SPOT 22/03/14 U.S.A North Atlantic Ocean 117

T101 137337 F 352 SPOT 12/05/14 U.S.A North Atlantic Ocean 186

T102 137336 F 383 SPOT 12/05/14 U.S.A North Atlantic Ocean 251

T103 137738 F 296 SPOT 13/05/14 U.S.A North Atlantic Ocean 26

T104 130580 F 300 SPOT 11/07/14 U.S.A North Atlantic Ocean 27

T105 146598 M 245 SPOT 14/11/14 U.S.A North Atlantic Ocean 162

T106 144397 F 270 SPOT 16/11/14 U.S.A North Atlantic Ocean 209

T107 144019 M 352 SPOT 15/03/15 U.S.A North Atlantic Ocean 102

T108 144020 F 356 SPOT 26/04/15 U.S.A North Atlantic Ocean 332

T109 73058 M 131 Mk10 28/06/08 Brazil South Atlantic Ocean 31

T110 78010 M 193 Mk10 24/07/09 Brazil South Atlantic Ocean 12

T111 90816 F 128 Mk10 01/06/10 Brazil South Atlantic Ocean 75

T112 78012 M 150 Mk10 07/08/10 Brazil South Atlantic Ocean 79

T113 97760 F 190 Mk10 21/12/10 Brazil South Atlantic Ocean 26

T114 78008 F 295 Mk10 06/01/11 Brazil South Atlantic Ocean 79

T115 98008 M 190 Mk10 08/02/11 Brazil South Atlantic Ocean 23

T116 78004 F 120 Mk10 05/03/11 Brazil South Atlantic Ocean 70

T117 97758 F 270 Mk10 22/03/11 Brazil South Atlantic Ocean 118

T118 78012a M 130 Mk10 11/07/11 Brazil South Atlantic Ocean 52

T119 98005 M 170 Mk10 11/07/11 Brazil South Atlantic Ocean 62

T120 97762 F 134 Mk10 25/07/11 Brazil South Atlantic Ocean 53

T121 98006 M 153 Mk10 14/08/11 Brazil South Atlantic Ocean 60

T122 98007 F 172 Mk10 23/08/11 Brazil South Atlantic Ocean 10

T123 112679 F 212 MiniPAT 01/08/12 Brazil South Atlantic Ocean 123

T124 112682 F 240 MiniPAT 01/08/12 Brazil South Atlantic Ocean 124

T125 112678 F 238 MiniPAT 02/08/12 Brazil South Atlantic Ocean 13

T126 112681 F 310 MiniPAT 12/08/12 Brazil South Atlantic Ocean 57

T127 52663 F 180 Mk10 24/08/12 Brazil South Atlantic Ocean 32

T128 112683 F 156 MiniPAT 09/09/12 Brazil South Atlantic Ocean 124

T129 122811 F 184 MiniPAT 31/08/13 Brazil South Atlantic Ocean 60

T130 78012b F 151 MiniPAT 31/08/213 Brazil South Atlantic Ocean 11

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Figure S 5.1 Representative example of a tiger shark track (black points) analysed with Biased

Random Bridges showing both residency (points within the 0.75 UD (red lines) and migration

(points outside the 0.75 UD).

Figure S 5.2 Figure extracted from: A) (Bouchet 2015) indicating hotspots in fish abundance

off the coast of Western Australia, B)Figure re extracted from (Double et al. 2014) showing

areas of high occupancy by pygmy blue whales off the coast of Western Australia.

