Feeding Ecology and Habitat Use of the Giant Manta Ray Manta Birostris at a Key Aggregation Site Off Mainland Ecuador

Home , Butterflyfish, Cortez rainbow wrasse, Feeding frenzy, Potamotrygon, Reef manta ray

Feeding ecology and habitat use of the giant manta ray Manta birostris at a key aggregation site off mainland Ecuador

Katherine Burn Burgess BSc (Hons) Marine Biology

A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in December, 2017 Faculty of Medicine

Abstract

A newly discovered aggregation site at Isla de la Plata, off mainland Ecuador, hosts the largest known population of M. birostris from June to October each year, with over 2400 individuals recorded since 2009. However, the drivers for this seasonal aggregation within the Humboldt upwelling system are unknown. As aggregative behaviour of planktivorous elasmobranchs is often attributed to food availability, the aim of this study was to examine whether feeding opportunities constitute the primary driver of this sizeable aggregation of M. birostris at Isla de la Plata.

We first define the inter-annual and seasonal variation in recorded numbers of M. birostris and investigate the relationship between M. birostris sightings and environmental variables at Isla de la Plata. The primary source of variability in physical processes and biological production in the Humboldt Current upwelling system is the El Niño-Southern Oscillation (ENSO), where primary production declines during the negative El Niño phase, and increases during the positive La Niña phase. Data comprising the daily sightings of M. birostris were collected over a five-year period (2011 – 2015), and a generalised linear model was used to identify the effect of El Niño on the presence of individual M. birostris. Overall, month and ENSO activity explained 27.7 % of the variability in M. birostris sightings between years. The number of individual M. birostris sighted off mainland Ecuador was significantly higher during years with La Niña and neutral ENSO conditions in comparison to El Niño conditions, suggesting that overall food availability in this region is likely to be important for this species.

To investigate the importance of local food availability and occurrence of M. birostris at Isla de la Plata, we determined near-surface zooplankton biomass, size structure and community composition in relation to M. birostris daytime foraging activity. When M. birostris was feeding there was higher zooplankton biomass in comparison to background non-feeding events. However, foraging activity was rarely observed at this aggregation site (6 % of all observations), which suggests that local food

availability at Isla de la Plata during the day is not a major driver of M. birostris aggregative behaviour. In comparison, cleaning was the most common behaviour recorded during daytime hours (54 % of all observations), with significant predictors conducive to efficient cleaning interactions such as, weak currents and good water clarity, explaining 44.8 % of the total variance in M. birostris sightings at Isla de la Plata. As boat-based plankton sampling and observations of M. birostris were only possible around Isla de la Plata during the daytime, M. birostris activity and location during night time hours were not able to be determined.

Non-lethal and minimally-invasive molecular methods were used to indirectly examine the feeding ecology and habitat use of M. birostris. To investigate the contribution of near-surface zooplankton to the long-term diet of M. birostris, we conducted bulk stable isotope (SIA) and fatty acid (FA) analyses on M. birostris muscle biopsies, as these provide an integrated (‘time-averaged’) signal of overall dietary intake. In addition, we conducted SIA on epidermal mucus, a rapid-to-medium turnover rate ‘tissue’, collected from M. birostris to assess the importance of near-surface zooplankton to the recent diet of this species. There was large variation in M. birostris mucus and muscle δ15N and δ13C values among individuals, indicating either a generalist feeding strategy or a population made up of individual specialists. Trophic position (TP) estimates from M. birostris muscle δ15N placed this species at a secondary consumer level, however, the broad range of δ15N values and subsequent calculated TPs are likely indicative of the targeting of higher level trophic prey, as well as primary producer baseline shifts. There was a broader range in mucus δ13C values in comparison to muscle, suggesting that resource use during aggregative behaviour off Ecuador is broader, but with no obvious resource switching. Manta birostris muscle tissue δ13C values were not consistent with this species feeding predominantly on surface zooplankton. Further, the FA profiles between M. birostris muscle and surface zooplankton were markedly different, with the FA profile of M. birostris muscle depleted in polyunsaturated fatty acids, which were the dominant constituent of surface zooplankton FA profiles. Instead, the FA profile of M. birostris was dominated by 18:1w9 isomers, which are commonly found in high proportions in deep-sea organisms. Taken together, findings from SIA and FA challenge the traditionally held view that surface zooplankton is the dominant component of M. birostris diet, and instead, suggest that the majority of dietary intake for M. birostris is mesopelagic in origin.

The rarity of observed M. birostris feeding activity at the Isla de la Plata does not provide support for aggregative behaviour of this species being driven by daytime feeding opportunities. Alternatively, it is possible that this daytime aggregation of M. birostris is related to the presence of cleaning stations at Isla de la Plata, and that the island is an important cleaning site given the prevalence of cleaning iii

behaviour. However, as sightings of M. birostris increased during La Niña conditions, when increased upwelling creates a more favourable food environment in the eastern Pacific, the broader, regional availability of food may ultimately drive M. birostris to aggregate at this site. The diet of M. birostris was dominated by mesopelagic prey sources, which may be targeted by M. birostris in offshore, possibly shelf-edge habitats, where they could feed on vertically migrating prey assemblages, such as euphausiids during night time hours. This possibility is consistent with the fact that Isla de la Plata is situated <40 km from the continental shelf-edge and that a recent acoustic tracking project suggests that M. birostris leave the island at night. Overall, results here indicate that Isla de la Plata provides both a good cleaning and social environment during the day, and its proximity to the shelf-edge means that M. birostris can access relatively nearby productive habitats to feed during the night. Future research could investigate whether major aggregation sites for manta rays involve the joint provision of cleaning habitat and nearby food resources, and whether manta rays aggregate if there is a provision of one without the other. Given the fishing pressure and conservative life history of manta rays, identifying vertical and horizontal locations of foraging grounds, and other important drivers of aggregative behaviour, is essential to develop effective conservation measures for these species.

Declaration by author

This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis.

Publications during candidature

Peer-reviewed publications

Pardo SA, Burgess KB, Teixieira D, & Bennett MB. (2015) Local-scale resource partitioning by stingrays on an intertidal flat. Marine Ecology Progress Series, 533, 205–218. doi:10.3354/meps11358

Burgess KB & Bennett MB. (2016) Effects of ethanol storage and lipid and urea extraction on δ15N and δ13C isotope ratios in a benthic elasmobranch, the bluespotted maskray Neotrygon kuhlii. Journal of Fish Biology. 90, 417–423. doi:10.1111/jfb.13164

Burgess KB, Couturier LIE, Marshall AD, Richardson AJ, Weeks SJ & Bennett MB. (2016) Manta birostris, predator of the deep? Insight into the diet of the giant manta ray through stable isotope analysis. Royal Society Open Science. doi:10.1098/rsos.160717

Rohner CA, Burgess KB, Rambahiniarison JM, Stewart JD, Ponzo A and Richardson AJ. (2017) Mobulid rays feed on euphausiids in the Bohol Sea. Royal Society Open Science. doi:10.1098/rsos.161060.

Burgess KB, Guerrero M, Richardson AJ, Bennett MB & Marshall AD. (2017) The use of epidermal mucus in elasmobranch stable isotope studies: a pilot study using the giant manta ray Manta birostris. Marine and Freshwater Research. doi:10.1071/MF16355.

Broadhurst MK, Laglbauer B, Burgess KB and Coleman MA. (2017) Reproductive biology and range extension for Mobula kuhlii cf. eregoodootenkee. Endangered Species Research. doi:10.3354/esr00876

Burgess KB, Guerrero M, Marshall AD, Richardson AJ, Bennett MB & Couturier LIE. (2017) Novel signature fatty acid profile of the giant manta ray suggests reliance on an uncharacterised mesopelagic food source low in polyunsaturated fatty acids. PLoS ONE. Accepted.

Conference abstracts

Burgess KB, Couturier LIE, Marshall AD, Richardson AJ, Weeks SJ & Bennett MB. (2014) Manta spp. as secondary consumers of a δ13C enriched food source. Sharks International Conference, Durban, South Africa.

Marshall AD, Guerrero M, Winstanley GW, and Burgess KB. (2014) A study on the migration and habitat use of a population of manta rays Manta birostris in the coastal waters of Isla de la Plata and its surroundings. First Latin American Congress on Rays and Chimeras, Manta, Ecuador.

Burgess KB, Couturier LIE, Marshall AD, Richardson AJ, Weeks SJ & Bennett MB. (2014) Manta birostris, an unlikely deep sea predator? Insights into the diet of giant manta rays using stable isotope analysis. International Postgraduate Symposium in Biomedical Sciences. Brisbane, Australia.

Burgess KB, Couturier LIE, Marshall AD, Richardson AJ, Weeks SJ & Bennett MB. (2015) Where do the world’s largest rays eat? An insight into the diet of Manta birostris in the Eastern Pacific using stable isotope analysis. Oceania Chondrichthyan Society Conference, Auckland, New Zealand.

Burgess KB, Winstanley GW, Bennett MB, Marshall AD, Weeks SJ and Richardson AJ. (2015) Sighting trends of the giant manta ray Manta birostris at a key aggregation site in the eastern equatorial Pacific. Oceania Chondrichthyan Society Conference, Auckland, New Zealand.

Burgess KB, Winstanley GW, Bennett MB, Marshall AD, Weeks SJ, Guerrero M and Richardson AJ. (2015) What influences the distribution of the world’s largest ray? International Postgraduate Symposium in Biomedical Sciences. Brisbane, Australia.

