Spatial Ecology, Genetic Barcoding, and Vulnerability of Tropical Indo-West Pacific Batoids, with a Focus on Australian Species

Spatial ecology, genetic barcoding, and vulnerability of tropical Indo-West Pacific batoids, with a focus on Australian species

Florencia Cerutti-Pereyra Bachelor of Marine Biology (Honours)

A thesis submitted for the degree of Doctor of Philosophy, Research Institute of Environment and Livelihoods, Faculty of Engineering, Health, Science, and the Environment, Charles Darwin University, Australia 6th July, 2012

Spatial ecology, genetic barcoding, and vulnerability of tropical Indo-West Pacific batoids, with a focus on Australian species

A thesis submitted for the degree of Doctor of Philosophy at Charles Darwin University, Australia 6th July, 2012 Candidate Declaration

I hereby declare that the work herein, now submitted as a thesis for the degree of Doctor of Philosophy of the Charles Darwin University, is the result of my own investigations, and all references to ideas and work of other researchers have been specifically acknowledged. I hereby certify that the work embodied in this thesis has not already been accepted in substance for any degree, and is not being currently submitted in candidature for any other degree.

Full name: Florencia Cerutti-Pereyra

Signature:

Date: 06/07/2012

iv Acknowledgements

I am very grateful to all my supervisors for your recently and patiently acquired expertise in Spanglish. Thank you to: Mark Meekan for the opportunity (and funding) of doing a dream of mine: studying rays in Australia. I’ll do my best to learn from the challenges faced through this project. Chris Austin for sharing your knowledge and love for genetics despite the inexperienced newbies; it is great to work with a scientist that, above all the bureaucratic daily responsibilities, is still passionate about his work. I did enjoy the genetics part of this project and learnt much from working with you. I am especially grateful to my ‘rescue’ supervisors Karen Edyvane and Peter Kyne for joining this project and taking the responsibility of it. I very much wanted to have a ‘management’ and an ‘applicability of all this’ sections in my thesis and you both provided that unexpected opportunity. Although, my biggest thanks to both of you are for being encouraging and supportive through the last part of PhD. I’m very grateful to Karen for your always kind way of dealing with it. I’m extremely grateful too to all the funding parties involved in this project: el Centro Nacional de Ciencia y Tecnología, CONACYT- México, Australian Institute of Marine Science (AIMS), Commonwealth Scientific and Industrial Research Organization (CSIRO), and Charles Darwin University. Many amazing scientists helped me with different parts of this project, from analysis to sampling, so I’m very, very grateful to the following people: Michelle Thums, Keith McGuinness, Yuval Berger, Conrad Speed, Clive McMahon, Shane Penny, and Aaron Petty for your incredible help and advice with data analysis and writing. Many thanks to the best-est GIS gurus Juno Rouwenhorst and Ron Ninnis. Thanks to Peter Last, John Stevens, Bob Ward, and Will White for the countless times I asked for rays’ taxonomic and genetic stories. Many thanks to Mark Shultz and Vivian Wei for you big help and time spent with me in the lab and processing samples. I thank Charles Darwin University staff for their help with all things administrative, electronic, and supportive. Extra thanks to Hale Williams, Roanne Ramsey, and Liz Lycett for sorting out all sorts of project issues; Craig Webster (ITMS) for un-fun technology matters; and Jayshree Mamtora and Vibeke Foss for

v your always friendly attitude, incredible support, and useful help. Thanks also to the ATRF/NARU staff for your help and friendliness while I ‘lived’ there. Thank you so very much to all the volees and people that helped during field work, I enjoyed so much sharing our passion for elasmobranchs and the marine world with: Frazer M., Conrad S., Charlie H., Aubanie R., Steph B. (your friendship was the best outcome of that trip!), Peter H., Kristel W., Michelle M., Heather, S. Stevens, and the dream team: Little Karen, Magnus the Norwegian, and el vato loco Owen O’Shea. Thanks to the Scary team (Scary, Ronny, monsieur Bernard, and Zac) for your help and encouragement during field work and conferences. Special and BIG thanks to the Viking, Mat Gray for teaching me how to attach a hook to a line, for catching only a couple of stingrays and for your many –and failed- attempts to be cooler than the Mexican!

vi I’m also very grateful to the Exmouth Light Engineering staff, Ningaloo Reef Dreaming, CDU metal workshop, Orca Marine, and Grant Johnson and Adrian Donati from NT Fisheries for your incredible help designing and fixing field work gear and sampling. Many thanks to Gavin Enever, the Anindilyakwa rangers, and Scott Whiting for your help getting around Groote Eylant and catching stingrays. ..Friends are angels who lift our feet when our wings have trouble to remember how to fly... I met many great people and made many good friends during my stay in Australia: the genetics team, Tuty, Rury, and Chelsea; the revolutionary friends, Evi, Rabia, Ve, & Salman; Lil Danielsen for showing me to never give up a climbing route! Maia Berman, Judith Szabo, and Kirsty McAllister for your world- travellers’ wisdom, easy laughs, last-minute editing advices, and full-time support. Many friends also became the family that looked after me, in one way or another, and made this experience more worthwhile. Although, I’d need an extra chapter to enlist so many things and situations where I was grateful for your presence in my life, thank you to Ve for always being a patient English (wait.. Irish) editor, but mostly thank you for always being a supportive friend despite your own personal fun; Costi (y Vale) for always welcoming me in your lives and sharing your ability to always make fun of un-fun situations; thank you Juno, Sabine, and Cindy for many great times of friendship, learning, never-fading support, and fun times. ..Patria y tribu se mezclan en mi mente dice Isabel Allende, así que muchas gracias a mis compañebrios latinos: Gabito, Miguelito, Alf, Rosita, Costi, Enrique y Lore, el sapo-mexican team, y otros más… A ustedes les agradezco nomas por aparecer en un momento de mi vida donde me hacían mucha falta los abrazos, bailes, comidas, cariños y palabrotas de esa cultura nuestra que no existe en ningún otro lado. Me recordaron donde quedaba mi familia y me hicieron sentir que no estaba tan lejos. Gracias a la Chilean family, Michael, Nicole, Alex, & Warrick for welcoming me in your family and your extra support despite my ‘newness’ among you. Salud. Michael Negrete, ojitos, no sé cómo agradecerte por tu presencia y amor constante durante mi último año de loquera y salvarme de la banca rota innumerables veces. Yo no sé de dónde sacaste tanta paciencia to stick around a pesar de mi full- time crisis mode. Tu presencia, sólida como una montaña, tus cariños y abrazos me dieron la estabilidad y fortaleza que me hacía falta para, por fin, terminar este sueño mío. En mi corazón estas pololin.

vii Lejos y cerca, como siempre, gracias a mis viejos amigos, Alice, Diego, Rubi, Alita, Lilu, y el Wacho-man por permanecer en mi vida, en mi sueños y echarme porras a pesar de las distancias y desconexiones. Ah como los extraño!! No me alcanzan las palabrotas para agradecer a mis papas y hermanotida por su apoyo todo este tiempo. De Monterrey a La Paz, luego a Darwin y luego a quiensabedondeporquequienserobómicompás??, gracias por recordarme, permitirme y apoyarme siempre para que siga mis sueños por los caminos que me lleven, aunque me lleven al otro lado del mundo, caray. Gracias porque durante cada hoja escrita, cada viaje, cada cosa aprendida, cada planta de albahaca (faltaba más!) y cada momento de estos años de doctorado y vida, ustedes estuvieron siempre presentes y siempre ayudándome a flotar.

“There is a pleasure in the pathless woods, there is a rapture on the lonely shore, there is society, where none intrudes, by the deep sea and music in its roar: I love not man the less, but Nature more, from these our interviews, in which I steal from all I may be, or have been before, to mingle with the Universe, and feel what I can never express, yet cannot all conceal” G.G. Byron

viii Dedication

To my parents and my little big sister.

A mis padres, por siempre decirme que yo puedo hacer cualquier cosa que me proponga. Este trabajo está dedicado a los pilares de mi vida: Mamima y Papitufin, y mi forever-present angelito, Nat.

ix Abstract Tropical batoids are abundant and important members of coral reef fish communities. Their populations are subject to intense pressure from fishing and habitat degradation, but due to their distinct life-history traits, can only withstand modest levels of decline without stock collapse. Despite this, basic knowledge to ensure their effective conservation and management is lacking. This project addressed this need by combining several current and innovative research tools to assist the integrated management of batoids in northern Australia (NA) and the Indo-West Pacific (IWP). DNA-barcoding was used to confirm the identification of 16 batoid species at Ningaloo Reef (NRMP), Western Australia. It demonstrated its potential to confirm field identifications where taxonomic uncertainty might confound ecological data, although two major sets of problems limiting its application were also identified. Passive acoustic monitoring was used to study the spatial ecology of batoids in the lagoon of NRMP. By using acoustic receivers, the first confirmed nursery area for tropical juvenile batoids in the IWP was identified - a shallow embayment that recorded most of the detections. Adult rays showed sexual segregation of core areas of activity inside the lagoon, with juvenile and adult rays, moving beyond protected areas. Little evidence of habitat partitioning among species was found at large spatial scales, despite differential use of microhabitats. The current zoning of the NRMP provides effective protection for core areas of activity but not for larger movement ranges. As such, spatial management will require large areas of protection to ensure the conservation of these species. An ecological risk assessment (ERA) was employed to assess the vulnerability of tropical batoids to the risk of fishing and habitat degradation in NA and IWP. The ERA methodology was also used to identify and rank research gaps and priorities. Finally, the applicability of these tools, and findings, to the conservation and integrated management of tropical batoids in NA and the IWP, and future research directions, are discussed.

x Table of Contents

CANDIDATE DECLARATION ...... IV

ACKNOWLEDGEMENTS ...... V

ABSTRACT ...... X

TABLE OF CONTENTS ...... XI

LIST OF FIGURES ...... XIV

LIST OF TABLES ...... XVII

CHAPTER 1- GENERAL INTRODUCTION ...... 3

1.1 WHAT ARE BATOIDS? ...... 5 1.1.1 Taxonomy and phylogeny ...... 5 1.1.2 Distribution and habitat ...... 6 1.1.3 Biological characteristics ...... 7 1.1.3.1. Age and growth ...... 7 1.1.3.2. Reproduction ...... 8 1.1.3.3. Feeding ...... 9 1.1.4 Ecological role and significance ...... 9

1.2 BATOID THREATS AND CONSERVATION ...... 10 1.2.1 Exploitation ...... 10 1.2.2 Habitat loss and degradation ...... 13 1.2.3 Current conservation status and research priorities ...... 14

1.3 MULTI-DISCIPLINARY TOOLS AND APPROACHES TO ELASMOBRANCH MANAGEMENT ...... 16 1.3.1 Molecular ecology- DNA barcoding ...... 18 1.3.2 Spatial ecology- movement patterns...... 21 1.3.2.1. Passive acoustic monitoring, the Ningaloo Reef Ecosysem Tracking Array, and study sites 23 1.3.3 Integrated ecological risk assessment ...... 25 1.3.4 Ethics Statement ...... 27

1.4 AIMS ...... 30

1.5 OUTLINE OF THESIS ...... 31 1.5.1 Overview of data collected ...... 31 1.5.2 Chapter outline ...... 31

xi CHAPTER 2- IDENTIFICATION OF RAYS THROUGH DNA BARCODING: AN APPLICATION FOR ECOLOGISTS ...... 34

2.1 PREFACE...... 34

2.2 INTRODUCTION ...... 36

2.3 METHODS ...... 38 2.3.1 Study group ...... 38 2.3.2 Study site ...... 39 2.3.3 Sampling ...... 39 2.3.4 Laboratory procedures ...... 39 2.3.5 Analysis ...... 42

2.4 RESULTS ...... 53 2.4.1 General findings ...... 53 2.4.2 Taxonomic identification and barcoding ...... 54

2.5 DISCUSSION ...... 58

2.6 CONCLUSIONS ...... 64

CHAPTER 3- RESTRICTED MOVEMENTS OF JUVENILE RAYS IN THE LAGOON OF NINGALOO REEF, WESTERN AUSTRALIA – EVIDENCE FOR THE EXISTENCE OF A NURSERY...... 67

3.1 PREFACE...... 67

3.2 INTRODUCTION ...... 69

3.3 METHODS ...... 71 3.3.1 Study site ...... 71 3.3.2 Acoustic tagging ...... 71 3.3.3 Analysis ...... 71

3.4 RESULTS ...... 79

3.5 DISCUSSION ...... 88

CHAPTER 4- HABITAT PARTITIONING AND MOVEMENT PATTERNS OF ADULT RAYS IN A TROPICAL REEF SYSTEM ...... 94

4.1 PREFACE...... 94

4.2 INTRODUCTION ...... 96

4.3 METHODS ...... 97 4.3.1 Study site ...... 97 4.3.2 Acoustic tagging ...... 97 4.3.3 Analysis ...... 100

4.4 RESULTS ...... 101 4.4.1 Receiver performance at Mangrove Bay ...... 101

xii 4.4.2 Residency time and diel patterns of activity ...... 102 4.4.3 Home ranges ...... 104

4.5 DISCUSSION ...... 119

4.6 CONCLUSIONS ...... 122

CHAPTER 5- AN INTEGRATED ECOLOGICAL RISK FRAMEWORK FOR 16 SPECIES OF BATOIDS: ASSESSING VULNERABILITY AND RESEARCH PRIORITIES IN THE INDO-WEST PACIFIC ...... 125

5.1 INTRODUCTION ...... 125 5.1.1 Ecological risk assessments as a tool for fisheries & ecosystem-based management . 125 5.1.2 Types of ERAs ...... 128 5.1.3 ERA of elasmobranch fisheries ...... 128

5.2 METHODS ...... 130 5.2.1 Description of objectives ...... 130 Objective 1...... 130 Objective 2...... 130 5.2.2 Identification of units of analysis...... 132 5.2.3 Working definitions ...... 133 5.2.3.1. Study regions ...... 133 5.2.3.2. Defining components, categories, and attributes ...... 133 5.3 RESULTS ...... 139 5.3.1 Vulnerability assessment and integrative matrix ...... 139 5.3.2 Research priorities assessment ...... 140 5.3.3 Research priorities for highly vulnerable species ...... 148

5.4 DISCUSSION ...... 149 5.4.1 Vulnerability assessment ...... 150 5.4.2 Research assessment ...... 152 5.4.2.1 Research priorities ...... 153 5.4.3 Assumptions and limitations of this method ...... 154 5.4.3.1. The precautionary approach ...... 154 5.4.3.2. Mobility assumption ...... 155 5.4.3.3. Unpublished information ...... 155 5.4.3.4. Taxonomic reviews of species, new name but old data ...... 155 5.5 CONCLUSIONS ...... 156

CHAPTER 6 - GENERAL DISCUSSION ...... 160

6.1 MULTI-DISCIPLINARY APPROACH TO THE MANAGEMENT AND CONSERVATION OF BATOIDS ...... 160 6.1.1 DNA-barcoding and species-specific information ...... 160 6.1.2 Spatial ecology and key habitats ...... 162

xiii 6.1.3 Critical habitats and MPAs ...... 164 6.1.4 Vulnerability and research priorities assessment of batoids ...... 166

6.2 FUTURE RESEARCH DIRECTIONS ...... 168 6.2.1 Molecular biology ...... 169 6.2.2 Spatial ecology ...... 169 6.2.3 The Northern Territory and tropical Australia ...... 170

6.3 CONCLUSIONS ...... 171

REFERENCES ...... 172

APPENDIX 1 ...... 205

APPENDIX 2 ...... 209

APPENDIX 3 ...... 211

APPENDIX 4 ...... 215

APPENDIX 5 ...... 224

List of Figures Figure 1. Ningaloo Reef Ecosystem Array Tracking. A) Locations of receiver arrays along Ningaloo Reef, WA (1-Tantabiddi curtain, 2-Mangrove Bay array, 3- Point Cloates curtain, 4- Stanley Pool array, 5- Point Maud curtain, 6- Skeleton Bay array). B) Mangrove Bay array and the southern array were used for these studies...... 28 Figure 2. Mapping of Mangrove Bay array (A) and the southern array (B). Dashed lines are sanctuary zones...... 29 Figure 3. Field work for genetic sampling. A, B) Hand net used to catch and handle smaller rays; C) measuring disc with (DW); D, E) taking tissue sample from pelvic fin; F) Hawaiian sling with modified tip for tissue sampling, G) taking tissue samples of larger rays while snorkeling...... 41 Figure 4. Sampling sites. 1) Ningaloo Reef, Western Australia, 2) Darwin Harbour, Northern Territory, 3) Lizard Island, GBR, Queensland, 4) Ha Long Bay, Vietnam...... 42 Figure 5. Phylogenetic relationship of rays Part I. Reduced view of the neighbour-joining tree based on COI sequence data using Kimura-two-parameter substitution model (left); the first part of the tree (right). Names in red are the sequences obtained in this stud ...... 50 Figure 6. Phylogenetic relationship of rays Part II. Second part of the Neighbour-joining tree based on COI sequence data using Kimura-two-parameter substitution model (left); the second part of the tree (right). Names in red are the sequences obtained in this stud ...... 51

xiv Figure 7. Phylogenetic relationship of rays Part III. Third part of the Neighbour-joining tree based on COI sequence data using Kimura-two-parameter substitution model (left); the third part of the tree (right). Names in red are the sequences obtained in this study ...... 52 Figure 8. Multidimensional Scaling (MDS) of Neotrygon kuhlii. Ningaloo Reef (NR), northern Indian Ocean (NIO), Great Barrier Reef (GBR), Japan (Jap), Southeast Asia (SEA)...... 57 Figure 9. Study site, Ningaloo Reef, WA. Mangrove Bay receiver array, black lines show boundary of the sanctuary zone...... 73 Figure 10. Acoustic tagging of juvenile rays. A) Gill net set up for catching small rays in shallow water; B) & C) handling small ray with deep net; D) acoustic tag V13; E) external tagging with spaghetti tag; F, G) internal acoustic tagging; H) releasing ray after handling...... 74 Figure 11. Proportion of detections for each species and receiver at Mangrove Bay...... 81 Figure 12. Proportion of days spent by each species inside and outside of the Mangrove Bay sanctuary zone...... 82 Figure 13. Number of daily detections per ray at Mangrove Bay. Individual plots of detections of juvenile rays in primary receivers (solid shapes) and other receivers (hollow shapes)...... 83 Figure 14. Seasonality with generalised linear model. Upper panel shows the number of log-transformed detections on the y-axis against month of study from February 2008. Fitted line for each species obtained by the top-ranked, generalised, linear mixed-effects modelling of the log-transformed number of detections vs species, month and month squared. Dotted line is U. asperrimus, dashed line is G. typus (Note dotted and dashed lines are almost on top of one another) and solid line is P. atrus. Bottom plot shows the temperature per month for receivers at each site during the study period from February 2008 (mean ± se)...... 84 Figure 15. Cycles of detections using spectral analysis. Cycles of detections by hour for four species of juvenile rays, P. atrus (A), G. typus (B), U. asperrimus (C), H. uarnak (D) at Mangrove Bay...... 85 Figure 16. Detections and temperature at primary sites. Mean (± se) hourly temperatures at the primary receivers (3, 15 and 9) (A). The proportion of hourly detections (mean ± se) at all receivers for P. atrus (B), G. typus (C), U. asperrimus (D) and H. uarnak (n= 1) (E). The fitted line from the linear mixed- effects model of the relationship between mean proportion of hourly detections and mean hourly temperature (F)...... 86 Figure 17. Average hourly wind speed (± se) at Ningaloo Reef ...... 87 Figure 18. Average monthly wind speed (± se) at Ningaloo Reef ...... 87 Figure 19. A) locations of receiver arrays along Ningaloo Reef, WA (1-Tantabiddi curtain, 2-Mangrove Bay array, 3- Point Cloates curtain, 4- Stanley Pool array, 5-Point Maud curtain, 6-Skeleton Bay array). B) Mangrove Bay array sections 1- north (Mangrove Bay sanctuary zone), 2-centre, 3-south, and 4-open shelf). C) southern array sections (1- Stanley Pool, 2-Skeleton Bay). Dashed lines are sanctuary zones...... 98

xv Figure 20. Acoustic tagging of adult rays. A) Spear gun used to tag while snorkeling; B) snorkelling following rays; C- E) external acoustic tagging of adult rays; F) external spaghetti tag of adult ray...... 99 Figure 21. Performance for the primary receivers in Mangrove Bay over the 2 years of the study. Average wind speeds (A), mean code detection efficiency (B), mean rejection coefficient (C), and mean noise quotient (D). Error bars show +SE...... 106 Figure 22. Abacus plot of presecnce/absence of all rays during the period Feb 2008- April 2011. M = male, f = female, sub-adults are marked with (*), rays tagged at the southern array are marked with (**) ...... 107 Figure 23. Hourly spectral analysis of mean detections per species at Mangrove Bay...... 109 Figure 24. Hourly spectral analysis of mean detections per species in the southern array...... 110 Figure 25. Hourly spectral analysis of mean detections pooled by sex at Magrove Bay. Females (A) and males (B)...... 111 Figure 26. Hourly cycles of detections of eight species of rays. Only single individuals of U. asperrimus, H. fai, and R. laevis were tagged. Solid lines show detections at Mangrove Bay, dashed lines show detections in the southern array...... 112 Figure 27. Overall detections per receiver inside and outside sanctuary zones at (A) the Mangrove Bay and (B) southern arrays...... 113 Figure 28. Proportion of detections inside and outside sanctuary zone at Mangrvoe Bay (A) and southern array (B)...... 114 Figure 29. Kernel density (KD) estimates for H. fai (A), H. jenkinsii (B), H. uarnak (C), and P. atrus (D) at Mangrove Bay. Red shows detections during summer, blue show detections during winter. The 95% KD estimate contour is outer boundary (total area); 50% KD estimate is the large circles (core area). Smaller blue and red circles are the estimated positions from the centre of activity algorithm to show the full extent of this analysis. Reef habitats are shown as for Figure 1...... 115 Figure 30. Seasonal kernel density (KD) estimates for U. asperrimus (A), T. lymma (B), and R. laevis (C) at Mangrove Bay. Red is for summer, blue is for winter. The 95% KD estimate contour is the outer boundary (total area), 50% KD estimate is the large circles (core area). Smaller blue and red circles are the estimated positions from the centre of activity algorithm to show the full extent of this analysis. Reef habitats are shown as for Figure 1...... 116 Figure 31. Seasonal kernel density (KD) estimates for H. uarnak (A), and P. atrus (B), and T. lymma (C) at the southern array during summer (red) and winter (blue). Outer boundary shows the 95% KD estimate (total area), 50% KD estimate is the large circles (core area). Smaller blue and red circles are the estimated positions from the centre of activity algorithm to show the full extent of this analysis. Reef habitats are shown as for Figure 1...... 117 Figure 32. Kernel density (KD) estimates by sex at Mangrove Bay array, females (red), males (green). Lines are 95% KD estimate (total area), large circles are 50% KD estimate (core area). Smaller red and green circles are the estimated positions from the centre of activity algorithm to show the full extent of this analysis ...... 118

xvi Figure 33. Stages of the integrated vulnerability assessments framework by Chin et al. (2010)...... 127 Figure 34. Stages of the integrated vulnerability assessment and research priorities framework for batoids in the Indo-Pacific Ocean...... 131 Figure 35. Biogeographic regions used for the vulnerability assessment and research priorities assessment framework: the Indo-west Pacific Ocean (blue line), and the Arafura-Timor Sea/northern Australia (green line)...... 133 Figure 36. Three components for the vulnerability assessment...... 134 Figure 37. Diagram for the assessment of research priorities...... 138 Figure A2. 1. Mangrove Bay array range testing. Proportion of detections received by VR2 receivers at increasing distances from the test transmitter in the lagoon channel (filled circles, 4 m water depth) and at Mangrove Bay (white circles, 1 m water depth)...... 210 Figure A3. 1. Species with known distribution in the Indo-West Pacific ecoregion part I...... 212 Figure A3. 2. Species with known distribution in the Indo-West Pacific ecoregion part II...... 213 Figure A3. 3. Species with known distribution in the Arafura-Timor Sea and northen Australia ecoregion. . 214

List of Tables Table 1. Research needs for effective management and conservation of elasmobranchs (Simpfendorfer et al. 2011)...... 17 Table 2. Specimen collection details for all sequences obtained in this study ...... 44 Table 3. Identification of sampled rays using GenBank and BOLD databases...... 47 Table 4. Means and ranges of K2P distance values (%) for the COI gene region at different taxonomic levels for the ray species analysed in this study...... 54 Table 5. Location (in decimal degrees), habitat and detections of the receivers deployed in the Mangrove Bay array. Temperature data are from loggers deployed with some receivers...... 76 Table 6. Details of all tagged rays and detection data. G. typus measurements are total lengths. LD= last detection, TD= total detections, DD= detected days, #RD= number of receivers detecting...... 78 Table 7. Ranked generalized linear mixed effects models of log transformed number of detections per month explained by species, month, month squared and random effect individual ray (id). Degrees of

freedom (df), Akaike’s information criterion corrected for small samples (AICc), change in AICc relative

to the top ranked model (ΔAICc) and the AIC weights (wAICc)...... 80 Table 8. Details of all tagged rays and detection data. Total length in cm (*); rays tagged at Stanley Pool (**); discw width in cm (DW); monitored days (MD); monitored months (MM); detected days (DD); reeivers detecting (RD); primary site (PS); % of days at primary site (%DPS); adjacent site (AS); % of days at adjacent site (%AS)...... 103 Table 9. Summarized criteria to define ecosystem-based management (Arkema, Abramson & Dewsbury 2006) ...... 126 Table 10. Species included in this assessment and their known distributions...... 132

xvii

Table 11. Cut-off scores for sensitivity and rigidity categories...... 136 Table 12. Vulnerability assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion. M= Medium (green); H High (red)...... 139 Table 13. Vulnerability assessment of batoids in Indo- West Pacific ecoregion. M= Medium (green); H High (red)...... 140 Table 14. Research priorities assessment of batoids in the Arafura-Timor Sea & northern Australia. N. nin= N. ningalooensis;. Information was on previous name of: 1- M. birostris, 2- A. narinari, 3- P. sephen. (Categories: Fisheries & Spatial ecology). L= Low(clear); M= Medium (green); H High (red)...... 142 Table 15. Research priorities assessment of batoids in the Arafura-Timor Sea & northern Australia. N. nin= N. ningalooensis;. Information was on previous name of: 1- M. birostris, 2- A. narinari, 3- P. sephen. (Categories: Biology & Genetics). L= Low(clear); M= Medium (green); H High (red)...... 143 Table 16. High priority research categories for batoid species assessed in the ATSNA ecoregion. N. nin= N. ningalooensis...... 144 Table 17. Research priorities assessment of batoids in the Indo- West Pacific ecoregion. Information was on previous name of: 1- M. birostris, 2- A. narinari. (Categories: Fisheries & Spatial ecology). L= Low(clear); M= Medium (green); H High (red)...... 145 Table 18. Research priorities assessment of batoids in the Indo- West Pacific ecoregion. Information was on previous name of: 1- M. birostris, 2- A. narinari. (Categories: Biology & Genetics). L= Low(clear); M= Medium (green); H High (red)...... 146 Table 19. High priority research categories for species assessed in the IWP ecoregion...... 147 Table 20. Research priorities for species most vulnerable to the risk of fisheries and habitat degradation in the ATSNA ecoregion...... 148 Table 21. Research priorities for the species most vulnerable to to the risk of fisheries and habitat degradation in the IWP ecoregion...... 149 Table A1. 1. Online sequences used in this study with their GenBank accession numbers...... 206 Table A4. 1. Vulnerability assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion (Categories: Exposure & Sensitivity). L= Low; M= Medium; H=High ...... 216 Table A4. 2. Vulnerability assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion (Category: Rigidity). L= Low(clear); M= Medium (green); H High (red)...... 217 Table A4. 3. Summary of vulnerability of batoids in the Arafura-Timor Sea & northern Australia ecoregion. L= low (clear); M= Medium (green); H= High (red)...... 218 Table A4. 4. Integration of the vulnerability of batoids in the Arafura-Timor Sea & northern Australia ecoregion. L= low (clear); M= Medium (green); H= High (red)...... 219 Table A4. 5. Vulnerability assessment of batoids in the Indo-West Pacific ecoregion (Categories: Exposure & Sensitivity)...... 220 Table A4. 6. Vulnerability assessed from the vulnerability assessment of batoids in the Indo-West Pacific ecoregion (Category: Rigidity)...... 221

xviii Table A4. 7. Summary of the vulnerability of batoids in the Indo-West Pacific ecoregion...... 222 Table A4. 8. Integrative matrix for vulnerability of batoids in the Indo- West Pacific ecoregion...... 223 Table A5. 1. Research priorities assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion (Category: Fisheries). N. nin= N. ningalooensis. Information on 1= M. birostris, 2= A. narinari, 3= P. sephen...... 225 Table A5. 2. Research priorities assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion (Category: Spatial Ecology). N. nin= N. ningalooensis. Information on 1= M. birostris, 2= A. narinari, 3= P. sephen...... 226 Table A5. 3. Research priorities assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion (Category: Biology). N. nin= N. ningalooensis. Information on 1= M. birostris, 2= A. narinari, 3= P. sephen...... 227 Table A5. 4. Research priorities assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion (Category: Genetics) N. nin= N. ningalooensis. Information on 1= M. birostris, 2= A. narinari, 3= P. sephen...... 228 Table A5. 5. Research priorities assessment of batoids in the Indo- West Pacific ecoregion (Category: Fisheries). Information on 1= Manta birostris, 2= A. narinari...... 229 Table A5. 6. Research priorities assessment of batoids in the Indo- West Pacific ecoregion (Category: Spatial ecology) Information on 1= Manta birostris, 2= A. narinari...... 230 Table A5. 7. Research priorities assessment of batoids in the Indo- West Pacific ecoregion (Category: Biology). Information on 1= Manta birostris, 2= A. narinari...... 231 Table A5. 8. Research priorities assessment of batoids in the Indo- West Pacific ecoregion (Category: Genetics). Information on 1= Manta birostris, 2= A. narinari...... 232

xix Chapter 1

General introduction Chapter 1

General introduction Chapter 1. General introduction

Chapter 1- General introduction

The biological diversity of the planet is rapidly declining as direct and indirect consequences of human activities. Humans have affected the land, coasts and the ocean, transforming ecosystems through habitat degradation and over- exploitation of species leading to the decline and loss of many taxa, including large vertebrates (Baum et al. 2003; Frankham, Ballou & Briscoe 2002). In the past half century, many species of large marine vertebrates have been subject to intense exploitation and over-fishing and the oceans are experiencing swift and potentially permanent biological changes shown by the alarming declines of marine fishes (Baum et al. 2003; Hutchings & Reynolds 2004). Additionally, exploitation, pollution, and habitat destruction are considered direct causes of changes in marine biodiversity (Lotze et al. 2006; Worm et al. 2005). Many recent reviews have shown that increasing fishing pressure on fish stocks, particularly sharks, over the last decades have resulted in significant declines in their populations around the globe. For example, large predatory fishes have shown a rapid decline during the last 40 years, with an estimated 90% reduction in the biomass compared to pre-industrial levels of fishing (Worm & Myers 2003), and there is a concern that predator declines can generate unforseen ecosystem effects (Dulvy, Freckleton & Polunin 2004). Hutchings (2000) found that 90 marine fish stocks (representing 38 species from 11 families) experienced 15-year declines of 13 to 99%. Of these 90 stocks, 37 (41%) continued to decline after the 15-year period, 46 (51%) exhibited some recovery, and only seven (8%) had fully recovered. Similarly, Dulvy et al. (2003) concluded that the failure to predict the susceptibility of fish stocks to collapse from over-fishing over the last two centuries has resulted in the loss of more than 55 marine fishes from parts of their current geographical ranges and the complete extinction of one species. Using global catch and mortality data sets, Worm et al. (2013) showed that the average exploitation for sharks exceeds the average rebound rates of many species estimated from life history information from 62 species. In the current region of research interest, the Indo-West Pacific, there are also reported declines in shark and ray species, and concerns about their conservation have led to management plans, at least in the developed nations of the area, Australia

3 Chapter 1. General introduction and New Zealand (Blaber et al. 2009; White & Kyne 2010). Although caution must be given when interpreting particular studies, as results may be variable due to differing levels of fishing, ocean conditions, and life history of each species (Burgess et al. 2005)- it is now widely accepted that the overall condition of global sharks stocks is concerning. Similarly, there is an equal concern that such significant global predator declines can generate unforseen and widespread ecosystem effects (Dulvy, Freckleton & Polunin 2004). Elasmobranchs (sharks and rays or batoids) are ecologically important components of virtually every marine system except the deepest oceans. As they occupy the top trophic levels they are more vulnerable to extinction, and declines in their numbers can also have cascading ecological consequences within ecosystems (Heithaus et al. 2008). Byrnes et al. (2007) found that the top two trophic levels (predators and secondary consumers) contained 70% of the 133 documented global and regional marine extinctions. Similarly Baum et al. (2003) found that large sharks and other predatory marine fishes have declined more than 75% in the northwest Atlantic since 1986. Due to the international concern surrounding the status of elasmobranchs (particularly in the last two decades), several measures have been globally taken. One of them is the International Plan of Action for the Conservation and Management of Sharks (IPOA-Sharks), whose principal objective is to ensure that the harvest of sharks should be biologically sustainable, economically rational, using all part of the animal and managed to maintain biodiversity and healthy ecosystems (FAO 2000). In addition, the International Union for Conservation of Nature (IUCN) Red List of Threatened Species (www.iucnredlist.org) provides the most comprehensive information source on the conservation status of wild species, such as elasmobranchs. The IUCN aim is to express the urgency and scale of conservation problems to the public and policy makers. Since early 2000, the IUCN Species Survival Commission’s Shark Specialist Group (SSG) has been committed to assessing the conservation status of all chondrichthyan species (Class Chondrichthyes) for inclusion on the IUCN Red List. This Red List hast been vital in highlighting the global status of elasmobranch species (Subclass Elasmobranchii) (Berkes 2012).

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Despite severe range reductions in some species, no sharks or rays are known to have become extinct (Dulvy & Forrest 2009). At present 68 species are currently listed as Critically Endangered or Endangered on the IUCN Red List. Although attention and regulatory protection has largely been directed at sharks, rays are also highly vulnerable to over-fishing as they share similar life-history traits and are fished throughout the world (Bizzarro et al. 2007). For example, over-fishing has led to a decline of 72% of skates and rays in the Northeast Atlantic over the past 30 years (Chevolot et al. 2007). Approximately 52% of chondrichthyan are in the Red List compared to only an 8% of teleost, however, the categories of teleost are more frequently found to be of higher risk (Field et al. 2009b). Despite the great advances in the understanding of the biology and ecology of sharks and rays in recent decades, much research is still needed to fill knowledge gaps for appropriate management and conservation of these species. Only when managers and policy makers have access to the best possible scientific information can they make the most effective decisions to guide the conservation of these important and iconic species and their populations.

1.1 What are Batoids?

1.1.1 Taxonomy and phylogeny

The class Chondrichthyes is an ancient group divided into two major subclasses, the Holocephalani (chimeras) and the Elasmobranch (sharks and rays). They represent one of the oldest lineage of vertebrates (Arnason, Gullber & Janke 2001; Rasmussen & Arnason 1999) with a fossil record that extends as far back as the Late Ordovician period (Daeschler 2005; Grogan & Lund 2004). By the middle Devonian, chondrichthyans were represented by a number of different forms that inhabited environments ranging from fresh water to continental margins and oceans (Ivanov & Rodina 2002). Elasmobranchs represent the most successful and diverse group of chondrichthyans encompassing all of the extant sharks and batoids (rays and skates), in addition to several extinct clades. Morphological studies suggested that batoids are derived sharks (e.g. Compagno, Didier & Burgess 2005; De Carvalho 1996; Shirai 1992), but more recent molecular work suggest that Batoidea is a sister group to a

5 Chapter 1. General introduction clade consisting of all shark orders and comprises more than half of chondrichthyan species diversity (≈ 630 of ≈ 1170 species) (Douady et al. 2003; Winchell, Martin & Mallatt 2004) Although monophyly of batoids is now widely accepted, the phylogenetic relationships among groups within batoids are not yet well established and remain controversial (Naylor et al. 2005). Current classification schemes are provisional and await a well-supported phylogeny, but five major groups are typically recognized: electric rays (Torpediniformes), skates (Rajiformes), guitar fishes (Rhinobatiformes), sawfishes (Pristiformes), and stingrays (Myliobatiformes) (McEachran & Aschliman 2004; Rocco et al. 2007). Batoids differs from sharks in having ventral gills and pectoral fins fused to the sides of the head forming a wedge-shaped, circular, oval or rhomboidal disc delimited from the tail. Eyes and well developed spiracles are generally on the dorsal surface or lateral in some pelagic species. Skates and stingrays have a reduced caudal fin and their swimming is due to a sine-wave undulation or dorso-ventral flapping of the disc. Other batoids such as sawfishes and shovelnose rays have a well-developed tail and caudal fin used for swimming through lateral undulations (Compagno 1999b; Schaefer & Summers 2005; Summers 2000).

1.1.2 Distribution and habitat

Batoids are widely distributed in all oceans, from the tropics to the Arctic and Antarctic waters. They have adapted to a wide range of habitats and are commonly found in shallow estuarine coastal and shelf regions, although they are also found in rivers and lakes and the abyssal plains in depths exceeding 3000 m (Kyne & Simpfendorfer 2010; Last & Stevens 1994; McEachran & Aschliman 2004). Worldwide there are 20 families, 65 genera and approximately 630-700 species though many species are poorly known and require taxonomic study (Rocco et al. 2007; Winchell, Martin & Mallatt 2004). Batoids are generally marine but some stingrays live only in fresh water (Potamotrygonidae), many species can tolerate fluctuating salinities in estuaries and shallow bays, and some sawfishes (Pristiformes) are euryhaline and readily enter fresh water to breed (Oldfield 2005; Speers-Roesch, Ip & Ballantyne 2006; Tam et al. 2003; Whitty et al. 2009). Indo- Australia has a high diversity, comprising of a quarter of the global total of batoids (171 species) (White & Kyne 2010). Australia has a high diversity and endemism of 6 Chapter 1. General introduction chondrichthyan species, with 73% of batoids being endemic to Australian waters (Stevens, Simpfendorfer & Francis 2005). Although the importance of the northern Australian ecosystems for some species of batoids such as the sawfish (Australian Government 1999, 2008; Peverell 2005) has been identified, the rest of species of this group are largely understudied in this area.