145

Chapter 6 General Discussion

An understanding of movement ecology is essential for the conservation and

management of mobile, top-order predators in marine systems. In this thesis I examined

how environment and diet influenced movement patterns and habitat use of tiger sharks

at multiple spatial and temporal scales. Firstly, I compiled available information on the

biology and ecology of tiger sharks and identified current threats to the species in order

to update their conservation status for the International Union for Conservation of

Nature (IUCN) Red List (Chapter 2). I then examined patterns of movement of the

species along the coast of Western Australia and showed that tiger sharks made

extensive movements between tropical and temperate ecosystems and exhibited high

individual variability in distances travelled and degree of residency within a habitat

(Chapter 3). Isotopic analyses (Chapter 4) showed that these large-scale movements

were accompanied by changes in diet, trophic niche and role across multiple habitats at

a continental scale. Finally, I analysed a large dataset of satellite tracking from locations

in the Indian, Pacific and Atlantic Ocean basins and identified bathymetry and

temperature as key drivers of movements and habitat use by tiger sharks at global scales

(Chapter 5). The relationship between these physical variables and movement patterns

varied among ocean basins and had low predictive power to explain patterns of

utilisation of habitats due to their variability in space (spatial non-stationarity). A

common thread across all my chapters was the high degree of variation in behaviour

that occurred among individual sharks, likely as a consequence of their role as a

generalist top-order predator in marine ecosystems.

6.1 Variability in movement behaviour

Why are movement patterns of these large sharks so variable? One key reason might be

that such species are not tied to any fixed point in the marine habitat during their

ontogeny, such as discrete nursery areas that are commonly used by several sharks

(Heupel et al. 2007) or philopatry to specific nesting beaches by sea turtles (Brothers &

Lohmann 2015). For example, taxa that have portions of their life cycle associated with

land, either for breeding or moulting, such as most pinnipeds, marine reptiles and

seabirds, typically show a high degree of predictability in movement patterns and

habitat use (Shaffer et al. 2006; Broderick et al. 2007; Egevang et al. 2010; Fitzpatrick

et al. 2012; Raymond et al. 2015) because these behaviours are associated with the

146

regular return to natal beaches, islands and nesting grounds in order to breed. Southern

elephant seals (Mirounga leonina), for instance, exhibit site fidelity to breeding colonies

and foraging grounds, with many individuals returning to the same haul-out site after

extended foraging trips (Jonker & Bester 1998; Bradshaw et al. 2004b; Fabiani et al.

2006; Mulaudzi et al. 2008). This highly predictable pattern of movement has allowed

key environmental and habitat requirements to be identified throughout the species

range (Biuw et al. 2007; Hindell et al. 2016).

In contrast, tiger sharks and other top-order predatory fishes including mako (Isurus

sp.), thresher (Alopias sp.), billfishes and tunas rarely display such predictable

behaviours. This is probably a reflection of the nomadic nature of their ecology with no

easily identifiable specific start or end point to their migrations, combined with a lack of

association with static points in the environment such as land masses, beaches or ice

caps where breeding colonies occur. As a consequence, these animals display a high

degree of individual variability in movement patterns and space use (Boustany et al.

2002; Walli et al. 2009; Carlisle et al. 2012) and a plasticity in behaviour and flexibility

in habitat associations that hampers our ability to identify the drivers of migration and

residency.

6.2 Drivers of movement and habitat use

The Biased Random Bridges (BRB) analytical technique provided a means to analyse

and visualise tracking data for tiger sharks, while incorporating inherent autocorrelation

in the computation of utilisation distributions and addressing gaps in the dataset

(Bullard 1991; Benhamou 2011; Benhamou & Riotte-Lambert 2012). The description of

utilisation distributions allowed me to assess if residency was potentially associated

with foraging (Chapter 3) and to identify the environmental drivers of patterns of high

utilisation of some habitats (Chapter 5). In Chapter 3, I found that residency at regional

spatial scales was associated with bathymetry and sea surface temperatures (SST), and

was more likely to occur in shallow coastal waters with temperatures above 23°C. The

same environmental variables drove patterns of residency of tiger sharks in other ocean

basins (Chapter 5). Although the distribution and habitat use of a wide range of fauna

has been associated with topographic covariates (McConnell et al. 1992; Woodley &

Gaskin 1996; Sjöberg & Ball 2000; Bouchet et al. 2015), tiger sharks are an epipelagic

species that spend most of their time in the uppermost surface layer of the water

column, above the thermocline (Meyer et al. 2010; Fitzpatrick et al. 2012; Hazin et al.