Burgess KB, Marshall AD and Bennett MB. (2016) Mucus in elasmobranch dietary studies using stable isotope analysis: preliminary findings from the giant manta ray Oceania Chondrichthyan Society Conference, Hobart, Australia.

Burgess KB, Marshall AD, Richardson AJ and Bennett MB. (2016) Surface snacking and deep sea banquets: Differences in short and long term dietary intake of the giant manta ray. International Postgraduate Symposium in Biomedical Sciences. Brisbane, Australia.

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Publications included in this thesis

Chapter 4 (Part I) Burgess KB, Couturier LIE, Marshall AD, Richardson AJ, Weeks SJ & Bennett MB. (2016) Manta birostris, predator of the deep? Insight into the diet of the giant manta ray through stable isotope analysis. Royal Society Open Science. doi:10.1098/rsos.160717 Contributor Statement of contribution Burgess KB (Candidate) Designed experiments (70 %) Collected data (80 %) Analysed data (100 %) Wrote the paper (80 %) Couturier LIE Designed experiments (10 %) Wrote and edited the paper (4 %) Marshall AD Designed experiments (20 %) Collected data (20 %) Wrote and edited the paper (4 %) Richardson AJ Wrote and edited the paper (4 %) Weeks SJ Wrote and edited the paper (4 %) Bennett MB Wrote and edited the paper (4 %)

Chapter 4 (Part II) Burgess KB, Guerrero M, Richardson AJ, Bennett MB & Marshall AD. (2017) The use of epidermal mucus in elasmobranch stable isotope studies: a pilot study using the giant manta ray Manta birostris. Marine and Freshwater Research. doi:10.1071/MF16355. Contributor Statement of contribution Burgess KB (Candidate) Designed experiments (90 %) Collected data (80 %) Analysed data (100 %) Wrote the paper (80 %) Guerrero M Wrote and edited the paper (5 %) Richardson AJ Wrote and edited the paper (5 %) Bennett MB Wrote and edited the paper (5 %) Marshall AD Designed experiments (10 %) Collected data (20 %)

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Wrote and edited the paper (5 %)

Chapter 5 Burgess KB, Guerrero M, Marshall AD, Richardson AJ, Bennett MB & Couturier LIE. (2017) Novel signature fatty acid profile of the giant manta ray suggests reliance on an uncharacterised mesopelagic food source low in polyunsaturated fatty acids. PLoS ONE. Accepted. Contributor Statement of contribution Burgess KB (Candidate) Designed experiments (30 %) Collected data (80 %) Analysed data (100 %) Wrote the paper (80 %) Guerrero M Designed experiments (5 %) Wrote and edited the paper (1 %) Collected data (5 %) Marshall AD Designed experiments (5 %) Collected data (15 %) Wrote and edited the paper (3 %) Richardson AJ Wrote and edited the paper (3 %) Bennett MB Wrote and edited the paper (3 %) Couturier LIE Designed experiments (60 %) Wrote the paper (10 %)

Contributions by others to the thesis

Chapter 1 The introductory review on the feeding ecology of manta rays is my own work. Contributions were made from Anthony Richardson, Michael Bennett, Andrea Marshall, Lydie Couturier and Chris Rohner who critically revised this chapter. Chris Rohner also provided unpublished data of giant manta ray sightings from Mozambique.

Chapter 2 This chapter was co-authored by Michel Guerrero, Giles Winstanley, Andrea Marshall, Anthony Richardson and Michael Bennett. Mark Harding, Giles Winstanley and Andrea Marshall provided logbook data for years prior to the start of this PhD study. Michel Guerrero contributed data from outside the main study period and Anthony Richardson assisted in statistical analyses of the data. Identification photos were also provided by a number of recreational divers from Ray of Hope Expeditions. All co-authors improved the quality of this chapter through critical revisions.

Chapter 3 Julian Uribe-Palomino and Amelia Armstrong provided technical assistance in the laboratory and aided in the interpretation of research data. Rena Ono provided practical assistance in the laboratory. Anthony Richardson, Michael Bennett, Andrea Marshall, Chris Rohner and Asia Armstrong provided assistance by critically revising the chapter.

Chapter 4 (Part I) This chapter was published in Royal Society Open Science in December, 2016 and was co-authored by Lydie Couturier, Andrea Marshall, Anthony Richardson and Michael Bennett. Andrea Marshall collected and provided samples prior to the start of this study. Michel Guerrero provided assistance in exportation of the samples. Rene Diocares and Rad Bak assisted in the processing of samples for stable isotope analysis. All co-authors and two anonymous reviewers improved the quality of this chapter through critical revisions and suggestions.

Chapter 4 (Part II) This chapter was published in Marine and Freshwater Research in September, 2017 and was co- authored by Michel Guerrero, Anthony Richardson, Michael Bennett and Andrea Marshall. Andrea Marshall devised the sampling protocol, Michel Guerrero assisted in the exportation of samples, and

Rene Diocares and Rad Bak provided laboratory assistance. All co-authors critically revised this chapter prior to its publication.

Chapter 5 This chapter has been accepted for publication in PLoS ONE and has been critically revised by Michel Guerrero, Andrea Marshall, Michael Bennett, Anthony Richardson and Lydie Couturier. The original study framework for this chapter was devised by Lydie Couturier who conducted a similar study in eastern Australia and Mozambique. Andrea Marshall collected and provided samples prior to the start of this study and Michel Guerrero assisted in the exportation of samples. Laboratory assistance was also provided by Frank Coman, Nick Bourne, Sue Cheers, Heidi Pethybridge and Peter Nichols.

Chapter 6 The general discussion is my own work. Anthony Richardson, Michael Bennett and Andrea Marshall critically revised and edited earlier versions of this chapter.

Statement of parts of the thesis submitted to qualify for the award of another degree

None

Acknowledgements

This thesis was a collaborative project between the Marine Megafauna Foundation, The University of Queensland and Proyecto Mantas Ecuador. Additional support was generously provided by the Australian Postgraduate Award, CSIRO, The Rufford Foundation, Ray of Hope Expeditions and NAZCA. I am indebted to the Minesterio del Ambiente and Machalilla National Park authorities for allowing me to conduct this research in Ecuador. During this PhD I have been lucky enough to visit some amazing locations, however, as with every PhD, there have been some considerable challenges, and the completion of this thesis would not have been possible without the academic and emotional support of a number of individuals.

First and foremost, I would like to thank my exceptional supervisory team. To my primary supervisor, Mike Bennett, who despite being a Tottenham fan, consistently provided sound advice on all things academic. Mike, thank you so much for your unwavering support and always making time for me. To Andrea Marshall, thank you for giving me the opportunity to come on board with MMF and develop this project. It would be impossible to list all the things you have taught and done for me over the past few years, but know that I am truly grateful and I would not have wanted to do field work with anyone else. Anthony Richardson, everyone needs a supervisor like you. Ant, you have coached me through countless scientific endeavours, and I admire the way you lead by example, ‘be inclusive not exclusive’ are words that will echo with me throughout my future career. Scarla, I am so grateful for your input and guidance at the beginning of this project. You opened up a whole other field of marine research to me, and always made sure I went out into the field with the best equipment possible. Lydie, while not an official supervisor, you took on that role without any complaints for many of the chapters in this thesis. I am so appreciative of your swift and constructive feedback on everything I have ever sent you, but most importantly, thank you for being such a supportive friend throughout this PhD.

In terms of support in the field I couldn’t have asked for a better research buddy than Stan, who selflessly supported and encouraged me throughout this PhD, and never let the coffee supplies dwindle. Janneman, thank you for your never-ending energy and enthusiasm for my project, you always went above and beyond to help me out. I would like to acknowledge Michel Guerrero for his help in obtaining research permits, exporting samples, and for hiring such amazing staff at Exploramar Diving. In particular, Capitan Luis Lucas, who has put up with my erratic requests for plankton collections and appalling Spanish like an absolute champion. To Daniel and Alejandro, thank you for your muscle power and good humour, plankton towing would have been much more

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difficult and less fun without you both. To Jose, Raymond, Pepe, Alessandro, Alejandra, and Javier, thank you for all your help underwater, your enthusiasm about this project, and recovering bits of equipment for me. None of which were part of your job description, but you did it all anyway. To Mark Harding, your passion and dedication to marine research and conservation inspired this PhD, and I am forever thankful for the opportunity to get involved in this project at its inception. Who would have thought a chance meeting at the London dive show in 2009 could have led to such a life altering journey? I would also like to thank the countless Ray of Hope Expedition volunteer divers that helped with sampling and contributed vital data to this project. Of these, Larry, Ralph and Peg, thank you so much for your support, particularly in the early days of this project.

A large part of what got me through this PhD was the support and advice given by the shark and ray research group at the University of Queensland. The range of specialities and fields covered by lab colleagues is truly outstanding and inspiring, and all of you in some way have made me a better scientist. Thank you Carlos, for your guidance and for all your vital conference poster assistance. Carolina, Deb and Bonnie, thank you for helping me through numerous milestones and bureaucracy. Sam, thank you for all the coffees, football chats, and for riding out some serious web situations. Chris Dudgeon, thank you for your constant words of encouragement, and for being such an inspiration both in life and science. Milly, I’m not sure what I would have done without you this past year, thank you for all the meals, swims and manta discussions. Asia, I’d like to think that a portion of your infectious enthusiasm has rubbed off on me over the past few years, thank you for being so positive and pragmatic, as well as always having time to listen to me whinge.