1.1.3 Biological characteristics

1.1.3.1. Age and growth Of those batoids whose age and growth have been estimated and verified, most are long-lived and very slow to reach maturity. For example, longevities for temperate water species of batoids such as skates are as high as 15 to 50 years (Ainsley, Ebert & Cailliet 2011; Frisk 2010; Gburski, Gaichas & Kimura 2007; Kyne & Simpfendorfer 2010), and some species may take as long as 13 years to reach maturity (Coelho & Erzini 2006). However there are some exceptions to this common rule, such as the fan ray (Platyrhina sinensis) in Japan which seems to be a relatively fast-growing species with and estimated maximum ages of 5-12 and maturity ages of 2-3 years old (Kume, Furumitsu & Yamaguchi 2008). Stingarees (Urolophidae) in Australian waters attain ages of at least 11-14 years with age at maturity of 3-5 years (White, Platell & Potter 2001; White & Potter 2005). Eagle rays (Myliobatis californica) are reported to live up to 24 years with reproduction first occurring at 2.5 years (Martin & Caillet 1988). Pacific electric ray (Torpedo californica) lives at least 16 years, with males maturing at six years, and females at nine years (Neer & Cailliet 2001). Eastern Pacific guitar fishes (Zapterix exasperata) live to about 11 years with an age of maturity of eight years (Downton- Hoffman 2007; Villavicencio-Garayzar 1995). Sawfishes (Pristis pectinata and P. perotteti) have longevity ranging from 30 to 44 years (Simpfendorfer 2000). Although little information is available for the family Dasyatidae, their longevity has been reported between 3-28 years and an estimated maturity age of 3-8 years (Henningsen 2002; Ismen 2003; Mollet, Ezcurra & O’Sullivan 2002), where females generally attain maturity later and at larger sizes. For example the blue- spotted mask ray (Neotrygon kuhlii) has a maximum age reported of 10-13 yrs, and the age at maturity ranged from 3-4 in males and 6-8 yrs in females (Pierce, Pardo & Bennett 2009), although Jacobsen and Bennett (2010) reported a maximum age of

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15-17 yrs. Jacobsen and Bennett (2010) also reported on two other species of the genus Neotrygon, N. picta with a maximum age of 11-18 yrs and N. annotata with a maximum age of 9-13 yrs. Larger rays such the brown stingray (Dasyatis lata) shows a longevity of 25-28 years, attaining maturity at 8.3 years old in males and 15 years old in females (Dale & Holland 2012); the black-spotted whipray (Himantura astra) ranged from 18 to 29 yrs (Jacobsen & Bennett 2011); Dasyatis pastinaca had a maximum age of 8-12 (Yeldan, Avsar & Manaşırlı 2009).

1.1.3.2. Reproduction In batoids, reproduction is always internal; males have cylindrical copulatory organs or claspers which are modifications of the pelvic fins to facilitate internal fertilization. Well-developed young are produced requiring considerable maternal input and modes of reproduction are classified depending on the kind and extent of this input. For example, some batoids such as skates are egg layers, while others like stingrays are live bearers with varied modes of reproduction (Carrier, Pratt & Marint 1994; Dulvy & Reynolds 1997; Hamlett 1997). The life-histories of batoids are more similar to marine mammals and marine turtles than to those of teleosts, and as a group, they have lower fecundity and have a more delayed maturity than teleosts (Frisk 2010). The information available shows a wide range of productivity in batoids, although species-specific information of batoids reproductive biology is still scarce. Many species are slow growing with low productivity while a few species, particularly the smaller ones, may not be as extreme in their life-histories as the larger K-selected species. For example, skates are thought to be more productive with an average of 58.9 eggs/year compared to myliobatiforms that have an average of 5.5 pups/year (Musick & Ellis 2005). Dasyatids have litters of 1-13 (Musick & Ellis 2005). For example: the Atlantic stingray Dasyatis sabina has a litter size of 1-3 embryos (Johnson & Snelson Jr 1996); the golden cownose rays (Rhinoptera steindachneri) has a litter size of 1 (Bizzarro et al. 2007); the blue stingray D. chrysonota of 1-7 embryos (Ebert & Cowley 2009); devil rays from genus Mobula (Myliobatiformes) have 1-2 embryos (Notarbartolo di Sciara 1988) and blue-spotted mask ray (N. kuhlii) in captivity have 1 embryo (Janse & Schrama 2010). In contrast, the eastern-Pacific guitarfish Rhinobatos productus (Rhinobatiformes) has a litter size of 4-18 (Downton-Hoffman 2007) and the Japanese guitarfish (R. hynnicephalus) has a litter size of 1-9 embryos

8 Chapter 1. General introduction per litter (Kume et al. 2009) but this species has a long inter-birth interval (‘resting’ years between pregnancies). Gestation rates are unknown for most batoids but are long compared to teleosts (Cailliet et al. 2005). For example stingarees may take 10-12 months to gestate (White & Potter 2005); the southern stingray D. americana has a gestation period of 4-7 months (Henningsen & McEachran 2000); N. kuhlii has an average gestation period of 4.8 (Janse & Schrama 2010; Pierce, Pardo & Bennett 2009); D. chrysonata has a gestation period of 9 months (Ebert & Cowley 2009); D. marianae and D. guttata for 5-6 months (Yokota & Lessa 2007), while the guitar fish has a gestation period of 9-14 months (Kume et al. 2009) (Downton-Hoffman 2007).

1.1.3.3. Feeding The feeding habits of rays range from planktonic filters (Dewar et al. 2008; Notarbartolo di Sciara 1988) to benthic feeders preying on invertebrates, bony fishes, and cephalopods (Collins et al. 2007; Dale et al. 2011; Last & Stevens 2009; Romanelli et al. 2006). No ray species have developed cutting teeth like sharks but some have specialized structures like the torpedo skates that have electric organs that stun large fishes, or sawfishes that kill small fishes with their rostral saws (Compagno 1999a; Macesic & Kajiura 2009). Last and Stevens (2009) reported general diets of the Indo-Australian species; particularly, different proportions of annelids, crustaceans, mollusks and nematodes were found to comprise the diets of coastal batoids from the family Dasyatidae in North-Western Australia (O'Shea unpublished data, Vaudo & Heithaus 2011).

1.1.4 Ecological role and significance

Elasmobranchs can play key roles in marine communities and ecosystem by influencing the species diversity and population sizes of their prey (Cortés 1999; Heithaus et al. 2008). Several studies examining the effects of shark removal concluded that ‘top-down’ effects must be widely expected whenever entire functional groups of predators are reduced (Myers et al. 2007; Navia 2010; Stevens et al. 2000). Although studies of the effects of batoid removal are limited, similar effects can be expected as they occupy trophic levels comparable to small or medium size sharks (3rd or 4th order consumers) and are likely to have a significant top-down effects in their communities (Dulvy et al. 2000; Martin 2005; Myers et al. 2007).

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Ecologically, batoids can significantly impact the benthic environment and often create extensive disturbance of the sediment that may affect benthic community structure (Hines et al. 1997; O'Shea et al. 2012; Thrush et al. 1994). Sand pits associated with rays’ feeding activities have been implicated in damaging commercial shellfish industries and seagrass habitat destruction (Cross & Curran 2000; Peterson et al. 2001). They also have effects on benthic meiofaunal communities in intertidal and subtidal sediments. Over time, ray pits lead to a succession in the communities creating mosaics of infaunal communities throughout the benthic habitat in different stages of colonization (Collins, Heupel & Motta 2007; Cross & Curran 2000; VanBlaricom 1982).

1.2 Batoid threats and conservation

1.2.1 Exploitation

Elasmobanch fisheries have likely expanded and intensified because this group provide a versatile fisheries resource (Dulvy et al. 2008; García, Lucifora & Myers 2008) and also, because other important marine resources, such as invertebrates, have been overfished. Consumption of elasmobranch meat has been recorded as early as the fourth century (Clarke et al. 2005). Bonfil (1997b) classifies the commercial use of sharks and rays as: a) traditional uses, where they are used for consumption, traditional medicine, and manufacture of weapons and tools; b) modern uses, like the manufacture of leather, abrasives, anticoagulants and other medial applications, production of vitamin A, paints, cosmetics and other products derived from squalene; c) novel uses such as the cartilage pill, which is claimed to “cure of cancer” (see Clarke et al. 2005 for a comprehensive review of the socio- economic significance of Chondrichthyan fish). A recent global assessment reported 830,000 tonnes of sharks and rays landed annually with an increase of landings at an approximate rate of 2% annually (Camhi et al. 2009) and globally, three-quarters (16 of 21) of oceanic pelagic sharks and rays have an elevated risk of extinction due to overfishing (Dulvy et al. 2008). Many of these species are also taken regularly as by-catch in mid-water trawl fisheries, longline, purse seine, and gillnet fisheries targeting tuna and billfishes. Several studies report an increase in global landings in the last 30 years and until 2000 it was about 760,000 tonnes per year. However, the total global catch is probably nearer to

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1.5 million tonnes, due to a large unreported by-catch (see reviews by Baum et al. 2003; Bonfil 1997b; Dulvy et al. 2008; Stevens et al. 2000; Walker 1998). Although these numbers were based primarily on reported catches and may still be underestimates considering the shark fin trade. Up to the 1990s, the distribution of catches among world economies indicates that developing countries produce about two thirds of the total elasmobranch world catch though the lack of effort or success in sustainably managing these fisheries seems to be a general pattern among all fishing nations (Bonfil 1997b; Lucifora, García & Worm 2011). Particularly, illegal, unreported, and unregulated (IUU) fishing is intensified in tropical developing nations where there is a rich marine biodiversity and fisheries provide a substantial source of protein for local communities. However, the lack of resources, capacity and inadequate infrastructure prevent the undertaking of relevant research to formulate and implement effective conservation plans (Ban et al. 2009; Field et al. 2009a; White & Kyne 2010). Although more scientific and regulatory attention has been dedicated to sharks, batoids are also extremely vulnerable to fishing pressure and are taken in artisanal fisheries, by inshore commercial fisheries, and by large fishing fleets in offshore waters. Data on the range of gear used in the capture of batoids is scant, but pelagic longlines, fixed and floating gill nets, bottom trawls, fixed fish traps, and purse seines are used either to target batoids or to take them as a by-catch (Bizzarro et al. 2007; Compagno 1999a). The most striking depletions within this group have occurred on rajids (skates) in the North Atlantic. Skates are the most important targeted elasmobranch in this area (Ellis et al. 2005) and this is one of the longest established fisheries in the northeast Atlantic (Gallagher, Jeal & Nolan 2005). These stocks have experienced localized extinctions in the northern seas of Europe creating an urgent need for management plans. In Alaskan waters, skate landings greatly exceed those of all the states of the USA combined (Matta & Gunderson 2007); the incidental capture of Alaskan skates increased in recent years creating a new targeted fisheries with little regulation (Ebert, Smith & Cailliet 2008). Many other species of batoids have suffered widespread exploitation, such as oceanic rays, however, catches have been poorly reported. Although this has improved over the last decade in some countries such as Australia, New Zealand and

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USA, generally there are very few effective domestic or international regulations for reporting ray catch and by-catch and even when catches are reported, they are usually not recorded to species level (Dulvy et al. 2000). Sawfishes (Pristidae) have also undergone remarkable global declines in their numbers and ranges; consequently, all sawfishes species are classified as Critically Endangered worldwide by the International Union for the Conservation of Nature (IUCN) (Compagno, Cook & Fowler 2006; Simpfendorfer 2000; Stevens et al. 2000). Batoids are a primary component of artisanal fisheries in Mexico. This fishery is among the largest in the world, although no quotas, seasonal closures or gear restrictions are established (Bizzarro et al. 2007; Bonfil 1997a; Stevens et al. 2000). Batoids also have a high biomass as by-catch in the trawl fishery of Argentina (Tamini et al. 2006) and freshwater stingrays are facing intense fishing pressure throughout their range in South American river systems as several species are targeted for ornamental purposes and as a food source (Oldfield 2005). Batoids comprise a significant part of the elasmobranch landings in most countries of the Indo-Australian region. For example, the landings of mobulid rays have increased in the last decade in Indonesia due to the increasing demand of branchial filters for traditional Chinese medicine in Asia (White, Giles, et al. 2006). As a result, the annual catch of was estimated to be ∼1,500 units (Dewar 2002), which is similar to the estimated number of mobulid rays landed as by-catch annually. While the landings of elasmobranchs in Australia and New Zealand are lower than other counties, the shark fisheries are among the most researched, managed, and documented in the world (Stevens, Simpfendorfer & Francis 2005). Graham et al. (2001) reported that the biomass of most species of sharks and batoids declined rapidly as the trawl fishery developed in the south east Australian region and is now at very low levels. Although the authors include rays and skates in their study, the species-specific information is focused on sharks. Field et al. (2009a) reported the major role in exploitation that the illegal, unreported, and unregulated (IUU) fishing plays in the Australasian region, and particularly in northern Australia as fishing is displaced towards healthier ecosystems, again this study focuses on sharks and large bony fishes. Batoids are not targeted in Australia’s biggest fisheries but are commonly caught as by-catch. For example, Kyne et al. (2002) reported that

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Dasyatidae is one of the families with most species recorded from by-catch in the eastern trawl fisheries. Similarly, the northern prawn fishery reported about 50 species of elasmobranchs and has recently implemented measures attempting to reduce the by-catch, particularly of batoids (Australian Government & Australian Fisheries Mangement Authority 2009). In spite of the intensive regulation and management on sharks in this region, the biology and ecology of batoids remains poorly known and the catch unregulated. Furthermore, batoids have an important economic and cultural role in Indigenous Australian communities, particularly in the northern coastal region (National Oceans Office 2008). Stingrays are significant to coastal communities as food source, as seasonal indicators, and for cultural and spiritual significances such as totems, and they are present in cave paintings (Layton 1992) and dietary studies (Gracey 2000). Although these practices are common, there are very few records of this fishery, its characteristics, cultural value and impacts are unknown. Henry and Lyle (2003), reported that indigenous fishing in the category of ‘sharks/rays’ is most intensive in the Northern Territory compared with the rest of Australia. They report that lines and spears are the most frequently used fishing gear to catch members of this group, but nets and traps are also used. The records in the category of ‘sharks/rays’ includes several families of rays, such as Rhinobatidae and Dasyatidae but it only identifies sharks to species level. Unfortunately, the lack of information about batoid populations makes it very difficult to determine how the level of regional exploitation affects stocks status. Significantly, species-specific information of by-catch and its effect on batoid populations is still lacking in the Indo-Australian region.

1.2.2 Habitat loss and degradation

Although the major factor held responsible for the decline of batoids populations is over-fishing, other human-related factors should not be overlooked. Habitat degradation and pollution can either reduce or affect fecundity, dispersal, and foraging ecology because many species use particular types of habitats both for particular life history stages (juveniles) and activities (breeding, shelter) (Field et al. 2009b; Musick et al. 2000a; Walker 1998). Habitat modification by bottom-deployed fishing gear can negatively affect demersal species by altering species composition,

13 Chapter 1. General introduction structural complexity, trophic structure, size and productivity of benthic communities (Dulvy, Sadovy & Reynolds 2003; Hutchings & Reynolds 2004). The effects of habitat degradation on fish populations are generally more difficult to study and historically have been less intensively studied than the effects of fishing (Walker 1998). However, the continuous loss or alteration of vital habitats could increase the extinction risk of certain species in addition to direct threats posed by over-harvesting (Kinney & Simpfendorfer 2009). Further, species that have highly specialized life histories and restricted spatial or environmental ranges will face higher risks of depletion with habitat changes (Field et al. 2009b). An example of this is the regional disappearance of all species of sawfish (Pristidae) in Thailand due to the modification of freshwater and coastal habitats by mining and mangrove deforestation and pollution due to the increased human population and industry in the vicinity of rivers (Vidthayanon, 1997). Similarly, the estuary stingray (Dasyatis fluviorum) has also suffered range contraction along the New South Wales coast likely due to the degradation of estuaries and coastal environments in the Australian coastline (Pierce & Bennett 2009).

1.2.3 Current conservation status and research priorities

The life-history characteristics of batoids have evolved over millions of years and remain a successful strategy in an environment where they frequently occupy high trophic levels. These species need only to produce a few slow-growing young, capable of reaching maturity to maintain a stable population under natural conditions. However, these traits have serious implications when batoids are overfished. Unlike most teleosts, batoids have difficulty compensating to this increased mortality and are therefore, more vulnerable to catastrophic population declines and extinction (Cailliet et al. 2005; Dulvy & Reynolds 2002; Musick et al. 2000b). Batoid life-history characteristics result in very low intrinsic rates of increase and very low resilience to fishing mortality compared to teleosts (Field et al. 2009b; Stevens et al. 2000). For example, Myers and Worm (2005) found that sharks showed twice the fishing extinction risk of teleosts (2005). This slow rate of recovery is exacerbated for species that have specific habitat requirements and localized distributions. The degradation of key habitats will affect species reliant on those

14 Chapter 1. General introduction habitats and the species with limited ranges may have little immigration from non- exploited populations (Field et al. 2009b; Walker 1998). The decline in batoid populations has been documented for a number of species over the last 20 years and international concerns about these declines have arisen due to the global continuous and poorly managed fisheries, as previously described (Bonfil 1997b; Dulvy et al. 2008; Quero 1998). Indeed, batoids appear to be among the most susceptible marine taxa to fisheries exploitation (Dulvy and Reynolds 2002; Dulvy et al. 2008). Among batoids, large body size, associated with increased age at maturity, longevity, and size at maturation, appears to correlate with a higher risk of overexploitation and in some cases local extirpations (Dulvy & Reynolds 2002). The remote and sparsely populated northern waters of Australia is a region of global significance for marine biodiversity, including elasmobranchs (National Oceans Office 2008). The region also, shares a marine boundary with one of the most threatened marine regions and ecosystems in the world (Indonesia) (Field et al. 2009a). Despite this, elasmobranchs, particularly batoids, remain a data-poor group in this region. The International Union for Conservation of Nature (IUCN) was created in 1956 to evaluate species status using five criteria; species are categorized as critically endangered, endangered or vulnerable (http://www.iucn.org/). A considerable effort has been invested on assessing the status of elasmobranchs in the Indo-Australian region. As a result, 77.2% of batoids have been assessed according to the IUCN red List of Threatened species and the proportion of species classified as ‘threatened’ and of ‘conservation concern’ is higher in batoids than sharks (White & Kyne 2010). However, the information on batoids and their fishery within this geographic region and other regions is still lacking, particularly, in northern Australian which is still a relatively pristine ecosystem compared with neighboring areas. Effective management and conservation of wildlife must rely on solid and reliable scientific information. Unfortunately, the lack of consistent fisheries records and species-specific information is a major impediment to the appropriate management of batoids and needs to be urgently addressed world-wide (Dulvy et al. 2000; Góes de Araújo et al. 2004; Hunter et al. 2006; Powles et al. 2000). Appropriate scientific information should include a range of critical topics such as

15 Chapter 1. General introduction the knowledge of distribution, age, growth and demography, genetic diversity, reproduction systems, foraging ecology, movements and migration cycles, habitat use and the threatening processes affecting a species (Karlsson et al. 2005; White & Kyne 2010). Furthermore, with the rapid degradation of natural habitats and major declines in some populations of batoids, an ecosystem-based management regime that identifies the spatial scales of population connectivity over relevant time scales is required (Cowen, Paris & Srinivasan 2006). Field et al. (2009b) concluded that the extinction risk can be estimated and reduced only through a comprehensive compilation of data describing demographic rates, habitat affinities, trophic linkages, geographic ranges, and the role of environmental stressors in modifying these factors. The list of research fields that would aid in the conservation and management of batoids is extensive and has a broad range of topics. Some biological questions have been addressed using the available ray body parts coming from fisheries landings (e.g. Henderson, Arkhipkin & Chtcherbich 2004; Matta & Gunderson 2007; Notarbartolo di Sciara 1988; Smith, Cailliet & Melendez 2007; Sosebee 2004). However, ecological questions such as habitat use, habitat partitioning, migration cycles, and movement patterns are harder to address. In a comprehensive review, Simpfendorfer et al. (2011) presented a summary of research topics and requirements for elasmobranchs, highlighting the questions that needed to be addressed for the development of effective conservation and management. They emphasised the importance of research on spatial ecology, including stock structure, movements and migrations, which will have important implications for the design, evaluation, and implementation of management and conservation strategies at appropriate spatial and temporal scales.

1.3 Multi-disciplinary tools and approaches to elasmobranch management

Elasmobranchs and particularly batoids are generally a data-poor group, particularly in the northern Australian region, and several major topics need to be addressed for management and conservation of their populations. Species identification/delimitation, population/stock structure, or movement patterns (spatial ecology) are some of the major topics needed for appropriate management. Therefore

16 Chapter 1. General introduction we need an ecosystem-based approach that includes several objectives and tools to address and integrate multiple questions.

Table 1. Research needs for effective management and conservation of elasmobranchs (Simpfendorfer et al. 2011).

Topic Research needs Outcomes for conservation Taxonomy Description of new species; Identifies scope for conservation relationships among species management and associated research Life history Production of identification guides, Basis for designing management Reproductive biology; age & growth; strategies and setting sustainable mortality estimation; demography; limits population dynamics Spatial ecology Stock structure, movement and Design and evaluation of migrations; habitat use and preference spatiotemporal management; appropriate scales of conservation management actions Environmental Environ effects on life history, Adaptation strategies for effects behaviour, and spatial ecology; effects species, ecosystems, industries, of rising sea temperatures, effects of communities, and conservation ocean acidification management plans Ecosystem role Diet and trophic structure; ecosystems' Application to ecosystem-based response to elasmobranchs removal; management ecosystem models Fishery status Catch & effort; species, sex, and size Identify threats to populations composition; gear selectivity Population status Ecological risk assessments; Identification of at-risk species; demographic models; stock decision-support tools for assessments, management-strategy resource managers to set evaluations sustainability limits Human Human values, attitudes, beliefs, and Design of conservation dimensions behaviours; economic value of management plans that take elasmobranchs to extractive and non- account of human behaviour, extractive uses; government models optimise economic outcomes for people, industries ,and communities

My thesis is a contribution to the research needs previously stated. Here, I first examine the species identity and composition of batoids in a coral reef environment using genetic tools. I then examine the movement patterns, habitat use, and residency patterns of these animals within this environment. These data are considered in light of current spatial and conservation management strategies for the reef system. And finally I evaluate the vulnerability and further research needs of

17 Chapter 1. General introduction tropical Indo-Pacific species to determine priorities for management and science. The rationale and backgrounds for each of these major parts of the thesis is given below.

1.3.1 Molecular ecology- DNA barcoding

Molecular ecology has recently assumed an increasingly important role in modern conservation biology although there still is a gap between molecular studies and its practical issues in wildlife management (Moritz 1994; Reiss et al. 2009; Ward 2000). The description of the levels of within- and among-populations genetic differentiation can help focus efforts on specific populations needing recovery, on newly-identified cryptic species that have limited ranges and relatively small population sizes, and can also provide guidance in population translocation and reintroductions. This kind of information can lead to more accurate and successful population management (Avise 1998; Haig 1998). One of the first goals of molecular genetics work in fishes was to find stock- specific markers, thus genetic tools have been recognised for their usefulness in defining stock structures (Ferguson 1994; Ward 2000). A stock of marine animals that has a solid genetic base is the most useful information for fisheries management because management policies may not achieve long-term conservation goals without knowledge of the number and distribution of independent interbreeding populations within an exploited species (Ovenden 1990). Furthermore, overexploited fish populations can undergo a loss of genetic diversity and with it the ability for the species to adapt and evolve to new environmental circumstances (Mace & Purvis 2008; Reiss et al. 2009). Other important applications of molecular tools for marine management include species identification. Accurate species identification is a prerequisite for effective fisheries management and biodiversity conservation because species that are morphologically very similar can lead to mistakes in identification that greatly reduces the value of data to managers (Ward 2000). Molecular approaches to species identification have been used for many years starting with allozyme electrophoresis in the 70s, then followed by mitochondrial DNA markers in the 90s (Avise 1975, 1994) and more recently the widespread use of microsatellite markers (e.g. Cassens et al. 2005; Chevolot et al. 2005; Chistiakov, Hellemans & Volckaert 2006; Feldheim, Gruber & Ashley 2004; Keeney et al. 2005). In species identification, molecular tools are valuable to clarify the taxonomy of a particular taxon by 18 Chapter 1. General introduction revealing species complexes or the extent of hybridization between closely related species (Hebert, E.R. Penton, et al. 2004; Ovenden et al. 2010; Quattro et al. 2006; Sandoval-Castillo et al. 2004). Species misidentifications can also potentially compromise the utility and veracity of ecological studies and management plans. Therefore taxonomic clarity, including the accurate delimitation of species boundaries and individuals in the field, is an important conservation need especially for exploited species and can result in re-prioritization of conservation efforts (Bortolus 2008; Moritz 1994; Richards et al. 2009; Vecchione et al. 2000; Ward et al. 2008). Despite over 200 years of taxonomic research, few of the extant batoid species are well characterized morphologically and some groups are continuously being revised and new species described (Lorenz et al. 2005; Manjaji-Matsumoto 2004; White & Kyne 2010). Difficulties arise when trying to identify species morphologically similar species, species at different stages of development, from body parts, or from tissues already processed for food (Ward 2000; Ward, R. Hanner & Hebert 2009). In the particular case of elasmobranchs, Holmes et al. (2009) mentioned that species misidentifications stored in logbooks from commercial fishers and observers is a major problem for the implementation of successful management plans in Australian waters. It is now critical for conservation, fisheries management and research that commercial species of some groups such as elasmobranchs are properly identified (Holmes, Steinke & Ward 2009; Moura et al. 2008) particularly in elasmobranchs that are capable of undertaking large-scale movements (Zemalak et al. 2009). Consequently, efforts need to be invested in the applicability and use of a standardized and robust tool to a) aid in the identification of target species by ecologists and fisheries managers, and b) in the delineation of species boundaries for other types of management such as marine protected areas (Khuroo et al. 2007; Ward et al. 2008). DNA-barcoding potentially offers a powerful tool to support fishery managers and non-taxonomist scientists with an efficient and accurate identification for morphologically conservative or polytypic taxa (Hebert, S Rarnasingham & deWard 2003; Ward et al. 2008). The proponents of DNA barconding (Hebert et al. 2003) propose that a short and standardized sequence of a single gene region can be

19 Chapter 1. General introduction used for a global identification system to distinguish species. A 655 base pair (bp) fragment of the 5’ end of the cytocrome c oxidase subunit I (COI) is used for species identification and its variation within species is low compared with its variation between-species. Thus, species are delineated by a particular sequence or a group of similar sequences: the barcode (Hajibabaei et al. 2007; Hebert, M.Y. Stoeckle, et al. 2004; Ward, R. Hanner & Hebert 2009; Ward et al. 2005). An important advantage of COI DNA-barcoding is the large and rapidly growing data base of barcode sequences: the Barcode of Life Data System (BOLD) (Holmes, Steinke & Ward 2009). The Fish Barcode of Life (FISH-BOL, http://www.fishbol.org) campaign was conceived in 2004 with the goal of barcoding all the world’s fishes and put together a standardized reference sequence library for all fish species. This system (BOLD) is a tool now shared by taxonomy, population genetics and phylogenetics. However, the DNA-barcoding approach has also generated controversy. Most of the critiques are a response to the view that a single gene should be the primary identifier for species as this assumes that each species has a fixed, invariant characteristic and suitably diagnostic for this gene which stands against the idea of a great evolutionary diversity and the dynamic nature of the speciation process (Moritz & Cicero 2004). Consequently, focusing on a single gene can lead to identification errors due to heteroplasmy, hybridization, incipient speciation and incomplete lineage sorting (Funk & Omland 2003; Rubinoff 2006). Another critique argues that most of DNA studies don’t include new sister species, which are likely to share haplotypes and show low interspecific divergence either because of ongoing gene flow or because of recent ancestry (Tautz et al. 2003; Toffoli et al. 2008). Mortiz and Cicero (2004) argue that one of the pitfalls of DNA barcoding is that it has been used for resolving phylogeny issues or the ‘tree of life’, when it should be used to at best, identify an individual to species level or reveal inconsistencies between molecular variation and current species boundaries. They suggest that its application is twofold and should be clearly separated: a) genetic diagnosis of individuals in relation to described taxa and b) the discovery of new species. Lastly, there is another problem associated with mtDNA and the polymerase chain reaction (PCR): the presence of mitochondrial-like DNA sequences in the

20 Chapter 1. General introduction nuclear genome of many organisms and their contamination of actual mtDNA during PCR amplification (Bensasson et al. 2001; Nguyen, Murphy & Austin 2002; Zhang & Hewitt 1996). These nuclear mitochondrial pseudogenes (NUMTs) are likely to get incorporated into the nuclear genome during the repair of chromosomal breaks by recombination. The prevalence of reported NUMTs varies widely among eukaryotes (reviewed in Bensasson et al. 2001), and are abundant in mammals and birds. Bensasson et al. (2001) report that NUMTs are absent in fish, although Antunes and Ramos (2005) not only found them in a high ratio in fish. In spite of these concerns, DNA-barcoding has proven successful for a wide range of both vertebrates and invertebrates. For example neotropical butterflies (Hebert, E.R. Penton, et al. 2004), north American birds (Hebert, M.Y. Stoeckle, et al. 2004), marine fishes (Ward et al. 2005), north American salmons and trouts (Rasmussen, Morrissey & Hebert 2009), elasmobranchs (Ward et al. 2008), primates (Lorenz et al. 2005) and even in biosecurity, for the surveillance of disease vectors (Besansky, Severson & Ferdig 2003). By 2008, FISH-BOL had 5334 fish species barcoded. A particular effort has been invested in DNA-barcoding elasmobranchs fishes from Australia and Indonesia (Ovenden et al. 2010; Ward et al. 2007; Ward, R. Hanner & Hebert 2009; Ward et al. 2005; Wynen et al. 2009) and these efforts should be expanded to other nations with high elasmobranch diversity. In conclusion, COI barcoding can be a useful tool that can potentially aid in the identification of species if a) its short-comings are kept in mind, and b) is used in combination with other methods applied to understand biological diversity such as morphology, ecology, etc (Rubinoff 2006; Tautz et al. 2003; Toffoli et al. 2008).

1.3.2 Spatial ecology- movement patterns.

Once animals have been unambiguously identified to species, it is then necessary to develop knowledge of distribution and abundance patterns for conservation management and sustainable exploitation, particularly the degree to which animals are residents in certain locations and how their movements change over time (Andrews et al. 2007; Jorgensen 2010). The knowledge of movements over smaller spatial scales can help to determine key habitats and the effectiveness of existing or proposed boundaries for protecting the species of interest (Garla, Chapman, Shivji, et al. 2006; Garla, Chapman, Wetherbee, et al. 2006; Heupel & 21 Chapter 1. General introduction

Simpfendorfer 2005). Additionally, identifying important habitats at each stage of a species’ life-history, including those stages when they are more vulnerable to certain stressors is critical for management, particularly for migratory species (Roberts 2000; Roberts & Sargant 2002). The knowledge of movement over larger spatial and temporal scales is also critical information for sustainable exploitation. Particularly in relation to protected areas, this information can help assessing the effectiveness of protective boundaries and spatial management regulations by determining patterns of moments and habitat use (Arnold & Dewar 2001; Chapman et al. 2005; Heupel & Simpfendorfer 2005). Understanding the different behavior between sexes is also important in this group as sharks can show a surprising pattern of female natal site-fidelity and male-based dispersal (Duncan et al. 2006; Feldheim, Gruber & Ashley 2002; Pardini et al. 2001) as well as long migration cycles (Bonfil et al. 2005; Eckert et al. 2002). Several species of rays can reach large sizes and form seasonal aggregations suggesting they are capable of both site fidelity and large-scale movement, yet movement patterns have been little studied in rays. Large scale movements, annual migration cycles and repeated seasonal migrations were found in the thornback ray (Raja clavata) in British and north European waters over several years of studies (Chevolot et al. 2006; Chevolot et al. 2005; Holland et al. 2001; Hunter, A. A.Buckley, et al. 2005; Whittamore & McCarthy 2005). In contrast, a surprising pattern of local residency has been observed in other species of rays such as the coastal manta rays (Manta alfredi) (Couturier et al. 2011; Dewar et al. 2008), cownose rays (Rhinoptera bonasus) (Collins, Heupel & Motta 2007) and the short- tailed stingray (Dasyatis brevicaudata) (Le Port, Sippel & Montgomery 2008), though seasonal movements either to deeper waters or over a scale of a few 100 km are also common in these species. Knowledge of distribution and movements associated with environmental factors can help the seasonal management of protected areas and interactions with humans (Braun-McNeill & Epperly 2002). Salinity, temperature, and depth have been proposed as important environmental factors in determining the distribution and abundance of elasmobranchs, furthermore size and sex could show a difference on spatial distribution relating to these factors (Vögler, Milessi & Quiñones 2008; Whitty et al. 2009). Although tidal flow has been proposed to strongly influence the

22 Chapter 1. General introduction movement of flat-bodied elasmobranchs such as batoids (Ackerman et al. 2000; Medved & Marshall 1983), temperature is possibly the most important environmental factor affecting this group (Fangue, Bennett & Douglas 2003). Effects of temperature on migrations, feeding and parturition are prevalent in the literature (Bruce, Stevens & Malcolm 2006; Heupel & Heuter 2001a; Sims 2003). For example, bat rays (Myliobatis californica) show diel patterns mostly related to thermoregulation because they maximize efficiency by feeding in warm water, then moving to cooler temperatures to promote nutrient absorption (Hopkins & Cech 1994; Matern, Cech & Hopkins 2000). Hunter et al. (2005; 2005) report migration patterns of the thornback ray and suggests they return to shallow and warmer areas for reproduction and spawning in the Thames Estuary. Wallman and Bennet (2006) found that temperature responses differed between genders in the Atlantic stingray (Dasyatis sabina). Fangue et al. (2003) also proposed that the use of habitats with elevated temperatures is common in pregnant Atlantic stingrays (D. sabina). Aggregations of female leopard sharks (Triakis semifasciata) (Hight & Lowe 2007) and grey reef sharks (Carcharhinus amblyrhynchos) (Economakis & Lobel 1998) are thought to migrate to warmer waters to increase embryo development rates and shorten gestation periods. Females of these species can show different preferences in response to reproductive states, where gravid females preferred warmer temperature than non-gravid females. These studies show the interesting influence that environmental factors can have on movement patterns of elasmobranchs, thus information about these potential correlations, will also aid in the creation of more appropriate management plans for this group (Andrews et al. 2007; Heupel, J.M. Semmens & Hobday 2006).

1.3.2.1. Passive acoustic monitoring, the Ningaloo Reef Ecosysem Tracking Array, and study sites Recent developments in tagging and tracking technology have revolutionized the description and investigation of movement patterns and behavior of marine animals, including elasmobranchs. The use of an array of automated acoustic hydrophones and acoustic tags provides opportunities to investigate animals 24 hrs a day and in all weather conditions for months at a time. While this tool does not produce the extremely detailed kind of information provided by continuous tracking, networks of monitors provide the opportunity to test hypotheses concerning large-

23 Chapter 1. General introduction and fine-scale movement, major migrations, seasonal periodicity, range expansion, habitat shifts (e.g. Arnold & Dewar 2001; Heupel & Heuter 2001a; Heupel, Simpfendorfer & Hueter 2004), the use of nurseries (e.g. Conrath & Musick 2010; DeAngelis 2008; Yokota & Lessa 2006) or the likely effectiveness of marine protected areas (e.g. Chapman et al. 2005; Heupel & Simpfendorfer 2005). A series of receivers forming arrays or curtains were deployed by the Australian Animal Tagging and Monitoring System (AATAMS, http://imos.org.au/aatams.html) in the Ningaloo Reef Marine Park. Ningaloo Reef is the largest fringing reef system in the Southern Hemisphere and extends along 270 km of coastline in the north of Western Australia. The reef is separated from the coast by a 0.2 to 7 km wide sandy lagoon, which is backed by a dry coastal plain (Figure 1). It has 270 km of reef and it was declared a marine park (Ningaloo Reef Marine Park, NMP) in 1996 (Leprovost Dames and Moore 2000; Taylor & Pearce 1999). An estimate of 118 elasmobranch species was documented at this reef in 2009, which is equivalent diversity to the Great Barrier Reef which is about 70 times the area of the NMP. Interestingly, the most abundant species observed were batoids (Stevens et al. 2009). The Ningaloo Reef Ecosystem Tracking Array (NRETA) aims to study the movement patterns of large fishes and elasmobranchs. Established in 2007, it consists of 61 acoustic receivers running along 137 km of coast, from the shoreline through the Ningaloo lagoon, onto the reef slope and out to a depth of 50 m (Figure 1). Two arrays were used in this thesis, Mangrove Bay and the southern array. Mangrove Bay is the northernmost sanctuary zone of the Ningaloo Marine Park (Figure 1, Figure 2) and is a broad sandy bay bordered by a fringing coral reef at the seaward edge with small areas of mangrove forest lining the shore. The bay includes a sanctuary zone that covers 11.35 km2 and is adjacent to recreational zones where fishing is permitted (Department of Conservation and Land Management 2005; Leprovost Dames and Moore 2000). Mangrove Bay has 51 receivers (VR2w VEMCO©, Halifax, Canada) covering an area of approximately 29 km2, including the Mangrove Bay sanctuary. Range testing within this array was done by placing an acoustic transmitter (VEMCO v13) for 4 – 6 hours at increasing distances from receivers. While receiver detection ranges in the embayment overlapped, there was there was little overlap of detection range in the southern- and northern-most parts of

24 Chapter 1. General introduction the array where receivers were 650-800 m apart. The range testing determined that the maximum detection range for receivers in Mangrove Bay was ~300 m and in the lagoon channel was ~ 500 m (Appendix 2). Temperature loggers (Minilog –II-T submersible temperature data logger, accuracy: ± 0.1 °C from -5 °C to 35 °C; range: -30 °C to +80 °C; VEMCO©, Halifax, Canada) were also attached on 20 receivers in the lagoon and open-shelf of Mangrove Bay array to record temperature at 30 minute intervals during 2008-09 (Figure 2). Arrays were serviced and data were downloaded every four to six months.The southern array comprises 15 receivers deployed at Stanley Pool and Coral Bay (Figure 1). This area is dominated by coral reef habitats and sandflats and had a both a no-take sanctuary (Maud Sanctuary zone) and areas open to recreational fishing (Department of Conservation and Land Management 2005; Leprovost Dames and Moore 2000). Range testing details for this array was done by a companion study and can be found in Speed et al. ( 2011).