147

2013; Vaudo et al. 2014; Afonso & Hazin 2015). In my study, bathymetry acted as a

surrogate measure of the use of oceanic and shelf habitats for the species (Chapter 3 and

5). As ectotherms, elasmobranchs are strongly influenced by changes in water

temperature (Crawshaw 1977; Bernal et al. 2012), implying that these animals are likely

to be constantly searching for optimal environmental conditions (Klimley & Butler

1988; Block et al. 1997, 2011; Bestley et al. 2013; Kessel et al. 2014; Schlaff et al.

2014). Despite the fact that water temperatures was a predictor of migration, it seems

unlikely that this behaviour was a search for optimal conditions since tiger sharks

selected temperatures during migrations to both tropical and temperate regions that were

outside the thermal range they preferred while resident (Chapters 3 and 5).

6.3 Low predictability of models

Although SST and bathymetry were selected as major predictors of migration, residency

and areas of high use, these explained only a small proportion of the variance in

movement behaviour (Chapters 3 and 5). In Chapter 3, this result might have been an

outcome of relatively low sample size. However, in Chapter 5, I gathered and analysed

the largest global dataset of tiger shark tracks available to date. Despite the large

number of tracks, generalised additive mixed models still explained only small amounts

of variance in movement and had low power to predict the utilisation distributions (UD)

of tiger sharks. To understand why this was the case, I then applied geographically

weighted regression (GWR) to determine if patterns of utilisation were changing in

space and causing non-stationarity in the relationship between model predictions and

observed values. The GWR calculated localised coefficients for a regression by

allowing the relationship between variables to vary in space and subsequently mapped

the range of coefficients in a spatial context (Brunsdon et al. 1996; Fotheringham et al.

1998). This allowed me to determine the presence of non-stationarity in the relationship

and to identify areas where observed values of UD where much higher or lower than

predicted by mixed models (Chapter 5). When non-stationarity is detected, there are a

number of possible explanations including, model misspecification, missing variables,

autocorrelation or inappropriate spatial scales of measurement (resolution) (Brunsdon et

al. 1996; Fotheringham et al. 1998; Foody 2004; Osborne et al. 2007). Autocorrelation

was incorporated in the computation of utilisation distributions (Benhamou 2011) and

so was not an issue here, and the consistent selection of the same predictors (depth and

SST) from the suite of fitted models suggested that principal environmental drivers of

movement had been identified. There were obvious issues with resolution of data in

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tracks from the South Atlantic where geolocations were associated with large errors

(Lam et al. 2010) that most likely drove non-stationarity. However, in the Atlantic and

Indian oceans, where non-stationarity was also identified, (as evidenced by a large range

in estimates of GWR coefficients), location estimates were obtained from satellite

telemetry and had less error. For these regions, areas where values of utilisations were

higher than predicted by the models seemed to be related to two main issues: missing

covariates in the model (such as food availability) and changes in the scale of movement

behaviour by tiger sharks between oceanic and coastal habitats.

In the Indian Ocean, off the coast of Western Australia, there were areas that had greater

than predicted utilisation giving highly positive values of GWR estimates. These areas

overlapped with hotspots for abundances of pelagic fish and occupancy by pygmy blue

whales (Balaenoptera musculus brevicauda) (Double et al. 2014; Bouchet 2015),

suggesting that prey availability may be accounting for some of the variation not

explained by the model and the resulting non-stationarity. In the North Atlantic, patterns

of higher than predicted UD values were found in areas with high average utilisation by

all sharks off the eastern coast of the USA. This suggests that our models properly

explained high use of oceanic areas, but failed to predict use of coastal habitats. In the

open ocean, the influence of large and mesoscale scale oceanographic features such as

large patterns of SST fields and fronts (Ullman & Cornillon 1999; Fryxell et al. 2008;