I would like to thank all students, staff and helpers at the Marine Megafauna Foundation who have helped me with fieldwork and admin over the years. In particular, Chris Rohner, thank you for your unwavering confidence in my abilities and Simon Pierce, thank you for your support throughout some of the more difficult moments. I would also like to express my sincere gratitude to a number of other individuals at The University of Queensland, CSIRO and Griffith University who have contributed their expertise and time to this PhD. Tess Canto, thank you for being the most amazing remote sensing teacher, it has been such a pleasure to work with you. To Julian Uribe-Palomino, Frank Coman, Nick Bourne, Sue Cheers, Heidi Pethybridge, Rene Diocares and Rad Bak, thank you all so much for all your assistance in the laboratory, most of the chapters in this thesis would not have been possible without your collective input.

Last but not least, I would like to thank the most important people in my life, who were there for me before this journey started, and who will continue to be there when this PhD is a distant memory. xiii

Despite being over the other side of the world, I would never have completed this project without the support of my parents. Mum and Dad, thank you for getting me through that year when I didn’t have a scholarship, and for your unconditional love even when my social courtesies had gone out the window. Perry, thank you for putting up with me throughout this entire time, I am incredibly grateful for your support and the sacrifices you have made, you have been amazing. I would also like to thank my second set of parents, Ross and Beth, as well as the Noosa and Bali Burgi. All of your support during this PhD has meant so much. Thank you for the constant words of encouragement and a never- ending supply of delicious meals. To my nearest and dearest in Brisbane, Elly, Shaun, James, Nicki and Matt, thank you all for the endless streams of reassuring chats, usually accompanied by a bottle of wine or two, you have all helped me maintain a small degree of sanity during this PhD.

To my friends and family in the UK, thank you for making my fleeting home visits so wonderful over the past few years. Anna, thank for always being just one phone call away even when the time difference made it difficult. To Jamie, Fern and Jess, thank you for always looking after me in Thailand and for humouring me with trips to the aquarium. To Sara, Sarah, Roxy, Siobhan, Alice, and Rhiannon, thank you for always being there and for not letting me take myself too seriously, I am so lucky to have such an incredible group of girls. To Jools, Celine, Maxine, Michelle, Charlie, Ellen, Matt and Ian, thank you for getting me through those undergraduate years at Bristol and for continuing to make my world a brighter place.

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Financial Support

This research was supported by an Australian Postgraduate Award and The Marine Megafauna Foundation

Keywords

El Niño, diet, stable isotope analysis, fatty acid analysis, Isla de la Plata, sightings record, zooplankton, mesopelagic, cleaning habitat, foraging grounds

Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 060205, Marine and Estuarine Ecology (Incl. Marine Ichthyology), 60 % ANZSRC code: 060809, Vertebrate Biology, 40 %

Fields of Research (FoR) Classification

FoR code: 0602, Ecology, 60 % FoR code: 0608, Zoology, 40 %

Table of Contents

Abstract ...... ii

Declaration by author ...... v

Publications during candidature ...... vi

Peer-reviewed publications ...... vi Conference abstracts ...... vii

Publications included in this thesis ...... viii

Contributions by others to the thesis ...... ix

Statement of parts of the thesis submitted to qualify for the award of another degree ...... xi

Acknowledgements ...... xii

Financial Support ...... xv

Keywords ...... xv

Australian and New Zealand Standard Research Classifications (ANZSRC) ...... xv

Fields of Research (FoR) Classification ...... xv

List of Figures ...... xx

List of Tables ...... xxiv

List of Abbreviations used in the thesis ...... xxvi

Chapter 1 ...... 1

Introduction ...... 1 1.1 Manta rays ...... 2 1.2 Manta birostris aggregations ...... 3 1.3 Drivers of aggregative behaviour ...... 4 1.4 Manta birostris feeding ecology ...... 5 1.5 Aims and Significance ...... 9

Chapter 2 ...... 10

Drivers of habitat use and sighting trends of the giant manta ray Manta birostris at a key aggregation site in the eastern equatorial Pacific ...... 10 2.1 Introduction ...... 11

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2.2 Material and methods ...... 13 2.2.1 Study site and manta ray sightings ...... 13 2.2.2 All year daily sightings logbook ...... 15 2.2.3 Seasonal duration ...... 16 2.2.4 Peak aggregation time dive logbook ...... 16 2.2.5 Model validation ...... 17 2.2.6 Manta birostris behaviour ...... 18 2.3 Results ...... 22 2.3.1 All year daily sightings logbook ...... 22 2.3.2 Peak aggregation time dive logbook ...... 24 2.4 Discussion ...... 28 2.4.1 Regional environment effects on manta ray sightings ...... 28 2.4.2 Feed, rest and clean hypothesis ...... 29 2.4.4 Limitations and future work ...... 31 2.5 Conclusions ...... 32

Chapter 3 ...... 33

Sporadic feeding on small-bodied surface zooplankton during the day by the giant manta ray Manta birostris at Isla de la Plata, Ecuador...... 33 3.1 Introduction ...... 34 3.2 Materials and methods ...... 37 3.2.1 Study site ...... 37 3.2.2 Sample Collection ...... 37 3.2.3 Zooplankton Biomass ...... 38 3.2.4 Zooplankton size-spectra & Zooscan wet biomass ...... 39 3.2.5 Zooplankton identification ...... 39 3.2.6 Environmental influences on zooplankton biomass ...... 40 3.2.7 Manta ray behaviour ...... 40 3.3 Results ...... 40 3.3.1 Zooplankton biomass ...... 41 3.3.2 Zooplankton biomass ...... 44 3.3.3 Zooplankton community composition ...... 47 3.4 Discussion ...... 48 3.4.1 Manta birostris feeding behaviour ...... 48 3.4.2 Zooplankton biomass calculations ...... 50

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3.4.3 Drivers of zooplankton abundance ...... 50 3.4.4 Zooplankton composition at Isla de la Plata ...... 52 3.4.5 Stomach contents analysis vs. in-situ tows ...... 53 3.4.6 Conclusions ...... 54

Chapter 4 (Part I) ...... 56

Manta birostris, predator of the deep? Insight into the diet of the giant manta ray through stable isotope analysis ...... 56

4.1 Introduction ...... 57 4.2 Methods ...... 58 4.3 Results ...... 60 4.4 Discussion ...... 64 4.4.1 Manta ray feeding ecology ...... 64 4.4.2 δ13C enriched food source origin ...... 65 4.4.3 Variability in isotopic baselines ...... 66 4.4.4 Limitations ...... 67 4.4.5 Future Work ...... 68 4.5 Conclusions ...... 68

Chapter 4 (Part II) ...... 69

The use of epidermal mucus in elasmobranch stable isotope studies: a pilot study using the giant manta ray Manta birostris ...... 69 4.6 Introduction ...... 70 4.7 Materials and methods ...... 71 4.8 Results and discussion ...... 73 4.9 Conclusions ...... 77

Chapter 5 ...... 78

Novel signature fatty acid profile of the giant manta ray Manta birostris suggests reliance on an uncharacterised mesopelagic food source low in polyunsaturated fatty acids ...... 78 5.1 Introduction ...... 79 5.2 Methods ...... 80 5.2.1 Sample collection ...... 80 5.2.2 Lipid extraction ...... 81 5.2.3 Fatty acid signature analysis ...... 81 5.2.4 Lipid class profiles ...... 82 xviii

5.2.5 Data processing and statistical analyses ...... 82 5.3 Results ...... 83 5.3.1 Manta ray fatty acid profile ...... 83 5.3.2 Surface zooplankton fatty acid profile ...... 85 5.3.3 Lipid class profiles ...... 88 5.4 Discussion ...... 89 5.4.1 PUFA-poor fatty acid profiles of Manta birostris ...... 90 5.4.2 Dietary derived fatty acids in giant manta rays ...... 92 5.4.3 Other fatty acids potentially derived from diet ...... 92 5.4.4 Lipid content and class composition ...... 94 5.4.5 Limitations of molecular tracers ...... 94 5.4.6 Conclusions ...... 95

Chapter 6 ...... 96

General Discussion ...... 96

6.1 Overview ...... 97 6.2 Manta birostris aggregations ...... 97 6.2.1 Temporal trends in the tropical eastern Pacific ...... 97 6.2.3 Aggregative behaviour of Manta rays ...... 100 6.2.4 Mesopelagic feeding ...... 102 6.3 Study limitations and future work ...... 103 6.3.1 Habitat use ...... 103 6.3.2 Manta birostris at Isla de la Plata ...... 104 6.3.3 Designation of ‘critical’ foraging habitats ...... 105 6.3.4 Trophic role and broader ecological context ...... 106 6.4 Conclusion ...... 107

Bibliography ...... 109

Appendices ...... 140

Chapter 3 ...... 140 Chapter 4 (Part I) ...... 141 Chapter 5 ...... 142 Lipid extraction ...... 142 Fatty acid signature analysis ...... 142

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List of Figures

Figure 1.1 A) Number of peer-reviewed publications split into research fields from 1916 – 2017 for Manta alfredi, Manta birostris, both M. alfredi and M. birostris, and the putative third species, Manta giorna (Marshall et al., 2009). Due to the taxonomic revision of the genus Manta in 2009, pre-2009 manta ray publications were ascribed to the most likely species according to reported morphological measurements and/or descriptions, and then verified alongside distribution data for that species. B) Total number of manta ray publications by species...... 3