1.3.3 Integrated ecological risk assessment

It is widely acknowledged that current management practices lack a multidisciplinary approach and are therefore inappropriate to manage marine ecosystems. There has generally only been management for each particular activity or sector using the sea (e.g. fishing, shipping, coastal development) and this sector- based management has resulted in these impacts being viewed individually and only within the context of each sector. However, all these activities exert pressures on marine ecosystems and have cumulative impacts on ecosystem structure and function, thus managing these impacts individually may be inadequate. Importantly, such single-focus management has also lacked a consistent integration of socio- economic factors to the management measures proposed, therefore generally ignoring its effects on directly-related human communities (deReynier, Levin & Shoji 2010). Integrative frameworks are being widely used to manage marine ecosystems in a way that integrates many features of an ecosystem instead of simply individual components of each (Samhouri & Levin 2012). This type of management approach recognizes that both human and ecological wellbeing are intrinsically linked and sustainability can only occur when both are addressed (Curtin & Prellezo 2010). One of the main challenges of this type of approach is to demonstrate connections between specific human activities and ecosystem components in a way that enables managers to prioritize problems (Bottrill et al. 2008). 25 Chapter 1. General introduction

In recent years, the awareness of the interconnectivity of ecosystems has grown, particularly in fisheries management which has undertaken a gradual evolution from a target-species primary focus on target species to a broader focus on ecosystems and the impacts of fisheries upon them (Curtin & Prellezo 2010; Smith et al. 2007; Stobutzki, Miller & Brewer 2001). Ecosystem-based management (EBM) when applied principally to fisheries management is commonly referred as ecosystem-based fisheries management (EBFM). Although there is a large list of definitions and shades of meaning of the EMB, there is a widespread agreement about the need to move towards a fisheries management approach that recognises how marine food webs, humans, and all the fisheries-related activities operating at regional or ecosystem level can affect the ecosystem (Pitcher et al. 2009). It proposes that an improved understanding and management of fish stocks and the influence that fisheries have on different aspects of such stocks will result in more sustainable fisheries. This assessment should also include social and economic outcomes generated by these activities (Christie et al. 2007; Fletcher et al. 2010; Pikitch et al. 2004). A development from the ecosystem-based management approach, is the ‘ecological risk assessment’ (ERA) framework, which aims to evaluate the likelihood that adverse ecological effects may occur as a result of exposure to stressors (Suter 2007). This method assesses the species’ or ecosystem’s sensitivity and response to the exposure to threats (or stressors), either qualitatively or quantitatively, which is usually crucial for environmental decision making (Gallagher, Kyne & Hammerschlag 2012; Guo et al. 2011; Smith et al. 2007). In Australia, the management of individual fisheries has used an EBM approach for the last decade (Fletcher et al. 2010). ERAs have also been used mostly to assess the effects of fisheries, particularly in the case of elasmobranchs, Gallagher et al. (2012) reviewed the applicability of such assessments for sharks populations. However, despite the now recognized ecological and economic importance of batoids, there are no risk assessments available for this group. Because of the high by-catch of batoids in Australia, the intensive fishery in neighbouring waters, and the high rates of coastal development in both, management plans based in holistic approaches such as ERAs are increasingly needed to ensure the sustainability and persistence of batoids’ populations.

26 Chapter 1. General introduction

1.3.4 Ethics Statement All animal work was approved by the Charles Darwin University Animal Ethics Committee (permit no. A07026). The Department of Environment and Conservation (DEC) of Western Australia issued the permit to enter a DEC managed area for the purpose of undertaking research (regulation no. 4. permit no. CE001842 and a license to take (i.e. capture, collect, disturb, study) fauna for scientific purposes (regulation no. 17, permit no. SF006661). The Department of Fisheries of Western Australia issued the research permit to collect fish (permit no. 185-2008-28).

27 Figure 1. Ningaloo Reef Ecosystem Array Tracking. A) Locations of receiver arrays along Ningaloo Reef, WA (1-Tantabiddi curtain, 2-Mangrove Bay array, 3- Point Cloates curtain, 4- Stanley Pool array, 5- Point Maud curtain, 6- Skeleton Bay array). B) Mangrove Bay array and the southern array were used for these studies.

28 Figure 2. Mapping of Mangrove Bay array (A) and the southern array (B). Dashed lines are sanctuary zones.

29 Chapter 1. General introduction

1.4 Aims

The overall objective of this thesis is to advance the understanding of the spatial ecology of 16 species of coastal rays in a reef environment and to identify primary threats in the Indo-Pacific region. There are three major components of this study, the DNA-barcoding of tropical rays, the monitoring and analysis of movement patterns, and the threat and research gaps assessment as specified below.

(1) Use and assessment of the DNA-barcoding tool for rays. (i) To use DNA-barcoding of COI to test the field identifications of rays at Ningaloo Reef. (ii) To use DNA-barcoding to accurately identify unknown specimens coming from Northern Territory and Viet Nam fisheries. (iii) To use the data generated above in combination with sequences from genetic data bases to critically evaluate the utility of DNA-barcoding as a tool for the genetic identification rays.

(2) Spatial ecology of tropical rays at Mangrove Bay, Ningaloo Reef, WA using NRETA array of acoustic receivers. (i) To study the movement patterns, residency and habitat use of seven species of rays using passive acoustic monitoring at several time-scales at Ningaloo Reef. (ii) The correlation between age and/or sex and movement patterns, residency and habitat use was examined for six species of rays. (iii) The correlation of movement patterns, residency and habitat use with as temperature and tides was also examined for all tagged species.

(3) Vulnerability and research priorities assessment using an Ecological Risk Assessment (ERA) framework. (i) To assess the vulnerability of 16 species of tropical rays to two major threats, fisheries and habitat degradation in the Indo-Pacific Ocean. (ii) To identify the research priorities for the management of 16 species in the Indo-Pacific Ocean.

30 Chapter 1. General introduction

1.5 Outline of thesis

1.5.1 Overview of data collected

Four tagging field trips occurred at Mangrove Bay and southern array during 2008- 2009 (February and November 2008; September and December 2009). Twelve species were tagged and monitored until April 2011 producing a total of 160,196 detections during 1,144 days of monitoring which were analyzed for movement patterns. A total of 341 samples was collected through the Ningaloo Reef marine park for the genetic analysis; an additional 37 samples from the Northern Territory, Australia, 11 from the Great Barrier Reef, Australia, and five from northern Viet Nam were collected to extend the DNA-barcoding analysis. A total of 16 species was barcoded and used for the vulnerability and research priorities assessment.

1.5.2 Chapter outline

The first research chapter of this thesis (Chapter 2) presents the results of the use of DNA-barcoding as an identification tool to confirm field identification of rays. This chapter was submitted and will be published in PLoS ONE in 2012. Chapter 3 details the movement patterns of juvenile rays and their relationship with water temperature. It describes the first reported nursery area for juvenile rays at Ningaloo Reef. This chapter has been published in Environmental Biology of Fishes. Chapter 4 extends this study by analyzing the movement patterns and habitat partitioning of adult rays in this tropical reef system. Chapter 5 identifies the most vulnerable species of rays to the risk of fisheries and habitat degradation in the Arafura-Timor Sea / northern Australia region and the rest of the Indo- West Pacific. This chapter also used a novel approach to identify research priorities for both regions. Finally Chapter 6 describes the major outcomes of this study. It focuses on the multi-disciplinary approach to management and provides recommendations for enhancing the effectiveness of management strategies of batoids.

31 Chapter 2

Identification of rays through DNA- barcoding: an application for ecologists Chapter 2

Identification of rays through DNA- barcoding: an application for ecologists Chapter 2. Identification of rays through barcoding

Chapter 2- Identification of rays through DNA barcoding: an application for ecologists

2.1 Preface

Chapter 2 has been published in PLoS ONE. I am the primary author of the manuscript; two of my supervisors Mark Meekan (AIMS) and Chris Austin (Monash university formerly CDU), as well as Corey Bradshaw (University of Adelaide, formerly CDU), Owen O’Shea, (University of Western Australia), and Vivian Wei Nu-Wei (CDU) are additional co-authors. The format of this chapter has undergone minor reformatting for inclusion here according to CDU formatting regulations for PhD thesis. I conducted part of the sampling and laboratory work as well as analysis, writing, and literature research. Owen O’Shea, M. Meekan, and C. Austin contributed with samples. V. Wei Nu-Wei contributed with substantial laboratory work and aided with analysis. Mark Meekan, and C. Bradshaw provided support and editorial comments. C. Austin assisted with analysis, interpretation of sequence data, access to softwares and laboratory facilities, substantial support, and editorial comments.

Chapter 2 was presented at the Oceania Chondrichthyan Society’s 3rd Annual Conference at Sea World Resort and Water Park, Gold Coast, 13-15 September, 2011. This work was also included in the following report:

Barcoding of rays in Western Australia, Science Network, WA. 2012 http://www.sciencewa.net.au/topics/fisheries-a-water/item/1673-dna-identification- of-rays-for-fisheries-management-and-conservation.html

Paper citation: CERUTTI-PEREYRA, F., MEEKAN, M. G., WEI, N.-W. V., O'SHEA, O., BRADSHAW, C. J. A. & AUSTIN, C. M. 2012. Identification of Rays through DNA Barcoding: An Application for Ecologists. PLoS ONE, 7, e36479.

34 Chapter 2. Identification of rays through barcoding

Identification of Rays through DNA Barcoding: an Application for Ecologists

Florencia Cerutti-Pereyra1, Mark G. Meekan2, Nu-Wei V. Wei1, Owen O’Shea3, Corey J. A. Bradshaw4,5, Chris M. Austin6

1Research Institute of Environment and Livelihood, Charles Darwin University, Northern Territory, Australia 2Australian Institute of Marine Science, UWA Oceans Institute, Western Australia, Australia 3Murdoch University, Western Australia, Australia 4The Environment Institute and School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, South Australia, Australia 5South Australian Research and Development Institute, South Australia, Australia 6School of Science Monash University Sunway Campus, Selangor, Malaysia

Abstract DNA barcoding potentially offers scientists who are not expert taxonomists a powerful tool to support the accuracy of field studies involving taxa that are diverse and difficult to identify. The taxonomy of rays has received reasonable attention in Australia, although the fauna in remote locations such as Ningaloo Reef, Western Australia is poorly studied and the identification of some species in the field is problematic. Here, we report an application of DNA-barcoding to the identification of 16 species (from 10 genera) of tropical rays as part of an ecological study. Analysis of the dataset combined across all samples grouped sequences into clearly defined operational taxonomic units, with two conspicuous exceptions: the Neotrygon kuhlii species complex and the Aetobatus species complex. In the field, the group that presented the most difficulties for identification was the spotted whiptail rays, referred to as the ‘uarnak’ complex. Two sets of problems limited the successful application of DNA barcoding: (1) the presence of cryptic species, species complexes with unresolved taxonomic status and intra-specific geographical variation, and (2) insufficient numbers of entries in online databases that have been verified taxonomically, and the presence of lodged sequences in databases with inconsistent names. Nevertheless, we demonstrate the potential of the DNA barcoding approach to confirm field identifications and to highlight species complexes where taxonomic uncertainty might confound ecological data.

35 Chapter 2. Identification of rays through barcoding

2.2 Introduction Taxonomic misidentification and the presence of cryptic species can seriously compromise the veracity of ecological, fisheries and conservation-related research and management (Austin & Ryan 2002; Bortolus 2008; Moura et al. 2008; Vecchione et al. 2000). These problems are further compounded by the ‘greying’ of the taxonomic workforce and the decline in the teaching of taxonomy and training of field biologists at universities, both issues identified as major impediments to the conduct of biodiversity science and conservation biology (Khuroo et al. 2007). Within this context, a key question is: how much confidence can be placed in the application of correct scientific names of taxa reported in ecological studies? In a review of high-ranking ecological journals, Bortolus (2008) reported that 62.5 % of papers did not provide any supporting information justifying or guaranteeing the correct identification of the organisms under investigation. The challenges for ecologists seeking verification of their field-based identifications are not trivial. Even when adequate taxonomic keys and field guides are available, it is often difficult to identify organisms in the field with confidence, as ecologists can be dealing with juveniles, undocumented geographic variants, or sexual dimorphism, such that accurate identification might require examination of microanatomy or measurements of a complex combination of morphometric attributes. Handling, examining and measuring individuals is often impractical, inappropriate for ethical reasons, or simply dangerous, thus exacerbating the problem of securing accurate identification. Furthermore, even if experienced taxonomists have studied the target organisms, it is unlikely that they can be encouraged to assist in the field, especially in remote locations. Voucher specimens can be taken for subsequent lodgement in museums; however, this is often impractical for large species, samples obtained in remote locations and studies involving multiple species. Even where voucher specimens can be obtained, they will not necessarily guarantee reliable and timely identification. DNA barcoding potentially offers scientists who are not expert taxonomists a powerful tool to support the efficiency and accuracy of field studies involving the identification of challenging taxa (Ward et al. 2008). The proponents of this approach mostly advocate the use of a single gene for global identification of animals based on the availability of a library of sequences linked to voucher specimens, thus

36 Chapter 2. Identification of rays through barcoding making these sequences, in effect, a DNA barcode (Hebert, M.Y. Stoeckle, et al. 2004; Ward, R. Hanner & Hebert 2009). A 650-base fragment of the cytochrome c oxidase I (COI, cox1) is proposed as a ‘global’ standard because the variation in COI within species is lower relative to that among species. While the DNA barcoding approach has its critics when touted as a solution to impediments presented by traditional taxonomy (Moritz & Cicero 2004; Tautz et al. 2003), it does potentially provide a quick and reliable means to confirm the identification of individuals in the field and to identify groups where there is discordance in the delineation of species boundaries that require further research. In their paper on DNA Barcoding Australian chondrichthyans, Ward et al. (2008) recommended this approach for marine ecologists working on chondrichthyans in the absence of expert taxonomists. While the taxonomy of rays has received reasonable attention in some parts of the world, including Australia (Last & Stevens 2009) where DNA information is accumulating, the fauna in remote locations such as Ningaloo Reef, Western Australia remains relatively poorly studied. It is now becoming apparent that the field identification of some species without access to taxonomic expertise or the ability to evaluate diagnostic traits (e.g. morphometrics or microanatomy) is problematic. Recent studies indicate that morphologically cryptic elasmobranchs might be common, as some groups show ontogenetic colour variation and colour pattern similarities among different species (Sandoval-Castillo et al. 2004; Sandoval- Castillo & Rocha-Olivares 2011; Toffoli et al. 2008; Wynen et al. 2009). For example, a recent revision of the ‘whiptail ray complex’ found that coloration patterns changed with life stage and different habitats (Manjaji-Matsumoto & Last 2008), thus complicating field identification. Here, I report an application of DNA-barcoding to confirm the identification of rays as part of ecological studies at Ningaloo Reef. The establishment of the Ningaloo Reef Ecosystem Tracking Array (NRETA), which is part of the Australian Animal Tagging and Monitoring System (AATAMS, www.imos.org.au/aatams.html), a national network of acoustic stations, provided the opportunity to address the lack of knowledge of the spatial ecology of these animals by enabling a study of the fine-scale movement of a diverse community of rays inhabiting this reef system (Cerutti-Pereyra et al. unpublished data). In these studies, 70 individual rays including both juveniles and adults representing 17 presumed species were captured and fitted with acoustic tags and monitored for more than two

37 Chapter 2. Identification of rays through barcoding years. Tissue samples were taken from each tagged individual for DNA barcoding. I therefore present 67 new COI sequences from these 17 putative species of rays to confirm field identification based on sequences deposited in the GenBank database. The over-arching aim was to assess the potential of DNA barcoding as an aid to batoid species identification for the tagging study.

2.3 Methods

2.3.1 Study group Rays, or batoids, include a variety of fishes closely related to sharks. Recent immunological and molecular studies show an ancient split between the two groups, where batoids are a sister group to the clade consisting of all shark orders (Douady et al. 2003; Lawson et al. 1995; Winchell, Martin & Mallatt 2004). Even though the monophyly of batoids is widely accepted, interrelationships within batoids remain controversial. Although early research established six orders, recent work now recognizes five: electric rays (Torpediniformes), skates (Rajiformes), guitar fishes (Rhinobatiformes), sawfishes (Pristioformes), and stingrays (Myliobatiformes) (2004; Rocco et al. 2007). Worldwide, there are between 507 and 630 species, many of them poorly known and requiring further taxonomic studies. Recent molecular evidence focuses on relationships among elasmobranch orders, but few studies have addressed interrelationships within the rays, (e.g. Lovejoy 1995; Rocco et al. 2007; Sezaki et al. 1999). The central Indo-Pacific is a major centre of origin and radiation of stingrays (McEachran & Aschliman 2004) and within this region, the Indo-Australian archipelago contains 30% of all species of sharks and rays worldwide (Last & Stevens 2009; Last & White 2011), including many species of tropical rays. Rays are exploited directly or indirectly in commercial fisheries; however, detailed data on landings and by-catch are often lacking. Global reviews of batoid fisheries indicate that in most cases there are large gaps in the basic biological information required to implement strategic management plans for stocks (Bonfil 1997b; Frisk 2010) and over-fishing has been suggested to be one of the critical reasons for the decline and local extinction of populations of rays and sharks in both hemispheres (Brander 1981; Dulvy et al. 2000; Field et al. 2009b; Frisk 2010; Graham, Andrew & Hodgson 2001; Stevens et al. 2000; White & Dharmadi 2007).

38 Chapter 2. Identification of rays through barcoding

DNA information for species of rays is accumulating, including COI sequences with 1255 lodged on GenBank to date. This suggests that there is now a sufficient DNA database available to at least partially support a DNA barcoding approach for taxonomic identification of batoids.

2.3.2 Study site Samples were taken from many locations along Ningaloo Reef. Study site details are in Chapter 1, section 1.3.2.1.

2.3.3 Sampling Gill and hand nets, hook and line, a Hawaiian sling with a modified tip (Bilgmann et al. 2007), and indigenous spear fishing were used to obtain rays for tissue samples of rays on the field (Figure 3). These were stored in a salt-saturated dimethyl sulphoxide (DMSO) solution (20 % DMSO, 0.25 M EDTA, saturated with NaCl) in the field, then at -80 ºC in the laboratory. Rays were visually identified and measured (disc-width, DW) during handling or prior to taking tissue samples in the case of free-swimming rays (Figure 3). Tissue samples were collected from two individuals per species per site where possible. Samples were also obtained from the Northern Territory, Lizard Island (Queensland), and Ha Long Bay (Vietnam) for comparison (Figure 4). The samples were collected by different researchers and fishers; when possible, a provisional identification was made in the field. The individual samples, their geographic origin and initial taxonomic identification based on information provided by Last and Stevens (2009) are shown in Table 2

2.3.4 Laboratory procedures Genomic DNA was extracted from muscle tissue using DNeasy Blood & Tissue Kit, and amplified the COI gene by polymerase chain reaction (PCR) using the universal primers FishF2 (5’TCGACTAATCATAAAGATATCGGCAC3’) and FishR2 (5’ACTTCAGGGTGACCGAAGAATCAGAA3’) designed by Ward et al. (2008). Each 50 µl reaction contained 5 µl of DNA tissue (ca. 10 ng), 4 µl (0.2 mM) of total Bioline dNTPs, 3 µl (0.6 µM) of each primer, 0.1 µl of 5 U/µl Mango taq, 5

µl of 10x Mango buffer, and 2 µl (2 mM) of MgCl2. PCR cycle conditions were an initial 3 min denaturation at 94 °C, followed by 35 cycles of 50 sec at 94 °C, 2 min at 50 °C, 1.5 min at 72 °C and finished with 6 min at 72 °C. PCR products were examined on 1 % agarose gels, purified with QIAGEN QIAquick PCR Purification

39 Chapter 2. Identification of rays through barcoding kit and sequenced with the automated sequenced using the dye-termination method (BigDye Terminator v3.1, Applied Biosystems). Amplicons were sequenced in both forward and reverse directions. Chromatograms were inspected for noisy and ambiguous base calling and translated to check for stop codons. Noisy tails were trimmed. Only those consisting of more than 519 bp were used for the analysis. Several sequences trimmed to less than 519 bp were excluded from the phylogenetic analysis but were submitted to online databases for identification. Sequences used for the phylogenetic analysis were submitted to GenBank database under the accession numbers given in Table 2.

40 Chapter 2. Identification of rays through barcoding

Figure 3. Field work for genetic sampling. A, B) Hand net used to catch and handle smaller rays; C) measuring disc with (DW); D, E) taking tissue sample from pelvic fin; F) Hawaiian sling with modified tip for tissue sampling, G) taking tissue samples of larger rays while snorkeling.

41 Chapter 2. Identification of rays through barcoding

Figure 4. Sampling sites. 1) Ningaloo Reef, Western Australia, 2) Darwin Harbour, Northern Territory, 3) Lizard Island, GBR, Queensland, 4) Ha Long Bay, Vietnam.

2.3.5 Analysis Sequence data were assembled using Mesquite 2.74 and the identification of samples was revised after considering the results of two analyses. First, sequences were submitted one at a time to the BOLD Identification Engine (www.boldsystems.org) and GenBank nucleotide database (www.ncbi.nlm.nih.gov/nucloetide). Both engines matched each uploaded sequence with every other sequence present in their databases and provided a percentage similarity with matching sequences (Table 3). In the second analysis, phylogenetic trees were constructed using ray sequences downloaded from both the GenBank nucleotide database and BOLD identification engine (Appendix 1). Sequences were chosen from GenBank/BOLD on the basis that they represented either the same species, a congeneric species, or they showed a high similarity to the sequences produced here and submitted to a blast search in GenBank or BOLD engines. If a species on GenBank displayed multiple divergent haplotypes, sequences were chosen to represent this variation. These online sequences were assembled and aligned with the sequenced produced in this study using MEGA 4 (Tamura et al. 2007). The data set used for phylogenetic analysis was composed of only those sequences that consisted of a minimum of 519 bp after trimming. Both neighbour-

42 Chapter 2. Identification of rays through barcoding joining (NJ) and Bayesian methods of phylogenetic tree construction for analysis were used. Neighbour-joining has a strong track record of being able to rapidly analyze large datasets (Kumar & Gadagkar 2000). Modeltest 3.7 showed that the Hasegawa, Kishino and Yano (Hasegawa 1985) (HKY85) model of molecular evolution was the most appropriate for this dataset (Posada & Crandall 1998). However, the simple Kimura two-parameter model was also used to estimate genetic distance (Kimura 1980) as it is the standard model of molecular evolution used in barcoding studies (Hebert, S Rarnasingham & deWard 2003). Sequences were used from two species of sharks (Carcharhinus amblyrhyncos Bleeker, 1856 and C. plumbeus Nardo, 1827) and two species of rays (Pristis clavata Garman 1906 and Torpedo californica Ayres, 1855) from GenBank as outgroups in separate analyses. As the relationships at the level required for species discrimination did not change with the use of different outgroups, only the tree using shark taxa is presented because we can be certain that these are an outgroup rather than an ingroup for batoids. Trees were constructed using both nucleotide models with PAUP* 4.0b10 (Swofford 2002) and MrBayes (Ronquist, JP Huelsenbeck & Mark 2005). As these provided similar outcomes, only results based on the neighbour-joining tree using the Kimura two-parameter model with bootstrap values and posterior probabilities are presented. Uncorrected pair-wise distances in PAUP* 4.0b10 (Swofford 2002) were generated, updating the name of the sequences used as detailed in Table 2. For initial species delineation, individuals that clustered with similarity < 3.5% of divergence were grouped because this value is the threshold recommended for COI of marine fish (Holmes, Steinke & Ward 2009; Ward et al. 2008; Ward et al. 2005; Zemlak et al. 2009) and equates to approximately 10x the intra-species variation proposed by Hebert et al. (2004). Multi-dimensional scaling (MDS) in SPSS was also used to explore patterns of variation in groups displaying high intra-speciation or geographic variation. For ease of interpretation and readability, the neighbour-joining tree was divided into 3 sections (Figure 5-Figure 7).

43 Table 2. Specimen collection details for all sequences obtained in this study

New Initial ID Locality Latitude Longitude Collector Used Identified Tagged GenBank No. for by accession # 1 Aetobatus ocellatus Skeleton Bay, Ningaloo 23° 7'53.21"S 113°46'14.76"E FC analysisYes taxonomistYes JQ765503 2 Aetobatus ocellatus Skeleton Bay, Ningaloo 23° 7'53.21"S 113°46'14.76"E OOS Yes Yes Yes JQ765504 3 Dasyatis parvonigra Shoal Bay, NT 12°20'49.44"S 131° 0'51.37"E FC Yes Yes JQ765505 4 Glaucostegus typus Skeleton Bay, Ningaloo 23° 7'53.21"S 113°46'14.76"E FC Yes No JQ765506 5 Glaucostegus typus Mangrove Bay, Ningaloo 21°58'5.23"S 113°56'27.02"E FC Yes No Yes JQ765507 6 Glaucostegus typus Shoal Bay, NT 12°20'49.44"S 131° 0'51.37"E FC Yes No JQ765508 7 Himantura astra Skeleton Bay, Ningaloo 23° 7'53.21"S 113°46'14.76"E OOS Yes No Yes JQ765509 8 Himantura astra Shoal Bay, NT 12°20'49.44"S 131° 0'51.37"E FC Yes Yes JQ765510 9 Himantura fai Point Cloates, Ningaloo 22°42'22.20"S 113°39'51.95"E FC Yes Yes JQ765511 10 Himantura fai Mangrove Bay, Ningaloo 21°58'5.23"S 113°56'27.02"E FC Yes Yes JQ765512 11 Himantura fai Mangrove Bay, Ningaloo 21°58'5.23"S 113°56'27.02"E FC Yes Yes Yes JQ815392 12 Himantura fai Skeleton Bay, Ningaloo 23° 7'53.21"S 113°46'14.76"E OOS Yes No Yes JQ765513 13 Himantura granulata GBR, Qld 14°41'27.55"S 145°27'29.05"E MM Yes Yes JQ765514 14 Himantura granulata Ranger Bay, Ningaloo 21°53'54.39"S 113°58'59.75"E OOS Yes No JQ765515 15 Himantura jenkensii Mangrove Bay, Ningaloo 21°58'5.23"S 113°56'27.02"E FC Yes Yes Yes JQ765516 16 Himantura jenkensii Mangrove Bay, Ningaloo 21°58'5.23"S 113°56'27.02"E FC Yes Yes Yes JQ765517 17 Himantura jenkensii Mangrove Bay, Ningaloo 21°58'5.23"S 113°56'27.02"E FC No Yes Yes JQ815393 18 Himantura jenkensii Shoal Bay, NT 12°20'49.44"S 131° 0'51.37"E FC Yes No JQ765518 19 Himantura leoparda Skeleton Bay, Ningaloo 23° 7'53.21"S 113°46'14.76"E OOS No No JQ929047 20 Himantura leoparda Ranger Bay, Ningaloo 21°53'54.39"S 113°58'59.75"E OOS Yes No JQ765519 21 Himantura leoparda Ranger Bay, Ningaloo 21°53'54.39"S 113°58'59.75"E OOS Yes Yes JQ765520 22 Himantura uarnak Skeleton Bay, Ningaloo 23° 7'53.21"S 113°46'14.76"E FC No Yes Yes JQ815394 23 Himantura uarnak Skeleton Bay, Ningaloo 23° 7'53.21"S 113°46'14.76"E FC Yes Yes Yes JQ765521 24 Himantura uarnak Mangrove Bay, Ningaloo 21°58'5.23"S 113°56'27.02"E FC Yes Yes Yes JQ765522

44 25 Himantura uarnak Skeleton Bay, Ningaloo 23° 7'53.21"S 113°46'14.76"E FC Yes No Yes JQ765523 26 Himantura uarnak Skeleton Bay, Ningaloo 23° 7'53.21"S 113°46'14.76"E FC Yes Yes JQ765524 27 Himantura uarnak Point Cloates, Ningaloo 22°42'22.20"S 113°39'51.95"E FC Yes Yes JQ765525 28 Himantura uarnak Point Cloates, Ningaloo 22°42'22.20"S 113°39'51.95"E FC Yes Yes JQ765526 29 Himantura uarnak Stanley's Pool, Ningaloo 22°59'29.39"S 113°48'6.04"E FC Yes Yes Yes JQ765527 30 Himantura uarnak Mangrove Bay, Ningaloo 21°58'5.23"S 113°56'27.02"E FC Yes Yes Yes JQ765528 31 Himantura uarnak Shoal Bay, NT 12°20'49.44"S 131° 0'51.37"E FC Yes No JQ765529 32 Himantura uarnak Shoal Bay, NT 12°20'49.44"S 131° 0'51.37"E FC Yes Yes JQ765530 33 Himantura uarnak Meckets Creek, NT 12°20'23.42"S 130°57'22.38"E FC No No JQ815395 34 Manta alfredi Stanley's Pool, Ningaloo 22°59'29.39"S 113°48'6.04"E FM* Yes Yes JQ765531 35 Manta alfredi Stanley's Pool, Ningaloo 22°59'29.39"S 113°48'6.04"E FM* Yes Yes JQ765532 36 Neotrygon kuhlii GBR, Qld 14°41'27.55"S 145°27'29.05"E MM Yes No JQ765533 37 Neotrygon kuhlii GBR, Qld 14°41'27.55"S 145°27'29.05"E MM Yes No JQ765534 38 Neotrygon kuhlii GBR, Qld 14°41'27.55"S 145°27'29.05"E MM Yes Yes JQ765535 39 Neotrygon kuhlii Point Edgar, Ningaloo 22°34'52.46"S 113°39'9.36"E OOS Yes No JQ765536 40 Neotrygon kuhlii Skeleton Bay, Ningaloo 23° 7'53.21"S 113°46'14.76"E OOS Yes No JQ765537 41 Neotrygon leylandi 5 Fingers, Ningaloo 23°11'38.34"S 113°46'1.45"E FC Yes No JQ765538 42 Neotrygon 5 Fingers, Ningaloo 23°11'38.34"S 113°46'1.45"E FC Yes Yes JQ765539 ningalooensis 43 Neotrygon 5 Fingers, Ningaloo 23°11'38.34"S 113°46'1.45"E FC Yes No JQ765540 ningalooensis 44 Pastinachus atrus Mangrove Bay, Ningaloo 21°58'5.23"S 113°56'27.02"E FC Yes No Yes JQ765541 45 Pastinachus atrus Skeleton Bay, Ningaloo 23° 7'53.21"S 113°46'14.76"E FC Yes No JQ765542 46 Pastinachus atrus Ranger Bay, Ningaloo 21°53'54.39"S 113°58'59.75"E OOS Yes No JQ765543 47 Pastinachus atrus Ranger Bay, Ningaloo 21°53'54.39"S 113°58'59.75"E OOS Yes No JQ765544 48 Pastinachus atrus Meckets Creek, NT 12°20'23.42"S 130°57'22.38"E FC Yes Yes JQ765545 49 Pastinachus atrus Shoal Bay, NT 12°20'49.44"S 131° 0'51.37"E FC Yes Yes JQ765546 50 Taeniura lymma Mangrove Bay, Ningaloo 21°58'5.23"S 113°56'27.02"E FC No No Yes JQ929048 51 Taeniura lymma Mangrove Bay, Ningaloo 21°58'5.23"S 113°56'27.02"E FC Yes No JQ765547

45 52 Taeniura lymma Mangrove Bay, Ningaloo 21°58'5.23"S 113°56'27.02"E FC Yes No JQ765548 53 Taeniura lymma Skeleton Bay, Ningaloo 23° 7'53.21"S 113°46'14.76"E OOS Yes No Yes JQ765549 54 Taeniura lymma Point Look, Ningaloo 21°55'37.51"S 113°53'38.99"E OOS No No JQ825396 55 Taeniura lymma Groote Island, NT 13°39'15.60"S 136°56'25.49"E FC Yes No JQ765551 56 Taeniura lymma Groote Island, NT 13°39'15.60"S 136°56'25.49"E FC Yes No JQ765552 57 Taeniura lymma Groote Island, NT 13°39'15.60"S 136°56'25.49"E FC Yes No JQ765553 58 Taeniurops meyeni Point Look, Ningaloo 21°55'37.51"S 113°53'38.99"E OOS Yes No JQ765550 59 Taeniurops meyeni 5 Fingers, Ningaloo 23°11'38.34"S 113°46'1.45"E FC Yes Yes JQ765554 60 Taeniurops spp Point Edgar, Ningaloo 22°34'52.46"S 113°39'9.36"E OOS Yes No JQ765555 61 Urogymnus asperrimus GBR, Qld 14°41'27.55"S 145°27'29.05"E MM Yes No JQ765556 62 Urogymnus asperrimus Ranger Bay, Ningaloo 21°53'54.39"S 113°58'59.75"E OOS Yes No JQ765557 63 Urogymnus asperrimus Point Look, Ningaloo 21°55'37.51"S 113°53'38.99"E OOS Yes No JQ765558 64 unknown Darwin Harbour, NT 12°24'26.35"S 130°47'20.82"E GJ* Yes No JQ765559 65 unknown Darwin Harbour, NT 12°24'26.35"S 130°47'20.82"E GJ* Yes No JQ765560 66 unknown Ha Long Bay, Vietnam 20°45'38.93"N 106°52'47.17"E CA Yes No JQ765561 67 unknown Ha Long Bay, Vietnam 20°45'38.93"N 106°52'47.17"E CA Yes No JQ765562 Locality: GBR, Great Barrier Reef; NT, Northern Territory; Qld, Queensland *GJ, Grant Johnson, NT Fisheries **FM, Frazer McGregor, Murdoch University ***Only those sequences of 519 bp were used in the phylogenetic analysis

46 Table 3. Identification of sampled rays using GenBank and BOLD databases.

Used for Initial Id Locality GenBank Id % BOLD Id % analysis 1 Aetobatus ocellatus Kuhli, 1823 Skeleton Bay, Ningaloo A. narinari 100 A. narinari/ocellatus 100 Yes 2 Aetobatus ocellatus Kuhli, 1823* Skeleton Bay, Ningaloo A. narinari 99 A. narinari/ocellatus 100 Yes 3 Dasyatis parvonigra,Last and White, 2008 Shoal Bay, NT D. fluviorum 98 Dasyiatis sp 99 Yes 4 Glaucostegus typus (Bennett, 1830) Skeleton Bay, Ningaloo G. typus 100 G. typus/ R. typus 100 Yes 5 Glaucostegus typus (Bennett, 1830)* Mangrove Bay, Ningaloo G. typus 99 G. typus/ R. typus 100 Yes 6 Glaucostegus typus (Bennett, 1830) Shoal Bay, NT G. typus 100 G. typus/ R. typus 100 Yes 7 Himantura astra, Last et al., 2008* Skeleton Bay, Ningaloo Rajiformes sp 100 H. uarnak 100 Yes 8 Himantura astra, Last et al., 2008 Shoal Bay, NT H. toshi 100 H. toshi/ H. astra 98 Yes 9 Himantura fai, Jordan & Seale, 1906 Point Cloates, Ningaloo H. fai 99 H. jenkinsii/fai 99 Yes 10 Himantura fai, Jordan & Seale, 1906 Mangrove Bay, Ningaloo H. fai 99 H. jenkinsii/fai 99 Yes 11 Himantura fai, Jordan & Seale, 1906* Mangrove Bay, Ningaloo H. fai 99 H. jenkinsii/fai 99 Yes 12 Himantura fai, Jordan & Seale, 1906* Skeleton Bay, Ningaloo H. fai 99 H. jenkinsii/fai 99 Yes 13 Himantura granulata (Macleay, 1883) GBR, Qld H. hortlei 86 H. granulata 99 Yes 14 Himantura granulata (Macleay, 1883) Ranger Bay, Ningaloo H. fai 100 H. jenkinsii/fai 100 Yes 15 Himantura jenkinsii (Annandale, 1990)* Mangrove Bay, Ningaloo H. jenkinsii 100 H. jenkinsii/fai 100 Yes 16 Himantura jenkinsii (Annandale, 1990) * Mangrove Bay, Ningaloo H. jenkinsii 99 H. jenkinsii 100 Yes 17 Himantura jenkinsii (Annandale, 1990) * Mangrove Bay, Ningaloo H. jenkinsii 100 H. jenkinsii 100 No 18 Himantura jenkinsii (Annandale, 1990) Shoal Bay, NT H. fai 100 H. jenkinsii/fai 100 Yes 19 Himantura leoparda Manjaji-Matsumoto and Last, 2008 Skeleton Bay, Ningaloo Rajiformes sp 99 H. uarnak 100 No 20 Himantura leoparda Manjaji-Matsumoto and Last, 2008 Ranger Bay, Ningaloo Rajiformes sp 100 H. uarnak 100 Yes 21 Himantura leoparda Manjaji-Matsumoto and Last, 2008 Ranger Bay, Ningaloo Rajiformes sp 100 H. uarnak 100 Yes 22 Himantura uarnak (Forskal, 1775)* Skeleton Bay, Ningaloo Rajiformes sp 99 H. uarnak 100 No 23 Himantura uarnak (Forskal, 1775)* Skeleton Bay, Ningaloo Rajiformes sp 100 H. uarnak 100 Yes 24 Himantura uarnak (Forskal, 1775)* Mangrove Bay, Ningaloo Rajiformes sp 100 H. uarnak 100 Yes