Belkin et al. 2009) can be identified given the resolution of the environmental data

provided by remote sensing. Here, mixed models identified a preference for deeper and

colder waters, probably associated with high use of areas around the Gulf Stream as

seen in previous studies (Lea et al. 2015; Queiroz et al. 2016). The association of tiger

sharks and thermal fronts in the North Atlantic (Queiroz et al. 2016) suggests that

mesoscale features such as eddies and coastal frontals may be important habitats for the

species, and could explain some of the patterns found here. Whereas, complex shelf

habitats influenced by topography and interactions with coastal oceanographic

processes would likely have higher explanatory power for areas of high use near the

coast if a finer-resolution spatial scale had been used in the analysis. My results suggest

that non-stationarity was likely to be a result of scale-dependent patterns in

environmental signals and the behaviour of tiger sharks, causing the models to have low

predictive power to describe utilisation patterns in shelf environments. The

environmental signals created by mesoscale oceanic features and coastal features will be

difficult to describe using remotely sensed data ad with a global dataset, however in-situ

149

environmental data such as provided by CTD-SRL tags combined with high resolution

tracking data (such as provided by Fastloc GPS tags and Diary tags) would likely be

necessary to define and explain those scale-dependent associations.

Tiger sharks are generalist predators that explore multiple food-webs (Chapter 4), likely

with foraging strategies adapted to specific prey in different habitats, a trait that may

drive scale- and habitat-dependent space use. Identifying scale-dependent movements of

top-predators can be challenging (Austin et al. 2006), especially when tracking data has

low resolution (Bradshaw et al. 2007), as is typically the case for tiger sharks. Although

my findings show that large scale patterns of SST and bathymetry can explain use of

oceanic areas, the application of GWR has allowed me to identify, for the first time that

the relationship between space utilisation by tiger sharks and environment operates at

finer scale in shelf than in oceanic habitats. The limited predictability of environmental

preferences (Chapter 5) and highly generalist behaviour (Chapter 4) currently preclude

our understanding of the potential effects of large-scale climate change in the

distribution and resiliency of tiger sharks. The prediction of movements and habitat use

for the species will likely be only possible in habitat-dependent contexts (e.g. use of

specific features or habitats in coastal and oceanic environments), when considering

different sections of the populations separately, i.e. movement of females to potential

mating and pupping grounds (Papastamatiou et al. 2013) for example. Spatial patterns

in trophic ecology

Variability in movement patterns of tiger sharks was paralleled by variability in diet.

Comparisons of isotopic signatures of sharks from locations on both the west and east

coasts of Australia suggested that tiger sharks have a diet that is size, context and

habitat specific. Signatures from muscle tissue showed that larger sharks fed on a diet

that had high values of δ13

C, consistent with these animals targeting large herbivores

such as dugong (Dugong dugon) and turtles that are part of a seagrass food chain.

Smaller tiger sharks had lower values of δ13

C, probably reflecting the more limited

ability of these size classes to subdue these large herbivores and thus resulting in diets

that have a larger component of smaller species such as fishes. These patterns were

consistent with previous studies of stomach contents (Simpfendorfer 1992; Lowe et al.

1996; Heithaus 2001).

Diets of tiger sharks also varied by location. The isotopic signatures of sharks sampled

on the Great Barrier Reef (GBR) and in Shark Bay were characteristic of a diet based on

150

a seagrass food chain, whereas sharks sampled on the coast of New South Wales (NSW)

had signatures indicative of a plankton-based food chain. Signatures of sharks sampled

at Ningaloo Reef were characterised by a diet based both on planktonic and benthic

food-webs. Despite the large differences in communities and habitats between Shark

Bay (Walker et al. 1988; Heithaus 2004; Vaudo & Heithaus 2011; Heithaus et al. 2013)

and on the GBR (Wachenfeld et al. 1998; Carruthers et al. 2002; Espinoza et al. 2014),

tiger sharks still remained at the top of the food web in both locations, showing that in

these tropical ecosystems, they acted as top-order predators (Heithaus et al. 2008a;

Wirsing & Ripple 2011). This did not appear to be the case off the temperate coast of

NSW (32° - 35°S) where seals, other oceanic sharks and tunas rather than tiger sharks

were located in the top-order positons of the food web (Davenport & Bax 2002; Revill

et al. 2009) and may have a larger role in structuring communities in this habitat.