Figure 1.2 Global Manta birostris distribution (light blue), sightings (dark blue (Couturier et al., 2012)) and the number of currently identified individuals off Ecuador, Indonesia, Mozambique, Myanmar, New Zealand, Thailand and the Red Sea derived from MantaMatcher (Marshall and Holmberg, 2011), with literature sourced numbers of M. birostris identified off Mexico (Rubin, 2002) and Brazil (Luiz Jr et al., 2009)...... 4

Figure 1.3 Literature published, or featuring information on Manta spp. feeding ecology (1999 – 2017). Included is total publications and studies per research methodology...... 6

Figure 1.4 The sampling of Manta birostris muscle tissue using a hand spear with a modified biopsy punch...... 7

Figure 1.5 The minimally invasive sampling of epidermal mucus from Manta birostris at Isla de la Plata, Ecuador...... 8

Figure 2.1 Location of the principal study site, Isla de la Plata, Ecuador. Dive sites are indicated by the red and white flags with the transect line represented by the dashed arrow...... 14

Figure 2.2 A) Recreational SCUBA diver taking an identification image. B) Sample identification image of Manta birostris showing ventral area of interest and visualisation of features extracted using the MantaMatcher algorithm (Town et al., 2013)...... 15

Figure 2.3 Behaviours of Manta birostris observed in Ecuador. Clockwise from top left: M. birostris (A) Cruising, (B) Cleaning, (C) Socialising and (D) Feeding...... 18

Figure 2.4 Average number of Manta birostris sighted per month at Isla de la Plata in Ecuador (2010 – 2015), Tofo in Mozambique (2003 – 2016) (C. Rohner, unpubl. data) and Laje de Santos in Brazil (2003 – 2009) (Luiz Jr et al., 2009)...... 22

Figure 2.5 Interaction plot between monthly Manta birostris sightings and El Niño–Southern Oscillation events. Here, El Niño refers to an Oceanic Niño Index sea surface temperature anomaly with a value ≥0.5 °C, Neutral was where anomalies were -0.5< °C <0.5, and anomaly values ≤-0.5 °C were classified as La Niña...... 23

Figure 2.6 Observed behaviours and number of M. birostris identified per dive at Isla de la Plata, Ecuador. The central box spans the interquartile range with the middle line denoting the median. Whiskers above and below the box show the location of the 5 % and 95 % confidence intervals and individual outlying data points are displayed as unfilled circles...... 24

Figure 2.7 Yearly spine plots of predominant plankton type in the water column (left panel) and Manta birostris behaviour during research dives (right panel). The y-axis on both panels shows the proportion of times a category was recorded out of all total observations...... 25

Figure 2.8 General additive model outputs showing the relationship between the number of individual Manta birostris sighted and predictor variables. The dashed line for the categorical predictors and the grey shading on the Tidal Range predictor represent the 95 % confidence interval, and the rug representation at the bottom of each panel indicates sampling effort...... 27

Figure 3.1 Isla de la Plata, Ecuador with the zooplankton sampling location is indicated by the diagonally-hatched box...... 37

Figure 3.2 Zooplankton biomass during occasions where Manta birostris were feeding and not feeding: a) total volumetric wet biomass calculated using the displacement volume (g m-3), b) volumetric spherical wet biomass calculated from ZooScan data (g m-3), c) oven-dried zooplankton biomass (mg m-3). The central box spans the interquartile range with the median denoted by the bold line. Whiskers above and below the box show the 5 % and 95 % confidence intervals, and outlying data points are shown as unfilled circles...... 41

Figure 3.3 Linear regression analyses for zooplankton biomass between a) the dry biomass and wet displacement volume, b) dry biomass and the spherical wet biomass calculated from ZooScan, and c) the displacement volume and spherical wet biomass. Dashed lines represent 95 % confidence intervals...... 42

Figure 3.4 Final generalised additive models for drivers of zooplankton dry and wet biomass. Zooplankton biomass (wet) used zooplankton biomass (g m-3) as the response and Current Direction, Wind Speed and Time to Low Tide as predictors (r2 = 54.5 %, n = 63). The model for Zooplankton

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biomass (dry) used zooplankton biomass (mg m-3) as the response and Month as the predictor (r2 = 20.8 %, n = 84). Dashed lines represent 95 % confidence intervals...... 43

Figure 3.5 Mean size structure with 95 % shaded confidence intervals of in situ zooplankton collected at Isla de la Plata, Ecuador during times where Manta birostris were feeding or not feeding...... 44

Figure 3.6 Mean size structure with 95 % shaded confidence intervals of in situ zooplankton tows collected in at Isla de la Plata, Ecuador during Manta birostris feeding events. Also included are the size structure of zooplankton tows during feeding events for Manta alfredi (Armstrong et al., 2016) and Rhincodon typus (Rohner et al., 2015)...... 46

Figure 3.7 Mean size structure with 95 % shaded confidence intervals of zooplankton tows collected during Manta birostris feeding activity (this study) in comparison to stomach content particles from mobulid species collected in the Philippines (Rohner et al., 2017)...... 46

Figure 3.8 Zooplankton community composition at Isla de la Plata, Ecuador in relation to when Manta birostris were feeding or not feeding...... 47

Figure 3.9 Manta birostris barrel roll feeding on a dense patch of near surface zooplankton...... 49

Figure 4.1 Mean δ15N and δ13C values for Manta birostris, surface zooplankton (δ13C lipid normalised), mesopelagic sources (Choy et al., 2015), and co-occurring turtle species (Chelonia mydas, Lepidochelys olivacea, Caretta caretta) (Kelez Sara, 2011), thresher shark Alopias pelagicus (Polo-Silva et al., 2013), and yellowfin tuna Thunnus albacares (Olson et al., 2010) from Ecuador and the broader eastern equatorial Pacific region. Error bars represent standard deviation...... 61

Figure 4.2 Bi-plot of δ15N and δ13C values with Bayesian ellipses overlaid for Manta birostris and surface zooplankton (this study), mesopelagic sources (Choy et al., 2015), turtle species (Chelonia mydas, Lepidochelys olivacea, Caretta caretta) (Kelez Sara, 2011), thresher shark Alopias pelagicus (Polo-Silva et al., 2013), and yellowfin tuna Thunnus albacares (Olson et al., 2010) from the eastern equatorial Pacific...... 62

Figure 4.3 Boxplots of Bayesian stable isotope mixing models for surface zooplankton (light grey), lipid normalised surface zooplankton (LN) (dark grey) and mesopelagic (yellow) prey dietary source contributions to Manta birostris. Model number is pictured in the bottom left corner of each panel. On the secondary Y axis is the DTDF from large shark species (Hussey et al., 2010) used in models 1 and 2 and the DTDF from Triakis semifasciata (Kim et al., 2012a) used for models 3 and 4. The central box spans the 2.5 – 97.5 % confidence intervals with the middle line denoting the median. 63 xxii

Figure 4.4 Scatterplot of mean (± s.d.) δ15N and non-lipid normalised δ13C values for surface zooplankton (black fill triangle), Manta birostris muscle (black fill circle) and epidermal mucus (black fill square). Lipid normalised (LN) δ13C values are shown as unfilled symbols. Also shown is expected prey isotopic compositions for M. birostris muscle (grey circle) calculated using diet tissue discrimination factors from Hussey et al. (2010) and epidermal mucus (grey square) calculated using diet tissue discrimination factors from Church et al. (2009)...... 75

Figure 5.1 The first and second principle components of Manta birostris and surface zooplankton signature fatty acid (FA) profiles (including all FAs >1 % total FA), sampled from coastal Ecuador. Similarity clusters (40 %) are indicated as well as fatty acids contributing most to the separation on the axes (eigenvector coefficient > [0.3])...... 83

Figure 5.2 Non-metric multi-dimensional scaling plot of Manta birostris fatty acid profiles (Bray- Curtis Similarity Index). Fatty acid labels represent the main coefficients (> 0.5) contributing to each axis. Clusters A and B refer to two distinct FA profile groups of M. birostris as revealed by cluster analysis at 70% similarity...... 84

Figure 5.3 Non-metric multi-dimensional scaling plot for surface zooplankton fatty acid profiles (Bray-Curtis Similarity Index). Fatty acid labels represent the main coefficients (>0.7) contributing to each axis. Cluster 1 and 2 designate the two distinct FA profile groups of surface zooplankton as revealed by cluster analysis at 80 % similarity...... 88

Figure S3.1 Monthly climatology composites (2000 – 2014) of chlorophyll a concentration for August (top panel), September (middle panel), and October (bottom panel). Isla de la Plata is indicated by the green circle and the extensive dark blue patches are representative of cloud cover in this eastern equatorial region...... 140

Figure S5.1 Comparison of surface zooplankton signature fatty acid (FA) profiles among sampling months with 95 % ellipses. Non-metric multi-dimensional scaling ordinations of surface zooplankton FA profiles sampled at Isla de la Plata, Ecuador, from August 2013 to October 2013, and August 2014 to September 2014. There was a significant difference among samples (ANOSIM, R value = 0.26, P <0.05), with pairwise comparisons revealing a significant difference between August and September, and September and October (P <0.05), but no significant differences between August and October (P = 0.14)...... 143

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List of Tables

Table 2.1 Response and predictor variables used in the generalised linear (GLM) and generalised additive (GAM) models for Manta birostris sightings at Isla de la Plata, Ecuador...... 20

Table 2.2 Day of the year for Manta birostris aggregation season start (SeasonS), middle (SeasonM), and end (SeasonE) at Isla de la Plata alongside the Oceanic Niño Index (ONI) classification for each year. Also included is seasonal duration (SeasonD), which represents the number of days over which 90 % of individual M. birostris were identified, and the total number of M. birostris individuals sighted for each year...... 23