47 25 Himantura uarnak (Forskal, 1775)* Skeleton Bay, Ningaloo Rajiformes sp 100 H. uarnak 100 Yes 26 Himantura uarnak (Forskal, 1775) Skeleton Bay, Ningaloo Rajiformes sp 99 H. uarnak 100 Yes 27 Himantura uarnak (Forskal, 1775) Point Cloates, Ningaloo Rajiformes sp 99 H. uarnak 99 Yes 28 Himantura uarnak (Forskal, 1775) Point Cloates, Ningaloo Rajiformes sp 99 H. uarnak 99 Yes 29 Himantura uarnak (Forskal, 1775)* Stanley's Pool, Ningaloo Rajiformes sp 100 H. uarnak 100 Yes 30 Himantura uarnak (Forskal, 1775)* Mangrove Bay, Ningaloo Rajiformes sp 98 H. uarnak 100 Yes 31 Himantura uarnak (Forskal, 1775) Shoal Bay, NT Rajiformes sp 100 H. uarnak 100 Yes 32 Himantura uarnak (Forskal, 1775) Shoal Bay, NT Rajiformes sp 100 H. uarnak 100 Yes 33 Himantura uarnak (Forskal, 1775) Meckets Creek, NT Rajiformes sp 95 H. uarnak 100 No 34 Manta alfredi (Krefft, 1868) Stanley's Pool, Ningaloo M. birostris 99 M .birostris 99 Yes 35 Manta alfredi (Krefft, 1868) Stanley's Pool, Ningaloo M. birostris 99 M .birostris 99 Yes 36 Neotrygon kuhlii (Last and White, 2008) GBR, Qld Rajiformes sp 99 N. kuhlii 100 Yes N. kuhlii 96 37 Neotrygon kuhlii (Last and White, 2008) GBR, Qld Rajiformes sp 99 N. kuhlii 100 Yes N. kuhlii 96 38 Neotrygon kuhlii (Last and White, 2008) GBR, Qld Rajiformes sp 99 N. kuhlii 99 Yes N. kuhlii 96 39 Neotrygon kuhlii (Last and White, 2008) Point Edgar, Ningaloo Rajiformes sp 99 N. kuhlii 99 Yes N. kuhlii 96 40 Neotrygon kuhlii (Last and White, 2008) Skeleton Bay, Ningaloo Rajiformes sp 99 N. kuhlii 99 Yes N. kuhlii 96 41 Neotrygon leylandi (Last, 1987) 5 Fingers, Ningaloo D. leylandi 99 N. leylandi 100 Yes 42 Neotrygon ningalooensis (Last et al., 2010) 5 Fingers, Ningaloo Rajiformes sp 94 unable to match Yes 43 Neotrygon ningalooensis (Last et al., 2010) 5 Fingers, Ningaloo Rajiformes sp 93 unable to match Yes 44 Pastinachus atrus (Macleay, 1993)* Mangrove Bay, Ningaloo P. sephen 100 P. atrus/sephen 99 Yes 45 Pastinachus atrus (Macleay, 1993) Skeleton Bay, Ningaloo P. sephen 99 P. atrus/sephen 100 Yes 46 Pastinachus atrus (Macleay, 1993) Ranger Bay, Ningaloo P. sephen 100 P. atrus/sephen 100 Yes

48 47 Pastinachus atrus (Macleay, 1993) Ranger Bay, Ningaloo P. sephen 100 P. atrus/sephen 100 Yes 48 Pastinachus atrus (Macleay, 1993) Meckets Creek, NT P. sephen 99 P. atrus/sephen 94 Yes 49 Pastinachus atrus (Macleay, 1993) Shoal Bay, NT P. sephen 99 P. atrus/sephen 99 Yes 50 Taeniura lymma (Forsskal 1775)* Mangrove Bay, Ningaloo T. lymma 98 T. lymma 99 No 51 Taeniura lymma (Forsskal 1775) Mangrove Bay, Ningaloo T. lymma 99 T. lymma 100 Yes 52 Taeniura lymma (Forsskal 1775) Mangrove Bay, Ningaloo T. lymma 99 T. lymma 100 Yes 53 Taeniura lymma (Forsskal 1775)* Skeleton Bay, Ningaloo T. lymma 99 T. lymma 100 Yes 54 Taeniura lymma (Forsskal 1775) Point Look, Ningaloo T. lymma 99 T. lymma 99 No 55 Taeniura lymma (Forsskal 1775) Groote Island, NT T. lymma 99 T. lymma 100 Yes 56 Taeniura lymma (Forsskal 1775) Groote Island, NT T. lymma 99 T. lymma 100 Yes 57 Taeniura lymma (Forsskal 1775) Groote Island, NT T. lymma 99 T. lymma 98 Yes 58 Taeniurops meyeni (Muller & Henle, 1841) Point Look, Ningaloo Taeniura meyeni 98 Taeniura meyeni 100 Yes 59 Taeniurops meyeni (Muller & Henle, 1841) 5 Fingers, Ningaloo Taeniura meyeni 98 Taeniura meyeni 100 Yes 60 Taeniurops spp Ranger Bay, Ningaloo Taeniura meyeni 98 Taeniura meyeni 100 Yes 61 Urogymnus asperrimus (Bloch & Scneider, 1801) GBR, Qld Rajiformes sp 88 U. asperrimus 100 Yes 62 Urogymnus asperrimus (Bloch & Scneider, 1801) Ranger Bay, Ningaloo Rajiformes sp 88 U. asperrimus 99 Yes 63 Urogymnus asperrimus (Bloch & Scneider, 1801) Point Look, Ningaloo Rajiformes sp 88 U. asperrimus 98 Yes 64 unknown Darwin Harbour, NT A. narinari 98 A. narinari/ocellatus 99 Yes 65 unknown Darwin Harbour, NT A. narinari 98 A. narinari/ocellatus 99 Yes 66 unknown Ha Long Bay, Vietnam N. kuhlii 99 N. kuhlii 99 Yes 67 unknown Ha Long Bay, Vietnam N. kuhlii 100 N. kuhlii 100 Yes *Tagged ray

49 Chapter 2. Identification of rays through barcoding

Figure 5. Phylogenetic relationship of rays Part I. Reduced view of the neighbour-joining tree based on COI sequence data using Kimura-two-parameter substitution model (left); the first part of the tree (right). Names in red are the sequences obtained in this stud

50 Chapter 2. Identification of rays through barcoding

Figure 6. Phylogenetic relationship of rays Part II. Second part of the Neighbour-joining tree based on COI sequence data using Kimura-two-parameter substitution model (left); the second part of the tree (right). Names in red are the sequences obtained in this stud

51 Chapter 2. Identification of rays through barcoding

Figure 7. Phylogenetic relationship of rays Part III. Third part of the Neighbour-joining tree based on COI sequence data using Kimura-two-parameter substitution model (left); the third part of the tree (right). Names in red are the sequences obtained in this study

52 Chapter 2. Identification of rays through barcoding

2.4 Results

2.4.1 General findings Sixty-seven individuals representing 17 putative ray species and five unidentified individuals were barcoded for a fragment of the COI gene with an average length of ~550 base pairs. When translated all sequences showed no stop codons, indication of heteroplasmy or NUMTs. All 67 sequences were compared with those in BOLD and GenBank databases (Table 2) to confirm the initial identification. Sixty one individuals with a minimum of 519 bp were included in the phylogenetic analysis verified by forward and reverse primers. Six sequences of less than 519 bp were excluded from this analysis. A neighbour-joining tree (Figure 5Figure 7) summarizes the relationships among samples from this study and matching sequences from the same or related species available on both nucleotide databases. One species was misidentified leaving data for 16 species belonging to 10 genera, 3 families and 2 orders is presented after comparisons of this study’s sequences with those on BOLD and GenBank databases. Twenty rays tagged as part of an ecological study at Ningaloo Reef (Cerutti- Pereyra et al. unpublished data) were barcoded to confirm or correct field identifications (Table 3). Sequences of Himantura uarnak, H. fai, H. granulata, Aetobatus ocellatus, Pastinachus atrus, Taeniurops meyeni, Manta alfredi, Taeniura lymma, and Urogymnus asperrimus represent new sequences from Australia for the GenBank nucleotide database. Data for M. alfredi, and P. atrus represent new sequences from Australia for both BOLD and GenBank databases. Sequences for Neotrygon ningalooensis have no matching sequences in either the GenBank or BOLD databases and new sequences of N. kuhlii from Vietnam are also presented. The average congeneric distance (D = 8.5 %) was 14 times the average conspecific distance (D = 0.63 %) (Table 4). These calculations excluded the aberrant samples D. parvonigra from Indonesia (D = 9 %) (GenBank accession number EU398732) and H. uarnak from India (D = 12 %) (GenBank accession number EU541309.1). Approximately 90 % of within-species values had < 2 % divergence; ~ 20 % of these had < 1 % divergence and 10 % had between 2 and 3 % divergence.

53 Chapter 2. Identification of rays through barcoding

Table 4. Means and ranges of K2P distance values (%) for the COI gene region at different taxonomic levels for the ray species analysed in this study.

No. of Comparisons Mean Minimum Maximum comparisons Between individuals within species 60 0.63 0 3.00 Between species within genera 20 8.85 3.40 14.00

2.4.2 Taxonomic identification and barcoding The COI sequences for the combined dataset (Figure 5-Figure 7) grouped sequences into clearly defined operational taxonomic units, with two conspicuous exceptions. These consisted of what we refer to as N. kuhlii and Aetobatus species complexes. Spotted whip-tail rays presented the most difficulties for field identification and are referred to as the ‘uarnak’ complex. Complete consistency in field identification (often by different researchers) and the nomenclature of records held on GenBank and BOLD occurred for only one species, T. lymma, although the tree suggests a phylogeographic disjunction between the Indonesian and Australian samples of this species. Sequences of the recently described species Neotrygon ningalooensis (Last, White & Puckridge 2010) were placed in the same lineage with N. leylandi and N. kuhlii in the tree, but formed a clear and isolated cluster with an average genetic distance of 9 % compared with other species within this genus. Of the 67 sequences we tested, only 19 had consistent matches on both BOLD and GenBank (Table 3). As a consequence, there were a number of anomalies that meant that taxonomic identification was not straightforward or consistent. These anomalies were due to the presence of cryptic species, misidentification of species associated with sequences in the databases, or field misidentification of species in this study. These taxonomically complex groups and anomalies (Table 2Table 3) are discussed in more detail below:

Manta birostris (Walbaum, 1792) / Manta alfredi (Krefft, 1868) (Marshall, Compagno & Bennett 2009) – The submission of sequences identified as M. alfredi (# 34, 35) in both online databases produced matches of 99-100 % with M. birostris. Sequences of this species showed a phylogeographic disjunction in the NJ tree between the Indonesian and Australian samples in the tree, but a genetic divergence of < 1 %.

54 Chapter 2. Identification of rays through barcoding

Urogymnus asperrimus (Bloch & Schneider, 1801) – Sequences of this species from Ningaloo Reef (# 62, 63) and the Great Barrier Reef (# 61) clustered together in the tree and had an average genetic divergence of 0.32 %. The submission of sequences to GenBank produced either incorrect matches or matches only to the level of order . Sequences had matches of 98-100 % in the BOLD database for U. asperrimus. Glaucostegus typus (Bennet, 1830) – Sequences from Queensland (EU398732.1), Northern Territory (# 6), and Western Australia (#4, 5) were identical. Sequences in GenBank had 99-100 % similarity with G. typus and 100 % similarity in BOLD with G. typus and Rhinobatos typus (senior synonym of Glaucostegus). Dasyatis parvonigra (Last & White, 2008) (Last & White 2008a) – A single specimen identified as D. parvonigra from Shoal Bay, Northern Territory (# 3) had a 98 % similarity with a sequence on GenBank labelled as D. fluviorum (GenBank accession number DQ108183.1) from New South Wales, Australia and a 99 % similarity with a sequence on BOLD labelled as Dasyatis sp. from Indonesia. Furthermore, a sequence from Indonesia recorded as D. parvonigra (EU398732.1), while placed in the same lineage, differed by 9 %, whereas the average divergence with D. fluviorum from New South Wales was 1.6 %. Pastinachus sephen (Forsskal 1775) / Pastinachus atrus (Macleay, 1883) – Six sequences from rays identified by different researchers as P. atrus from Ningaloo Reef (# 44-47) and the Northern Territory (# 48, 49) clustered tightly with samples of P. sephen from Malaysia and Indonesia. The average genetic distance among samples was 0.29 %. In GenBank, the most closely matched sequences were labelled P. sephen. In BOLD the highest matches (100 %) included sequences identified as both P. atrus and P. sephen. Taeniurops meyeni (Muller & Henle, 1841) – Of the three Taeniurops rays sampled from Ningaloo Reef, two were initially identified as Taeniurops meyeni (Last & Stevens 2009) (# 58, 59) whereas the other was thought possibly to represent a new species because of an unusual colour pattern. The latter was provisionally referred to as Taeniurops spp (# 60). These sequences from Ningaloo Reef (n = 3) and one sequence under the name of Rajiformes (GenBank accession number GU673424.1) from Queensland were clustered tightly in the tree. There was a small difference between the Australian cluster and the sequence from India; however, the genetic distance among these sequences was low (0.36 %).The matching entries in both

55 Chapter 2. Identification of rays through barcoding

GenBank and BOLD were labelled as Taeniura meyeni. Last and Stevens (2009) revised the nomenclature of this species from Taeniura to Taeniurops. Neotrygon leylandi (Last & White 2008) (Last & White 2008b)– Sequences from Western Australia (n = 4) and Queensland (n = 3) for this species showed geographic variation with an average genetic distance among groups of 3 % compared to 0.13 % within groups. The sequence from Ningaloo Reef, W.A. (# 41), matched 100 % with sequences in BOLD labelled as N. leylandi and 99 % with sequences in GenBank labelled Dasyatis leylandi (Last 1987) (Last 1987) (senior synonym of Neotrygon). Himantura fai, Jordan & Seale, 1906 / H. jenkinsii (Annandale, 1909) – Three samples (# 15-17) from Ningaloo Reef identified in the field as H. jenkinsii matched sequences (99-100%) in both GenBank and BOLD. However, a different sample from the Northern Territory (# 18) also initially identified as H. jenkinsii matched a different species in GenBank (H. fai) and both Himantura fai and H. jenkinsii in BOLD. Four other individuals identified in the field as H. fai (# 9-12) and H. granulata (# 14) also clustered with this sample and were identified as H. fai in GenBank and H. fai and H. jenkinsii in BOLD. The average conspecific genetic distances for H. fai (including a sample initially identified as H. granulata, #14) and H. jenkinsii were 0.03 and 0.4 % respectively, while the average genetic distance between H. fai and H. jenkinsii was ~ 13 %. H. jenkinsii showed phylogeographic disjunction between samples from Indonesia/Southeast Asia and Australia, but a small genetic distance of < 1 %. Another sample, also identified as H. granulata (# 13) was clearly divergent in the tree, and matched H. hortlei (Last, Manjaji-Matsumoto & Kailola 2006) on GenBank (86 %) and H. granulata on BOLD (99 %). Neotrygon kuhlii complex (Last & White 2008b) – (formerly Dasyatis kuhlii). Sequences of N. kuhlii had overall levels of similarity of 99-100 % with sequences in both GenBank and BOLD databases. Sequences of unidentified rays from Vietnam (# 66-67) matched closely with N. kuhlii (99-100 % similarity) on BOLD. The sequences (n = 11) provisionally assigned to this species formed five distinct subgroups in the tree and multi-dimensional scaling analysis (Figure 8) and had an average genetic distance of ~ 3 %. These subgroups were: Great Barrier Reef (# 36- 38), Ningaloo Reef (# 39-0), Japan (AB485685.1), northern Indian Ocean (HM467799.1), Indonesia (EU398745.1), and Southeast Asia (Vietnam: # 66-67; Taiwan: EU398735.1). Average distances among and within these groups were 3 and

56 Chapter 2. Identification of rays through barcoding

0.15 %, respectively. The most divergent lineage was from the Great Barrier Reef, which had an average genetic difference of 3.5 % from the other sequences from this species. While there was generally a correspondence between the genetic distance and geographical proximity, the two Australian lineages from the western and eastern coasts had the greatest genetic distance (3.8 %).

Figure 8. Multidimensional Scaling (MDS) of Neotrygon kuhlii. Ningaloo Reef (NR), northern Indian Ocean (NIO), Great Barrier Reef (GBR), Japan (Jap), Southeast Asia (SEA).

Aetobatus complex: A. narinari Euphrasen 1790 / A. ocellatus Kuhli 1823 – Sequences from Ningaloo Reef identified by different researchers in the field as A. ocellatus (# 1-2) and from unidentified samples from the Northern Territory (# 64- 65), were identified as A. narinari using GenBank and as A. narinari and A. ocellatus using BOLD. Sequences of the species commonly referred to as the white-spotted eagle ray from the Caribbean region (Cayman Islands, Belize, and South-East Mexico) (Appendix 1) and sequences from the Indo-Pacific (Hawaii, Japan, Indonesia, and Australia) showed genetic differences (x¯ = 3.4%). The genetic distance of sequences from A. narinari within the Indo-Pacific, including sequences of A. ocellatus from Australia was low (x¯ = 0.86%). While the name A. narinari is consistently applied to what might be a distinct biological entity in the Caribbean, the

57 Chapter 2. Identification of rays through barcoding names A. narinari and A. ocellatus seemed to be applied interchangeably to a different biological entity that is widespread in the Indo-Pacific. The ‘uarnak’ complex: Himantura uarnak (Forsskal 1775), H. leoparda (Manjaji-Matsumoto & Last 2008), H. astra (Last, White & Pogonoski 2008), H. toshi (Whitley, 1939) – Samples of this group were identified by several researchers in the field. They were identified as H. leoparda (# 19-21), H. astra (# 7, 8) and H. uarnak (# 22-33) and all (except #19) were grouped together within the tree with an average genetic distance of 0.15 % (ignoring the aberrant sequence of H. uarnak from India). Comparisons with the BOLD database identified all these sequences as H. uarnak. In contrast, identifications from GenBank were either uninformative or misleading and applied only the name of the order (Rajiformes) (Table 2). The aberrant sequence of H. uarnak from India had a genetic distance of 12 % from this lineage. The BOLD database identified this sequence as H. uarnak (similarity of 100 %), H. gerrardi (Gray 1851) (similarity of 99 %) and Dasyatis microps (similarity of 99 %). A sequence from an individual collected from Shoal Bay identified as H. astra (# 8) matched sequences labelled as H. toshi in GenBank (100 %) and as H. toshi and H. astra (98 %) in BOLD. This sequence had an average genetic distance of 0.1 % with both H. astra and H. toshi, suggesting these two species may refer to the same biological species.

2.5 Discussion The aim of this study, to investigate the applicability of DNA barcoding for confirming field-based identifications of rays, was at best, a partial success. Two kinds of problems limited the successful application of DNA barcoding to rays. First, biological and taxonomic issues included: a) the presence of cryptic species, b) species complexes with a number of named species of uncertain or unresolved taxonomic status and c) widespread species with substantial intra-specific geographical variation. The second set of problems involved the limitations associated with the online databases including: a) insufficient numbers of taxonomically verified entries on GenBank and BOLD databases; and b) the presence of lodged sequences with incorrect, duplicated, outdated, inconsistent or unhelpful names (e.g. insufficient taxonomic resolution). Nevertheless, this study has demonstrated the potential power of the DNA Barcoding approach to confirm

58 Chapter 2. Identification of rays through barcoding field identifications, detect misidentifications, and discover cryptic species and species complexes with taxonomic issues. As with other barcoding studies of rays (Ward et al. 2008), the COI gene region was effective for their taxonomic identification and delineation. This was particularly the case for species in which the complexity of their colour patterns made identification difficult without the input from an expert taxonomist. The average intra-specific genetic distance within species (0.63 %) obtained here was larger than that reported for Australian chondrichthyans (0.37 %) by Ward et al. (2008). This could have arisen because the geographic extent of sampling was increased for a number of species. In contrast, the average congeneric distance we recorded (7.5 %) was similar (7.4 %) to that found by Ward et al. (2008). Twenty species of rays representing 9 species tagged as part of an ecological project were correctly and consistently identified using BOLD, albeit with some inconsistent nomenclature (Table 2). In a study of marine fish, Zemlak et al. (2009) suggested that similarity below 96.5 % could be used as a rule of thumb for discriminating species. All of these samples had BOLD matches  98 %, with these levels well within the tolerance range for intra-specific genetic divergence. DNA Barcoding has also been useful when only parts of an animal are available for identification (e.g. Domingo-Roura et al. 2006; Holmes, Steinke & Ward 2009; Valière et al. 2003). The value of barcoding in this context was confirmed by the identification of ‘unknown’ species from tissues samples obtained from rays in markets near Ha Long Bay, Vietnam (#66,67) and from fishers in the Northern Territory (#65,65) as belonging to the N. kuhlii and A. narinari species complexes, respectively. In both cases, the match between this study’s sequences and the BOLD database was  99%. Furthermore, as both species groups displayed significant geographic variation, the confidence of identifications was enhanced due to lodgements on data bases of sequences from individuals from a range of geographic localities. Barcoding has been used successfully to aid in the identification of species with morphological complexity (e.g. Amaral, Sequeira & Coelho 2007; Ovenden et al. 2010; Toffoli et al. 2008). Not all field identifications in this study were correct or reliable, with a total of nine specimens representing four species identified incorrectly. Field identification was particularly challenging in the ‘uarnak’ complex group due to similarities in colour patterns among species. While the DNA sequences 59 Chapter 2. Identification of rays through barcoding as summarized in the tree indicated clear taxonomic groupings, the fact that identical reference sequences on the BOLD database were labelled with two different names further complicated taxonomic identification. Lastly, one specimen thought to be a possible new species of Taeniurops (# 60) based on colour patterns was unambiguously identified from the BOLD database as Taeniura meyeni and was genetically identical to other samples of this species from Ningaloo Reef. Another example of ambiguous taxonomy, which limited the value of barcoding for rays, involved the species P. atrus and P. sephen. The low sequence divergence and the absence of any geographic structure in the relationships among the sequences of P. atrus and P. sephen indicated that the sequences available online under these different names are most likely the same species. Furthermore, the close relative found in the Red Sea that was originally named as Pastinachus sephen was morphologically different from the Indo-Pacific form (Last & Manjaji-Matsumoto 2010; Last & Stevens 2009). The databases were uninformative for two species. D. parvonigra (# 3) was identified simply as Dasyatis sp in the BOLD database while GenBank matched an entirely different species, D. fluviorum to the sequence obtained here. Neotrygon ningalooensis (# 42,43) represents a new species (Last, White & Puckridge 2010) for which sequences are not yet available in the databases with no matching sequences greater than a similarity of 89 %. Overall, these results show that a great deal of care must be taken when using DNA barcoding to confirm field identifications, particularly with groups that have a recent history of nomenclatural changes. When the online search engines gave ambiguous responses to sequence submissions, the phylogenetic tree and genetic distances analyses proved useful aids to identification. The misidentification of several species belonging to the genus Himantura on the basis of morphology confirms the taxonomic complexity of the genus, which has been continuously reviewed for the last 10 years (Last & Stevens 1994; Last & Stevens 2009; Last, White & Pogonoski 2008; Manjaji-Matsumoto & Last 2008). The ‘uarnak’ complex is a group of whip-rays with spotted, ocellated and reticulated dorsal patterns that up until 2008, had 7 valid nominal species (Last, White & Pogonoski 2008). Identification of members of this complex was further complicated due ontogenetic changes in colour patterns that can lead to misidentification of different life-history stages of the same species (Manjaji-Matsumoto & Last 2008). Field identification of species within this group was challenging because of the

60 Chapter 2. Identification of rays through barcoding similarities in colour patterns among H. uarnak, H. leoparda, and H. astra. The clustering of H. leoparda as H. uarnak in the tree suggests that these two named species represent the same biological species in this study. While the Australian samples are clearly a distinct species, a sequence from India (GenBank accession number EU541309.1) lodged under the same name is genetically quite different (12 %) when compared with the rest of H. uarnak sequences and may represent a new species more closely related to H. gerrardi. Himantura fai and H. jenkinsii also proved difficult to distinguish in the field. As discussed above, the obtained sequences matched both H. fai and H. jenkinsii in the BOLD database; however, the tree clearly showed that these are distinct species, suggesting that a revision of the names attached to sequences in the BOLD database is required. Sequences assigned to H. astra and H. toshi also need to be reviewed (Last, White & Pogonoski 2008). The tree suggests there is only one species, but the BOLD database again produced ambiguous results with sequences being identified as both H. toshi and H. astra with similarities of 100 %. Confusion in taxonomy was also a problem for the genus Aeotobatus. Aetobauts narinari represents a widespread species complex and the pattern of geographic variation in COI indicates that there are two closely related forms. One distinct species, A. narinari, occurs in the north Atlantic and the other that occurs in the Indo-Pacific should be referred to A. ocellatus (Richards et al. 2009; White et al. 2010). To add to the uncertainty involving these species, the BOLD database identified these sequences as both A. narinari and A. ocellatus. This results were consistent with those of Richards et al. (2009) and Schluessel et al. (2010) who analysed sequences of cytochrome b and COI and found that individuals of A. narinari from the west Atlantic formed a distinct lineage compared with those from the Indo-Pacific. Based on a morphological review, White et al. (2010) proposed that A. ocellatus is a separate species restricted to the Indo-West Pacific and distinct from the A. narinari complex. The average genetic distance between sequences of A. narinari from the Caribbean Sea and sequences from the Indo-Pacific region labelled as A. narinari in this study was 3.4 %, consistent with the idea that the Atlantic and Indo-Pacific lineages are separate species. This pattern and geographical divergence between Atlantic and Pacific stocks has been observed in other elasmobranchs such as Squalus acanthias (Ward et al. 2007).

61 Chapter 2. Identification of rays through barcoding

The Neotrygon kuhlii species complex is also widespread, with the maximum divergence close to the rule of thumb for discriminating species. Geographic differences in genetic divergences indicate the possibility of three differentiated clades consisting of a) east Asia (Vietnam, Taiwan, and Japan); b) the eastern Indian Ocean (India, Indonesia, and Ningaloo Reef, Australia) and c) the Great Barrier Reef (Australia). This is consistent with the suggestion by Ward et al. (2008) of the possibility of cryptic species within N. kuhlii. Further research is required to determine geographic boundaries and to examine variation in other genes (e.g. microsatellite loci) to establish if this group is undergoing incipient speciation. The geographic spread of genetic sampling for several rays in the tropical Indo- Pacific was increased and a number of contrasting patterns have emerged that might be of taxonomic or biological importance. Several species were noteworthy for having little genetic divergence over large (1000s of km) distances. For example, Glaucostegus typus (# 4-6) shared haplotypes between Ningaloo Reef, Western Australia and Northern Territory; U. asperrimus (# 61-63) shared haplotypes between Ningaloo Reef, Western Australia and Queensland; H. fai (# 9-12, 18) and P. atrus (# 44-49) both shared haplotypes between Ningaloo Reef, the Northern Territory and Malaysia. These results suggest that these species all have high vagility, at least at generational time scales. In contrast, T. lymma (# 50-53, 56, 57), H. jenkinsii (# 15, 16), and M. alfredi (# 34, 35) showed little (< 1 %) but potentially biologically relevant variation in sequences between Australia and Indonesia. While these sample sizes were small, this result implies that the biogeographic factors responsible for population differentiation could potentially act on the three species in a similar way. The possibility of population differentiation in M. alfredi is supported by the observations of strong residency patterns in Indonesia (e.g. Dewar et al. 2008) and in Ningaloo Reef (F. McGregor, pers. comm.) based on acoustic tagging and photo-identification studies. An individual misidentified in the field as D. parvonigra (# 3) from the Northern Territory was in fact a new record of D. fluviorum, a species that was previously thought to occur only along the eastern coast of Australia (Last & Stevens 2009). The extent of genetic divergence within several species (N. kuhlii, N. leylandi) from the north-west and east of Australia might reflect historical isolation when the land bridge between New Guinea and northern Australia formed during the

62 Chapter 2. Identification of rays through barcoding

Holocene and late Pleistocene (Voris 2000). A number of marine and coastal species (including elasmobranchs) show this pattern of differentiation caused by vicariant events (Avise 1992; Keenan 1994; Rawlings, Barker & Donnellan 2004; Sandoval- Castillo et al. 2004; Sandoval-Castillo & Rocha-Olivares 2011). Further investigation of this idea would require intensive sampling of these rays for both nuclear and mitochondrial markers between Torres Strait and the Arafura-Timor Seas to understand the geographic basis for genetic differentiation. It was, however, surprising to find such discordance between genetic differentiation and body size in some rays (e.g. T. lymma vs M. alfredi) because it is generally assumed that body size and dispersal capacity are correlated in elasmobranchs (e.g. Bruce, Stevens & Malcolm 2006). Several genetic studies have found surprisingly strong population structure in sharks and rays considered vagile that might be related to site fidelity in both adults and juveniles or deep water acting as barriers to dispersal (Keeney et al. 2003; Sandoval-Castillo et al. 2004; Sandoval-Castillo & Rocha-Olivares 2011; Schrey & Heist 2003). The general limits and pitfalls of DNA barcoding as a stand-alone tool for identifying species and delimiting taxonomic boundaries have been dealt with elsewhere (e.g. Ebach & Holdrege 2005; Hajibabaei et al. 2007; Rubinoff 2006; Tautz et al. 2003; Valentini, Pompanon & Taberlet 2009). However, it is worth reiterating that taxonomic decision-making solely on the basis of a single maternally inherited marker will not identify all biological species. Other studies of rays have found that mtDNA sequences have not been useful for delimiting species boundaries since haplotypes can be shared, particularly between newly evolved species (Toffoli et al. 2008). Conversely, it is possible that some species with higher genetic distances that approach the (arbitrarily defined) species-level thresholds might be able to interbreed. Such rules-of-thumb for genetic distance will vary in their usefulness among gene regions and across taxonomic groups and will inevitably be a “one-way” test for species discrimination (Meyer & Paulay 2005; Munasinghe, Murphy & Austin 2003; Ward et al. 2008). While barcoding for rays was largely successful as an identification tool in this study, there were several limitations. To succeed, barcoding must be able to reference a stable and well-defined taxonomy and have access to a sufficient number of barcodes lodged on databases that have been verified taxonomically (Meyer & Paulay 2005). Several species groups were found to require taxonomic review both

63 Chapter 2. Identification of rays through barcoding to define confidently species boundaries and revise nomenclatures. Furthermore, the continued updating of sequences lodged on GenBank and BOLD is a vital, but a rarely considered issue in the practical application of barcoding. The specimens from which sequences are derived must first be identified by a competent taxonomist. The names assigned to sequences need to be updated on the online genetic data bases when taxonomies are revised and names changed. Fifty-eight percent of these sequences did not matched entries on GenBank and 30 % showed ambiguous results on BOLD due to confusing nomenclature (Table 2). For example, in the cases of H. fai versus H. jenkinsii and H. astra versus H. toshi, the BOLD search engine showed a 99-100 % similarity with both names in each case, thereby invalidating the simple use of BOLD as an identification tool. Furthermore, a number of ray sequences on GenBank were identified only to genus or family level making them uninformative for DNA barcoding-based identification.

2.6 Conclusions DNA barcoding was successful in validating field identifications and correcting misidentifications of tagged rays at Ningaloo Reef, WA, although application of the technique was somewhat problematic due to the inconsistency and ambiguity of taxonomic information available on the online data bases. The genetic analyses have resulted in a better understanding of intra-species diversity and biogeographic patterns along the coast of northern Australia and at localities across the Indo-Pacific that will ultimately be useful for delimiting species boundaries, fisheries management and conservation of tropical rays. In the future, the usefulness of ray barcoding will be directly related to the quantity and geographic representation of sequences, the number of sequences from taxonomically verified specimens, taxonomic revisions of key species complexes and a revision of the taxonomic nomenclature assigned to existing sequences on genetic data bases. With these advances, together with the recent production of COI sequences and taxonomic studies in Australia (Last & Stevens 2009; Ward et al. 2008) and Indonesia (Last, White & Pogonoski 2010; White, Last, et al. 2006), barcoding for species identification of rays will become far less problematic, at least for this region. Such an approach needs to be extended to areas with high diversity of rays around the world.

64 Chapter 3

Restricted movements of juvenile rays in the lagoon of Ningaloo Reef, Western Australia – evidence for the existence of a nursery. Chapter 3

Restricted movements of juvenile rays in the lagoon of Ningaloo Reef, Western Australia – evidence for the existence of a nursery. Chapter 3- Restricted movements of juvenile rays

Chapter 3- Restricted movements of juvenile rays in the lagoon of Ningaloo Reef, Western Australia – evidence for the existence of a nursery.

3.1 Preface

Chapter 3 has been published in Environmental Biology of Fish. I am the primary author of the manuscript; two of my supervisors Mark Meekan (AIMS) and Chris Austin (Monash University, formerly CDU), as well as Corey Bradshaw (University of Adelaide, formerly CDU), Michele Thums (University of Western Australia/AIMS), John Stevens (CSIRO), Russ Babcock (CSIRO), and Richard Pillans (CSIRO) are additional co-authors. The format of this chapter has undergone minor reformatting for inclusion here according to CDU formatting regulations for PhD thesis. I conducted most of the internal tagging as well as analysis, writing, and literature research. C. Austin and C. Bradshaw provided support and editorial comments. J. Stevens, R. Babcock, and R. Pillans contributed with deployment and servicing of acoustic receivers, data downloading, tagging, and editorial comments. M. Thums and M. Meekan assisted with substantial support on analysis and providing editorial comments.

Chapter 3 was presented at the Sharks International Conference, 6-11 June, Cairns, 2010. Information and results from Chapter 3 have also been included in the following reports:

Stevens, J., P. R. Last, W. T. White, R. B. McAuley, and M. G. Meekan. 2009. Diversity, abundance and habitat utilisation of sharks and rays. Final report to Western Australian Marine Science Institute. CSIRO

Cerutti-Pereyra, F. 2011. Investigating stingray movements, populations and mating systems. Ningaloo research. CSIRO Ningaloo Collaboration Cluster, WAMSI, and AIMS.

Paper citation: CERUTTI-PEREYRA, F., MEEKAN, M. G., THUMS, M., STEVENS, J. D., BABCOCK, R. C., PILLANS, R. D. & AUSTIN, C. 2013. Restricted movements of juvenile rays in a nursery area of Ningaloo Reef. Environmental Biology of Fishes, DOI 10.1007/s10641-013-0158-y.

67 Publication front-page

Restricted Movements of Juvenile Rays in the lagoon of Ningaloo Reef, Western Australia – evidence for the existence of a nursery

Florencia Cerutti-Pereyra1,2, Michele Thums2,3, Chris M. Austin4 ,Corey J. A. Bradshaw5,6, John D. Stevens7, Russ C. Babcock8, Richard D. Pillans8 and Mark G. Meekan2

1Research Institute of Environment and Livelihood, Charles Darwin University, Northern Territory, Australia 2Australian Institute of Marine Science, UWA Oceans Institute, Western Australia, Australia 3School of Environmental Systems Engineering and UWA Oceans Institute, The University of Western Australia, Australia 4 School of Science Monash University Sunway Campus, J Selangor, Malaysia 5The Environment Institute and School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, South Australia, Australia 6South Australian Research and Development Institute, P.O. Box 120, Henley Beach, South Australia 5022, Australia 7CSIRO Marine and Atmospheric Research, Hobart, Tasmania, Australia 8CSIRO Marine and Atmospheric Research, Ecosciences Precinct, Brisbane, Queensland, Australia

Abstract Little information is available on the movements and behaviour of tropical rays despite their potential ecological roles and economic value as a fishery and a tourism resource. We provide the first description of the movement patterns and site fidelity of juvenile rays within a coral reef environment. We used acoustic telemetry to focus on the use of potential nursery areas and describe movement patterns of 15 individuals of four species monitored for 1-21 months within an array of 50 listening stations deployed across a lagoon, reef crest, and reef slope at Ningaloo Reef, Western Australia. We found that juveniles used a small (< 1 km2), shallow (1-2 m depth) embayment where three receivers recorded 60-80% of total detections of tagged animals, although individuals of all species moved throughout the array and beyond the lagoon to the open reef slope. Detections at these primary sites were more frequent during winter and when water temperatures were highest during the day. Long-term use of coastal lagoons by juvenile rays suggests that they provide an important habitat for this life stage. Current marine park zoning appears to provide an effective protection for juveniles within this area.