The shift in isotopic signatures of tiger sharks between these tropical and temperate

environments was not solely due to latitude, it also appeared to be driven by the

oceanographic setting of the environment where these sharks occur. The signatures of

sharks sampled in offshore environments of NSW were very similar to those of sharks

sampled on the isolated reefs of Reunion Island in the tropical western Indian Ocean

(Trystram et al. 2016). Both localities are exposed to the open ocean and these

signatures probably reflect the accessibility of planktonic food chains. Similarly, at

Ningaloo Reef the continental shelf is very narrow, so that boundary currents occur

within 5 - 10 km of the coast accounting for the use of mixed food chains revealed by

signatures.

These results emphasise the flexibility of the diet and trophic role of tiger sharks.

Whether such traits are a cause or a consequence of their wide-ranging movements is

difficult to determine. These sharks are not simply predators, as opportunistic

scavenging is also an important feeding behaviour (Gallagher et al. 2011; Bornatowski

et al. 2012a,b; Clua et al. 2013; Hammerschlag et al. 2016) and may make an important

contribution to energy intake. In some localities, such as at Raine Island, where dead or

dying sea turtles make a significant part of seasonal diets (Hammerschlag et al. 2016), it

will be very difficult to distinguish between active predation and scavenging, since

these will give an equivalent isotopic signal.

Although there is no evidence that tiger sharks display the hunting patterns of other top-

order marine predators, such as the stalking strategy of white sharks (Klimley 1994;

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Klimley et al. 2001b; Huveneers et al. 2015) or the cooperative group hunting

behaviour of killer whales (Orcinus orca) (Hoelzel 1991; Saulitis et al. 2000) and their

isotopic signatures place them at the lowest trophic position of assemblages of large

sharks in some coastal environments (e.g. Hussey et al. 2015b), few species are likely to

represent a predation threat for large tiger sharks (Heupel et al. 2014), with the possible

exception of white sharks (Carcharodon carcharias) and killer whales (Pyle et al.

1999).

6.4 Implications for conservation

Biological characteristics such as high growth rates, relatively low fishing mortality

(Chapter 2) combined with the high variability in movement and space use (Chapter

3,5) and flexibility in feeding preferences and trophic role (Chapter 4), suggests that

tiger sharks could potentially display greater resilience to fishing pressure and human-

induced changes in habitats, including climate change (Hazen et al. 2013), compared to

other large marine predators. However, a key issue hampering the quantification of

resilience to threats and evaluation of the conservation status of tiger sharks is a lack of

data on population structure and dynamics, and catch trends of fisheries for the species

at both regional and global scales (Chapter 2). This combination of knowledge gaps and

somewhat constraining reproductive traits (i.e. producing many pups but having a

triennial reproductive cycle) is reflected in the species classification as “Near

Threatened” in the IUCN Red List. Recently, a global analysis of tiger shark genetics

showed significant genetic separation between sharks from the western Atlantic and

Indo-Pacific, with limited gene flow and different population structures among different

ocean basins (Bernard et al. 2016). Although insufficient to be designated as sub-

species (Bernard et al. 2016), these distinct genetic and population structures suggests

that tiger sharks from different ocean basins should be assessed separately by the IUCN.

New regional assessments could facilitate the identification of threats and population

trends, and possibly result in different, but more appropriate, classifications of

conservation status.

Mounting evidence of the key role of top predators in regulating ecosystem health

(Heithaus et al. 2008a; Beschta & Ripple 2009; Estes et al. 2011; Ruppert et al. 2013;

Atwood et al. 2015), combined with the severe impacts of human activities to their

populations (Dulvy et al. 2008, 2014; Schipper et al. 2008; McCauley et al. 2015;

Payne et al. 2016) highlight the importance of understanding how predators relate to the

152

environment if we are to create appropriate conservation measures to remediate and halt

the consequences of their removal from ecosystems worldwide. Tiger sharks are top-

order predators in tropical and seagrass habitats (Chapter 4) where they have been

shown to regulate grazing on seagrass by communities of large herbivores (Burkholder

et al. 2013). Their removal could result in a reduction of the control of grazing by

herbivores, which in turn could lead to declines in seagrass productivity and eventually

result in the collapse of this habitat (Heithaus et al. 2014; Atwood et al. 2015). Marine

reserves in the Great Barrier Reef, Ningaloo Reef and Shark Bay in Australia can

provide limited spatial protection for mobile species (Preen et al. 1997; McCook et al.