Table 2.3 Summary and percentage deviance explained (% Dev. Exp) by each significant predictor of Manta birostris sightings...... 25

Table 4.1 Mean (± s.d.) δ 13C and δ 15N values for Manta birostris ...... 61

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Table S4.1 Mixing model summary statistics on the mean credible interval source contributions of surface zooplankton and mesopelagic sources to Manta birostris diet. Model 1 source inputs comprised of mesopelagic fishes and lipid normalised surface zooplankton δ13C values and used DTDFs from large sharks (Hussey et al., 2010). Model 2 source inputs were mesopelagic fishes and non-lipid normalised surface zooplankton δ13C with the large shark DTDF (Hussey et al., 2010). Models 3 and 4 comprised the same source inputs as model 1 and 2 respectively, but used DTDF values from Triakis semifasciata (Kim et al., 2012a). Model 5 used the highest δ13C and δ15N values of surface zooplankton and mesopelagic fishes to assess whether extreme surface zooplankton values could account for the discrepancy in stable isotope profiles between surface zooplankton and M. birostris...... 141

Table S5.3 Signature fatty acid (FA) profiles (% of total FA ± s.d.) of surface zooplankton collected from Isla de la Plata, Ecuador among sampling months...... 145

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List of Abbreviations used in the thesis

% Dev. Exp Percentage deviance explained % FA Percentage of total fatty acids °C Degrees Celsius ∆ Stable isotope enrichment AIC Akaike’s Information Criterion ANOSIM Analysis of similarities ANOVA Analysis of variance ARA Arachidonic acid BULK Non-lipid normalised c cis C:N Percentage carbon to percentage nitrogen ratio CITES Convention on International Trade in Endangered Species of Wild Fauna and Flora CTD Conductivity, temperature, depth recorder df Degrees of freedom DHA docosahexaenoic acid dpi dots per inch DTDF Diet tissue discrimination factor DW Disc width E East EBC Eastern boundary current ENSO El Niño Southern Oscillation activity EPA Eicosapentaenoic acid F F-statistic from ANOVA or regression analysis FA Fatty acid FAME Fatty acid methyl esters FFA Free fatty acids Fig Figure g-1 per gram GAM Generalised additive models GC Gas chromatography gel gelatinous zooplankton GLM Generalised linear models GPS Global positioning system h hour(s)

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IUCN International Union for Conservation of Nature Kcal Kilocalorie KJ Kilojoules km kilometres LC Lipid content LCMUFA Long chain monounsaturated fatty acids LEI Lady Elliot Island LN Lipid normalised m metre(s) m-3 per cubic metre mg milligram min minutes(s) mL millilitre mm millimetre mm3 Cubic millimetre MODIS Moderate Resolution Imaging Spectroradiometer MUFA Monounsaturated fatty acid n number of samples N North NE Northeast nMDS Non-parametric multi-dimensional scaling NPSG North Pacific Subtropical Gyre NW Northwest OCI Ocean Colour Index ONI Oceanic Niño Index P Calculated probability PCR Polymerase chain reaction pers. comm. personal communication Photo-ID Photographic identification PL Phospholipids pp phytoplankton PUFA Polyunsaturated fatty acid R Ratio of heavy to light isotopes r2 coefficient of determination s second(s) S South xxvii

s.d. standard deviation s.e. standard error SBv Spherical biovolume SCA Stomach contents analysis SCMUFA Short chain monounsaturated fatty acids SCUBA Self-contained underwater breathing apparatus SE Southeast

SeasonD Season duration

SeasonE Season end

SeasonM season middle

SeasonS Season start SFA Saturated fatty acid SIA Stable isotope analysis SIMPER Similarity percentage analysis SOI Southern Oscillation Index spp. species ss suspended sediment ST Sterols SW Southwest t trans TAG Triacylglycerol

TL Trophic level

TLSIA Trophic level determined by stable isotope analysis unpubl. data unpublished data W West W Mann-Whitney test statistic WE wax esters zp zooplankton δ Delta δ13C the ratio of 13C: 12C δ15N the ratio of 15N: 14N µl microlitre µm micrometre χ2 Chi-squared ω Omega

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Chapter 1

Introduction

1.1 Manta rays

Manta rays (Manta spp.) are among the largest elasmobranch fishes and currently comprise two recognised species, the reef manta ray Manta alfredi and the giant manta ray Manta birostris (Marshall et al., 2009). Both Manta spp. have circumglobal distributions in tropical and temperate oceans, with M. alfredi generally residing in nearshore environments or along continental coastlines (Dewar et al., 2008, Couturier et al., 2011), and displaying high fidelity to specific sites (Braun et al., 2015). In comparison, M. birostris is typically found in more pelagic locations such as around offshore islands, seamounts and in surface waters of continental shelves (Luiz Jr et al., 2009, Kashiwagi et al., 2011). It is also suspected that M. birostris undertakes large-scale oceanic migrations, but the extent and pattern of movements is poorly known, which reflects the difficult-to- study nature of this species (Fig. 1.1) (Marshall et al., 2011a, Graham et al., 2012, Dulvy et al., 2014).

Over the past decade, the demand for manta and other mobulid ray gill plates, which are used in Asian medicine, has led to an increase in targeted fisheries for these species (Lack and Sant, 2009). In addition, mobulid bycatch rates of have also increased (Couturier et al., 2012), with individuals susceptible to capture in a wide range of fishing gear including gill nets, purse seines and long lines (Croll et al., 2015). Both currently recognised manta ray species are listed as Vulnerable to extinction on the IUCN Red List of Threatened Species and are characterised by conservative life history traits such as very low fecundity and late age of maturity (Dulvy et al., 2014). These traits, coupled with an assumed low level of interchange among sub-populations, means that localised sub-populations of manta rays are at particular risk of depletion from direct and indirect fisheries (Dulvy et al., 2014).

1.2 Manta birostris aggregations

The relatively large size, off-shore habitat occupancy (Marshall et al., 2011a), and ability to travel large distances and dive to depths of hundreds of metres provide considerable challenges to collecting even fairly basic information on the biology and ecology of M. birostris (Stewart et al., 2016b). Both species of manta ray predictably aggregate in relatively small areas in comparison to their overall home ranges (Rohner et al., 2013b, Jaine et al., 2014), with previously identified aggregation sites for M. birostris typically harder to reach in comparison to those identified for M. alfredi (Marshall et al., 2011a). However, recently discovered M. birostris aggregation sites located within close proximity to mainland Ecuador prove to be an exception. One of these aggregation sites, Isla de la Plata (1.2786° S, 81.0686° W), is an oceanic island on the continental shelf, <40 km from the coast and includes sheltered and shallow environments where SCUBA diving and in-water observations of M. birostris can be easily conducted.

Over 2400 individual M. birostris have been identified off mainland Ecuador (Marshall and Holmberg, 2011), which makes it the largest regional population of this species (Fig 1.2). The closest reported population in both geographical and numerical context is off Mexico, which currently comprises ~350 identified individuals (Rubin, 2002). There has been no observed interchange using photo identification methods of individuals between M. birostris aggregations in Ecuador and Mexico. In addition, satellite telemetry as well as stable isotope and genetic analyses, have demonstrated that the aggregations of M. birostris off Mexico exhibit restricted movements and fine- scale population structure to the extent where there appears to be distinct onshore and offshore sub- populations off the same coast line (Stewart et al., 2016a). It is therefore unlikely aggregations of M. birostris off Ecuador and Mexico constitute one larger sub-population in the eastern tropical Pacific .

1.3 Drivers of aggregative behaviour

For reef manta rays, food availability is the most commonly cited driver of aggregative behaviour, with surface feeding activity frequently documented in small and productive areas around islands (Dewar et al., 2008, Anderson et al., 2011, Jaine et al., 2012). Drivers of aggregative behaviour of M. birostris are yet to be identified, but the occurrence of this species in areas during a time of high seasonal productivity indicates that availability of food resources over a larger scale might be of importance (Luiz Jr et al., 2009). Ecuador’s coastal and offshore waters comprise part of the Humboldt Current system, which is categorised as an eastern boundary current (EBC) coastal upwelling ecosystem. EBCs are characterised by high biological activity and have short food chains 4

that respond rapidly to environmental variability (Carr, 2001). Upwelling intensity increases during austral winter, and the time of M. birostris aggregation, (Echevin et al., 2008), but whether this subsequently leads to a greater abundance of zooplankton that this species might target is undetermined.

The physical processes and biological production in the Humboldt Current system are also subject to inter-annual variability (Feely et al., 1987) with the El Niño-Southern Oscillation the primary source of this variability (Chavez et al., 1999). In the eastern Pacific during El Niño, the thermocline deepens and new production declines. This can have a number of negative repercussions on fauna in the tropical eastern Pacific such as coral bleaching (Glynn, 1990), loss of body condition in penguins (Dee Boersma, 1998), and a collapse in anchovy stocks (Chavez et al., 2003). In contrast, trade winds intensify and upwelling of cooler, nutrient-enriched subsurface waters increases in the eastern Pacific leading to greater productivity during La Niña episodes (Meinen and McPhaden, 2000a). How these various events might affect M. birostris sightings in the eastern Pacific is hard to predict, but for long- lived species like manta rays, this environmental variability is likely to be reflected in changes in distribution, home range or seasonal timing of life history events, as opposed to dramatic changes such as decreases in abundance.