68 Chapter 3- Restricted movements of juvenile rays

3.2 Introduction Batoids are more vulnerable to exploitation than teleosts due to their life history traits of low fecundity, late sexual maturity, long gestation and relatively slow grow rates (Frisk 2010). Although rays are targeted for fisheries throughout the world and are frequently taken as by-catch (Dulvy et al. 2000; Dulvy & Reynolds 2002; Heupel & Heuter 2001a; Romanov 2001; Stevens 2002; Ward, R. Hanner & Hebert 2009), relatively little is known about their ecology and movement patterns. The low fecundity of this group suggests that survival during juvenile stages is a key element of population persistence, and thus behaviour and movement patterns during the juvenile phase is an important focus for research (Heupel, Carslon & Simpfendorfer 2007). Many sharks and rays are thought to use nursery areas (Ache de Freitas et al. 2009; Castro 1993; DeAngelis 2008), which are defined as areas where gravid females give birth and where the young spend their first weeks, months or years (Ache de Freitas et al. 2009; Castro 1993; DeAngelis 2008; Garla, Chapman, Shivji, et al. 2006). Heupel et al. (2007) proposed that within a nursery area, higher abundances of individuals less than one year old should be found and that these animals should display long residency times and site fidelity across years. Such nurseries often occur in estuarine and lagoon systems and are thought to provide enhanced feeding opportunities and protection from predation during early life. Lower predation rates can occur in nurseries because predators enter these environments infrequently and higher relative turbidity protects young from detection and attack (Holland et al. 1993; Yeiser, Heupel & Simpfendorfer 2008; Yokota & Lessa 2006). As individuals grow, food demands increase and activity space increases to meet this need (Collins et al. 2007; Morrisey & Gruber 1993) so that young are eventually forced to venture beyond the nursery area. Although there is unequivocal evidence for the use of nursery areas in a restricted number of species of sharks (Heupel et al. 2006; Speed et al. 2010) there have been few studies of this phenomenon in rays. Yokota and Lessa (Yokota & Lessa 2006) found evidence for the use of nursery areas by eight species of ray along the shallow coast of north-eastern Brazil, based on catches in artisanal and small- scale fisheries. Their study supported the idea that nursery areas were most critical

69 Chapter 3- Restricted movements of juvenile rays for slow-growing species that remained at higher risk of predation for longer than fast-growing species (Branstetter 1990; Kinney & Simpfendorfer 2009). For many sharks the use of nurseries coincides with changes in water temperatures. For example, a rise in water temperatures in early summer elicits a movement of juvenile sandbar sharks (Carcharhinus plumbeus, (Nardo, 1827)) into coastal nursery areas for the summer (Grubbs et al. 2005). Similarly, juvenile reef sharks such as Carcharhinus amblyrhynchos (Bleeker, 1856) occupy a small bay at Ningaloo Reef when water temperatures are warmest over the summer (Speed et al., unpublished data). Such studies and the large amount of evidence for the effects of temperature on diel movements, seasonal migrations, distribution and abundance of elasmobranchs (Conrath 2008; Heupel 2007; Hopkins & Cech Jr 2003; Springer 1960) suggest that water temperature may be an important factor determining the temporal scales of use of coastal nurseries by rays. Recent advances in tagging technology have made studies of movement patterns of elasmobranchs in coastal waters more tractable. Acoustic telemetry has been used to examine the small-scale (metres to 10s of km) movement and residency patterns of sharks and rays in shallow coastal waters over temporal scales ranging from hours to years (Dewar et al. 2008; Klimley, Kihslinger & Kelly 2005; Speed et al. 2010). In contrast to catch data from fisheries, acoustic monitoring is non- destructive and provides multiple records of position for an individual assuming that tagged animals remain within the bounds of the receiver array. This is useful given the vulnerability of many species to over-exploitation and restrictions on destructive sampling in marine protected areas. In addition, information provided by tracking studies can inform management by identifying critical habitats such as nurseries and can be used to assess the effectiveness of protected areas (Heupel, Simpfendorfer & Hueter 2004; Hight & Lowe 2007; Simpfendorfer & Heupel 2004). The first study of the movement patterns of rays within a coral reef environment at Ningaloo Reef, Western Australia is described here. Restricted patterns of movement and long-term (months to years) residency within defined habitats would be expected if nurseries are important to this life history stages. The extent to which residency and movement patterns of rays are related to water temperature was also examined because water temperatures affect the growth, embryonic development, parturition, and feeding rates of elasmobranchs (Economakis & Lobel 1998; Fangue, Bennett & Douglas 2003; Heupel & Heuter

70 Chapter 3- Restricted movements of juvenile rays

2001a; Hight & Lowe 2007; Kinney & Simpfendorfer 2009). Predictable patterns in habitat use that are strongly correlated with water temperatures would be expected if water temperature drives patterns of distribution.

3.3 Methods

3.3.1 Study site This study was done using the acoustic array at Mangrove Bay (Figure 1, Figure 2, Figure 9). The study site is described in Chapter 1, section 1.3.2.1.

3.3.2 Acoustic tagging Rays were tagged in February and November 2008 near receiver 3 (Figure 9). Gill and throw nets were used to catch rays in shallow (0.5 m) water. Each individual was then immobilized in a hand net, photographed, measured (cm disc width, DW) and externally tagged with a spaghetti tag (FD-94 Anchor tags, Floy Tag & Manufacturing) to avoid the possibility of double-tagging with acoustic tags (Figure 10, Table 6). Tissue samples were taken from the right pelvic fin for genetic analyses of Chapter 2 (Cerutti-Pereyra et al. 2012) to confirm initial species IDs based on Last and Stevens (2009). Rays were then turned upside-down and an acoustic tag (VEMCO v13-1H transmitter; battery life approximately 16 months; transmitting interval: 120-240 sec) was inserted in the gut cavity following the methods of Heupel et al., (2006) (Figure 10). Prior to implantation, each tag was coated with paraffin to reduce any possibility of an immune response to the presence of a foreign body within the ray (Heupel et al. 2006). Measurement and tag insertion required between 5-10 minutes, after which individuals were released at the site of capture and monitored until recovery for 10 minutes. All of the 16 rays tagged were juveniles based on published sizes at maturity (Last & Stevens 2009). Rays were monitored within the array until May 2009.

3.3.3 Analysis The mean, minimum, and maximum temperature per hour and month was calculated (Table 5). Hourly wind speed data were collected by the Australian Government Bureau of Meteorology (BOM) climate station at Learmonth, the closest monitoring station to the study site (approx. 30 km). The data were requested from the website: http://www.bom.gov.au/.

71 Chapter 3- Restricted movements of juvenile rays

Numbers and proportions of detections from each ray at each receiver were used to estimate site fidelity. For each individual, the receivers that recorded the most detections of a tag was identified as ‘primary’ receivers (Garla, Chapman, Wetherbee, et al. 2006). The number of days each ray was recorded within the array was used to calculate the minimum percentage of days it was present at its primary receiver (Garla, Chapman, Wetherbee, et al. 2006). Temporal patterns in numbers of detections at the primary receivers were analyzed in order to determine patterns of seasonal movement of rays. The number of monthly detections at the primary receivers was modelled as a function of month and species. Total detections per ray per month were used as the response variable rather than the proportion of detections per ray per month as the latter were not normally distributed and this problem was not improved by transformation. Temporal patterns in occurrence were the same for both total and proportional detections. A non-linear relationship between the number of detections and time (month) was hypothesized, therefore a quadratic term (the square of month) was also included in some models. Due to low sample sizes, data from Himantura uarnak (Forskål, 1775) and detections from the final month of the study (May 2009) were excluded. The number of monthly detections was log- transformed to normalize distributions of data. Month was coded as an integer from 1 (February 2008) – 15 (April 2009). A suite of generalised linear mixed-effect models were then constructed using all combinations of the explanatory variables where the random effect was the individual ray. Temporal autocorrelation using the acf function in R was tested for but none was found.

72 Figure 9. Study site, Ningaloo Reef, WA. Mangrove Bay receiver array, black lines show boundary of the sanctuary zone.

73 Chapter 3- Restricted movements of juvenile rays

Figure 10. Acoustic tagging of juvenile rays. A) Gill net set up for catching small rays in shallow water; B) & C) handling small ray with deep net; D) acoustic tag V13; E) external tagging with spaghetti tag; F, G) internal acoustic tagging; H) releasing ray after handling.

74 Chapter 3- Restricted movements of juvenile rays

All models were fitted in R (R Development Core Team 2011) using package lme4 (Bates & Sarkar 2007), and ranked them according to weights of Akaike’s information criterion corrected for small sample sizes (wAICc). The wAICc varies from 0 (no support) to 1 (complete support) (Burnham & Anderson 2002).A linear mixed-effects models was also applied to examine whether the number of monthly detections varied between the sexes, again comparing model AICc weights. These models were fitted using the R package nlme (Pinheiro et al. 2011). The corAR1 function was used to account for temporal autocorrelation. The weight of the slope model (number of monthly detections ~ sex + random effect) was divided by that of the intercept-only model (number of monthly detections ~ 1 + random effect) to provide an information-theoretic evidence ratio (Burnham & Anderson 2002). The random effect was the individual ray as above. Similarly, the relationship between the mean proportion of hourly detections and the mean hourly temperature was examined by comparing the slope model to the intercept only model and explored the spatial distribution of diel detections. Average wind speed per day and month were plotted against proportion of detections to look for any correlations between wind speed and receiver detection efficiency. High winds creating noisy surface waves and breakers would be expected to reduce receiver efficiency and thus reduce the likelihood of tag detections, particularly in the shallow waters of the lagoon, while calm weather would have the opposite effect, reducing ambient noise from waves and increasing receiver efficiency and the number of tag detections (Payne et al. 2010). Fast Fourier Transformations (FFTs) with Hamming window smoothing were used in the package MATLAB (MATLAB 2010) to search for cyclical patterns in detections. FFTs reveal cycles in the data as frequency peaks in a power spectrum (Meyer et al. 2009; Papastamatiou et al. 2009). Detections were summed up in each hour of every day for all individual per species and the FFT searched for monthly, weekly, and hourly patterns.

75 Table 5. Location (in decimal degrees), habitat and detections of the receivers deployed in the Mangrove Bay array. Temperature data are from loggers deployed with some receivers.

Min Max Mean Receiver Total % Latitude Longitude Habitat temp temp temp Number Detections Detections (°C) (°C) (°C) 0 -22.001000 113.925983 rocky 1 0.00 1 -21.979750 113.902972 deep water 4 0.01 2 -21.948033 113.921417 rocky 4 0.01 18.6 31.5 25.05 3 -21.967150 113.936278 mud 1474 2.00 22.4 32.5 27.45 4 -21.996800 113.930467 rocky 17 0.02 25.1 31.7 28.4 5 -21.993867 113.925183 rocky 8 0.01 6 -21.994600 113.905767 deep water 0 0.00 24.5 32.7 28.6 7 -21.979933 113.920817 sand 4 0.01 23.9 28.4 26.15 8 -21.971478 113.919250 rocky 0 0.00 21.8 29.4 25.6 9 -21.980433 113.929150 sand 111 0.15 10 -21.992250 113.920233 rocky 0 0.00 11 -21.950428 113.943828 sand 310 0.42 12 -21.987217 113.925433 rocky 0 0.00 23.1 29.4 26.25 13 -22.001067 113.903267 deep water 2 0.00 14 -21.978500 113.911450 deep water 1 0.00 15 -21.962569 113.941978 mud 23438 31.83 16 -21.997000 113.915017 rocky 6 0.01 17 -21.947928 113.932500 sand 288 0.39 22.2 29.3 25.75 18 -21.983150 113.912278 deep water 4 0.01 20.2 31.7 25.95 19 -21.967619 113.939169 mud 44563 60.51 15.5 33.4 24.45 20 -21.955769 113.913100 deep water 1 0.00 21 -21.977050 113.918767 sand 3 0.00 21.8 31.3 26.55

76 22 -21.983883 113.903617 deep water 6 0.01 23 -21.989150 113.914817 rocky 0 0.00 24 -21.964219 113.938828 mud 335 0.45 25 -22.006083 113.920833 sand 2 0.00 19.9 28.4 24.15 26 -21.969150 113.919164 rocky 6 0.01 24.2 26.6 25.4 27 -21.975733 113.907300 deep water 0 0.00 28 -21.989150 113.901633 deep water 0 0.00 29 -21.973900 113.930033 sand 56 0.08 30 -21.968478 113.929900 sand 44 0.06 31 -21.974533 113.923833 sand 5 0.01 32 -22.994880 113.796150 sand 2 0.00 33 -22.987420 113.799867 sand 6 0.01 34 -21.991317 113.897367 deep water 4 0.01 35 -21.984517 113.931683 rocky 38 0.05 36 -21.973017 113.910650 rocky 0 0.00 37 -21.948506 113.926383 sand 18 0.02 22.9 31.3 27.1 38 -21.957167 113.941100 rocky 683 0.93 23.4 28.2 25.8 39 -22.004794 113.911528 rocky 2 0.00 20.3 28.4 24.35 40 -21.962067 113.933617 sand/rock 311 0.42 41 -21.999333 113.921117 sand 41 0.06 42 -22.012750 113.898550 deep water 0 0.00 43 -23.041500 113.748500 sand 0 0.00 22.4 30.6 26.5 44 -21.996233 113.901267 deep water 0 0.00 45 -21.947481 113.938720 sand 42 0.06 46 -21.983164 113.907800 deep water 3 0.00 47 -21.989200 113.908467 sand 0 0.00 48 -21.985583 113.918917 deep water 0 0.00

77 49 -22.005900 113.915778 sand 14 0.02 50 -21.971828 113.902219 deep water 1781 2.42 22.1 27.1 24.6 51 -21.965483 113.90955000 sand 0 0.00 24.4 29.3 26.85

Table 6. Details of all tagged rays and detection data. G. typus measurements are total lengths. LD= last detection, TD= total detections, DD= detected days, #RD= number of receivers detecting.

DW Date Species Sex LD TD DD #RD (cm) tagged G. typus F 85.2 23-Feb-08 26-Apr-09 3842 274 17 G. typus M 97.8 24-Feb-08 10-Sep-08 4391 75 8 G. typus M 105 26-Feb-08 15-Jul-08 170 26 8 G. typus F 72 23-Nov-08 5-Mar-09 466 76 16 G. typus M 119 24-Feb-08 3-Apr-08 119 17 11 H. uarnak M 76 23-Nov-08 21-May-09 2436 108 5 P. atrus F 45.8 23-Feb-08 27-Sep-08 524 76 13 P. atrus F 55 23-Feb-08 1-Aug-08 4801 63 16 P. atrus M 81 28-Feb-08 21-May-09 6206 269 7 P. atrus F 76 6-Dec-08 21-May-09 4316 148 9 P .atrus M 84 20-Nov-08 20-May-09 1069 74 6 P. atrus M 49 25-Feb-08 18-May-08 1700 20 13 U. asperrimus M 74.5 24-Feb-08 10-Feb-09 9305 256 5 U. asperrimus M 55 24-Feb-08 8-May-09 16192 326 8 U. asperrimus M 58.5 24-Feb-08 22-Feb-09 5796 138 14 U. asperrimus F 53.7 23-Feb-08 3-Aug-08 9960 78 12

78 Chapter 3- Restricted movements of juvenile rays

3.4 Results Water temperature at Mangrove Bay from February 2008 to May 2009 ranged from 15.5 to 33.4 °C, varying over different seasons by almost 18°C (Table 5). Temperatures in the bay peaked in February 2008 at 33.4°C and reached a low of 15.5°C in June-August, 2008. A total of 71293 detections from the 16 individual rays tagged in February and November 2008 are summarized in Table 5. The majority (94 %) of all detections for all rays were obtained from three receivers (nos. 3, 15 & 19, Figure 11) (Table 6) located in the shallowest area of the array (< 2 m water depth) on muddy sediments close to the mangrove forest (Figure 9, Table 5) and within the sanctuary zone (Figure 9,Figure 12 , Figure 13). These receivers were considered primary sites, although 82 % of the receivers had at least one detection of a tagged ray at some point during the study. Pastinachus atrus (Macleay, 1993) (n = 5) was detected on receivers within the sanctuary zone for 93 % of the total time the species was monitored here. Three individuals of this species were never detected on receivers outside the sanctuary zone. Juvenile Glaucostegus typus (Bennett, 1830) (n = 4) were detected for 92 % of the time they were monitored inside the sanctuary zone, while Urogymnus asperrimus (Bloch & Schneider, 1801) (n = 4) was detected for 98 % of the time they were monitored inside the zone. Almost 99% of all detections of one tagged H. uarnak occurred on receivers within the sanctuary boundaries. Modeling suggested that detections of tagged animals varied by month and species. Two plausible models described the number of detections per month, with a model that included month, its quadratic term and species having 50% support and a model that only including month and its quadratic term having 49% support (Table 7). Detections increased from February 2008 until August and September of 2008 and then declined (Figure 14). Peak detections were similar for all species although P. atrus had the least detections. The highest number of detections (May-Aug, 2008) coincided with the lowest temperatures (Figure 14) and wind speeds of the year (Figure 18). Pastinachus atrus were detected within the area of the array from 5-18 months after tagging. Similarly, juvenile U. asperrimus were detected for between 7- 15 months, while G. typus were recorded from 2-15 months after tagging. The upper time limit for detections of these species probably reflects the lifetime of the tag battery (around 16 months).

79 Chapter 3- Restricted movements of juvenile rays

Table 7. Ranked generalized linear mixed effects models of log transformed number of detections per month explained by species, month, month squared and random effect individual ray (id). Degrees of freedom (df), Akaike’s information criterion corrected for small samples (AICc), change in AICc relative to the top ranked model (ΔAICc) and the AIC weights (wAICc).

Model df AICc ΔAICc wAICc ~ species + month + month2 + (1|id) 7 530.27 0.00 0.50 ~ month2 + month + (1|id) 5 530.30 0.03 0.49 ~ species + month + month2 + species × month + (1|id) 9 538.17 7.90 0.01 ~ species + (1|id) 5 555.51 25.23 < 0.01 ~ 1 + (1|id) 3 556.51 26.24 < 0.01 ~ species + month + (1|id) 6 561.23 30.96 < 0.01 ~ month + (1|id) 4 562.20 31.93 < 0.01 ~ species + month + species × month + (1|id) 8 569.37 39.10 < 0.01

There was no evidence for a difference in monthly detections between male and female juvenile rays, with the intercept-only model (wAICc = 0.60) having slightly more bias-corrected support than the slope model (wAICc = 0.40). While the spectral analysis did not indicate weekly or monthly cycles of detections, there was a strong peak of detections for all species every 24 hours and a secondary peak every 12 hours (Figure 15). Plots of hourly detections show that these coincided with highest water temperatures (Figure 16) and peaked between 15.00 – 19.00 hrs for P. atrus (Figure 16, B), U. asperrimus (Figure 16, C) and H. uarnak (Figure 16, E). Detections of G. typus peaked later around 23.00 hrs (Figure 16, C). Overall, there were more detections during the afternoon and night than the early morning and day. However, for all these species, around 80-90 % of detections at night occurred at only one or two receivers, while detections during the day were spread over more than two receivers. There was evidence for a positive relationship between the mean proportion of hourly detections and the mean hourly temperature (Figure 16) with the slope model (wAICc = 1) having 100% support over the intercept only model (wAICc < 0.01). Both detections and water temperature were positively correlated with wind speed on an hourly basis (Figure 17).

80 Chapter 3- Restricted movements of juvenile rays

Figure 11. Proportion of detections for each species and receiver at Mangrove Bay.

81 Chapter 3- Restricted movements of juvenile rays

Figure 12. Proportion of days spent by each species inside and outside of the Mangrove Bay sanctuary zone.

82 Figure 13. Number of daily detections per ray at Mangrove Bay. Individual plots of detections of juvenile rays in primary receivers (solid shapes) and other receivers (hollow shapes). 83 Chapter 3- Restricted movements of juvenile rays

Figure 14. Seasonality with generalised linear model. Upper panel shows the number of log- transformed detections on the y-axis against month of study from February 2008. Fitted line for each species obtained by the top-ranked, generalised, linear mixed-effects modelling of the log-transformed number of detections vs species, month and month squared. Dotted line is U. asperrimus, dashed line is G. typus (Note dotted and dashed lines are almost on top of one another) and solid line is P. atrus. Bottom plot shows the temperature per month for receivers at each site during the study period from February 2008 (mean ± se).

84 Chapter 3- Restricted movements of juvenile rays

Figure 15. Cycles of detections using spectral analysis. Cycles of detections by hour for four species of juvenile rays, P. atrus (A), G. typus (B), U. asperrimus (C), H. uarnak (D) at Mangrove Bay.

85 Chapter 3- Restricted movements of juvenile rays

Figure 16. Detections and temperature at primary sites. Mean (± se) hourly temperatures at the primary receivers (3, 15 and 9) (A). The proportion of hourly detections (mean ± se) at all receivers for P. atrus (B), G. typus (C), U. asperrimus (D) and H. uarnak (n= 1) (E). The fitted line from the linear mixed-effects model of the relationship between mean proportion of hourly detections and mean hourly temperature (F).

86 Chapter 3- Restricted movements of juvenile rays

Figure 17. Average hourly wind speed (± se) at Ningaloo Reef

Figure 18. Average monthly wind speed (± se) at Ningaloo Reef

87 Chapter 3- Restricted movements of juvenile rays

3.5 Discussion The use of nurseries appears to be a phenomenon common to many sharks and rays in coastal regions worldwide (Heupel 2007; Speed et al. 2010; Yokota & Lessa 2006). At Ningaloo Reef, juvenile rays were detected most frequently at three listening stations that were located in a small area of muddy habitat (approximately 3 km2) in shallow water (< 2 m depth) along the rim of a mangrove forest. Juveniles of P. atrus, H. uarnak, U. asperrimus and G. typus were present here for many months over both years of the study. Evidence from a companion study that surveyed ray distributions across the reef and lagoon showed that juvenile rays were found only within this shallow bay (O’Shea et al. unpublished data). These primary sites may thus form part of a nursery as defined by Heupel et al. (2007). Primary sites in Mangrove Bay were at the entrance to a small embayment in the vicinity of mangroves that was accessible only during high tide. While this area might provide better opportunities for feeding, it was also more turbid than the waters of the reef nearby, which might offer some protection against predation (Holland et al. 1993). Vaudo and Heithaus (2009) described nurseries for juvenile P. atrus, H. uarnak and G. typus in shallow habitats in Shark Bay, 300 km to the south of Ningaloo Reef. They suggested that these functioned as predation refuges, reducing mortality of juveniles due to seasonally abundant predators such as tiger sharks (Galeocerdo cuvier (Peron & Lesueur, 1822)). Surveys show that sharks, including blacktip, (Carcharhinus melanopterus (Quoy & Gaimard, 1824)), lemon (Negaprion acutidens (Rüppell, 1837)) and tiger sharks (G. cuvier) are abundant at Mangrove Bay (Stevens et al. 2009), suggesting that the embayment could offer some degree of protection against the threat of predation for relatively vulnerable juvenile rays in the lagoon habitat. In other species of batoids, including the freshwater sawfish (Pristis microdon, Latham 1794), the spatial segregation of juveniles into shallow water habitats may occur as a result of the reduced manoeuvrability of larger animals in shallow estuaries (Whitty et al. 2009). This did not appear to influence the use of the primary sites in this study because adults of species of different sizes such as the relatively small Taeniura lymma (Forsskål 1775) (around 75 cm total length) (Last & Stevens 2009) and the much larger G. typus (up to 270 cm total length) (Last & Stevens 2009) were also present within this habitat in Mangrove Bay (Cerutti-Pereyra et al., unpublished data). Juvenile rays were detected more frequently in the primary sites during the late autumn – winter, suggesting that they may range less widely at this time of the year. However, this result should be treated with caution given that seasonal changes in wind 88 Chapter 3- Restricted movements of juvenile rays strength and direction are likely to alter receiver range (Huveneers et al. 2009; Payne et al. 2010), particularly in shallow water where the sites were located. The greatest number of detections generally coincided with the months of reduced wind speed, however detections peaked in August, some months after the lowest wind speeds in April and May. This lag is difficult to reconcile if the increase in detections was solely due to the improved ability of receivers to detect tags under low wind conditions. Assuming that temporal patterns in detections were not confounded by wind speed (see below), there was little evidence that use of the primary sites during autumn was due to behavioural thermoregulation, as water temperatures were similar among sites outside the reef, within the lagoon and in the primary sites. Interestingly, this seasonal pattern contrasts with many studies of the use of nurseries by sharks, which tend to occupy these habitats in summer months when water temperatures are warmest (Speed et al. 2010). The use of nurseries during summer is thought to provide optimum temperatures to increase rates of growth and thus reduce the duration of vulnerable life-history stages (Heupel, Carslon & Simpfendorfer 2007). The restriction of detections of juvenile rays to two or three receivers in a small area of the array contrasts with movement patterns by adults of these species. Additional tagging of mature animals showed that they routinely moved over the entire area of the array and in some cases, more than 125 km south along the Ningaloo Reef in only a few weeks (Cerutti- Pereyra et al., unpublished data). This broad-scale movement is probably indicative of a requirement for greater habitat diversity by adults than by juveniles (Heupel, Simpfendorfer & Hueter 2004; Rousset 1990; Vaudo & Lowe 2006). Alternatively, or in addition, juveniles may be restricted to feeding on the softer, muddy sediments present within Mangrove Bay, while adults can excavate coarser and harder sediments, allowing them to roam more widely over reef habitats (Marshall, Kyne & Bennett 2008). The overlapping nature of the diel patterns of detections among species suggests that there is little evidence for habitat partitioning at this temporal scale, with all juveniles showing 12 and 24 hr patterns in detections. For all species, the lowest number of detections occurred around dawn or the early morning and the greatest numbers around dusk and the early evening. The consistency of these patterns suggest that these cycles in detections were not related to tides, as is the case in several other elasmobranchs (Ackerman et al. 2000; Medved & Marshall 1983), since the timing of high and low tides shift progressively during the day throughout the lunar cycle. No evidence was found that these daily cycles in detections were an artefact of ambient noise produced by wind and waves. Had this been the case, a strong negative correlation between detections and the peak in wind speed during the 89 Chapter 3- Restricted movements of juvenile rays day would have been seen, as the noise from wave chop and breakers reduced the receiver ranges (Rousset 1990). Instead, the number of detections was positively related to wind speed, so that the greatest number of detections occurred in the late afternoon and early evening when average wind speed was highest. This implies that any noise generated by wind and waves on the surface had only a minor influence on receiver ranges. Furthermore, given that the range in daily wind speed (12 – 20 km/hr) was very similar to that occurring on a seasonal basis, this provides further support for the idea that seasonal patterns in detections were not simply a consequence of variation in receiver efficiency due to ambient noise. Daily use contrasted to seasonal patterns of occupancy of the primary sites. On a seasonal basis, rays occupied these sites as water temperatures declined in late autumn and early winter. In contrast, over a 24 hr cycle, rays tended to be found in primary sites when water temperatures were warmer at night time and they ranged more widely in the cooler hours of the early morning and at dawn. This pattern conflicts with the hypothesis that elasmobranchs obtain bioenergetic advantages by feeding in warm waters during daytime and resting during cooler hours for digestion (Matern, Cech & Hopkins 2000; Sims et al. 2006). Such behavioural thermoregulation is common in other benthic elasmobranchs, such as bat rays (Myliobatis californica, Gill, 1865) that feed during the warmest times of the day (Matern, Cech & Hopkins 2000). Similarly, Snelson et al. (1988) showed that Atlantic stingrays (Dasyatis sabina, (Lesueur, 1824)) that live in shallow bays show patterns of movements closely related to diel temperature cycles, moving onto shallow seagrass flats to feed as temperatures rise during the day. However, the primary sites inhabited by rays at Mangrove Bay are known to be a focal point for intense feeding. Recent work suggests that at least half the sediment in the shallow, intertidal areas of the bay is overturned by stingray feeding each year and that feeding in the embayment occurs at a far greater rate than in other areas of the lagoon (O'Shea et al. 2012). Thus, restricted movement in the late afternoon may not necessarily indicate periods of resting, as suggested by other studies. The possibility of a nursery raises the issue of natal site fidelity by females. Although studies are limited, it seems that many elasmobranchs that utilise nurseries also display this behaviour. Examples include the hammerhead shark Sphyrna lewini (Griffith & Smith, 1834) (Duncan et al. 2006), black-tip shark Carcharhinus limbatus (Valenciennes, 1839) (Keeney et al. 2005), lemon shark Negaprion brevirostris (Poey, 1868) (Feldheim, Gruber & Ashley 2002) and bull shark Carcharhinus leucas (Müller & Henle, 1839) (Tillet et al. 2011). Such behaviour can result in high genetic structuring among sub-populations at spatial scales consistent with those over which nurseries are dispersed. This has major implications for 90 Chapter 3- Restricted movements of juvenile rays conservation since the preservation of genetic diversity within populations is, ideally, a goal of management strategies. Future work will investigate these possibilities using tagging and molecular genetic studies of tropical rays. The delineation of essential habitat and nurseries for rays in a tropical reef environment has important implications for management and conservation, although due to low sample sizes, these results must be treated with caution. It is not known what proportion of the adult population the primary sites at Mangrove Bay serve, or from what spatial extent the habitat draws neonates and juveniles. Given that Ningaloo Reef stretches over more than 320 km of coastline, it is unlikely that it is the only potential nursery, even though equivalent habitats with fringing mangroves are rare along this coastline. It is thus fortunate that Mangrove Bay is designated as a protected area and is of a sufficient size to protect juveniles within this habitat.

91 Chapter 4

Habitat partitioning and movement patterns of adult rays in a tropical reef system Chapter 4

Habitat partitioning and movement patterns of adult rays in a tropical reef system Chapter 4- Habitat partitioning and movements of adult rays

Chapter 4- Habitat partitioning and movement patterns of adult rays in a tropical reef system

4.1 Preface

Chapter 4 is currently being prepared for submission to the Journal of Fish Biology. I am the primary author of the manuscript; two of my supervisors Mark Meekan (AIMS) and Chris Austin (Monash University, formerly CDU), as well as John Stevens (CSIRO), Russ Babcock (CSIRO), and Richard Pillans (CSIRO) are additional co-authors. The format of this chapter will undergo reformatting and minor changes in preparation for journal submission and publication. I conducted the acoustic tagging as well as analysis, writing, and literature research. C. Austin and M. Meekan provided support and editorial comments. J. Stevens, R. Babcock, and R. Pillans contributed with deployment and servicing of acoustic receivers, data downloading, tagging, and editorial comments.

Information and results from Chapter 4 have also been included in the following reports:

Cerutti-Pereyra, F. 2011. Investigating stingray movements, populations and mating systems. Ningaloo research. CSIRO Ningaloo Collaboration Cluster, WAMSI, and AIMS.

94 Chapter 4- Habitat partitioning and movements of adult rays

Habitat partitioning and movement patterns of adult rays in a tropical reef system

Florencia Cerutti-Pereyra1, Chris Austin5 , John D. Stevens3, Russ C. Babcock4, Richard D. Pillans4, Mark G. Meekan2

1Research Institute of Environment and Livelihoods, Charles Darwin University, Darwin, Northern Territory, Australia 2Australian Institute of Marine Science, The UWA Oceans Institute Western Australia, Australia 3CSIRO Marine and Atmospheric Research, Hobart, Tasmania 7001, Australia 4CSIRO Marine and Atmospheric Research, Ecosciences Precinct, Brisbane, Australia 5School of Science Monash University Sunway Campus, Selangor, Malaysia

Abstract Habitat use and partitioning by tropical rays is still poorly understood despite the economic and ecological importance of these species. We used passive acoustic monitoring to look at movement pattern of adult rays in the lagoon and open shelf of a coral reef system at Ningaloo Reef, Western Australia. Adult rays used reef lagoons for long periods of time (2 years). We found little evidence of habitat partitioning among species at large spatial scales (km-100s km), although it is likely that these species had microhabitat partitioning (m-100s m). Adult rays showed sexual segregation of core areas of activity with females using shallow waters (2 m water depth) near a mangrove forest and males using the lagoon near reef patches. Although core areas of activity of all species were within protected zones of the Ningaloo Reef Marine Park, the movements of most species included unprotected areas where they could be exposed to the risk of fishing. These patterns of movement and habitat use suggest that spatially-fixed management approaches such as marine parks will require relatively large areas of protection to ensure the conservation of these species.

95 Chapter 4- Habitat partitioning and movements of adult rays

4.2 Introduction Due to the relatively limited energy flow available in the upper trophic levels of ecosystems, there is strong selective pressure for resource partitioning to reduce the intensity of inter-specific competition (Krebs 2001; Papastamatiou et al. 2009; Ross 1986). In marine systems, adult and juvenile elasmobranchs are typically apex or secondary predators and for this reason, there are many examples of niche partitioning in these taxa, where co-occurring species utilize different depth ranges (Babel 1967; Marshall, Kyne & Bennett 2008), different habitats within an environment (White, Platell & Potter 2004), or different resources within the same habitat (Brickle et al. 2003; Platell, Potter & Clarke 1998). An understanding of niche partitioning is essential for management of elasmobranchs, since this can be a principal determinant of patterns of distribution and abundance in space and time. Such information is increasingly important given concerns about the conservation status of many species and rapid declines in abundance of populations worldwide (Dulvy & Forrest 2009; White & Kyne 2010). This is particularly the case in the tropics, where reef fisheries provide a major source of food for large and growing human populations (Bonfil 1997a; White & Kyne 2010). Due to their conservative life-history traits of slow growth and low rates of reproduction, these animals are extremely susceptible to over-exploitation, a problem that threatens elasmobranch populations even in developed countries (Heupel et al. 2009; Robbins et al. 2006), . Many species of rays inhabit the tropical coasts of Western Australia (Last & Stevens 2009; White & Potter 2004). While juvenile rays tend to concentrate in protected embayments (Cerutti-Pereyra et al. unpublished data, Yokota & Lessa 2006), adults can also be found within the lagoon system of the Western Australian tropical coral reefs and bays (Vaudo & Heithaus 2009; White & Potter 2004). Unlike the nearby coasts of South-East Asia, there is relatively little fishing pressure in these environments and the shallow waters and coral reefs of the region are characterised by high diversity and abundance of rays. In such habitats these animals are “keystone” species, with major effects on patterns of bioturbation and the structure of soft-sediment communities (O'Shea et al. 2012). These relatively undisturbed environments offer the opportunity to examine patterns of movement and habitat residency of co-occurring species without the confounding effects of fishing pressure Chapter 4-Habitat partitioning and movements of adult rays and thus to determine the degree to which these animals partition shallow water habitats in space and time. Thus study examines the residency and movement patterns of adult rays in the shallow waters of the lagoon of Ningaloo Reef, WA. Arrays and curtains of acoustic listening stations maintained by the Australian Animal Tagging and Monitoring System in this environment offer the opportunity to track these animals at scales ranging from m- 100s of km. In addition to partitioning of spatial resources among species, the degree to which sexual segregation occurs is also eximined. Many elasmobranchs show sexual segregation in patterns of use of space, thought to be driven by social/predatory avoidance or thermal niche/fecundity behavior. In the latter, male and female distributions differ because females choose areas with optimum temperatures for fertilization, gestation, and growth (Economakis & Lobel 1998; Hight & Lowe 2007; Sims 2005). The following hypotheses aretested: (1) different species of rays show temporal and spatial habitat partitioning in the use of shallow water habitats; (2) there are sex-specific patterns of habitat use by adult rays

4.3 Methods

4.3.1 Study site This study focused on two sections of the reef, Mangrove Bay and the southern receivers (Figure 1, 2). Both arrays are described in Chapter 1, section 1.3.2.1. For smaller scale habitat partitioning, Mangrove Bay was divided in 3 zones: northern, center, and southern sections (Figure 19).

4.3.2 Acoustic tagging Smaller rays (>80 cm disc with, DW) (n=5) were caught with gill and hand nets as in Chapter 3. Larger rays (> 80 m DW) were externally tagged 18 with VEMCO v16 tags (battery life of approximately 42 months) (Figure 20). The tags were attached using a stainless steel wire and dart, covered with anti-fouling paint, and were inserted on the right side of the disc using a spear gun. The field identification of all tagged animals was confirmed using genetic techniques of Chapter 2 (Cerutti-Pereyra et al. 2012).

97 Figure 19. A) locations of receiver arrays along Ningaloo Reef, WA (1-Tantabiddi curtain, 2-Mangrove Bay array, 3- Point Cloates curtain, 4- Stanley Pool array, 5-Point Maud curtain, 6-Skeleton Bay array). B) Mangrove Bay array sections 1- north (Mangrove Bay sanctuary zone), 2-centre, 3-south, and 4-open shelf). C) southern array sections (1- Stanley Pool, 2-Skeleton Bay). Dashed lines are sanctuary zones.

98 Chapter 4-Habitat partitioning and movements of adult rays

Figure 20. Acoustic tagging of adult rays. A) Spear gun used to tag while snorkeling; B) snorkelling following rays; C- E) external acoustic tagging of adult rays; F) external spaghetti tag of adult ray.

99 Chapter 4-Habitat partitioning and movements of adult rays

4.3.3 Analysis The performance of the nine receivers at Mangrove Bay that had 95% of the total detections were examined. Of these, four receivers were primary sites for several species. Three metrics of receiver performance developed by Simpfendorfer et al. (2008) were used, these were calculated from the code detection parameters for each receiver. These were the percentage of tagged rays present with valid detections (code detection efficiency), an estimate of rejected detections (rejection coefficient), and errors due to tag code collisions or environmental noise (noise quotient). These metrics for receiver performance at Skeleton Bay array (southern array) were calculated by Speed et al. (2011). The monthly average wind speed data were requested from the Australian Government Bureau of Meteorology (BOM) web site http://www.bom.gov.au/. It was obtained at the climate station at Learmonth, Western Australia during 2008-2009. Total detections, total numbers of monitored days, proportion of days detected, primary and adjacent sites and day-night detections were determined for each individual (Table 8). The primary site was the receiver with the highest amount of detections for each individual. One or two adjacent sites were designated when detections at the primary site were < 50% of all detections. The number of days each ray was recorded within the array was used to calculate the minimum percentage of days it was present (Garla, Chapman, Wetherbee, et al. 2006). The residence of rays within the Mangrove Bay and southern arrays was examined by determining the number of days individuals were present within the study site. Individuals were considered present when more than one detection was recorded for that individual during a single day. Monitoring days and months were defined as the time between the first and the last detections for each individual. A mean position algorithm was used to calculate centre of activity locations (COA), for tagged individuals every 30 min (Simpfendorfer, Heupel & Heuter 2002). Processed data were used to define home ranges using kernel density (KD) estimates (50% and 95%) for all species to provide core (50%) and total (95%) areas of activity. Winter (April-October) and summer (November to March) KD estimates were also calculated for all species, with exception of Glaucostegus typus due to a very low number of days detected. Data for all species (with the exception of G. typus) was pooled for and used to calculate total area and core areas of activity for

100 Chapter 4-Habitat partitioning and movements of adult rays females and males. Home ranges and KD estimates were mapped using the Hawth’s tool extension in ArcView 3_2 GIS (Hooge & Eichenlaub 2000). All detections by species were pooled together by hour of the day (0-24). Significant differences in the total detections by hour within each species were determined using Chi-squared goodness of fitness test (Heupel, Simpfendorfer & Fitzpatrick 2010). A Fast Fourier Transformations (FFTs) with Hamming window smoothing was used in MATLAB to identify cyclic patterns in detections of each species and by sex per hour, day, and month.

4.4 Results Eight species (n=23 individuals) were tagged in shallowest area of Mangrove Bay and the southern array (>2 m depth) during February, 2008, August-September, and November, 2009. Twenty rays were mature and three were sub-adults based on published sizes at maturity (Last & Stevens 2009) (Table 8). During 1157 days of total monitoring time, 88903 detections were recorded.