2010; Speed et al. 2010; Heupel et al. 2015). However, due to the very wide-ranging

nature of tiger sharks (Chapter 3 and 5) such reserves will not provide protection for

individuals throughout their life history. The tracks shown in Chapter 5 show that some

sharks routinely moved across 1000s of km of open ocean and coastline and across the

territorial waters of different countries, each with different protections, fishery

regulations and conservation strategies. Protection for these animals can even differ

within the territorial waters of the same country. Thus, it may make sense to aim to

identify and focus conservation strategies on those key areas that serve as foraging or

breeding habitat and to unify and strengthen management approaches to fisheries that

capture these sharks on a regional basis. In the case of tiger sharks in Western Australia,

large marine parks such as Ningaloo Reef and Shark Bay seem to offer sufficient

protection for the species despite the varied classes of zoning present, and modification

of zoning within these reserves would unlikely change the level of protection offered for

the species. However, in the North Atlantic areas of high use by large sharks overlap

with highly exploited fishing grounds in the North Atlantic Current – Labrador Current

convergence zone, and the Mid-Atlantic Ridge, indicating the necessity of ocean-scale

management of fisheries (Queiroz et al. 2016). Therefore, the identification of

environmental and habitat preferences for tiger sharks, and other large migratory sharks,

in areas where no protection currently exists will be a major step for the development of

alternative management actions (fisheries regulation, catch quotas, dynamic ocean

management) where the development of large-scale marine reserves might not be

feasible. Additional tagging effort and larger spread across the population (i.e. also

including juveniles and males) that allow the identification of movements related to

reproduction and habitats associated with pupping and juvenile grounds, combined with

the use of analytical approaches such as geographically weighted regression could aid

this goal.

153

6.5 Future directions

The need for more tracking data to better describe movement patterns derived from all

components of the population of sharks was identified as a key goal for conservation

strategies. Poor monitoring of catches in commercial and artisanal fisheries that prevent

stock assessments and definition of long-term population trends have been identified as

one of the major issues regarding management and conservation of tiger sharks (Chapter

2). Management strategies that integrate near-real-time tracking and predictive

modelling of habitat and environmental preferences are able to account for the dynamic

nature of species and oceanographic features (Hobday & Hartmann 2006; Hobday et al.

2009; Hobday & Hartog 2014; Dunn et al. 2016; Hazen et al. 2016) and offer great

potential for management of highly mobile marine predators.

To date, nearly all studies of tiger sharks have focused on horizontal movements and

environmental variables obtained by satellite remote sensing techniques from the

superficial layers of the water column (Chapter 3 and 5). However, tiger sharks occupy

a three dimensional environment where movements also occur in the vertical plane

often to depths well beyond the thermocline (Nakamura et al. 2011; Iosilevskii et al.

2012; Chapter 3). I have presented evidence of differential utilisation of the water

column between tropical and temperate environments (Chapter 3) and that diet and

trophic role of tiger sharks also change between those ecosystems (Chapter 4), which

reinforces the need for additional efforts to deploy tags that can be used to link

horizontal and vertical movements of individuals across multiple ecosystems. Many

predators utilise water column and vertical movements in their hunting strategy

(Klimley 1994) or forage while diving (Gallon et al. 2013). Defining the link between

horizontal and vertical displacements of tiger sharks, how they relate to and vary

according to environmental conditions, could resolve much of the lack of predictability

and ‘noise’ associated with ecological models applied to tracking data.