1.4 Manta birostris feeding ecology

Of all research fields covered by manta ray publications to date, feeding ecology is the second best represented behind sightings information in terms of the number of studies published (Fig 1.1). As is the case with many other large marine species, the majority of existing information on manta ray diet is based on data from feeding events at the sea surface during daylight hours. There are more feeding publications for M. alfredi with a greater range of methodologies used to elucidate dietary information (Fig 1.3), which has resulted in the availability of high to low taxonomic resolution, and short to long- term dietary data for this species (Couturier et al., 2013a, Armstrong et al., 2016, Bennett et al., 2016). For M. birostris, there are only four publications that solely present information on its diet (Fig 1.3). Of these, two present low resolution, descriptive molecular data with no quantitative inferences on dietary contributions (McCauley et al., 2012, Stewart et al., 2016a), while another investigates feeding behaviour in response to prey stimuli within an aquarium setting (Ari and Correia, 2008). Another publication from Stewart et al. (2016b) presents an opportunistic observation of M. birostris foraging at depth, as well as seasonal shifts in diving behaviour likely related to zooplankton abundance and location. While this publication details potential drivers of vertical habitat use, it does not provide any taxonomic information on M. birostris dietary sources. 5

High resolution dietary information is important for the identification of preferred foraging grounds and the implementation of effective conservation strategies at these sites (López-Mendilaharsu et al., 2005). The conventional approach for determining elasmobranch dietary composition is through stomach contents analysis (SCA). This technique involves intrusive or lethal sampling of the specimen, but provides high unparalleled taxonomic information on dietary items. However, given the conservation status of M. birostris, characterisation of diet for this species via the targeted use of lethal or highly stressful approaches required for SCA would be inappropriate and impractical unless animals caught in fisheries that are already dead can be examined. A different, non-lethal technique for characterising diet in large planktivores involves the collection of plankton alongside feeding animals. This approach, similar to SCA, can give high resolution information on a species’ diet, and can provide insight into prey preferences (Rohner et al., 2015, Armstrong et al., 2016). However, the approach is only feasible when manta rays are seen feeding in surface waters, where plankton tows can be conducted simultaneously.

Manta spp. Manta alfredi 15 Manta birostris

5 Peer-reviewed studies published published studies Peer-reviewed 2000 2005 2010 2015 Year

Manta alfredi feeding publications Manta birostris feeding publications Both Manta spp feeding publications n=9 n=6 n=4

Telemetry Biochemical Morphology Biochemical (this thesis) Biochemical Stomach Contents

Plankton Observations Observations Observations Oceanography Sensory Telemetry

Figure 1.3 Literature published, or featuring information on Manta spp. feeding ecology (1999 – 2017). Included is total publications and studies per research methodology.

Up until 2016, it was assumed that M. birostris predominantly fed on surface zooplankton at aggregation sites throughout their distribution, with little known about the foraging habits of M. birostris when individuals were not in surface waters, or in areas other than aggregation sites. Minimally invasive molecular methods are useful tools for examining the feeding of large, mobile and difficult to observe marine species as certain molecular components can act as ecological tracers (Carlisle et al., 2012, McMeans et al., 2013). These ecological tracers are representative of trophic interactions over time and provide an average time-integrated signal of long term dietary intake (Dalsgaard et al., 2003, West et al., 2006). Another benefit to using these methods is that only a small tissue sample is required, which can be obtained from free swimming animals (Fig 1.4).

One of these molecular methods, bulk stable isotope analysis (SIA), has been successfully used to examine aspects of the biology and ecology of M. alfredi and several other planktivorous elasmobranch species (Sampson et al., 2010, Borrell et al., 2011, Couturier et al., 2013a). Underlying its use is that the isotopic composition of a tissue is representative of dietary sources (δ13C (‰), (DeNiro and Epstein, 1978) and the relative trophic position at which an organism feeds (δ15N (‰), (DeNiro and Epstein, 1981). Bayesian models can also be used to address natural variation in stable isotope data and to generate probability distributions of prey source contributions as percentages of total diet (Parnell et al., 2013).

Figure 1.4 The sampling of Manta birostris muscle tissue using a hand spear with a modified biopsy punch.

Stable isotope analysis of tissues with different turnover rates allows for a broader temporal view of a species’ feeding habits. In elasmobranchs, tissues with relatively slow turnover rates, such as muscle, provide an integrated signal that is representative of total ingested dietary intake ≥12 months (Vander Zanden et al., 2015), whereas faster turnover rate tissues, such as blood, have the potential to identify more recent resource switches (Kim et al., 2012a). Currently, validated fast turnover tissues in elasmobranchs are internal, making them difficult or impossible to sample from free-living animals. Epidermal mucus is a rapid to medium turnover ‘tissue’ in teleost fishes (~30 days) (Church et al., 2009), however, its utilisation in elasmobranch dietary studies is unknown, despite the relative ease by which this ‘tissue’ can be obtained non-intrusively from elasmobranch species (Fig 1.5).

Figure 1.5 The minimally invasive sampling of epidermal mucus from Manta birostris at Isla de la Plata, Ecuador.

Fatty acid (FA) analysis has also proved to be another useful molecular tool to assess the diet of planktivorous elasmobranchs (Couturier et al., 2013b, Rohner et al., 2013a). Fatty acids (FA) are the main constituents in the majority of lipid molecules and certain fatty acids are used to follow energy transfer in ecosystems and highlight predator-prey relationships (Dalsgaard et al., 2003). For example, 20- and 22-carbon length chains of mono-unsaturated fatty acids are characteristically found in Calanus spp. and subsequently in consumers of these copepods (Dalsgaard et al., 2003). Another application of FA analysis is that it can examine the FA profiles of a predator species over different

spatio-temporal scales to provide qualitative information about diet within and between populations over time (Herman et al., 2005). Like SIA, the main assumption underlying this technique is that the profile of a consumer represents that of its prey, and can therefore provide information on dietary sources (Young et al., 2010a).

1.5 Aims and Significance

Manta birostris is an iconic marine species that generates a large amount of public interest and dive industry related socio-economic benefits (O’Malley et al., 2013), but this species has only recently received scientific attention (Couturier et al., 2012). Published ecological and biological information for M. birostris remains scant, primarily due to the lack of predictable and accessible locations where this species can be found. However, the recent discovery of aggregation sites for M. birostris off mainland Ecuador provide an exceptional opportunity to explore drivers of aggregative behaviour of this generally elusive species. This overall aim of this thesis was to use a variety of non-lethal approaches to examine whether feeding is the primary driver of the largest known M. birostris aggregation in the world, at Isla de la Plata, Ecuador. Here, we define the inter-annual and seasonal variation in M. birostris occurrence at Isla de la Plata over a 5-year period, to provide the first insights into environmental drivers of aggregative behaviour and habitat use of this species. To investigate the importance of local food availability in driving M. birostris occurrence during the day at Isla de la Plata, we quantify and describe near-surface zooplankton size spectra, biomass and community composition collected in relation to M. birostris feeding activity. The contribution of near-surface zooplankton to the diet of M. birostris outside of observed foraging events was also examined by conducting SI and FA analyses on M. birostris muscle tissue, which is representative of long-term (~12 months) integrated diet. In the first example of using SIA on epidermal mucus to investigate elasmobranch feeding ecology, seasonal switches in resource use of M. birostris were then investigated by comparing the isotopic profiles of mucus and muscle to explore differences in short- term and long-term dietary intake, respectively. Determining drivers of M. birostris aggregative behaviour and movement patterns, and how these are linked to foraging opportunities, helps identify where this vulnerable species is most susceptible to direct or incidental capture in fisheries (Stewart et al., 2016a), or subject to other unsustainable anthropogenic pressures such as poorly managed ecotourism activities (O’Malley et al., 2013).

Chapter 2

Drivers of habitat use and sighting trends of the giant manta ray Manta birostris at a key aggregation site in the eastern equatorial Pacific

2.1 Introduction

The identification of biotic and abiotic drivers of habitat use in large and highly mobile marine species is important for the implementation of effective conservation and management strategies (Schlaff et al., 2014). However, drivers of movement in migratory marine animals can often be hard to determine as relationships with physical features can be either direct or indirect (Papastamatiou et al., 2015), and are also influenced by prey response to environmental conditions (Sims and Reid, 2002), or reproductive requirements (Bonfil et al., 2005). Therefore, the relationship between marine species and habitat use patterns is rarely driven by a single physical or biological feature, but rather a combination of variables along complex gradients (Ballance et al., 2006).

Manta rays and other planktivorous elasmobranchs that rely on patchily distributed zooplankton prey (Folt and Burns, 1999), include some of the largest extant marine fish species (McClain et al., 2015). A strong reliance on zooplankton means that sightings of planktivorous elasmobranchs are usually linked to environmental variables, which influence the abundance and distribution of their prey (Hays et al., 2005). Both currently recognised species of manta ray, the giant manta ray Manta birostris and the reef manta ray Manta alfredi, are listed as Vulnerable to extinction on the IUCN red list and have conservative life history traits, which make them highly susceptible to overexploitation (Croll et al., 2015). Sightings of M. alfredi are often linked to environmental variables that affect the distribution and abundance of zooplankton prey (Dewar et al., 2008, Armstrong et al., 2016). However, relatively little is known about drivers of aggregative behaviour of M. birostris due to difficulties associated with accessing aggregation sites and this species’ preference for offshore waters (Marshall et al., 2011a).