4.4.1 Receiver performance at Mangrove Bay Wind speeds at the northern section of Ningaloo Reef averaged 20 km/hr over most of the year, but declined to 10 km/hr from April–August. (Figure 21, A). The code detection efficiency range for the primary receivers varied between 0 and 0.9731. The mean (+ SE) detection efficiency for all 9 receivers was 0.305 + 0.04, which suggests that approximately 30% of the codes transmitted were successfully detected (Figure 21, B). Five receivers had detections during the full two years of the study. The detections in the other four receivers declined at different times. Receiver 11 had the lowest mean detection efficiency. There was a general decline in detection efficiency in February 2008 (except for receiver 15) and again in October 2009 (except for receiver 17). The rejection coefficient for these receivers ranged between 0 and 0.0333 and had a mean (+ SE) of 0.009 + 0.01 (Figure 21, C). Two receivers show the highest peaks (3 and 38). Except for receiver 17, the rejection coefficient showed a general trend to decrease with time. The noise quotient was extremely variable, ranging from -3851 to 29410 with approximately half of the noise quotients (2735 of 5312) less than zero, indicating that tag signal collisions may have been a potential problem during the study (Figure 21, D). The overall noise quotient of all receivers was stable although receiver 15

101 Chapter 4-Habitat partitioning and movements of adult rays had a sharp peak in mean noise quotient in March 2008. Receiver 38 had a peak in the mean noise quotient in February 2009 that corresponded with a peak in the mean detection efficiency. Receiver 38 showed a noticeable drop in the mean noise quotient in May, 2008 which corresponded with a peak of mean detection efficiency. Also, receiver 11 showed a sharp drop in mean noise quotient of June-July 2009 that corresponded with a peak in the mean detection efficiency.

4.4.2 Residency time and diel patterns of activity Although some individuals left or were not detected by the array, all species except Glaucostegus typus were resident for extended periods of time both the Mangrove Bay and southern arrays (Figure 22). Summaries of monitoring days, detected days, primary and adjacent sites and peak months of detections are shown in Table 8. Females (n=2) of G. typus were detected for 13-19 days and the male (n=1) was only detected for four days during 15 months of total monitoring time for this species. A female Himantura fai was monitored for over eight months and detected on 58% of days during this time, with the highest number of detections during November 2009. Females (n=2) of H. jenkinsii had a monitoring period of 8.4 months and were detected for 62-79% of days within this time. The males (n=3) had a monitoring period of almost 14 months and were detected on 67-98% of days within this time. The largest male (100 cm DW) and female (120 cm DW) were detected for the longest times (413 and 252 days respectively). The months with the highest detections for both males and females were September and October 2009. Male H. uarnak (n=3) had a maximum monitoring period of 3.8 months and were detected 28-59% of days during this time. The greatest number of detections of these animals occurred in September 2009. The female Pastinachus atrus tagged at Mangrove Bay had the longest period of monitoring time (20 months) and the smallest proportion of days detected (40%) of this species. The female tagged in the southern array was monitored for almost 14 months and was detected for 27% of days. The monitoring periods for males of this species were between 30 days and 8 months. The largest male (150 cm DW) had the longest monitoring period among the males, during which it was detected on 56% of days. Detections of this species peaked in September and November of 2009, and again in September 2010. The tagged Rhincobatos laevis (n=1) was a subadult of unknown sex and had a monitoring period of 8.4 months during which time it was detected on 27% of days, 102 Table 8. Details of all tagged rays and detection data. Total length in cm (*); rays tagged at Stanley Pool (**); discw width in cm (DW); monitored days (MD); monitored months (MM); detected days (DD); reeivers detecting (RD); primary site (PS); % of days at primary site (%DPS); adjacent site (AS); % of days at adjacent site (%AS).

ID Tagged Total % % # Spp Sex DW Age Tag # code date hits Last hit MD MM DD DD RD PS DPS 2 G. typus* F 230 A 1046003 8213 26/02/2008 173 10/03/2008 13 0.4 13 100 6 15 50 4 G. typus* F 182 A 1046093 8254 27/02/2008 636 20/03/2008 22 0.7 19 86 22 15 83 3 G. typus* M 140.3 s-A 1046099 8360 26/02/2008 40 19/05/2009 448 14.9 4 1 9 774 60 139 H. fai F 150 A 1076866 60961 4/09/2009 5390 13/05/2010 251 8.4 146 58 21 41 32 120 H. jenkinsii F 90 A 1076860 60955 31/08/2009 3312 18/12/2009 109 3.6 86 79 12 41 44 121 H. jenkinsii M 90 A 1076864 60959 31/08/2009 15758 23/01/2010 145 4.8 142 98 11 41 80 122 H. jenkinsii M 90 A 1076861 60956 31/08/2009 12061 18/03/2010 199 6.6 129 65 8 41 40 141 H. jenkinsii F 120 A 1076871 60966 4/09/2009 6865 14/05/2010 252 8.4 155 62 7 11 96 123 H. jenkinsii M 100 A 1076865 60960 31/08/2009 6657 18/10/2010 413 13.8 275 67 16 5 31 115 H. uarnak M 100 A 1076859 60954 27/08/2009 698 23/11/2009 88 2.9 28 32 8 45 31 143 H. uarnak M 100 A 1076862 60957 3/09/2009 4055 25/12/2009 113 3.8 59 52 6 41 73 107 H. uarnak** M >100 A 1076854 60949 21/08/2009 815 15/11/2009 86 2.9 0.1 24 28 33 60 128 P. atrus M 110 A 1061739 14511 31/08/2009 1451 3/10/2009 33 1.1 30 91 27 29 44 114 P. atrus M 150 A 1076858 60953 27/08/2009 3747 1/05/2010 247 8.2 139 56 11 17 78 126 P. atrus F 80 s-A 1061743 14515 2/09/2009 5893 28/04/2011 603 20.1 239 40 8 15 70 144 P. atrus** F 120 A 1076855 60950 21/08/2009 1238 8/10/2010 413 13.8 0.5 110 27 33 77 119 R .laevis* ? 150 s-A 1061740 14512 30/08/2009 1412 8/05/2010 251 8.4 69 27 18 9 61 5 T. lymma F 28 A 1061710 53404 15/11/2008 69 6/04/2009 142 4.7 20 14 5 3 46 6 T. lymma M 28 A 1061712 53406 15/11/2008 2086 2/12/2010 747 24.9 212 28 5 50 95 112 T. lymma F 31 A 1076881 60974 25/08/2009 981 9/02/2011 533 17.8 180 34 5 50 50 171 T. lymma** F 28 A 1076888 60981 16/11/2009 11278 27/09/2010 315 10.5 0.4 258 82 845 87 182 U. asperrimus F 120 A 1076872 60967 25/11/2009 2428 31/05/2010 187 6.2 58 31 13 38 60

103 Chapter 4-Habitat partitioning and movements of adult rays

with detections peaking in October 2009. The male Taeniura lymma that was tagged at Mangrove Bay was monitored for 25 months and was detected for 28% of days during this time. Detections of females of this species peaked in December 2008 and March 2010 and for the male, in March-April 2009. The female Urogymnos asperrimus was recorded over almost 6 months, during which time it was detected on 34% of days. The most detections occurred in December 2009 and May 2010 There was significant difference in the detections by hour of the day within each species (p<0.00). Spectral analysis did not reveal weekly or monthly cycles in detections, although there were clear 10 and 24-hour patterns in six species at Mangrove Bay (Figure 23). In the southern array, H. uarnak showed no pattern, while P. atrus had clear cycles in detections at 8, 12, and 24 hrs and T. lymma had strong 12 and 24 hrs cycles in detections (Figure 24). Females showed a clear 8 and 24 hrs cycles of detections, and while males had also 24 hrs cycle, they showed a 12 hrs cycle (Figure 25). There was significant difference in the detections by hour of the day within each species (p<0.00). Plots of proportions of detections (Figure 26) showed that H. jenkinsii and G. typus had higher activity during the day at Mangrove Bay. Pastinachus atrus had more activity at night and early morning with a drop after midday at Mangrove Bay while it showed the opposite in the southern array, with a peak at midday night time. Taeniura lymma had more detections at night in Mangrove Bay with peaks before sunrise and after sunset, while it had a peak of detections at midday at the southern array. Both Rhincobatos laevis and U. asperrimus had more detections during the night, although the latter also had a narrow peak of activity during midday. Both H. fai and H. uarnak had more detections on only a few receivers during the night, and less detections spread over more receivers during the day at Mangrove Bay. The latter had more detections during day time in the southern array.

4.4.3 Home ranges Detections and primary sites were dispersed through the array at Mangrove Bay but were very localized in the southern array. The primary sites for all species were within the lagoon, except for one individual of T. lymma at Mangrove Bay. Both females of G typus had the same primary site located in shallow waters close to the mangrove forest in Mangrove Bay. The primary site of the male was located at the southern array (Stanley Pool), which was 125 km to the south of Mangrove Bay where it was originally tagged (Table 8, Figure 27, Figure 28). At Mangrove Bay, the female H.fai had a total of 53% of detections at two receivers

104 Chapter 4-Habitat partitioning and movements of adult rays located at the northernmost and southernmost sides of the array. This species was detected throughout the array during summer and had two core areas of activity: one in near the mangrove forest in the northern part of the array and another in the southern part of the array in area with many small patch reefs. During winter, the total area of use within the array was reduced to the northern part, but included the open shelf (Figure 29, A). Himantura jenkinsii had several primary and adjacent sites. Three individuals had approximately 70% of their detections at two receivers, while one had 80% and the other had 96% of detections at two receivers. The KD estimates of this species were located in the southern part of the Mangrove Bay array during both summer and winter, although this species also had detections in the northern part of the array during winter (Figure 29, B). In summer it was detected only in the southern section and open shelf. One male of H. uarnak had 73% of detections in one receiver, while the other male had 80% of detections spread over three receivers at Mangrove Bay. Total use and core areas of activity of H. uarnak were larger during winter (Figure 28, C) with individuals of this species using north, south and open shelf sections of the array. In summer, the total area of use was reduced and the core area was only in the southern part of the array. Pastinachus atrus had three different receivers as primary sites with >70% of detections at Mangrove Bay, this species used much of the array and had two core areas of activity. The northern core area of activity included most of the sanctuary zone during the summer. During winter, its range extended to the open shelf and the size of the core area in the north of the array was reduced (Figure 29, D). Urogymnos asperrimus had 60% of total detections in the one receiver and used the northern and southern areas of the array in summer, while total use was reduced during winter and its core area was in the northern-most section of the array (Figure 30, A). Rhincobatos laevis had 61% of detections in one receiver and its total area of use did not change throughout the year (Figure 30, C). The primary site of the male T. lymma was unusual in that it occurred beyond the reef on the open shelf with one receiver obtaining 95% of detections. For females, around 50% of detections occurred at primary receivers at Mangrove Bay. The total area of use by T. lymma (Figure 30, B) did not change throughout the year, although the core area of activity of T. lymma included the shallowest area of the array near the mangrove forest during winter. In the southern array, the male H. uarnak had 60% of detections at one receiver of Stanley Pool array had the same core area during summer and winter at Stanley Pool array; however, its total area of use during summer extended further south than in winter. Pastinachus atrus had 77% of detections at one receiver in the Stanley Pool array

105 Chapter 4-Habitat partitioning and movements of adult rays

Figure 21. Performance for the primary receivers in Mangrove Bay over the 2 years of the study. Average wind speeds (A), mean code detection efficiency (B), mean rejection coefficient (C), and mean noise quotient (D). Error bars show +SE.

106 Figure 22. Abacus plot of presecnce/absence of all rays during the period Feb 2008- April 2011. M = male, f = female, sub-adults are marked with (*), rays tagged at the southern array are marked with (**)

107 Chapter 4-Habitat partitioning and movements of adult rays

Taeniura lymma had 87% of detections at a primary site in Skeleton Bay. Its total area of use was larger during summer than winter. Both core and total areas of use by this species overlapped during summer and winter (Figure 31). Both males and females used the full extent of the Mangrove Bay array, however, the core areas differed, with females having a core area of activity in the northern part and males a core area in the southern part of the array (Figure 32).

108 Chapter 4-Habitat partitioning and movements of adult rays

Figure 23. Hourly spectral analysis of mean detections per species at Mangrove Bay.

109 Chapter 4-Habitat partitioning and movements of adult rays

Figure 24. Hourly spectral analysis of mean detections per species in the southern array.

110 Chapter 4-Habitat partitioning and movements of adult rays

Figure 25. Hourly spectral analysis of mean detections pooled by sex at Magrove Bay. Females (A) and males (B).

111 Chapter 4-Habitat partitioning and movements of adult rays

Figure 26. Hourly cycles of detections of eight species of rays. Only single individuals of U. asperrimus, H. fai, and R. laevis were tagged. Solid lines show detections at Mangrove Bay, dashed lines show detections in the southern array.

112 Figure 27. Overall detections per receiver inside and outside sanctuary zones at (A) the Mangrove Bay and (B) southern arrays.

113 Chapter 4-Habitat partitioning and movements of adult rays

Figure 28. Proportion of detections inside and outside sanctuary zone at Mangrvoe Bay (A) and southern array (B).

114 Figure 29. Kernel density (KD) estimates for H. fai (A), H. jenkinsii (B), H. uarnak (C), and P. atrus (D) at Mangrove Bay. Red shows detections during summer, blue show detections during winter. The 95% KD estimate contour is outer boundary (total area); 50% KD estimate is the large circles (core area). Smaller blue and red circles are the estimated positions from the centre of activity algorithm to show the full extent of this analysis. Reef habitats are shown as for Figure 1.

115 Figure 30. Seasonal kernel density (KD) estimates for U. asperrimus (A), T. lymma (B), and R. laevis (C) at Mangrove Bay. Red is for summer, blue is for winter. The 95% KD estimate contour is the outer boundary (total area), 50% KD estimate is the large circles (core area). Smaller blue and red circles are the estimated positions from the centre of activity algorithm to show the full extent of this analysis. Reef habitats are shown as for Figure 1.

116 Figure 31. Seasonal kernel density (KD) estimates for H. uarnak (A), and P. atrus (B), and T. lymma (C) at the southern array during summer (red) and winter (blue). Outer boundary shows the 95% KD estimate (total area), 50% KD estimate is the large circles (core area). Smaller blue and red circles are the estimated positions from the centre of activity algorithm to show the full extent of this analysis. Reef habitats are shown as for Figure 1.

117 Chapter 4-Habitat partitioning and movements of adult rays

Figure 32. Kernel density (KD) estimates by sex at Mangrove Bay array, females (red), males (green). Lines are 95% KD estimate (total area), large circles are 50% KD estimate (core area). Smaller red and green circles are the estimated positions from the centre of activity algorithm to show the full extent of this analysis

118 Chapter 4-Habitat partitioning and movements of adult rays

4.5 Discussion Niche theory suggests that secondary predators such as stingrays should partition resources within environments among similar species, in order to avoid competitive exclusion (Roughgarden 1976; Yick, Tracey & White 2011). For this reason, rays were predicted to show temporal and spatial patterns of habitat partitioning in the use of shallow water within the lagoon of Ningaloo Reef. Although only relatively small numbers of rays were tagged of each species, the results of the monitoring study were not consistent with this idea on a large spatial scale (km-10s km). On a temporal basis, the residency patterns of most species overlapped as rays were present throughout the year and. most species showed very similar daily cycles in detection patterns that peaked around midnight and then declined through the morning until dusk at Mangrove Bay. Rays also overlapped in spatial patterns of habitat use. Primary sites for nearly all individuals of all species were found in the northern and shallower part of the array close to the shore and the mangrove area likely due to the productivity associated with mangrove forests (Nagelkerken et al. 2008; White, Platell & Potter 2004). Core areas of activity were found in these habitats and overlapped among species, although most rays moved throughout the array during the study period. As expected, these adult and sub-adult rays used a greater area and wider variety of habitats than juveniles of the same species, which were mostly limited to shallow embayments at the lagoon edge near the mangrove forest (Cerutti-Pereyra et al. unpublished data). If there is little evidence that different species of ray partition habitats in space and time, at least at the scale of this study (km-10s km), how then do they coexist? Although home ranges overlap, the differential use of sections (northern, central, and southern areas) of the Mangrove Bay array and the seasonal intermittent use of the open-shelf suggest a fine-scale habitat partitioning among these species. The diets of some of these species of rays show little evidence for niche separation on the basis of diet composition although the available information is to higher taxa (O’Shea unpublished data). Despite having similar prey, there are some noticeable differences in the proportion of preferred preys that would reflect the overlap in the use of sections of the array. It is therefore likely that niche partitioning may be occurring at a relatively fine spatial scale (m – 100s m) where different species use

119 Chapter 4-Habitat partitioning and movements of adult rays different microhabitats. For example, H. uarnak and U. asperrimus overlap most of their range in winter and summer. However, these species had the greatest dissimilarity in diet, with former consuming a larger proportion of crustaceans (66%), and the latter consuming a larger proportion of nematodes (52%). Pastinachus atrus and T. lymma both feed primarily on annelids, however these species have very different body sizes and are likely to feed on different sizes of preys (Ellis, Pawson & Shackley 1996; Sommerville et al. 2011). Vaudo and Heithaus (2011) examined the diets of these species at Shark Bay, WA, 300 km to the south of Ningaloo Reef and also found similar patterns of diets. The shark-like body species G. typus is known to prefer crabs and feed on larger preys such as blue swimmer crabs Portunus pelagicus (Linnaeus, 1758) (Vaudo & Heithaus 2011) that the non shark-like bodied species and smaller species of rays cannot prey upon, therefore avoiding prey competition while overlapping habitats. This monitoring study was not capable of a fine-scale spatial resolution that might have explained better the overlapping of habitat at the Mangrove Bay array. In order to test this hypothesis, future studies will require active sonar tracking of rays (in contrast to passive monitoring) within this habitat to develop a better picture of m-scale patterns in the movement, distribution, and behavior of rays within the lagoon. While the overlap of diel patterns in detections suggested that rays did not partition habitats on a temporal basis, these results must be treated with caution since it is possible that detections may have been influenced by environmental factors such as wind speed (Payne et al. 2010). Evening sea breezes cause surface chop and waves, producing noise that can lower detection ranges of receivers (Payne et al. 2010). In this study, the patterns of detections were negatively correlated with wind speed, so that when wind speed was high, detections were low, as might be expected if wind speed confounded detection rates. However, two lines of evidence suggest that the patterns in detections recorded were in fact biological, rather than environmental artefacts. First, wind speed was positively correlated with detection rates of juveniles on the same receivers (see Chapter 3). Juveniles resided near receivers in the shallowest water, where noise from wave action would be expected to be greatest, yet detection rates increased as wind speeds increased in the evening. Second, detections of one species (P. atrus) were positively correlated with wind speed in the southern array. If wind speed were driving cycles of detections then

120 Chapter 4-Habitat partitioning and movements of adult rays patterns should be consistent among different ontogenetic stages of rays at the same location, and among species at different locations where wind conditions were the same. Since this was not the case, it is evidence that suggests that cycles in detections, at least on a daily basis, were possibly the result of biological factors. Assuming that cycles of detections were not an artifact of environmental signals, similar diel patterns were seen in P. atrus, T. lymma, H. uarnak, H. fai, H. jenkinsii and R. laevis at Mangrove Bay, where greater numbers of detections occurred in the late evening and early morning when water temperatures were cooler, and fewer detections in the late afternoon and early evening when water temperatures were warmer. This pattern was consistent with the “feed warm, rest cool” hypothesis of bioenergetic advantages in movement patterns for elasmobranchs (Matern, Cech & Hopkins 2000; Sims et al. 2006). However, the same species showed somewhat different patterns in the southern array (although monitoring data were obtained from only one or two individuals). For example, P. atrus in the southern array had the lowest number of detections in the early morning and the greatest number in the early evening – a pattern opposite to that of the array in Mangrove Bay. This implies that daily patterns in detections may be site- and habitat-specific, reflecting equivalent spatial variation in factors such as prey behaviour or predation risk. For example, differences in day/night activity patterns in the Hawaiian stingray, Dasyatis lata (Garman, 1880), which has a higher rate of movement and larger activity space during the night than the day, are thought to be due to activity cycles of its prey (Cartamil et al. 2003, Cerutti-Pereyra et al. unpublished data). Although most rays had core areas of use in shallow water close to the shore, adults ventured throughout the array. One relatively small adult ray (a male T. lymma), had a core area of use beyond the barrier reef in open waters of the channel. This ray made occasional forays within the lagoon to the area near where it was tagged. Similarly, other rays made occasional movements beyond the lagoon through the channel to open water. The purpose of these movements into the channel were unknown, but were an occasional feature of tracks of all species, with the exception of U. asperrimus. In the case of this latter species, this result must be treated with caution due to the very low numbers of individuals tagged. Such movements at 100s m – km scales meant that rays travelled routinely beyond the boundaries of the sanctuary zone at Mangrove Bay and would be regularly exposed to the possibility of fishing mortality elsewhere in the lagoon. The largest movements were recorded by

121 Chapter 4-Habitat partitioning and movements of adult rays the largest species, G. typus. One male was tagged at Mangrove Bay and was then detected 127 km to the south at Stanley Pool. Movements at these large scales suggest that programs to ensure the conservation of these species will require management strategies at relatively large spatial scales and for this reason may be very challenging to implement (Chapman et al. 2005; Hunter et al. 2006). One of the few clear differences in spatial patterns of distribution occurred between male and female rays within the Mangrove Bay array. Although rays of both genders were monitored throughout the array, females had a core area of activity centered on a small and shallow embayment that was fringed by mangrove forest in the northern part of the array. The area of core use for males was around 4 km to the south. Such patterns of sexual segregation are common in elasmobranchs. Females usually prefer shallower areas with higher temperatures that increase somatic rate, growth, and embryo development (Economakis & Lobel 1998; Hight & Lowe 2007) and are thought to gather in female-only groups to avoid male aggressive behavior (Economakis & Lobel 1998; Sims, Nash & Marritt 2001).

4.6 Conclusions Adult rays used Mangrove Bay and the southern array intermittently for long periods of time although with habitat use variability. There was little evidence of habitat partitioning at a large scale but this may to occur at microhabitat scales. Adult rays had core areas of activity within the sanctuary zone at Mangrove Bay but also moved beyond its boundaries and were likely to also move beyond the boundaries of the marine park or include unprotected areas within their ranges and seasonal movements. The information on the home ranges and habitat use provided in this study is a first step towards improving the understanding of activity and distribution patterns of these species and can therefore aid in the spatiotemporal management strategies to ensure the future of these species.

122 Chapter 5

An integrated ecological risk framework for 16 species of batoids: assessing vulnerability and research priorities in the Indo-West Pacific. Chapter 5

An integrated ecological risk framework for 16 species of batoids: assessing vulnerability and research priorities in the Indo-West Pacific. Chapter 5- Vulnerability and research priority assessment

Chapter 5- An integrated ecological risk framework for 16 species of batoids: assessing vulnerability and research priorities in the Indo-West Pacific

5.1 Introduction

5.1.1 Ecological risk assessments as a tool for fisheries & ecosystem-based management A shift from sector-based management to multi-sector management is occurring as scientists, managers, and policy makers realize the management plans must incorporate the values of ecosystem services and the multiple impacts on such ecosystems (Smith et al. 2007; Tallis et al. 2010). Because of this shift, newer approaches to management planning have evolved that include multiple management objectives, such as ecosystem-based management (EBM) and the ecological risk assessment (ERA). EBM goes beyond traditional management and considers a wide range of factors for the exploitation of resources: ecological, environmental, and human. It takes into account the inter-connectedness and interdependent nature of the components of an ecosystem and its importance to humans (Arkema, Abramson & Dewsbury 2006; Murawski 2007). Resource managers, scientists, and policy makers agree that this kind of integrated ecosystem-based approach to the management of marine resources may be the best method for restoring marine ecosystems and ensuring the long term delivery of the goods and services that humans require (deReynier, Levin & Shoji 2010; Espinosa-Romero et al. 2011; Tallis et al. 2010). Arkema et al. (2006) outlined 17 criteria to characterise EBM. Of these, three were general criteria, sustainability, ecological health and the inclusion of humans in the ecosystem while the other 14 were specific criteria (Table 9). Ecological Risk Assessment (ERA) has evolved from the ecosystem-based management to investigate the effects of ‘ecological hazards’ or risks from an identified stressor or proposed activity (Hobday et al. 2011). ERA is a useful and flexible tool that can define a critical issue and identify how an ecological asset will be affected (Gallagher, Kyne & Hammerschlag 2012); it provides a better understanding of the relationship between a hazard and its consequences or impacts. The ERA approach generally involves three steps: (1) defining the assessment context (i.e. identification of risk factors), (2) assessing components of vulnerability (exposure, sensitivity, resilience or adaptive capacity), and (3) integrating vulnerability components to derive the predicted vulnerability of each species to a stressor.

125 Table 9. Summarized criteria to define ecosystem-based management (Arkema, Abramson & Dewsbury 2006)

Category Criteria name Requirement General Sustainability Emphasizes maintenance of one or more aspects of the ecosystem Ecological health Goals for ecosystem health Humans inclusion Education and well-being of humans as part of management decisions Specific ecological Complexity Recognises links among components of an ecosystem Temporal Includes the temporal dynamics within an ecosystem Spatial Spatial scales of an ecosystem Specific human dimension Services Human uses and values on natural resources Economic Economic aspects of an ecosystem Stakeholder Engages interested parties Specific management Science-based Hypothesis-testing studies are incorporated Boundaries Spatial limitations of management plans Technological Tools to monitor ecosystems and assess management plans Adaptive Systematic assessment of management plans and improvement Co-management Shared responsibility among interested parties Precautionary approach Manages conservatively when uncertainty of threats are present Interdisciplinary Bases management plans on several sciences Monitoring Tracks changes in all components of the ecosystem

126 Chapter 5- Vulnerability and research priority assessment

Figure 33. Stages of the integrated vulnerability assessments framework by Chin et al. (2010).

127 Chapter 5- Vulnerability and research priority assessment

(Figure 33) (Fletcher 2005; Gallagher, Kyne & Hammerschlag 2012). This process ranks the vulnerabilities of species in response to particular stressors and provides a framework of information for management prioritization (Chin et al. 2010; Fletcher 2005). One of the defining features of ERA is that it follows a procedural, step-wise, framework that serves as guide and provides a basis by including all necessary components (Suter 2007).

5.1.2 Types of ERAs A range of ERAs methods addressing a variety of single and multiple management objectives and data availability is currently available. Quantitative ERAs use mathematical models to describe relationships between harvest levels and various parameters of the fish stocks and/or ecosystem (Astles et al. 2009) but are only viable in data-rich situations. However, a range of data deficiencies can occur for harvested species, marine habitats, non-target species, or ecological processes. , Consequently, a quantitative approach can present a a major challenge in a data deficient scenario. In contrast, qualitative methods which use attributes or properties of an ecological component, rather than exact numerical measurements, can be used to assess all major ecological components in a data deficient scenario (Astles et al. 2009; Astles et al. 2006). Semi-quantitative or qualitative ERA uses a combination of attributes of the ecosystem, target stocks, and risks. Attributes include generalized information, for instance, like relative levels of resilience and vulnerability (low, medium, high), and such data can be valuable and useful particularly for data deficient situations, such as fisheries in many developing countries, new fisheries, or where there is a lack of biological data (Astles et al. 2009; Astles et al. 2006). A semi-quantitative approach has the advantage that each component and attribute is defined and rated using a standard assessment (Chin et al. 2010).

5.1.3 ERA of elasmobranch fisheries ERA is a particularly essential tool to assess the overall risk and impacts facing threatened and data-poor marine species, such as elasmobranchs (Chin et al. 2010; Gallagher, Kyne & Hammerschlag 2012). Due to the important declines that elasmobranchs have been facing in recent decades, there is an increasing concern among scientists, managers, and policy makers of the need to understand the risks and impacts of populations’ declines on the ecosystem health from ecological, economic, and social perspectives. This tool has proven to be very effective in case

128 Chapter 5- Vulnerability and research priority assessment of data-poor fisheries such as when elasmobranchs are targeted in countries without appropriate regulations or when they are components of by-catch (Gallagher, Kyne & Hammerschlag 2012; Hobday et al. 2011) and it has been used for fisheries assessments (Aires-da-Silva & Gallucci 2007; Braccini, Gillanders & Walker 2006; Cortés et al. 2010; Tovar-Ávila, Day & Walker 2010), or by-catch in fisheries (Arrizabalaga et al. 2011; Lack M. Shellack Pty Ltd 2010; Stobutzki et al. 2002; Zhou & Griffiths 2008). Australia has implemented the use of ERAs for elasmobranch management (see Gallagher, Kyne & Hammerschlag 2012), for example Chin et al. (2010) integrated a framework to assess the vulnerability of elasmobranchs to climate change and Gallagher et al. (2012) summarized the implications of ERAs for elasmobranchs management. However, very few studies have addressed the pristine northern Australian region (Field et al. 2009a; Lack M. Shellack Pty Ltd 2010) in spite of the high levels of illegal, unregulated and unreported fishing in the region and in the adjacent waters of Indonesia, particularly of elasmobranchs for shark fin (Field et al. 2009a). Significantly, there are currently no published risk assessments specifically on batoids in Australia, or globally. Despite the depletions batoid populations are facing in many parts of their ranges (Dulvy & Forrest 2009; Field et al. 2009b; Frisk 2010), there are currently no assessment of batoid populations targeted by fisheries or assessment of the effects of habitat degradation specifically on populations, While some species are included ‘by default’ in the risk assessment of by-catch in the Australian northern prawn trawl fisheries (Lack M. Shellack Pty Ltd 2010; Stobutzki et al. 2002) or climate change in the Great Barrier Reef (Chin et al. 2010), studies and management plans are still primarily focused on sharks. While some countries like Australia, New Zealand, the UK, and USA have regulations in place for the catch of elasmobranchs that include batoids, species- specific information, particularly on by-catch or indigenous fishing, is still not effectively reported, making it challenging to manage such fisheries. Furthermore, tropical developing countries that specifically target batoid populations have barely any regulations or management regimes in place (Bizzarro et al. 2007; Field et al. 2009a; White & Kyne 2010; White, Last, et al. 2006). Management plans need to be developed and implemented to ensure fisheries sustainability and the persistence of batoid populations and research priorities need to be identified to ensure such plans are appropriate and effective. If management plans for batoid populations are to be

129 Chapter 5- Vulnerability and research priority assessment implemented in any particular country, addressing the limited information available remains the largest challenge. A framework that evaluates both the vulnerability of batoids to major threats and also identifies the key research priorities to manage such populations, would be a logical first step towards the creation of effective management plans. As stated by Bottrill et al. (2008), prioritizing research needs and budgets is now an unavoidable step for efficient conservation. The risk assessment framework proposed in this study has been adapted from a previously established method for an ecological risk assessment (Chin et al. 2010). This approach has been extended to develop a complementary research prioritization framework. This study aims to develop an integrated assessment of batoid populations in the Indo-West Pacific region, including 1) an assessment of the vulnerability of batoids to two key threats, fisheries and habitat degradation; and 2) the identification of the key research priorities to effectively conserve and sustainably manage such populations. Further discussion on the criteria will follow in subsequent sections of the thesis.

5.2 Methods The stages and rating system for this framework was adapted from the integrative vulnerability assessment framework for elasmobranchs and climate change proposed by Chin et al. (2010). It includes a scoping stage, an assessment stage (for each objective), and an integrative stage (for each objective). The stages of this framework are presented in Figure 34Figure 34. The scoping stage is the assessment context and includes three components: description of objectives, identification of units of analysis, and working definitions.

5.2.1 Description of objectives

Objective 1. Assess the vulnerability of the 16 species of batoids to two major threats (fisheries and habitat degradation) in the Indo-West Pacific Ocean.

Objective 2. Identify the key research priorities to effectively conserve and manage the populations of the 16 species of batoids in the Indo-West Pacific Ocean.

130 Chapter 5- Vulnerability and research priority assessment

Figure 34. Stages of the integrated vulnerability assessment and research priorities framework for batoids in the Indo-Pacific Ocean.

131 Chapter 5- Vulnerability and research priority assessment

5.2.2 Identification of units of analysis. Sixteen species barcoded in Cerutti-Pereyra et al. (2012) belonging to three families of batoids were included in this assessment. They are generally distributed through the Indo-Pacific Ocean and are all considered coastal benthic species, except for M. alfredi, which is a pelagic filter- feeding species (Table 10).

Table 10. Species included in this assessment and their known distributions.

Species Distribution Manta alfredi World-wide, coastal Aetobatus ocellatus Indo-West Pacific Pastinachus atrus Aus, New Guinea, Melanesia, Micronesia Urogymnos asperrimus Indo-West Pacific, NSW north to the central QLD Dasyatis fluviorum NSW north to the central Qld, and west from Cape York in Qld to Darwin in the NT. Southern New Guinea, off both Papua New Guinea and Indonesian Irian Jaya Himantura uarnak Indo Pacific, east Mediterranean, northern Aus. Indian and western Pacific Oceans, Taiwan, Ryukyu Islands, southern Japan Himantura jenkinsii Indo-Pacific, northern Aus Himantura fai Indo-Pacific, northern Aus. Ryukyu Islands, South China Sea and Sulu Sea off Sabah, Indonesia, Thailand, Vietnam and north to Myanmar, northern tropical Australia, the Java Sea, down to New South Wales. Papua New Guinea. Eiao, Marquesas Islands and Apia, Samoa, Philippines, Micronesia. Himantura granulata Indian ocean and western Pacific. North Aus. Indonesia, Borneo and Sabah. Papua New Guinea, Vanikoro, Pohnpei, Melanesia, and the Philippines. Guam and Fiji. Taeniura lymma Indo-West Pacific Taeniurops meyeni Indo-West Pacific Neotrygon kuhlii Indian ocean, western Pacific, northern Aus Neotrygon leylandi Western central WA, Aus Neotrygon ningalooensis Exmouth Gulf, Ningaloo Reef, Shark Bay (WA); Nhulunbuy NT Glaucostegus typus Indo-Pacific, Melanesia, north Aus Rhynchobatus laevis Indo-Pacific, northern Aus, south Africa, New Caledonia, south Japan *Aus=Australia, Qld= Queensland, NSW= New South Wales, WA= Western Australia, NT= Northern Territory. Sources: IUCN Red List, Last & Stevens 2009, Marshal et al. 2009, Last et al. 2010b, White et al. 2010

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5.2.3 Working definitions

5.2.3.1. Study regions The information assessed for batoid species was divided in two ecoregions: A) Indo-West Pacific Ocean (IWP) and B) Arafura-Timor Sea and northern Australia (ATSNA) (Figure 35)

Figure 35. Biogeographic regions used for the vulnerability assessment and research priorities assessment framework: the Indo-west Pacific Ocean (blue line), and the Arafura- Timor Sea/northern Australia (green line).

5.2.3.2. Defining components, categories, and attributes

5.2.3.2.1 Vulnerability assessment (Objective 1) This framework uses three components identified by Chin et al., (2010) to assess the vulnerability of 16 species of rays to two major threats in two ecoregions defined above. The components are ‘exposure’, ‘sensitivity’, and ‘adaptive capacity or rigidity’ (Figure 36). The first component, ‘exposure’, assessed the exposure of each species to the risk of two major threats (categories) in each region, fisheries and habitat degradation. Both categories were considered ‘negative’, with high exposure indicating an increased potential impact from a threat. The first category, fisheries, was assessed by comparing the extent of the species’ known distribution with the

133 Chapter 5- Vulnerability and research priority assessment known distribution of its fishery (Appendix 3). This category included both targeted fisheries and by-catch. The second category, habitat degradation, was assessed by the presence of each species in areas directly affected by coastal development, coastal pollution, and/or coral bleaching. The second component, ‘sensitivity’, was considered a ‘negative’ component, where high sensitivity equates to increased potential impact. It includes

Figure 36. Three components for the vulnerability assessment.

134 Chapter 5- Vulnerability and research priority assessment characteristics that are not easily changed and comprised four categories: maximum size, maturity size, and productivity and was evaluated by comparing these categories among the species assessed. For each category, the minimum and maximum values of the data for the 16 (ATSNA) and 12 (IWP) species assessed were log-transformed. The range of these data was divided in thirds and back-transformed to obtain reference values for the determination of ranks (Table 11). Criteria rules related to size were calculated for the species that the standard size measurement is disc width (DW) (myliobatoids, n=14). Only two species require total length (TL) as a standard measurement (G. typus and R. laevis). For those species, the reference values for could not be obtained but were assigned as high because both attain large sizes. The third component, ‘adaptive capacity or rigidity’ was considered a ‘positive’ component. High adaptive capacity means that a species is able to better alter behaviour to accommodate changing environmental conditions. It was characterized by three categories, trophic specificity (diet), habitat specificity, mobility (or immobility), and latitudinal range. Each component was individually rated, the level of exposure, sensitivity, and rigidity were rated as 0.33, 0.66 , or 1.00 ). These ratings were averaged to assess vulnerability that is expressed as a proportion ranging from 0.00 to 1.00, where numbers from 0.00 to 0.33 equals to low exposure, 0.34 to 0.66 equals to moderate exposure, and 0.67 to 1.00 equals high exposure. The colors, green and red were used to color-code the resulting tables: green was used to code for ‘Low’ vulnerability and red for ‘High’ vulnerability. When assessing a species’ exposure, sensitivity, or adaptive capacity, the highest ranking of any of the categories was used. Components were rated using literature, unpublished data and expert knowledge. If there was no information available to rank certain category or attribute, it was ranked as high which is consistent with the precautionary principle of the ERA approach that is applied to recognize that lack of information increases risk (Chin et al. 2010; Hobday et al. 2011; Stobutzki, Miller & Brewer 2001). Once components and categories were identified and rated, these ratings are combined to produce a matrix integrating these three components to calculate the vulnerability for each species.

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Table 11. Cut-off scores for sensitivity and rigidity categories.