Studies that provide long-term and high-resolution tracking of tiger sharks are rare,

likely due to tag failure, shedding, damage and biofouling (Kerstetter et al. 2004;

Heithaus et al. 2007c; Hays et al. 2007; Meyer et al. 2010), which results in patchy and

sparse position estimates and short track durations. Some of the tracks presented in this

thesis were considerably longer than many previously obtained with satellite tags for the

species (Heithaus et al. 2007c; Fitzpatrick et al. 2012; Hazin et al. 2013; Papastamatiou

et al. 2013) and were able to describe extensive latitudinal movements (Chapter 3) and a

154

larger utilisation of areas beyond the shelf break than previously recorded for the

species (Chapter 5). However, long, high quality tracks represented only a small number

of the tracks gathered in this thesis. For non-air-breathing marine taxa the rate of uplink

of satellite tags and the use of cutting-edge technology is somewhat limited (Hussey et

al. 2015a; Hays et al. 2016). For exemple, CTD-SRL tags (Boehme et al. 2009) have

provided great advances in the understanding of habitat use of marine mammals (Biuw

et al. 2007) and could aid in overcoming some of the issues with data resolution and

uplink rates of satellite tracking of sharks. However, a towed version that could be

deployed on sharks is still not available. Studies using this tag could accelerate our

understanding of relationships between locomotion, foraging and the physical

environment inhabited by tiger sharks.

My results showed that predicting drivers of movement of tiger sharks is challenging,

most likely because sharks are possibly targeting diverse resources and habitats in

different ecosystems (Chapter 3 - 4). At fine spatial scales, the deployment of multi-

sensor and camera tags such as Customized Animal Tracking devices (CATS-Cam tags)

across multiple habitats would allow the identification of inter-specific interactions and

feeding events (Heithaus et al. 2002; Nakamura et al. 2011), and illuminate the decision

process behind feeding behaviour and prey selection, while linking movement patterns

to the role of sharks within specific habitats. At large spatial scales, GWR could be used

to test predictions associated with habitat use and to identify areas where general

relationships defined by physical parameters fail to explain patterns of use (Chapter 5).

The overlap found between patterns of GWR estimates and areas of high utilisation by

multiple species aids the targeting of future research efforts in order to identify factors

structuring habitat use by tiger sharks. New satellite technologies and the expansion of

the tracking data to other sections of the population, such as juveniles and males, would

allow the identification ecologically important habitats yet to be defined that warrant

special attention for conservation. Tagging programs could also potentially provide the

real-time tacking of animals that could feed into dynamic ocean management actions.

The analytical methods utilised in this thesis showed great potential applicability to

tracking data from a variety of taxa of marine megafauna, particularly when predictive

and explanatory power of models are low. Similarly, the technological development of

tracking systems and a better understanding of variability in species’ behaviours have

also been flagged as a major necessity for directing future research in marine megafauna

movement (Hays et al. 2016).

155

6.6 Concluding remarks

This thesis demonstrated how increasing sample numbers and the use of large datasets

can provide new insights into the ecology of a cosmopolitan marine predator. My

results revealed that tiger sharks use oceanic habits more often than previously reported

for the species, with both movement and residency being influenced by sea surface

temperature and bathymetry. Large-scale movements displayed by tiger sharks imply

that they act as an important link between different habitats, which is also supported by

their context specific trophic ecology. My results also showed how the combination of

data obtained from tracking and stable isotopes analysis of multiple tissues can be used

for defining large scale habitat use of top-order predators, despite some clear limitations

regarding data resolution and temporal scale of sampling. Although ecological

modelling of sparse tracking datasets and coarse resolution environmental data are

problematic, methods to generate utilisation distributions that can overcome these issues

showed great value and possible broad application for tracking of other large marine

taxa. Moreover, I provided a novel method to test explanatory power of models, identify

when and where model predictions inadequately explain spatial patterns, and guide

future efforts in areas where the compilation of high resolution and multispecies

datasets should be targeted. This is essential for understanding the spatial ecology and

conservation of mobile top-order marine predators such as tiger sharks.

157

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Appendix B

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Appendix C