Recently discovered aggregation areas for M. birostris off mainland Ecuador are some of the most accessible sites for what appears to be the largest identified regional population of this species (Chapter 1). Ecuador’s coastal and offshore waters are part of the Humboldt Current system, where coastal and equatorial upwelling merge making it the most productive region in the world in terms of fish biomass (Bakun and Weeks, 2008), however, the characteristically short food chains in this system means that it responds rapidly to environmental variability (Carr, 2001). The primary source of inter-annual variability in physical processes and biological production in the Humboldt Current upwelling system is the El Niño-Southern Oscillation (ENSO) (Feely et al., 1987, Chavez et al., 1999). However, remote connections over longer time periods such as the Pacific Decadal Oscillation also affect the productivity of the region (Bakun and Broad, 2003). In the eastern Pacific during El Niño, sea surface temperatures warm, the thermocline deepens and primary production declines. El

Niño can have a number of negative repercussions on fauna in the tropical eastern Pacific such as coral bleaching (Glynn, 1990), a collapse in anchovy stocks (Chavez et al., 2003), and mass die-offs of seabirds and marine mammals (Stenseth et al., 2002). Conversely, during La Niña episodes, trade winds intensify and upwelling of cooler, nutrient-enriched subsurface waters increases in the eastern Pacific leading to greater productivity (Meinen and McPhaden, 2000b). However, sardine populations have been known to increase during El Niño and it has been suggested that extreme ENSO events might be an important factor behind the unrivalled fishery productivity and diversity in the Peru- Humboldt marine ecosystem (Bakun and Broad, 2003). In this dynamic system, regime shifts are common place and it is likely fauna here can take advantage of the transient opportunities that the fluctuations in environmental conditions between La Niña and El Niño provide (Bakun and Broad, 2003). While El Niño and climatic variability effects on population dynamics of commercially important teleost fish species are well known (Bakun and Broad, 2002), the effect of El Niño on highly mobile and long-lived elasmobranch species, such as M. birostris, in the eastern Pacific is harder to predict. Off California, during the extreme 1997 – 1998 El Niño event, several elasmobranch species extended their range in order to shift away from the pool of warm water (Lea and Rosenblatt, 2000). Therefore, it is expected that El Niño effects on M. birostris, and other long-lived species in the eastern equatorial region, are likely to be reflected by changes in distribution, home range or seasonal timing of life history events as opposed to dramatic changes such as decreases in abundance (Ballance et al., 2006).

In worldwide tropical and sub-tropical regions, temperature is considered an indirect or a direct driver of planktivorous elasmobranch aggregative behaviour and movement (Wilson et al., 2001, Rohner et al., 2013b). The majority of elasmobranch species are ectothermic and thermal preferences are likely to influence large-scale migratory movements and subsequent aggregative behaviour (Couturier et al., 2011, Graham et al., 2012). Temperature also influences nutrient availability and new production, which subsequently drives the distribution, abundance and composition of zooplankton (Richardson, 2008). Other abiotic factors known to influence elasmobranch movement patterns are tidal range and periodicity. Tidal influences of elasmobranch movements have been linked to energy conservation strategies (Campbell et al., 2012), predator avoidance (Schlaff et al., 2014) and foraging opportunities (Carlisle and Starr, 2010). However, previous studies that have found a correlation between tide and elasmobranch movement patterns have focused on smaller species that occupy estuarine habitats where tidal phase has a large effect on habitat size and consequently the size of foraging areas (Schlaff et al., 2014).

Lunar phase can also influence manta ray sightings, with full moon corresponding with a higher visitation rate of M. alfredi at South Komodo, Indonesia, whereas half and new moon phases correlated with more M. alfredi visitations in North Komodo (Dewar et al., 2008). Here, the authors proposed this could be due to the increased light levels aiding foraging activity in nearshore areas at night. In comparison, off Mozambique, higher numbers of M. alfredi and M. birostris were sighted during half to new moon than during full moon phases (Rohner et al., 2013b). Moon illumination has been shown to influence endogenous rhythms and subsequent reproductive behaviour in teleost reef fish (Heyman and Kjerfve, 2008). However, while sightings of many elasmobranch species are correlated with certain moon phases (Cuevas-Zimbrón et al., 2011, Weltz et al., 2013), if foraging is not observed, the influence of moon illumination on the sightings of wide-ranging manta species and whether this can be explicitly linked to reproductive behaviour is undetermined.

Regression analysis via generalised linear models (GLM) and generalised additive models (GAM) have been used previously to investigate ecological relationships among manta ray sightings and environmental variables (Jaine et al., 2012, Rohner et al., 2013b, Armstrong et al., 2016). Here, we aimed to define the relationship between environmental and temporal variables on sightings of M. birostris at an aggregation site off mainland Ecuador. A modelling approach was used to determine the importance of ENSO activity in relation to the number of M. birostris sighted throughout the year and to identify local environmental variables that affected M. birostris sightings during peak aggregation time. We hypothesised that sightings of M. birostris would be negatively affected by El Niño activity given this weather phenomenon is associated with decreased productivity and subsequent food availability in this region. Our second hypothesis was that local environmental conditions were unlikely to influence M. birostris sightings, as individuals in this sub-population are largely transient.

2.2 Material and methods

2.2.1 Study site and manta ray sightings

The study was conducted at Isla de la Plata (1.2759° S, 81.0672° W), Manabí Province, Ecuador (Fig 2.1). Isla de la Plata is an oceanic island located ~25 km west from mainland Ecuador and within the boundaries of Machalilla National Park. Near daily recreational SCUBA and boat based surface transects were used to observe and count M. birostris primarily at dive sites off the northern-most tip of the island (Fig 2.1).

While dedicated research dives were not possible throughout the entire year, boat survey effort and surface transects were consistent, as near daily excursions were undertaken by a tourism operator for recreational SCUBA diving. The boat captain and other staff of a local tourism operator (Exploramar Diving; www.exploradiving.com) used consistent protocols over the January 2011 – December 2015 period to document M. birostris occurrence on transects and at dive sites outside of the peak aggregation season, when researchers were not present. Dive sites visited during the off-peak season were the same as visited during the peak-season, with dive depths ranging from 10 – 30 m depending on the dive site, but with consistent intra-site dive depths used.

81°5′W 81°4′W 81°3′W

1°15′S Transect Line

1°16′S ISLA DE LA PLATA OCEAN PACIFIC

SOUTH AMERICA

1°17′S

2 km

Figure 2.1 Location of the principal study site, Isla de la Plata, Ecuador. Dive sites are indicated by the red and white flags with the transect line represented by the dashed arrow.

Each Manta birostris has a unique marking on its ventral surface that is used to identify individuals within a population (Marshall et al., 2008) (Fig 2.2). Images taken during dedicated expeditions to Isla de la Plata in August – September (2012 – 2015), were submitted by researchers and citizen scientists to MantaMatcher (www.mantamatcher.org) (Marshall and Holmberg, 2011, Town et al., 2013). The total number of individuals seen on each dive was recorded in a logbook.

A B

Logbook sighting data presented here, for August – September (2012 – 2015), include 1,298 individual M. birostris identified over 295 SCUBA dives. Mean dive duration was 46 ± 8.9 min (s.d.) 2 and the number of manta rays sighted was not affected by the length of the dive (F1,349 = 0.51, r = 0.0014, P = 0.47).

2.2.2 All year daily sightings logbook

To investigate whether El Niño or La Niña events were key drivers of M. birostris occurrence at Isla de la Plata, we built a generalised linear model (GLM) (v.3.2.3; R Core Development Team), with number of manta rays identified photographically per day as the response. In three instances manta ray images were not available for a day when M. birostris were present, and the number of individuals seen during the surface transect was used instead as the response. There is a significant positive correlation between number of M. birostris seen on the surface transect and number seen via photo 2 identification methods (F1,170 = 102.7, r = 0.38, P <0.05).

Predictors used in the model were the Oceanic Niño Index (ONI) and Month. ONI is the de-facto standard for identifying El Niño (warm) and La Niña (cool) events in the tropical Pacific and is the 3-month mean sea surface temperature (SST) anomaly for the Niño 3.4 region (5 °N – 5 °S, 120 ° – 170 °W) (NOAA, Chavez et al., 1999). While Isla de la Plata sits in the neighbouring Niño 1+2 region, the ONI has been used to reliably describe ENSO activity and effects throughout the entirety of the Pacific (Barnard et al., 2015). Further, the ONI has also been used to define El Niño events in the Galapagos, coastal Ecuador and northern Peru, all of which are in the Niño 1+2 region (Stramma

et al., 2016). Events are defined as 5 consecutive overlapping 3-month periods at or above the +0.5 °C anomaly for El Niño (warm) events or at or below the -0.5 °C anomaly for La Niña (cold) events (i.e. El Niño ≥ 0.5 °C, Neutral -0.5< °C <0.5, and La Niña ≤-0.5 °C). The ONI was used instead of a monthly El Niño-Southern Oscillation index, such as the Southern Oscillation Index, as it provides information on ENSO activity over prolonged periods and is not biased by month to month fluctuations. We also included a two-way interaction between ONI and Month to test the relationship between M. birostris sightings during the months that they aggregate and corresponding La Niña, neutral or El Niño events in the tropical Pacific.

2.2.3 Seasonal duration

To investigate differences in the seasonal duration of M. birostris aggregation each year, we calculated annual indices from the sighting data that represent the start, middle and end of the season. We used the method proposed by Greve et al. (2001), where the cumulative frequency distribution of the number of sightings of manta rays per day is calculated each year (i.e. the ogive). Thresholds of 5 %, 50 %, and 95 % were used to define the ‘‘start of season’’, ‘‘middle of season’’, and ‘‘end of season’’, respectively.