RANKING 0.33 0.66 1 COMPONENTS CATEGORIES DETAILS Low (L) Moderate (M) High (H) Known max size Max size Sensitivity ATSNA < 79 79-230 >230

IWP <94 94-250 >250 Averaged males Maturity size & females < 55 55-150 >150 Productivity Litter size >9 3-9 <3 Trophic Prey >4 gps 3-4 gps 1-2 gps Rigidity specificity Reef, mangrove, Coastal but Moderate Habitat Habitat estuary related; not habitat habitat specificity associated or other associated association Depending on body size, type, Large Moderate Restricted Immobility and movement mobility mobility mobility capacity All Indo- Arafura, Latitudinal Coral triangle/ West Timor Sea & range central IP Pacific north Aus

5.2.3.2.2 Research priorities assessment (Objective 2) The components of this assessment were disciplines and topics of research; and will be addressed as ‘components’ for practicality in this assessment. Four components were used for the research priorities assessment: fisheries, spatial ecology, biology, and genetics (Figure 37). All components were assessed by reviewing the available information on the components’ categories and attributes. The first component, ‘fisheries’, was assessed by reviewing the available information of fisheries of each species in three categories: records, fishery description, and socio-economic aspects. Records refers to information of species that are (attributes) targeted and species caught as by-catch. Fishery description refers to information on (attributes) fishing gear, fishing seasonality, catches, and fishing mortality rates. Socio-economic aspects refer to information on (attributes) dependency/incomes/trade-value, culture/traditions and alternative livelihoods related to the fishery of each species. The socio-economic aspects referred to indigenous fishing in Australia as these species are not targeted by other fisheries. The second component, ‘spatial ecology’, had four categories: key habitats, habitat partitioning, site fidelity, and movements/migration. Key habitats refers to the identified crucial habitats for the species’ survival. Habitat partitioning refers the habitat use and overlapping among species. Site fidelity refers to the ability of a

136 Chapter 5- Vulnerability and research priority assessment species to use and/or return to particular sites. Movements/migration refers to the ability of a species move to different habitats and/or to travel large distances. The third component, ‘biology ‘, had three categories: diet, reproduction, and age-&-growth. Diet refers to identified (attributes) preys and ontogenetic shifts of diet. Reproduction refers to (attributes) fecundity, gestation, and size at maturity. Age-&-growth refers to information on (attributes) longevity, ageing and growth. The last component, ‘genetics’, has four categories: species boundaries, genetic markers, population structure, and populations’ connectivity. The research needs for each species of batoids assessed, were rated as 0.33 (low), 0.66 (moderate), or 1.00 (high), depending on the available information (or lack of) for each attribute. The multiplicative approach was used for this assessment to a category level. A mathematical consequence of this approach is that when all components are moderate, the calculated value is low, and is arbitrarily assessed as moderate (Chin et al. 2010). A low level of available information equals high priority for research; a high level of available information equals a low research priority. Once components, categories, and attributes are identified and rated, these ratings are combined to produce an integrated matrix of research priorities for all species.

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Figure 37. Diagram for the assessment of research priorities.

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5.3 Results

5.3.1 Vulnerability assessment and integrative matrix

The vulnerability assessment of batoids in the Arafura-Timor Sea / northern Australia ecoregion included the 16 barcoded species listed in Table 10. This assessment identified that 50% of the species were highly vulnerable to the risk of fisheries and 37% were highly vulnerable to the risk of habitat degradation. Only N. ningalooensis and H. fai were highly vulnerable to both threats and 31% of the species had moderate vulnerability to both threats (Table 12). The attribute that had more high values was productivity (Appendix 4). The 16 species of batoids were ranked for each category (Appendix 4) and the extent of available species-specific information varied among categories. The vulnerability assessment of batoids in the Indo-West Pacific region included 12 species as P. atrus, N. leylandi, N ningalooensis, and D. fluviorum are endemic to the ATSNA ecoregion. This assessment identified that 75% of the species included were highly vulnerable to at least one threat and 67% were highly vulnerable to both threats. A percentage of 25 were moderately vulnerable to both threats (Table 13).

Table 12. Vulnerability assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion. M= Medium (green); H High (red).

SPECIES FISHERIES HABITAT DEGRADATION M. alfredi M H A. ocellatus M M D. fluviorum M H P. atrus H M T. lymma M M T. meyeni M M N. kuhlii H M N. leylandi H M N. ningalooensis H H H. fai H H H. granulata M H H .jenkinsii M M H. uarnak M M U. asperrimus H H G. typus H M R. laevis H M

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Table 13. Vulnerability assessment of batoids in Indo- West Pacific ecoregion. M= Medium (green); H High (red).

HABITAT SPECIES FISHERIES DEGRADATION M. alfredi H H A. ocellatus H H T. lymma M M T. meyeni M M N. kuhlii M M H. fai H H H. granulate H H H. jenkinsii H H H .uarnak H H U. asperrimus H H G. typus H H R. laevis H M

5.3.2 Research priorities assessment

The assessment of research priorities showed that studies on genetics (species delineations, genetic markers for population studies, population structure connectivity) are the most needed in both ecoregions (Table 14, Table 16) with 83% of the species showing high priority in the ATSNA ecoregion and 82% in the IWP. In the ATSNA ecoregion, socio-economics of fisheries, migration, and age & growth had high priority for research for most species based on the information (or lack of) available (Table 14Table 15). Neotrygon ningalooensis had high priorities in all components in this ecoregion. For the ‘fisheries’ category, records was best documented with no high ranks, although five species (H. granulata. H. jenkinsii, U. asperrimus, G. typus, and R. laevis) had moderate values. The socio-economic aspects of fisheries were ranked as high in 81% of the species, being T. lymma and N. leylandi the species with most need of fisheries information. For the ‘spatial ecology’ category, movements/migration had high rank in 75% of the species, with T. meyeni showing the highest need of research in this area. For the category of ‘biology’, age & growth had high ranking in 75% of the species, with H. granulata showing the highest need of research in this category. All species were ranked as high in the category of ‘genetics’ except A. ocellatus; M. alfredi had high ranking only in populations’ connectivity. The research priorities (topics ranked as high) for each species are listed in Table 16.

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For ‘fisheries’ in the IWP ecoregion, the socio-economic aspect was ranked as high in four species (33%) (Table 17, Table 18). Records and description were relatively well documented for all species except T. lymma. Spatial ecology had priority for research for most species in the IWP ecoregion, particularly habitat partitioning with 100% of species showing high rank on this component. Except for M. alfredi, all species were ranked as high for research on site fidelity, and except for M. alfredi and A. ocellatus, all species were ranked as high for research on movements/migration. For the category of ‘biology’, 92% of the species had high priority for research on age & growth; diet and reproduction are relatively well documented for all species except R. laevis. All species except M. alfredi and A. ocellatus had high priority for research in all aspects of genetics. The high ranked research priorities for each species are listed in Table 19.

141 Table 14. Research priorities assessment of batoids in the Arafura-Timor Sea & northern Australia. N. nin= N. ningalooensis;. Information was on previous name of: 1- M. birostris, 2- A. narinari, 3- P. sephen. (Categories: Fisheries & Spatial ecology). L= Low(clear); M= Medium (green); H High (red).

FISHERIES SPATIAL ECOLOGY SOCIO- KEY HABITAT SITE Species RECORDS DESCRIPTION ECONOMICS HABITATS PARTITIONING FIDELITY MIGRATION M. alfredi 1 L L M M H M M A. ocellatus2 L M L M H H H D. fluviorum L M H L H H H P. atrus3 L L H M M M H T. lymma L H H M M M H T. meyeni L M H H H H H N. kuhlii L L M H H H M N. leylandi L H H M H H H N. nin L H H H H H H H. fai L L H M M M H H. granulata M M H M H H H H. jenkinsii M L H M M M H H. uarnak L L H M M M M U. asperrimus M M H M M M H G. typus M M H M M M M R. laevis M M H M M M H

142 Table 15. Research priorities assessment of batoids in the Arafura-Timor Sea & northern Australia. N. nin= N. ningalooensis;. Information was on previous name of: 1- M. birostris, 2- A. narinari, 3- P. sephen. (Categories: Biology & Genetics). L= Low(clear); M= Medium (green); H High (red).

BIOLOGY GENETICS AGE & STOCK POPs' Species DIET REPRODUCTION GROWTH DELINEATION MARKERS STRUCTURE CONNECTIVITY M. alfredi 1 M L H M M M H A. ocellatus 2 M L H M M M M D. fluviorum M L M H H H H P. atrus 3 L L H H H H H T. lymma M H H H H H H T. meyeni M M H H H H H N. kuhlii M M M H H H H N. leylandi M M M H H H H N. nin H H H H H H H H. fai M M H H H H H H. granulata H H H H H H H H. jenkinsii H M H H H H H H. uarnak L M M H H H H U. asperrimus M M H H H H H G. typus M M H H H H H R. laevis M M H H H H H

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Table 16. High priority research categories for batoid species assessed in the ATSNA ecoregion. N. nin= N. ningalooensis

Species Research Categories HABITAT AGE & POPS M. alfredi PARTIT GROWTH CONNECT HABITAT SITE MOVS/ AGE & A. ocellatus PARTIT FIDELITY MIGR GROWTH SOCIO- HABITAT SITE MOVS/ ALL D. fluviorum ECON PARTIT FIDELITY MIGR GENETICS SOCIO- MOVS/ AGE & ALL P. atrus ECON MIGR GROWTH GENETICS MOVS/ AGE & T. lymma DESCRIP MIGR REPROD GROWTH SOCIO- KEY HABITAT SITE MIGRATIO ALL T. meyeni ECON HABITATS PARTIT FIDELITY N GENETICS KEY HABITAT SITE ALL N. kuhlii HABITATS PARTIT FIDELITY GENETICS HABITAT SITE MOVS/ AGE & ALL N. leylandi DESCRIP PARTIT FIDELITY MIGR GROWTH GENETICS

ALL SOCIO- SPATIAL ALL ALL N. nin DESCRIP ECON ECOLOGY BIOLOGY GENETICS SOCIO- MOVS/ AGE & ALL H. fai ECON MIGR GROWTH GENETICS SOCIO- HABITAT SITE MOVS/ ALL ALL H. granulata ECON PARTIT FIDELITY MIGR BIOLOGY GENETICS SOCIO- MOVS/ AGE & ALL H. jenkinsii ECON MIGR GROWTH DIET GENETICS SOCIO- MOVS/ ALL H. uarnak ECON MIGR GENETICS SOCIO- AGE & ALL U. asperrimus ECON GROWTH GENETICS SOCIO- AGE & ALL G. typus ECON GROWTH GENETICS SOCIO- MOVS/ ALL R. laevis ECON MIGR GENETICS

144 Table 17. Research priorities assessment of batoids in the Indo- West Pacific ecoregion. Information was on previous name of: 1- M. birostris, 2- A. narinari. (Categories: Fisheries & Spatial ecology). L= Low(clear); M= Medium (green); H High (red).

FISHERIES SPATIAL ECOLOGY SOCIO- KEY HABITAT SITE SPECIES RECORDS DESCRIPTION ECONOMICS HABITATS PARTITIONING FIDELITY MIGRATION M. alfredi 1 M M M M H M M A. ocellatus 2 M M H M H H M T. lymma H H H M H H H T. meyeni M M H M H H H N. kuhlii M M M M H H H U. asperrimus M M M M H H H H. uarnak L L M M H H H H. jenkinsii L L M M H H H H. fai L L M M H H H H. granulata M M M M H H H G. typus M M M M H H H R. laevis M M H M H H H

145 Table 18. Research priorities assessment of batoids in the Indo- West Pacific ecoregion. Information was on previous name of: 1- M. birostris, 2- A. narinari. (Categories: Biology & Genetics). L= Low(clear); M= Medium (green); H High (red).

BIOLOGY GENETICS AGE & STOCK POPs' SPECIES DIET REPRODUCTION GROWTH DELINEATION MARKERS STRUCTURE CONNECTIVITY M. alfredi 1 M L H M M M M A. ocellatus 2 M L L M M M M T. lymma M M H H H H H T. meyeni M M H M H H H N. kuhlii M M H M H H H U. asperrimus M M H H H H H H. uarnak M M H H H H H H. jenkinsii M M H H H H H H. fai M M H H H H H H. granulata M M H H H H H G. typus M M H H H H H R. laevis H H H H H H H

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Table 19. High priority research categories for species assessed in the IWP ecoregion.

Species Research Categories HABITAT AGE & M. alfredi PARTIT GROWTH SOCIO- HABITAT SITE A. ocellatus ECON PARTIT FIDELITY ALL HABITAT SITE MOVS/ AGE & ALL T. lymma FISHERIES PARTIT FIDELITY MIGR GROWTH GENETICS SOCIO- HABITAT SITE MOVS/ AGE & ALL T. meyeni ECON PARTIT FIDELITY MIGR GROWTH GENETICS HABITAT SITE MOVS/ AGE & ALL N. kuhlii PARTIT FIDELITY MIGR GROWTH GENETICS HABITAT SITE MOVS/ AGE & ALL U. asperrimus PARTIT FIDELITY MIGR GROWTH GENETICS HABITAT SITE MOVS/ AGE & ALL H. uarnak PARTIT FIDELITY MIGR GROWTH GENETICS HABITAT SITE MOVS/ AGE & ALL H. jenkinsii PARTIT FIDELITY MIGR GROWTH GENETICS HABITAT SITE MOVS/ AGE & ALL H. fai PARTIT FIDELITY MIGR GROWTH GENETICS HABITAT SITE MOVS/ AGE & ALL H. granulata PARTIT FIDELITY MIGR GROWTH GENETICS HABITAT SITE MOVS/ ALL G. typus PARTIT FIDELITY MIGR GENETICS SOCIO- HABITAT SITE MOVS/ ALL ALL R. laevis ECON PARTIT FIDELITY MIGR BIOLOGY GENETICS

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5.3.3 Research priorities for highly vulnerable species

The species of the ATSNA ecoregion that were assessed as highly vulnerable to the risk of both fishing and habitat degradation and the research needs assessed as priority for such species are shown in Table 20. Socio-economics of their fishery, age & growth, and all attributes of genetics are the most common topics for these species in the ATSNA region. The research priorities for the species assessed as highly vulnerable to both threats in the Indo-West Pacific ecoregion are shown in Table 21. Habitat partitioning, site fidelity, migration, age & growth and all attributes of genetics are the priority topics for these species in this region.

Table 20. Research priorities for species most vulnerable to the risk of fisheries and habitat degradation in the ATSNA ecoregion.

SPECIES RESEARCH PRIORITY Socio-economics, movs/migration, age & growth, all P. atrus genetics. Key habitats, habitat partitioning, site fidelity, all N. kuhlii genetics. Fisheries descriptions & socio-economics, habitat HIGH N. leylandi partitioning, site fidelity, movs/migration, all VULNERABILITY genetics. TO THE RISK OF N. ningalooensis All components except targeted fisheries. FISHERIES Socio-economics, movs/migration, age & growth, all H. fai genetics. Socio-economics, movs/migration, age & growth, all G. typus genetics. Socio-economics, movs/migration, age & growth, all R. laevis genetics. Habitat partitioning, age & growth, populations' M. alfredi connectivity. Socio-economics, habitat partitioning, site fidelity, D. fluviorum HIGH movs/migration, all genetics. VULNERABILITY N. ningalooensis All components except targeted fisheries. Socio-economics, movs/migration, age & growth, all TO THE RISK OF H. fai HABITAT genetics. DEGRADATION H. granulata Socio-economics, habitat partitioning, site fidelity, movs/migration, all biology, all genetics. Socio-economics, movs/migration, age & growth, all U. asperrimus genetics. Chapter 5- Vulnerability and research priority assessment

Table 21. Research priorities for the species most vulnerable to to the risk of fisheries and habitat degradation in the IWP ecoregion.

SPECIES RESEARCH PRIORITY M. alfredi Habitat partitioning, age & growth. A. ocellatus Socioeconomics-, habitat partitioning, site fidelity. Habitat partitioning, site fidelity, movs/migration, age H. fai & growth, all genetics. HIGH Habitat partitioning, site fidelity, movs/migration, age VULNERABILIT H .granulata & growth, all genetics. Y TO THE RISK Habitat partitioning, site fidelity, movs/migration, age OF FISHING H. jenkinsii & growth, all genetics. Habitat partitioning, site fidelity, movs/migration, age H. uarnak & growth, all genetics. Habitat partitioning, site fidelity, movs/migration, age U .asperrimus & growth, all genetics. M. alfredi Habitat partitioning, age & growth. Habitat partitioning, site fidelity, movs/migration, age H. fai & growth, all genetics. Habitat partitioning, site fidelity, movs/migration, age HIGH H. granulata & growth, all genetics. VULNERABILIT Habitat partitioning, site fidelity, movs/migration, age Y TO THE RISK H. jenkinsii & growth, all genetics. OF HABITAT Habitat partitioning, site fidelity, movs/migration, age DEGRADATION H. uarnak & growth, all genetics. Habitat partitioning, site fidelity, movs/migration, age U. asperrimus & growth, all genetics. Habitat partitioning, site fidelity, movs/migration, age G. typus & growth, all genetics.

5.4 Discussion Ecological risk assessment (ERAs) has been widely applied to evaluate the effects of fishing, for example in the Australian northern prawn trawl fisheries (Stobutzki et al. 2002) and in the Atlantic tuna long-line fisheries (Arrizabalaga et al. 2011). Gallagher et al. (2012) summarized the applicability of the ERA approach in elasmobranchs. This approach, however, has some limitations that need to be recognized and will be discussed below. With such limitations in mind, this approach can indeed be a suitable method to assess vulnerability and research priorities of batoids. While geographically, the ATSNA region is part of the Indo-west Pacific, these two regions were assessed separately in this study because of the significant differences in threats, research capacity, regulated fisheries, and current knowledge of fisheries and biodiversity. The conservation concerns in the developing countries of the IWP region arise from several and diverse socio-economic drivers, among

149 Chapter 5- Vulnerability and research priority assessment those are the high demand for protein coming from fish, including elasmobranchs, rapid population and industrialization growth, and the limited sources available for management (Ban et al. 2009; Field et al. 2009a; Talaue-McManus 2000; White & Kyne 2010). This situation has resulted in an additional factor, the increase in illegal and unregulated fishing which most developing countries in the region have little capability for documenting and policing (Field et al. 2009a; White & Kyne 2010). The diversity of elasmobranchs in the Indo-West Pacific is not well documented except for some areas of Indonesia (White, Last, et al. 2006). In contrast, the high elasmobranch diversity of Australia has been well documented and updated during the last decade (Last & Stevens 1994; Last & Stevens 2009; Last & White 2011). An overview of the fisheries and research in the Indo-Pacific was compiled in the IUCN Chondrichthyan status survey of 2005 (Chapter 7, Cavanagh 2005). This report shows the limited information and low level of management of elasmobranch fisheries within the IWP, highlighting the exception of Australia (and New Zealand) as being among the most researched, managed, and documented elasmobranch fisheries. A developing country in the IW, Indonesia, has the highest annual landings of elasmobranchs reported to FAO (Lack & Sant 2011), however there have been no reliable species-specific data from their fisheries and little specific management. A similar situation to that of Indonesia is likely occurring in other countries of the Indo- West Pacific. For example, 55% of the top 20 countries with the highest catch of elasmobranchs are located in the Indo- West Pacific region (Lack & Sant 2011). In contrast, the shark fishery in Australia is regulated by three separate Commonwealth- State/Territory Joint Authorities and the total catch of these fisheries is comparatively low (Blaber et al. 2009). Additionally, Australia has a much smaller percentage of threatened species and species of conservation concern in comparison to other tropical subregions of the IWP (White & Kyne 2010), and also a considerable quantity of risk assessments of elasmobranchs fisheries (Gallagher, Kyne & Hammerschlag 2012).

5.4.1 Vulnerability assessment

The vulnerability assessment in the ATSNA region identified that 69% (n=11) of the species had high vulnerability to at least one threat and three species (N. ningalooensis, H. fai and U. asperrimus) were highly vulnerable to both threats 150 Chapter 5- Vulnerability and research priority assessment in this region. Although the assessment suggested that the vulnerability of a species depends on the combination of its components and attributes, productivity had the most high rankings of all attributes included. The high values in productivity were mostly due to the lack of information and the precautionary principle that gives a high rank when information is not available (Chin et al. 2010; Hobday et al. 2011; Stobutzki, Miller & Brewer 2001). However, 50% of the species did have information available on productivity and the majority of those species (75%) had high ranking on this attribute which supports the popular knowledge that live-bearer elasmobranchs are particularly vulnerable because of their low productivity (Cortés et al. 2010; Field et al. 2009b). The species that had high vulnerability to fisheries were the species commonly caught in the Australian northern (including northwest and northeast) prawn trawl fisheries (Brewer, CSIRO & Marine and Atmospheric Research & Australian Fisheries Management Authority & Fisheries Research and Development Corporation (Australia) & Northern Prawn Fishery Bycatch Monitoring Project 2007; Kyne et al. 2002; Stobutzki et al. 2002) and the shark-like bodied species which are usually caught by the illegal shark fin fisheries (Holmes, Steinke & Ward 2009). Neotrygon ningalooensis was ranked with high vulnerability because of the lack of information as it is a newly described species (Last, White & Puckridge 2010), however, the limited knowledge available of its habitat and distribution indicates that this species could potentially be caught as by-catch in the Exmouth Gulf prawn trawl fisheries. The northern Australia trawl fisheries have included the use of turtle exclusion devices (TEDs) as a management measure, which reduced the by-catch of larger rays such as Rhynchobatus djiddensis, Gymnura australis, and Himantura toshi (Brewer et al. 2006) however, smaller species and juveniles (smaller individuals) of large species are still at high risk to get caught (Brewer et al. 2006; Stobutzki et al. 2002). Furthermore, the use of these devices helps a percentage of rays to avoid caught in, but does not help in avoiding the significant impact on habitat that rays depend on (Griffiths & Fuller 2007; Hiddink et al. 2011; Pikitch et al. 1997). Therefore, special attention to highly vulnerable species needs to be included in the management and planning of the fisheries in this region. The ATSNA region had fewer species with a high vulnerability to the risk of habitat degradation than to the risk of fisheries. This is very likely a result of the near pristine coastal environments and very low coastal population density of northern

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Australia. Despite this, species that had a high vulnerability to habitat degradation were species commonly associated with particular habitats such as reef, mangroves, or estuaries. It is recognized that specialization may increase the extinction risk and vulnerability of species (Davies, Margules & Lawrence 2004; Field et al. 2009b), furthermore, the degradation of habitats may also affect prey availability and the interhabitats linkages (Chin et al. 2010). Therefore, the habitat-associated species may be less adaptable and have less capacity to move to or utilize other areas when their sustenance sources and primary habitats are modified. In contrast with the ATSNA region, the IWP region had more species with high vulnerability to both threats (ie. fisheries and habitat degradation). Eight species (M. alfredi, A. ocellatus, H. fai, H. granulata, H. jenkinsii, U. uarnak, U. asperrimus, and G. typus) had high vulnerability to both threats likely due to a highly targeted fishery on batoids and the increase of coastal population and industry (e.g. Cassels, Curran & Kramer 2005; Vidthayanon 1997; White & Dharmadi 2007; White, Last, et al. 2006). Smaller species, which have less meat, and therefore less commercial value, were ranked as moderately vulnerable to fisheries. The smoothnose wedgefish (R. laevis) had also moderate vulnerability because of its high productivity (Last & Stevens 2009) (Appendix 2, Table 1).

5.4.2 Research assessment

Several studies have recently highlighted the need for research and more data to improve management of elasmobranch populations (e.g.Heupel & Simpfendorfer 2010; Simpfendorfer et al. 2011), however, the list of research needs is extensive and depends on which regions or countries are involved. In an ideal world, there would be enough resources to study and manage all biodiversity, but instead, we are faced with the need to prioritize conservation and science needs to efficiently allocate resources (Bottrill et al. 2008). This study applied an established ranking method (ERA) to a completely different scope: prioritizing research topics. The available information (or gaps of) on tropical batoids was rated and the research priorities were identified. Additionally, this study also integrated the highly vulnerable species identified in the previous assessment with their research priorities. This approach is a systematic method to discriminate and prioritize extensive lists of research needs and therefore can be extremely useful for scientists and managers.

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5.4.2.1 Research priorities

The research needs of the most vulnerable species in these two regions were different. The ATSNA region had priority in more diverse and ‘advanced’ research such as habitat partitioning or movements while the IWP region generally needing more ‘basic’ research such as age & growth of the highly vulnerable species (see Table 10Table 11). Regardless of such differences, ‘genetics’ had high priority in 80% of the species and ‘fisheries’ was the category better documented in both regions. Genetic data have the potential to improve the understanding of population structure and intraspecific diversity and consequently, the development of specific management strategies (Reiss et al. 2009; Selkoe, Henzler & Gaines 2008; Waples, Punt & Cope 2008); however it seems to be often excluded from fisheries management planning (Waples, Punt & Cope 2008). Simpfendorfer et al. (2011) put together a comprehensive list of research topics needed for the conservation and management of elasmobranchs, however there is no mention of genetics or molecular studies in such list and in this assessment, only two species (M. alfredi and A. ocellatus) have some information on population genetics. Although samples may be hard to collect for batoids, the first needed step in genetic studies would be to develop appropriate markers to study the populations of tropical species, and the following step would be identifying the genetic structure and limits of targeted and overfished stocks (Tinti et al. 2003; Valsecchi 2005). Age and growth was also highly ranked in both regions showing the general need of studies on this topic to better assess sensitivity of batoids to a variety of threats. Movements and migrations was a highly ranked priority for research in the ATSNA region along with habitat partitioning and site fidelity in the IWP region. This type of research can provide important information to understand the spatial scales to which management should be applied (Simpfendorfer et al. 2011). Because of the multiplicative and integrative approach used for this assessment, some important issues can be masked. For example, the ‘socio-economic aspects’ of the batoids’ fisheries in the IWP was mostly ranked as moderate because central Indonesia and Borneo have had considerable research investment from Australian researchers (Last, White & Pogonoski 2010; White & Dharmadi 2007; White, Last, et al. 2006). Such research has produced major taxonomic and biological data and, although in a much smaller quantity, also information on the

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Chapter 5- Vulnerability and research priority assessment economic value of elasmobranchs (Last, White & Pogonoski 2010; White & Dharmadi 2007; White, Last, et al. 2006). This small but important information led this attribute to be ranked as moderate even though the rest of the IWP does not have such information available. The socio-economic aspects of batoids of the rest of the Indo west- Pacific region, and in the case of Australia, the socio-economic use by Indigenous Australia are still considerably unknown and not considered in management planning. If the socio-economic context of a resource is not included in analysis and planning and the community is not aware of its benefits, the success of such plans will be considerably limited (Ban et al. 2009; Hilborn 2007; Sobel & Dahlgren 2004), therefore this topic should have a higher priority than stated in this assessment.

5.4.3 Assumptions and limitations of this method

5.4.3.1. The precautionary approach This study used the precautionary approach which assumes that threats pose higher risks in the absence of information (Hobday et al. 2077; Hobday et al. 2011). Although this approach has been widely used in risk assessments, its limitations must be recognized. This approach gives high rank in the absence of information which may result in more false positives: units are identified at a higher risk than they may have if assessed with more data (Hobday et al. 2011). The precautionary approach also encourages the collection of data to support future assessments but this approach could be unrealistic if the information available is not analyzed and criticized thoroughly. Another way to assess attributes is the ‘biological approach’ (Kyne 2010) which uses data from close related species that may produce a more realistic rating, assuming that closely related species would have the same or similar biological characteristics. In this study, the precautionary approach was chosen because of two reasons: 1) most of these species lack information, making it difficult to find a closely related species that would actually have any available and reliable information, and 2) because it is assumed that the high biodiversity of the IWP is related to the particular geographic characteristics of this area which creates diversity in habitats (Allen 2008; Carpenter & Springer 2005; Roberts et al. 2002) and it is likely that similar species of rays have different biological and behavioral patterns.

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5.4.3.2. Mobility assumption For many species of batoids, their exposure to fisheries and habitat degradation had moderate ranking because of low rigidity. However there are many knowledge gaps and this assessment had an assumption of mobility that should be recognized. This category assumes that individuals will be able to move to new areas and establish viable populations. This assumption however, must be treated with caution because alternative habitats may not be available, particularly in the case of mangroves or coral reefs that are especially at risk to localized pressures (Carpenter et al. 2008; Su et al. 2007). Thus, species may not be able to locate alternative habitats or cope with the increased competition in new viable habitats, in these cases, this assessment will have underestimated rigidity.

5.4.3.3. Unpublished information Both assessments were based on the available information, which may not report the full extent of exposure to some threats. For example, it is known that batoids are caught in the north and east of the Indian Ocean; however, there is no published information which may limit the applicability of this method and underestimate some results. It is likely then, that many species assessed have higher and wider exposure to both threats that what has been identified here.

5.4.3.4. Taxonomic reviews of species, new name but old data Another issue encountered in this assessment in which attributes are ranked in a species-specific basis, is the change of taxonomic names or split of species. This was the case of three species whose taxonomic name had recently been reviewed: M. alfredi (Marshall, Compagno & Bennett 2009), P. atrus (Last & Stevens 2009) and A. ocellatus (White et al. 2010). Since these names are relatively new, there is no information linked with each particular name and therefore the older names were used when applicable (M. birostris, P. sephen, and A. narinari). This is an important point because if the former names were not used, then these species would have high ranking on all attributes.

The approach adopted in this study examined different sensitivities and exposure to threats to identify the species most vulnerable to the risk of fisheries and habitat degradation in the Indo- West Pacific including Australia. The ERA approach has been utilised in different ways according to the goal of the study. For example,

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Stobutzki et al. (2002) Tovar-Avila et al. (2010), and Arrizabalaga et al. (2011) have assessed the resilience of elasmobranchs species to the risk of fishing. Although these studies have used different ways to assess such resilience, they focused on life- history characteristics that influence the recovery of populations and the habitats and distribution to assess susceptibility. This assessment is similar to these because it includes life-history traits and susceptibility to evaluate vulnerability. However, its scope and geographic scale were broader, as it included the exposure to different kinds of fisheries and habitat degradation in a larger region, the Indo-West Pacific. Furthermore, two factors highlight the importance of this work and its major differences with previous assessment: i) the comparison of regions that included developed and developing countries with noticeable economic and management differences, and ii) the inclusion a particular biogeographical region that covers some of most pristine and threatened habitats, the Arafura-Timor Sea and northern Australia. The semi-quantitative method used maximized what can be determined from the available information and made consistency possible across species. It therefore, provided a rapid assessment for prioritizing species for management and highlighted gaps for research so that efforts can be focused in particular regions and/or highly vulnerable species.

5.5 Conclusions The management of batoid populations includes a wide range of concerns and issues, and addressing these in a logical manner requires knowledge of the biological, environmental, economic, social, and cultural aspects of such populations. The limitations discussed previously point towards the same direction: more information is needed for better (or any) management. Whether the available information and assumptions may, in some cases, underestimate or overestimate species’ vulnerabilities, this method is a rapid and useful way to assess vulnerability status and identify/prioritize needs. The study of data-poor species needs a starting point where directions for management and research can be identified and this assessment has proven to fulfill such needs. The Indo-West Pacific region has a highly diverse marine biodiversity that is quickly decreasing (Allen 2008; Carpenter & Springer 2005; Roberts et al. 2002). Many species and ecosystems need to be managed to ensure their persistence but

156 Chapter 5- Vulnerability and research priority assessment priority must be given to highly vulnerable species and ecosystems. The vulnerability assessment integrated several aspects affecting these species and identified the species at highest risk to two major threats in the Indo-west Pacific region, including Australia. The species assessed had moderate or high vulnerability to both threats in both regions and this information can be used by managers to help prioritize management responses so that management efforts are directed where it’s most needed. The research assessment framework also provided a basis for further research and the development of informed management responses. Furthermore, by including and rating spatial information of the target species such as habitat use, dependency and seasonal use of critical habitats, and fishing pressures surrounding such habitats, this versatile system could be a very helpful tool to help prioritize spatial zoning in marine protected areas. Although the northern section of Australia is still unpopulated and relatively untouched, the ecosystems in this area need special attention to ensure the persistence of such a pristine environment. Five reasons aggravate this issue in the particular case of tropical batoids: 1) the presence of endemic species, 2) the intensity of the northern Australian trawl fisheries in this area, 3) the continuous effects of the illegal and unregulated and/or expansion-and-displacement fishery, 4) the lack of basic biological information for almost all species of rays in this area, and 5) the cultural importance (and lack of information of) of this group for Indigenous Australia. Although this region is relatively remote and poorly-researched (compared to fisheries in southern Australia), there is still the opportunity to invest in research and understand these important ecosystems, species, and populations before they are negatively affected.

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Chapter 6

General discussion Chapter 6 General discussion Chapter 6- General discussion

Chapter 6 - General discussion

6.1 Multi-disciplinary approach to the management and conservation of batoids

The use of several tools for answering ecological and management questions is becoming more common as technology advances, but more importantly, as scientists understand that management requires the use of multi-disciplinary studies for the development of comprehensive management plans. This thesis combines three scientific tools that are currently flourishing in the recent literature due to their high functionality and recent technological advances: genetic barcoding to inform species discrimination, passive acoustic monitoring to understand movements patterns, and integrated risk assessments to identify vulnerable species. With the combination of these tools in this thesis, we now have information about how to genetically identify tropical batoids, what habitats they prefer within a tropical reef system, short-term (1-3 yrs) use of such habitats, which tropical species are most vulnerable in the Indo- West Pacific region, and what research is needed to manage their exploited populations. The information presented here has implications for management at a range of scales, from species information, to regional and international management of batoid populations.

6.1.1 DNA-barcoding and species-specific information Species are the basic unit for sustainable management and conservation of biodiversity (King 2007) and species taxonomy is fundamental to determine species boundaries and recognize biodiversity. The appropriate identification of the unit of study or management is of critical importance as research outcomes and management measures, vary depending on such identification. Chapter 2 provided the first assessment of the DNA-barcoding tool for tropical rays and, if its limitations are well understood, it can prove to be a useful tool when appropriate taxonomic knowledge is not available (Ovenden et al. 2010; Valentini, Pompanon & Taberlet 2009; White & Last 2012). By barcoding the COI gene, I was able to confirm and/or correct the identification of several individuals tagged for the ecological studies conducted as part of this thesis and also, for other behavioral studies (e.g. O’Shea unpublished data). In addition to the species ID

160 Chapter 6- General discussion confirmation, new and important information was produced, such as new barcodes of batoids and new reports of species distribution. For example, nine species represented new barcode sequences from Australia for the GenBank nucleotide online database; two species represented new barcodes from Australia for both online databases, GenBank and BOLD. One species represented a new sequence from Vietnam and another species had no matching sequences in either data base and, therefore, represented the only barcoding sequence available for this species. Lastly, the sequence from one species from the Northern Territory (NT), Australia, significantly matched sequences of a species whose distribution does not include the NT waters and it is therefore, a new report of this species’ distribution; however, taxonomic studies should follow this result to appropriately describe this finding. The barcoding of COI also provided preliminary results of biogeographic patterns using the gene COI supporting pervious information on some tropical species (e.g. Ward et al. 2008) and giving a ‘bird’s-eye view’ of populations structures that can be followed up by specific genetic population studies. The downfalls of DNA-barcoding such as the presence of mtDNA pseudogenes or the issues related to the mitochondrial inheritance have been discussed elsewhere (eg reviews by Antunes & Ramos 2005; Funk & Omland 2003; Krishna Krishnamurthy & Francis 2012; Rubinoff 2006; Taylor & Harris 2012) and were not discussed in this thesis. Such molecular limitations must be understood but there was an additional set of issues that were encountered and were related to the uncertain taxonomy of the species barcoded and the quality of online sequences and data bases. This study highlighted the importance of confirming the ID of species identified in the field, particularly of those species known for presenting morphological or color complexities but caution must be taken when manipulating data from the online databases (GenBank and BOLD), because much of these online data need quality control and updates. As stated by Taylor and Harris (2012), new sequences should be compared with verified reference sequences from voucher specimens properly authenticated by taxonomists. If such limitations are understood and kept in mind, this molecular tool can contribute to conservation by accelerating local biodiversity assessments to prioritize management and by assessing phylogenetic diversity (Faith & Williams 2005; Krishna Krishnamurthy & Francis 2012). I therefore suggest that the use of this molecular tool should be promoted and routinely included in ecological studies,

161 Chapter 6- General discussion particularly those that are providing critical information for management and conservation of threatened or key species. Also, there is a significant opportunity for observers on board of fishing vessels to play an important role in obtaining vital information to underpin effective by-catch monitoring and reporting. Tissue sampling for DNA-barcoding could be a valuable standard procedure for some fisheries, particularly those that significantly by-catch impacts on batoids, such as trawl fisheries. Batoids have a high by-catch in such fisheries (Stobutzki, Miller & Brewer 2001; Stobutzki et al. 2002) and DNA-barcoding by-catch will help to accurately identify and assess species by-catch through more accurate and reliable species discrimination..

6.1.2 Spatial ecology and key habitats Telemetry and biologging techniques are being increasingly applied to the study of the spatial ecology of marine animals because they can provide detailed information on behaviour that would be otherwise unachievable using other techniques (Cooke 2008; Heupel & Heuter 2001b). Additionally, the long term and continuous records of behaviour achievable with these tools facilitate the detection of spatial trends through time. Such an approach is particularly important for organisms that inhabit large ranges, exhibit rapid movement, or occupy habitats that are difficult to access (Cooke 2008). Spatial ecology of batoids has not been well studied in the past. This is primarily because of the complications of studying large vertebrates with fishery independent methods, as it is usually more difficult to find and handle larger animals. However, a fundamental understanding of the spatial dynamics of commercially exploited batoids is required in order to predict the potential impacts of management measures - an understanding that cannot be determined from fisheries data alone (Hunter, Buckley, et al. 2005). Additionally, defining key or critical habitats is one of the most important topics for marine conservation at a local-scale because the understanding of habitat use within potentially threatened ecosystems and how species may respond and/or be impacted by habitat degradation or fragmentation is essential information for management (Chateau & Wantiez 2009; Chin et al. 2010; Roberts et al. 2003; Ward-Paige et al. 2012). For instance, two species of batoids were identified as the most vulnerable elasmobranchs to climate change in another tropical coral reef ecosystem, the Great Barrier Reef, likely because of their use of

162 Chapter 6- General discussion particular coastal and estuarine habitats (Chin et al. 2010). As such, identification of critical habitats, spatial ecology, and behaviour of target and key species are critical missing elements that should be incorporated into the reserve design and planning models to provide a realistic assessment of a marine reserve’s efficacy so conservation procedures can then be tailored to protect high-quality habitats and enhance species management (Roberts and Sargant 2002). Key habitats, spatio-temporal habitat use, and movement patterns of juvenile and adult rays in relation to a marine protected area (ie. the Ningaloo Reef Marine Park) were explored and described in Chapters 3 and 4. In Chapter 3, the first nursery area for tropical batoids in the Indo-Australian region was identified. Although elasmobranch scientists generally believe that batoids, like sharks, likely use shallow and protected habitats as nurseries grounds, there are few published studies identifying such critical habitats for batoids (e.g. Dale et al. 2011; Yokota & Lessa 2006) and significantly - none in the Indo-Australia region. At a regional scale, the temperature correlates of juvenile batoids behavior can also be used to predict the spatial patterns of some species within areas of conservation significance and also, to monitor the effects of anthropogenic disturbance. With knowledge of both critical habitats and batoid behavior, managers can ensure effective environmental safeguards in areas of urban coastal development – including monitoring of water quality, coastal runoff, and erosion control during coastal development, to protect critical habitats (Sheppard 2008). Chapter 4 identified that adult rays showed sexual segregation of core areas of activity, with females using shallow waters (2 m water depth) near a mangrove forest and males using the lagoon near reef patches. The identification of mangrove forests and reef patches as key habitats for juvenile and adult batoids is essential information for the management of coastal development, coastal fisheries, and the implementation marine parks focused on habitat protection. Significantly, this study provides information regarding the use of particular key habitats over time by different species of tropical rays within an established marine park. This information can be used not only to review the marine park’s current boundaries, but can also assist in the developing marine reserves in other important tropical ecosystems for batoids species in northern Australia or Indonesia.

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6.1.3 Critical habitats and MPAs My study site included the highly protected Mangrove Bay sanctuary zone within the Ningaloo Reef Marine Park. This sanctuary zone is significant because it includes the only mangrove forest in the area; the zone also has a high level of marine biodiversity (Cassata & Collins 2008), hence its status as a highly protected, ‘no-take’ zone. The movements of juvenile tropical rays were concentrated within the sanctuary zone confirming its ecological importance. The diel movements of such individuals in these core areas of activity were related to temperature changes rather than tidal cycles, suggesting behavioral thermoregulation (Hight & Lowe 2007; Matern, Cech & Hopkins 2000). However, movements of juvenile batoids also extended to other areas of the array within the lagoon and the open shelf indicating that this spatially-fixed management measurement is only partially protecting a critical life-stage of these rays. Because the size of an effective reserve will depend in part on the daily, seasonal, and ontogenetic movements of species, the evaluation of the home ranges, site fidelity, and habitat use of individuals in the population is critical (Chapman et al. 2005; Glazer & Delgado 2006; Heupel & Simpfendorfer 2005). The movements of adults and juvenile batoids outside the boundaries of the sanctuary zone, and also likely outside the park, makes spatially-fixed batoid management measures challenging. Furthermore, all the ‘no-take’ zones at Ningaloo Reef Marine Park are adjacent to recreational and general use zones where recreational and commercial fishing is permitted, respectively. Therefore, this current zoning and management regime may not be adequate for mobile organisms, such as batoids. Thus, alternatives zoning regimes need to be developed to ensure minimum impact on current batoid populations in the Marine Park. For example, a buffer zone where only selected gear is permitted, so it protects mobile organisms that use the sanctuary zones but move beyond. The detection of batoids outside protected areas identified through acoustic monitoring studies (such as this program of research), implies that conservation actions implemented at smaller scales elsewhere may not be sufficient to ensure the sustainability of populations targeted by fisheries. Marine reserves are considered a key conservation tool and strategy for protecting top-order predators (Simpfendorfer & Heupel 2004). However, the protection of areas and critical habitats such as nurseries would increase the survival of the young, although as the size of an 164

Chapter 6- General discussion organism increases, so does its habitat range - and a small reserve will leave unprotected a vulnerable life-stage(Heupel & Simpfendorfer 2005). If the movements of batoids are wide and marine reserves have spatial restrictions, there remains uncertainty regarding the effectiveness of this measure to encompass their critical habitats and adequately protect these species. Without information on the life stages protected by the reserve, the migratory behaviour of the species, and the level of fishing effort outside the reserve, its effectiveness of marine protected areas (MPAs) as a conservation management tool for top-order predators (such as sharks and rays), remains very challenging (Chapman et al. 2005; Simpfendorfer & Heupel 2004). . Heupel and Simpfendorfer (2005) summarized the ways of examining the effectiveness of MPAs in relation to the movement patterns of a species, including long- and short-term monitoring and individual modelling of movements. The most important outputs of acoustic tagging in relation to marine reserves are twofold: 1) the recognition of the need to incorporate spatial and temporal movement data to assess the habitat use and activity space to design spatial management plans, in particular the optimal design of marine reserves (or networks of) (Afonso et al. 2009); and 2) the realization that the actual size of the marine reserves under study is too small to encompass core areas of habitat use or residency (Afonso et al. 2009; Meyer, Papastamatiou & Holland 2007). Thus, acoustic tagging can be used to determine how long targeted species stay in protected or fishing areas, or to quantify the movements and habitat use of batoids and other large vertebrates within and/or related to MPA boundaries, to better evaluate temporal fishing closures and spatial reserves (Chapman et al. 2005). Targeting areas of greater vulnerability could offer much greater benefits than if the effort was homogeneous over entire fishing grounds (Roberts & Sargant 2002). To this end, socio-economic studies should be included to ensure a comprehensive plan of management of highly targeted species (deReynier, Levin & Shoji 2010). Integrated risk assessment frameworks that assess the vulnerability and sensitivity of key species, habitats, or ecosystems, such as the ecological risk assessment (or ERA) used in Chapter 5 could also potentially be used to assist the identification and assessment of MPA networks, as well as assist in development of individual spatial zoning plans for large MPAs. Therefore, an integrative assessment that evaluates the spatial ecology of target or iconic species and rank their life-stage,

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Chapter 6- General discussion movement patterns, habitat use, dependency to key habitats, and the fishing pressure in surrounding areas, could ideally be used at a local or regional scale, as a spatial conservation planning tool to develop spatial zoning plans and prescribe management zones (and activities). This is an exciting area of applied science- management that requires further inquiry.

6.1.4 Vulnerability and research priorities assessment of batoids Chapter 5 takes a different approach to previous research chapters as it is focuses on management tools. This section of my thesis applied an ecological risk assessment (or ERA) to identify the most vulnerable species of batoids from the 16 species DNA-barcoded in Chapter 2. These species are well distributed through the Indo-Pacific and are heavily targeted in developing countries and also, caught as by- catch in northern Australia. Therefore, this framework can help mangers to prioritize efforts on the species most vulnerable to fisheries and habitat degradation in Australia and the Indo-West Pacific. Furthermore, this framework could also be used as a standardized quantitative approach to assess particular research priorities of batoids and other large vertebrates, such as habitat use and spatial ecology, for a more effective design of potential MPAs. In addition, I used the prioritization and ranking approach from the ERA framework to assess the information available on each batoid species and identify research priorities. This approach identified specific research topics urgently needed for effective management of the highly vulnerable batoid species identified in the ERA. In this emerging era of multi-disciplinary management, managers need the current and latest research information, while scientists can ensure that their research provides information that fills current high priority knowledge gaps. Applying this integrated assessment approach to rank and identify priority research gaps is a novel approach that can assist with allocating funding and investing research efforts, where it is most needed. Importantly, it was possible to address several research topics under the ‘spatial ecology’ component through the information provided in Chapter 3 and 4 of this thesis. As a result, such topics had moderate priority in the Arafura-Timor Seas and northern Australia region. This highlights the utility of using acoustic monitoring for addressing management issues such as the identification of critical

166 Chapter 6- General discussion habitats (key habitats) or the dependence and interrelationships of rays with those habitats (habitat partitioning & site fidelity). Although data need to be produced for these species in the remainder of the Indo- West Pacific region, the current information obtained for northern Australian waters could potentially be used to assist spatial fisheries and conservation management of tropical batoids elsewhere. Comprehensive tools to manage fisheries and ecosystems, like ERAs or ecosystem-based fisheries management (or EBFM) are relatively flexible tools, as they can include several factors affecting the target species, the lack of information, and the uncertainty of some results (e.g. Chin et al. 2010; Stobutzki et al. 2002). However, these extremely useful approaches generally miss an important component: the socio-economic importance/impact of the managed resource. The combination of social and biological studies to manage a resource should be encouraged as scientists’ efforts are usually concentrated in preserving the source without acknowledging or taking into account the economic or social consequences of such protective measures, nor the socio-economic drivers of the affected community. For instance, socio-economic factors are usually considered secondary to biological factors in the design of MPAs and tend to be analysed after areas have been selected based on biophysical data (Klein et al. 2008). Another example is the use of traditional fisheries-sector analyses and approaches, which focus on conserving fish stocks, while making small-scale fisheries more economically efficient - this management and policy approach typically does not include the role of fisheries in a wider coastal economy (Allison & Ellis 2001). However, planning the best way to mitigate threats and promote recovery of biodiversity requires the understanding of the human social, political, and economic systems in which conservation functions and decisions cannot be reached without the full consideration of their implications on the coastal communities depending on such resources (Ban et al. 2009; Polasky 2008; Ward 2000). Fish production in the countries of the Indo-West Pacific region is largely based on artisanal or small-scale fisheries (Allison & Ellis 2001; Berkes 2003). Yet such community-based fisheries seem to be generally neglected as national government policies tend to favor large-scale, commodity-orientated fisheries (Berkes 2003). However, the small-scale fishers’ livelihood has particular dynamics and traditions that should be included in the planning and management of coastal and marine fisheries and ecosystems in the region. For example, exploring fishers

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Chapter 6- General discussion reactions to changes in resource harvesting and alternative livelihoods (Daw et al. 2012) or exploring the dynamics of alternative livelihoods occurring in small-scale fishers’ households and communities (Allison & Ellis 2001) could be added into the framework of species’ vulnerability or management planning. If such important socio- economic considerations of small-scale, community-based fisheries are not taken into account, future fisheries and coastal-marine conservation management and planning will likely be ineffective in the region as it is, after all, human action that has caused the biodiversity crisis and human communities who still depend on such endangered sources. The need for an integrated approach to small-scale, community-based, fisheries management that includes socio-economic and cultural factors, the ‘sustainable livelihoods approach’ (or SLA), has been articulated by several authors (e.g. Allison & Ellis 2001; Allison & Horemans 2006). This approach is similar to the ERA approach used in Chapter 5, in that it uses the concepts of resilience, sensitivity, and sustainability, with the difference that the SLA’s concepts are focused in the socio-economic aspect of the livelihood analyzed (e.g. fisheries). An integrated approach to a multi-disciplinary management of batoids populations in the Indo-Pacific could be done by combining an ERA and a SLA approach. In this combined approach, all important aspects of the target species used to determine its vulnerability (ERA) and all the important socio-economic (and cultural) aspects of the livelihood of the fisheries used to determine its resilience (SLA), would be included and assessed together to produce an integrated fisheries management plan. Consideration of this additional and vitally important socio-economic context of small-scale fisheries in the Indo-Pacific, is essential for effective community-based fisheries, risk-based fisheries, and ecosystem assessment and management in the region.

6.2 Future research directions The two major research priorities identified for tropical batoids in Chapter 5 for the Indo-West Pacific region (including Australia), were molecular biology and spatial ecology. Additionally, for northern Australia, research and management should also be a high priority, due to its relatively pristine, threatened, and data-poor environments.

168 Chapter 6- General discussion

6.2.1 Molecular biology Assessing genetic diversity has become an important component in conservation and management programs (Frankham, Ballou & Briscoe 2002), however this thesis has revealed significant gaps in molecular studies of batoids. Recently, DNA-barcoding has built up a large amount of data on batoids, but the uses of the COI gene are limited and may not be suitable for other molecular studies like population genetics or robust phylogeny. As mentioned in Chapter 1, broad phylogeny of the batoids orders is still limited and particular population studies of skates (Rajidae) in the European Northern Sea are available, but equivalent information on tropical batoids is still missing. Studying population genetics can provide quantitative data for evaluating the risk of losing genetic diversity and change in effective population size, evaluating the possibility of replenishment of depleted areas from sister areas, and the scale of genetic connectivity (Chevolot et al. 2007; Ward 2000). Therefore development of appropriate markers for population studies, stock structure of fished populations, and connectivity of such stocks are urgent ‘next steps’ to manage batoid populations. Additionally, combination of genetic and tagging data provide complementary sources of information on movement and migration patterns (Chevolot et al. 2006). This data is necessary to integrate short- and long-term movements for batoid conservation and to better understand larger spatial and temporal dynamics of population structure in relation to behaviour.

6.2.2 Spatial ecology These research findings constitute a snapshot of tropical rays’ spatial patterns and habitat use over a period of 2-3 years in particular areas of a tropical coral reef system. The costs and logistical constraints of batoid monitoring meant that sample sizes were small. Therefore, to enhance the understanding of tropical batoids’ spatial ecology, future monitoring need larger samples, encompassing a variety of size and sex classes. Connectivity among spatially isolated populations of batoids is potentially facilitated by the dispersal of adults. Thus, the increasing concerns about the impacts of humans on their populations have created a need for information on variations in their distribution, abundance, connectivity of subpopulations, and the processes that influence these patterns. Furthermore, the combination of genetic and non-genetic

169

Chapter 6- General discussion data along with oceanographic factors can provide an optimal multi-disciplinary approach for the management and conservation of batoid populations. For understanding their movement patterns at a larger spatial scale, comparative monitoring of several embayments through several arrays and curtains (arrays networks) could be used to investigate an inter-linked network of core habitats and large-scale spatial migrations. Additionally, the identification of ‘hotspots’, nursery grounds, and the habitat preferences of tropical batoids in different localities will also improve the spatial knowledge needed for conservation of these species.

6.2.3 The Northern Territory and tropical Australia Unfortunately, no effort was invested in monitoring batoid populations from the Northern Territory (NT), Australia, because the primary objective of field work in the NT area was to obtain tissue samples to complement the genetic studies. Despite this, we observed an extraordinary abundance and diversity of young (small) batoids and sharks in shallow estuarine embayments during field sampling in the coastal waters of the Northern Territory, particularly near-by mangrove forests. Shallow coastal estuarine and marine ecosystems in the NT, particularly in Darwin Harbor, are currently undergoing rapid coastal development, driven by major economic and population growth, resulting in habitat modification, mangrove deforestation, and shallow waters dredging for port development and boating access – all of which are likely to significantly affect marine wildlife, including batoids. With the increasing development and modification of coastal ecosystems and habitats in the NT, particularly near Darwin Harbor, research on their spatial ecology, critical habitats, nurseries, and the effects of habitat modification will be vital to ensure the conservation and maintenance of batoid populations in this still, near-pristine environment. Significantly, as mentioned in Chapter 5, batoids are also an important part of the diet, culture, and spiritual beliefs of coastal Australian Indigenous communities of the NT. However, unlike other important marine wildlife resources for Indigenous coastal communities, such as dugongs, information on ecological and social importance of batoids or indeed, the impacts of indigenous fishing on batoid populations is critically lacking. This information is urgently needed and should be formally included in the fisheries research and management plans for this batoid and shark fisheries in northern Australia. Moreover, there remains a significant research

170 Chapter 6- General discussion opportunity to document the traditional ecological knowledge of tropical batoids that coastal or ‘saltwater’ Indigenous communities hold from generations harvesting these coastal and totemic species. Therefore, both the ecological knowledge and cultural needs of Indigenous communities, needs to properly documented and incorporated in future batoid science, fisheries, and management plans (Berkes 2003, 2008).

6.3 Conclusions

Despite the great advances in our understanding of sharks, our understanding of tropical batoids and their relation to the environment is still far behind. While there are major differences in the levels of understanding of tropical batoids, levels of threats, research, and management capacity between developed countries (Australia) and developing countries (the remainder of the Indo-West Pacific), much research is still needed for this diverse group and also, the tropical coastal ecosystems in which they occur, including in the northern Australia (particularly in the Northern Territory). This means that there is a huge amount of research to be done to aid effective fisheries and batoid conservation management decisions. However, multi-disciplinary approaches that combine a range of tools can, and should now be used, to produce integrative assessment frameworks for the management and conservation of vulnerable, threatened, and data-poor species such as tropical batoids. Batoid spatial ecology, genetics, and integrated risk and research assessments are urgently needed for batoids in the Indo-West Pacific to better inform effective spatial conservation and small-scale fisheries. As this study has demonstrated, the correct identification of batoid species, the understanding of movement patterns, habitat use, the identification of critical habitats, and the understanding of species at risk and major threats will be essential approaches in assisting effective small-scale fisheries management and developing integrated conservation plans for tropical batoids in the region.

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204 Appendix 1

Online sequences used for barcoding tropical batoids Appendix 1- Chapter 2- Online sequences

TABLE A1. 1. ONLINE SEQUENCES USED IN THIS STUDY WITH THEIR GENBANK ACCESSION NUMBERS.

No. Species Locality GenBank No. Author 1 Aetobatus narinari Cayman Islands, Caribbean FJ812203.1 Richards et al, 2009 2 Aetobatus narinari Belize FJ812202.1 Richards et al, 2009 3 Aetobatus narinari Mexico FJ812200.1 Richards et al, 2009 4 Aetobatus narinari Indonesia FJ812198.1 Richards et al, 2009 5 Aetobatus narinari Japan FJ812196.1 Richards et al, 2009 6 Aetobatus narinari Hawaii FJ812197.1 Richards et al, 2009 7 Aetobatus ocellatus Northern WA, Au EU398507.1 Ward et al, 2008 8 Aetobatus ocellatus Lombok, Indonesia EU398508.1 Ward et al, 2008 9 Carcharhius amblyrhynchos Indonesia EF609308.1 Ward & Holmes 2007 10 Carcharhius plumbeus Lombok, Indonesia EU398639.1 Ward et al, 2008 11 Dasyatis fluviorum Logan River, Qld, Au DQ108183.1 Ward et al, 2005 12 Dasyatis leylandi North-West Shelf, WA, Au EU398751.1 Ward et al, 2008 13 Dasyatis leylandi North-West Shelf, WA, Au EU398750.1 Ward et al, 2008 14 Dasyatis leylandi North-West Shelf, WA, Au EU398749.1 Ward et al, 2008 15 Dasyatis leylandi Rockhampton, Qld, Au DQ108175.1 Ward et al, 2008 16 Dasyatis leylandi Northern Qld, Au DQ108174.1 Ward et al, 2008 17 Dasyatis parvonigra Bali, Indonesia EU398732.1 Ward et al, 2008 18 Glaucostegus typus Northern Qld, Au EU398999.1 Ward et al, 2008 19 Himantura astra Shark Bay, WA, Au EU398866.1 Ward et al, 2008 20 Himantura astra Shelburne Bay, Qld, Au DQ108170.1 Ward et al, 2008 206 Appendix 1- Chapter 2- Online sequences

21 Himantura fai Lombok, Indonesia EU398839.1 Ward et al, 2008 22 Himantura fai Sandakan, Malaysia DQ108176.1 Ward et al, 2008 23 Himantura fava Sandakan, Malaysia DQ108167.1 Ward et al, 2008 24 Himantura gerrardi Jakarta, Indonesia EU398844.1 Ward et al, 2008 25 Himantura gerrardi Jakarta, Indonesia EU398842.1 Ward et al, 2008 26 Himantura hortlei Java, Indonesia EU398848.1 Ward et al, 2008 27 Himantura jenkinsii Java, Indonesia EU398851.1 Ward et al, 2008 28 Himantura jenkinsii Legendre Island, WA, Au DQ108169.1 Ward et al, 2008 29 Himantura jenkinsii Sandakan, Malaysia DQ108168.1 Ward et al, 2008 30 Himantura pastinacoides Sarawak, Malaysia EU398857.1 Ward et al, 2008 31 Himantura pastinacoides Kampung, Malaysia EU398856.1 Ward et al, 2008 32 Himantura toshi Hervey Bay, Qld, Au EU398869.1 Ward et al, 2008 33 Himantura uarnak India EU541309.1 Persis et al, 2008 34 Himantura walga Jakarta, Indonesia EU398876.1 Ward et al, 2008 35 Himantura walga Jakarta, Indonesia EU398874.1 Ward et al, 2008 36 Manta birostris Lombok, Indonesia EU398904.1 Ward et al, 2008 37 Manta birostris Indonesia GU673824.1 Ward, Last, White pers. com. 38 Neotrygon kuhlii Japan AB485685.1 Yagishita et al, 2009 39 Neotrygon kuhlii India HM467799.1 Bineesh et al, 2010 40 Neotrygon kuhlii Indonesia EU398745.1 Ward & Holmes 2007 41 Neotrygon kuhlii Taiwan EU398735.1 Ward et al, 2008 42 Pastinachus sephen Malaysia EU398973.1 Ward et al, 2008 43 Pastinachus sephen Malaysia EU398972.1 Ward et al, 2008

207 Appendix 1- Chapter 2- Online sequences

44 Pastinachus sephen Indonesia EU398971.1 Ward et al, 2008 45 Pastinachus sephen Merauke, Indonesia EU398970.1 Ward et al, 2005 46 Pastinachus solocirostris Malaysia EF609431.1 Ward & Holmes 2007 47 Rajiformes (H. uarnak) WA, Au GU673418.1 Ward, Last, White pers.com. 48 Rajiformes (H. uarnak) WA, Au GU673414.1 Ward, Last, White pers.com. 49 Rajiformes (H. uarnak) WA, Au GU673417.1 Ward, Last, White pers.com. 50 Rajiformes (P. sephen) WA, Au GU673072.1 Ward, Last, White pers.com. 51 Rajiformes (T. meyeni) Qld, Au GU673424.1 Ward, Last, White pers.com. 52 Taeniura lymma Indonesia FJ584168.1 Steinke et al, 2009 53 Taeniurops meyeni India HM467797.1 Bineesh et al, 2010

Locality: GBR, Great Barrier Reef; NT, Northern Territory; Qld, Queensland

208

Appendix 2

Receiver range testing of Mangrove Bay array.

Appendix 2 Range testing

Figure A2. 1. Mangrove Bay array range testing. Proportion of detections received by VR2 receivers at increasing distances from the test transmitter in the lagoon channel (filled circles, 4 m water depth) and at Mangrove Bay (white circles, 1 m water depth).

210

Appendix 3

Biological and fisheries distribution maps of 16 species of tropical batoids Appendix 3 Chapter 5- Biological and fisheries distributions

Figure A3. 1. Species with known distribution in the Indo-West Pacific ecoregion part I.

212 Appendix 3 Chapter 5- Biological and fisheries distributions

Figure A3. 2. Species with known distribution in the Indo-West Pacific ecoregion part II.

213 Appendix 3 Chapter 5- Biological and fisheries distributions

Figure A3. 3. Species with known distribution in the Arafura-Timor Sea and northen Australia ecoregion.

214 Appendix 4

Vulnerability assessment integrative matrix for the Indo- West Pacific Appendix 4 Chapter 5- Integrative matrix for vulnerability assessment Table A4. 1. Vulnerability assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion (Categories: Exposure & Sensitivity). L= Low; M= Medium; H=High .

EXPOSURE SENSITIVITY FISHERIES HABITAT DEGRAD MAX SIZE MATURITY SIZE PRODUCTIVITY 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 SPECIES L M H L M H L M H L M H L M H M. alfredi 0.33 0.66 1 1 1 A. ocellatus 0.66 0.33 1 0.66 0.66 D. fluviorum 0.33 1 0.66 0.33 1* P. atrus 0.66 0.33 0.66 0.66 1 T. lymma 0.33 0.66 0.33 0.33 1 T. meyeni 0.66 0.66 0.66 0.33 1* N. kuhlii 1 0.33 0.33 0.33 1 N. leylandi 1 0.33 0.33 0.33 1 N. ningalooensis 1* 1 1* 1* 1* H. fai 0.33 0.66 0.66 0.66 1* H. granulata 0.33 0.66 0.66 0.66 1* H. jenkinsii 0.33 0.33 0.66 0.66 1* H. uarnak 0.33 0.33 0.66 0.66 1 U. asperrimus 0.66 0.66 0.66 0.66 1* G. typus 1 0.33 1 1 1* R. laevis 1 0.33 1 1 0.33 *No information available 216 Appendix 4 Chapter 5- Integrative matrix for vulnerability assessment

Table A4. 2. Vulnerability assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion (Category: Rigidity). L= Low(clear); M= Medium (green); H High (red).

RIGIDITY TROPHIC SPECIF HABITAT SPECIF IMMOBILITY LAT RANGE 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 Species L M H L M H L M H L M H M. alfredi 1 1 0.33 0.33 A. ocellatus 0.66 1 0.33 0.33 D. fluviorum 0.33 1 0.66 1 P. atrus 0.33 0.66 0.66 1 T .lymma 0.33 1 1 0.33 T. meyeni 0.33 0.66 0.66 0.33 N. kuhlii 0.33 0.33 1 0.33 N. leylandi 1 0.33 1 1 N. ningalooensis 1* 1* 1 1 H. fai 1 0.66 0.66 0.66 H. granulata 0.33 0.66 0.66 0.66 H. jenkinsii 0.33 0.33 0.66 0.33 H. uarnak 0.33 0.33 0.66 0.33 U. asperrimus 0.33 1 1 0.33 G. typus 0.33 0.33 0.33 0.33 R .laevis 1 0.33 0.33 0.33 *No information available 217 Appendix 4 Chapter 5- Integrative matrix for vulnerability assessment

Table A4. 3. Summary of vulnerability of batoids in the Arafura-Timor Sea & northern Australia ecoregion. L= low (clear); M= Medium (green); H= High (red).

EXPOSURE SENSITIVITY RIGIDITY HABITAT MAX MAT TROPHIC HABITAT LATITUDINAL SPECIES FISHERIES DEGRAD SIZE SIZE PROD SPECIFICITY SPECIFICITY IMMOBILITY RANGE M. alfredi L M H H H H H L L A. ocellatus M L M M M M H L L D. fluviorum L H M L H L H M H P. atrus M L M M H L M M H T. lymma L M L L H L H H L T. meyeni M M M L H L M M L N. kuhlii H L L L H L L M L N. leylandi H L L L H H L H H N. ningalooensis H H H H H H H H H H. fai L M M M H H M M M H. granulata L M M M H L M M M H. jenkinsii L L M M H L L M L H. uarnak L L M M H L L M L U. asperrimus M M M M H L H H L G. typus H L H H H L L L L R. laevis H L H H L H L L L

218 Appendix 4 Chapter 5- Integrative matrix for vulnerability assessment

Table A4. 4. Integration of the vulnerability of batoids in the Arafura-Timor Sea & northern Australia ecoregion. L= low (clear); M= Medium (green); H= High (red).

EXPOSURE SENSITIVITY RIGIDITY HABITAT SPECIES FISHERIES DEGRADATION M. alfredi L M H M A. ocellatus M L M M D. fluviorum L H M H P. atrus M L H M T. lymma L M M M T. meyeni L M M M N. kuhlii H L M M N. leylandi H L M H N. ningalooensis H H H H H. fai L M H H H. granulata L M H M H. jenkinsii L L H M H. uarnak L L H M U. asperrimus L M H M G. typus H L H L R. laevis H L H M

219 Appendix 4 Chapter 5- Integrative matrix for vulnerability assessment

Table A4. 5. Vulnerability assessment of batoids in the Indo-West Pacific ecoregion (Categories: Exposure & Sensitivity).

EXPOSURE SENSITIVITY HABITAT FISHERIES DEGRADATION MAX SIZE MATURITY SIZE PRODUCTIVITY

0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 SPECIES L M H L M H L M H L M H L M H M. alfredi 1 0.66 1 1 1 A. ocellatus 1 0.66 1 0.66 0.7 T. lymma 0.66 1 0.33 0.33 1 T. meyeni 0.66 0.66 0.66 0.33 1* N. kuhlii 1 1 0.33 0.33 1 H. fai 1 0.66 0.66 0.66 1* H. granulata 0.66 1 0.66 0.66 1* H. jenkinsii 0.66 0.66 0.66 0.66 1* H. uarnak 1 0.66 0.66 0.66 1 U. asperrimus 0.66 1 0.66 0.66 1* G. typus 1 0.66 1 1 1* R .laevis 1 0.66 1 1 0.33 *No information available

220 Appendix 4 Chapter 5- Integrative matrix for vulnerability assessment

Table A4. 6. Vulnerability assessed from the vulnerability assessment of batoids in the Indo-West Pacific ecoregion (Category: Rigidity).

RIGIDITY TROPHIC SPECIF HABITAT SPECIF IMMOBILITY LAT RANGE 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 Species L M H L M H L M H L M H M. alfredi 1* 1 0.33 0.33 A. ocellatus 0.66 1 0.33 0.33 T. lymma 0.33 1 1 0.33 T. meyeni 0.33 0.66 0.66 0.33 N. kuhlii 0.33 0.33 0.66 0.33 H. fai 1 0.66 0.66 0.66 H. granulata 0.33 0.66 0.66 0.66 H. jenkinsii 0.33 0.33 0.66 0.33 H. uarnak 0.33 0.33 0.66 0.33 U. asperrimus 0.33 1 1* 0.33 G. typus 0.33 0.33 0.33 0.33 R. laevis 1 0.33 0.33 0.33 *No information available

221

Appendix 4 Chapter 5- Integrative matrix for vulnerability assessment

Table A4. 7. Summary of the vulnerability of batoids in the Indo-West Pacific ecoregion.

EXPOSURE SENSITIVITY RIGIDITY HABITAT MAX MAT TROPHIC HABITAT LAT SPECIES FISHERIES DEGRADATION SIZE SIZE PRODUCTIVITY SPECIFICITY SPECIFICITY IMMOBILITY RANGE M. alfredi H M H H H H H L L A. ocellatus H M H M M M H L L T. lymma M H L L H L H H L T. meyeni M M M L H L M M L N. kuhlii H H L L H L L M L H. fai H M M M H H M M M H. granulata M H M M H L M M M H. jenkinsii M M M M H L L M L H. uarnak H M M M H L L M L U. asperrimus M H M M H L H H L G. typus H M H H H L L L L R. laevis H M H H L H L L L

222 Appendix 4 Chapter 5- Integrative matrix for vulnerability assessment

Table A4. 8. Integrative matrix for vulnerability of batoids in the Indo- West Pacific ecoregion.

EXPOSURE SENSITIVITY RIGIDITY HABITAT SPECIES FISHERIES DEGRADATION M. alfredi H M H M A. ocellatus H M H M T. lymma M H M L T. meyeni M H M L N. kuhlii H H M L H. fai H H H H H. granulata M H H M H. jenkinsii M M H M H. uarnak H M H M U. asperrimus M M H M G. typus H H H L R. laevis H M M M

223

Appendix 5

Research priorities assessment integrative matrix for the Indo- West Pacific Appendix 5- Chapter 5- Integrative matrix for research priorities assessment

Table A5. 1. Research priorities assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion (Category: Fisheries). N. nin= N. ningalooensis. Information on 1= M. birostris, 2= A. narinari, 3= P. sephen.

FISHERIES RECORDS DESCRIPTION SOCIO-ECONOMICS seasonality & culture/ targeted by catch gear catches mortality rates Trade & value traditions 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 Species L M H L M H L M H L M H L M H L M H L M H M. alfredi L L H L H L H A. ocellatus L H M H H L H D. fluviorum M L M H H H H P. atrus M M L H H H H T. lymma M L H H H H H T. meyeni L H M H H H H N. kuhlii L L L H L H M N. leylandi L M H H H H H N. nin L H H H H H H H. fai M M L H H H H H. granulata M H M H H H H H. jenkinsii M M L H H H H H. uarnak M M L H H H H U. asperrimus M M M H H H H G. typus H M M H H H H R. laevis H M M H H H H

225 Appendix 5- Chapter 5- Integrative matrix for research priorities assessment

Table A5. 2. Research priorities assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion (Category: Spatial Ecology). N. nin= N. ningalooensis. Information on 1= M. birostris, 2= A. narinari, 3= P. sephen.

SPATIAL ECOLOGY KEY HABITAT SITE HABITATS PARTITIONING FIDELITY MIGRATION 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 Species L M H L M H L M H L M H M. alfredi 1 M H M M A. ocellatus 2 M H H H D. fluviorum L H H H P. atrus 3 M* M* M* H T. lymma M* M* M* H T. meyeni H H H H N. kuhlii H H H M N. leylandi M H H H N. nin H H H H H. fai M* M* M* H H. granulata M H H H H. jenkinsii M* M* M* H H. uarnak M* M* M* M U. asperrimus M* M* M* H G. typus M* M* M* M R. laevis M* M* M* H M*= Information obtained in this thesis.

226

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Table A5. 3. Research priorities assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion (Category: Biology). N. nin= N. ningalooensis. Information on 1= M. birostris, 2= A. narinari, 3= P. sephen.

BIOLOGY DIET REPRODUCTION AGE & GROWTH ontogenetic size at preys shifts fecundity gestation maturity longevity ageing growth 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 Species L M H L M H L M H L M H L M H L M H L M H L M H M. alfredi M H M M M H H H A. ocellatus M H M M M H H H D. fluviorum M H H H L M H H P. atrus L H M H L H H H T. lymma M H H H H H H H T. meyeni M H M H H H H H N. kuhlii M M M M M M M M N. leylandi M M M H M M M M N. nin H H H H H H H H H. fai M H H H M H H H H. granulata H H H H H H H H H. jenkinsii H H H H M H H H H. uarnak L H M H M H H H U. asperrimus M H H H M H H H G. typus M H H H M H H H R. laevis M H M H H H H H

227 Appendix 5- Chapter 5- Integrative matrix for research priorities assessment

Table A5. 4. Research priorities assessment of batoids in the Arafura-Timor Sea & northern Australia ecoregion (Category: Genetics) N. nin= N. ningalooensis. Information on 1= M. birostris, 2= A. narinari, 3= P. sephen.

GENETICS STOCK POPs' DELINEATION MARKERS STRUCTURE CONNECTIVITY 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 Species L M H L M H L M H L M H M. alfredi 1 M M M H A. ocellatus 2 M M M H D. fluviorum H H H H P. atrus 3 H H H H T. lymma H H H H T. meyeni H H H H N. kuhlii H H H H N. leylandi H H H H N. ning H H H H H. fai H H H H H. granulata H H H H H. jenkinsii H H H H H. uarnak H H H H U. asperrimus H H H H G. typus H H H H R. laevis H H H H

228 Appendix 5- Chapter 5- Integrative matrix for research priorities assessment

Table A5. 5. Research priorities assessment of batoids in the Indo- West Pacific ecoregion (Category: Fisheries). Information on 1= Manta birostris, 2= A. narinari.

FISHERIES RECORDS DESCRIPTION SOCIO-ECONOMICS seasonality & culture/ alternative targeted by catch by product gear catches mortality rates trade & value traditions livelihoods SPECIES L M H L M H L M H L M H L M H L M H L M H L M H L M H M. alfredi 1 M M M M M H M H H A. ocellatus 2 M M H M M H H H H T. lymma H H H H H H H H H T. meyeni M H H M H H H H H N. kuhlii M M H M H H M H H U. asperrimus M M H M H H M H H H. uarnak M M H L H H M H H H. jenkinsii M M M L H H M H H H. fai M M H L H H M H H H. granulata M H H M H H M H H G. typus M M H M H H M H H R. laevis H M H M H H H H H

229 Appendix 5- Chapter 5- Integrative matrix for research priorities assessment

Table A5. 6. Research priorities assessment of batoids in the Indo- West Pacific ecoregion (Category: Spatial ecology) Information on 1= Manta birostris, 2= A. narinari.

SPATIAL ECOLOGY KEY HABITAT HABITATS PARTITIONING SITE FIDELITY MIGRATION SPECIES L M H L M H L M H L M H M. alfredi 1 M H M M A. ocellatus 2 M H H H T. lymma M H H H T. meyeni M H H H N. kuhlii M H H H U. asperrimus M H H H H. uarnak M H H H H. jenkinsii M H H H H. fai M H H H H. granulata M H H H G. typus M H H H R. laevis M H H H

230 Appendix 5- Chapter 5- Integrative matrix for research priorities assessment

Table A5. 7. Research priorities assessment of batoids in the Indo- West Pacific ecoregion (Category: Biology). Information on 1= Manta birostris, 2= A. narinari.

BIOLOGY DIET REPRODUCTION AGE & GROWTH ontogenetic size at preys shifts fecundity gestation maturity longevity ageing growth SPECIES L M H L M H L M H L M H L M H L M H L M H L M H M. alfredi 1 M H M M L H H H A. ocellatus 2 M H M M M M M M T. lymma M H H H M H H H T. meyeni M H H H M H H H N. kuhlii M H M H M H H H U. asperrimus M H H H M H H H H. uarnak M H H H M H H H H. jenkinsii H H H H M H H H H. fai H H H H M H H H H. granulata M H H H M H H H G. typus M H H H M H H H R. laevis H H H H H H H H

231 Appendix 5- Chapter 5- Integrative matrix for research priorities assessment

Table A5. 8. Research priorities assessment of batoids in the Indo- West Pacific ecoregion (Category: Genetics). Information on 1= Manta birostris, 2= A. narinari.

GENETICS STOCK POPULATIONS’ DELINEATION MARKERS STRUCTURE CONNECTIVITY 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 0.33 0.66 1 SPECIES L M H L M H L M H L M H M. alfredi 1 M M M M A. ocellatus 2 M M M M T. lymma H H H H T. meyeni H H M H N. kuhlii M M H H U. asperrimus H H H H H. uarnak H H H H H. jenkinsii H H H H H. fai H H H H H. granulata H H H H G. typus H H H H R. laevis H H H H

232 TERMINé!