2.2.4 Peak aggregation time dive logbook

Detailed logbook data of M. birostris sightings alongside fine-scale environmental variables were only available for peak aggregation time (August – September). A generalised additive model (GAM) (v.3.2.3; R Core Development Team) was constructed using the 'mcgv' package where M. birostris sightings per dive + 1 were log-transformed and used as the response variable with explanatory predictors: Year, Month, Sea surface conditions, predominant plankton type in the water column, mean water column temperature (0 – 30 m), Tidal range, Current strength and Direction (Table 2.1). A spline smoothing function (k = 4) was used for tidal range and mean temperature given the uncertainty about the linear relationship with these variables (Wood, 2003).

Tidal range was determined from the Guayaquil Ecuador reference station (http://tidesandcurrents.noaa.gov/). Temperature was recorded in situ as the average temperature between 0 – 25 m depths using a dive computer (Suunto D6i) and conductivity, temperature depth recorder (CTD) (Levelogger Junior conductivity data logger). Temperature recordings between the dive computer and CTD were found to be comparable with an average deviation of 0.3 °C between the two instruments when both were at a simultaneous depth at the same time. Moon illumination 16

was determined from the NASA US Navy Observatory (http://www.usno.navy.mil/USNO), and was categorised as New (<10 %), Half (10 – 90 %) or Full (>90 %).

For current measurements, a Holux GPS unit was placed inside a surface buoy surface attached to a subsurface sail and was deployed ~5 mins after each dive. The start and end time, and GPS location was recorded and used to calculate Current Direction and Speed. Current speed was categorised as Weak (0 – 0.3 m.s-1), Moderate (0.31 – 0.6 m.s-1) or Strong > 0.6 m.s-1. When surface buoy Current Speed and Direction were not available, Current Direction was estimated visually using a dive compass underwater and Speed was estimated by the ease SCUBA divers were able to move between dive sites. Diver perception of Current Speed was also validated with surface buoy Current Speed categories. Weak was recorded when there was little or no current, Moderate when movement between dive sites was possible, but only by holding onto objects underwater to aid movement, and Strong when movement between dive sites was not possible or when it was only possible to drift dive from one site to another.

Predominant plankton type was designated from systematic visual observations taken at the end of each dive as divers ascended, with any changes in plankton type throughout the water column noted. Observations were then divided into five categories (Rohner et al., 2013b): (1) gelatinous zooplankton (gel), (2) no obvious plankton (i.e. good visibility with no apparent particles present), (3) phytoplankton (pp) (i.e. when the water was visibly green), (4) suspended sediment (ss) (i.e. when visibility was reduced due to stirred up sediment), and (5) other zooplankton (zp) (i.e. dense visible groups of non-gelatinous zooplankton such as copepods, barnacles, decapod crustaceans or pteropods).

2.2.5 Model validation

Diagnostic plots were used to assess the normality and homogeneity of variance assumptions of the GAM/GLM visually. Models were improved by using a Gaussian distribution error structure, as Normal and Poisson distributions fitted poorly in comparison. The model of best fit was chosen using Akaike’s Information Criterion (AIC). Following the AIC selection process, the significance of each predictor in the final model of M. birostris sightings was assessed using a χ2 test (Venables and Ripley, 2002).

2.2.6 Manta birostris behaviour

Habitat use and intra-specific interactions were categorised using four commonly observed behaviours (Fig 2.3): viz. Cruising, Cleaning, Social, and Feeding. Cruising was recorded when M. birostris was observed with tightly rolled cephalic fins and not engaging in any other activity. Cleaning was when cleaner fish-manta ray interactions were seen. Social activity was when courtship trains or breaching was seen. Although social behaviour has not been well studied in manta rays, these types of behaviours are typical for other elasmobranch species (Sims et al., 2000, Marshall, 2008). Feeding was recorded when individuals were engaged in continuous ram-feeding on zooplankton patches, with an open mouth, visible gill rakers and unrolled cephalic fins.

To address the question of why M. birostris aggregate at Isla de la Plata, and to reduce the number of redundant levels in the instances when multiple behaviours were recorded in one dive (69 % of all records), the final recorded behaviour was subject to hierarchal classification (Feed > Social > Clean > Cruise). This hierarchal order was set according to current literature, whereby food availability (or local productivity) has been more commonly cited as a driver of aggregative behaviour of manta rays than breeding or cleaning opportunities (Homma et al., 1997, Dewar et al., 2008, Anderson et al., 2011, Jaine et al., 2012, Couturier et al., 2012).

A B

D C

Figure 2.3 Behaviours of Manta birostris observed in Ecuador. Clockwise from top left: M. birostris (A) Cruising, (B) Cleaning, (C) Socialising and (D) Feeding.

The occurrence of other megafauna, including sunfishes Mola ramseyi and whale sharks Rhincodon typus, was documented opportunistically to investigate what other transient species seasonally occur at Isla de la Plata, and whether these other species were displaying similar behaviours to M. birostris.

Table 2.1 Response and predictor variables used in the generalised linear (GLM) and generalised additive (GAM) models for Manta birostris sightings at Isla de la Plata, Ecuador. Model Variable Justification Type Description Range/Levels GLM M. birostris sighted Fluctuations of M. birostris sightings * Daily numbers of M. birostris 0 – 59 per day throughout the year observed via transect/photo identification GAM M. birostris sighted Dives conducted in AM and PM. Biotic and * Individually identified M. 0 – 30

Response per dive abiotic conditions change throughout day birostris via photo identification GLM Month Peak aggregation time for M. birostris off + Month of observation January – December mainland Ecuador during austral winter GLM ONI Year La Niña (cold)/El Niño (warm) events + 5 Consecutive 3-month periods El Niño (≥ 0.5 °C), known to effect productivity and resource that are ≥+0.5 °C anomaly for Neutral (-0.5< °C <0.5), availability in the eastern Pacific (Sidun, El Niño or ≤-0.5 °C anomaly La Niña (≤-0.5 °C ) 2015) for La Niña events.

GAM Year Inter-annual variation in oceanographic + Year of observation 2012 – 2015 processes (Meinen and McPhaden,

2000b) GAM Month Peak aggregation time for M. birostris off + Month of observation August, September, October mainland Ecuador during austral winter

Predictor GAM Temperature Influences occurrence of other * Average temperature per dive 17.9 – 25.8 °C planktivorous elasmobranchs (Afonso et (0 – 25 m) al., 2014), but thermal preferences of M. birostris unknown GAM Lunar Phase Correlation found with sightings of other + Daily value for percentage of New (<10 %), Half (10–90 %), large planktivorous elasmobranchs but moon illuminated Full (>90 %) mechanism unknown (Dewar et al., 2008) GAM Plankton Type Feeding often cited as important driver of + Predominant visible plankton Gelatinous (gel), no plankton aggregative behaviour of manta rays (Jaine type per dive (none), phytoplankton (pp), et al., 2012) suspended sediment (ss), zooplankton (zp)

GAM Tidal Range Zooplankton concentrating mechanism * Vertical distance between the 1 – 3.31 m (Bowman et al., 1986) or habitat space high and low tide determinant (McClain et al., 2015) GAM Current Strength Strong currents may impede divers + Weak (0 – 0.3 m.s-1), Moderate 0 – 0.9 m.s-1 capturing photo IDs or disrupt cleaning (0.31 – 0.6 m.s-1), interactions (O'Shea et al., 2010) Strong (> 0.6 m.s-1) GAM Current Direction Northwards currents bring cool nutrient rich + Compass degrees converted to None, N, NE, E, SE, water and southwards currents bring warm compass direction S, SW, W, NW nutrient depleted water

+categorical,*continuous

2.3 Results

2.3.1 All year daily sightings logbook

The majority of Manta birostris sightings at Isla de la Plata occur during the equatorial dry season (May – December) with most individuals recorded from August – September. The number of M. birostris sighted seasonally off Ecuador was far higher in comparison to Mozambique and Brazil, where long-term sightings data are also available for this species (Fig 2.4).

Ecuador Mozambique Brazil 400

20 20 300 per month 15 15 200 10 10 Manta birostris

100 5 5 Number of

0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 3 4 5 6 7 8 9

Month

The GLM with all year daily M. birostris sightings as the response with predictor variables ONI and Month explaining 27 % of the deviance observed. There was a statistically significant interaction between ONI and Month (P <0.05), with the lowest number of M. birostris sightings during peak aggregation time coinciding with a very strong El Niño climate event in 2015 (Table 2.2, Fig 2.5).

12 El Niño per day La Niña 10 Neutral

sighted 8

6 M. birostris 4

0 Mean number of 1 2 3 4 5 6 7 8 9 10 11 12 Month

Year ONI SeasonS SeasonM SeasonE SeasonD Total M. birostris 2011 La Niña* 241 247 251 10 411 2012 Neutral 220 244 269 49 739 2013 Neutral 143 232 265 122 320 2014 Neutral 199 246 257 58 436 2015 El Niño** 211 242 250 39 183 *Weak La Niña event (0.5 – 0.9 °C SST anomaly), **Very strong El Niño event (≥2 °C SST anomaly)

Mean seasonal duration of M. birostris aggregation at Isla de la Plata was 55.6 days (range 10 – 122 days). Years categorised as Neutral all had longer seasons in comparison to La Niña (2011) and El Niño years (2015) (Table 2.2, Fig 2.5). The shortest season was in 2011 (La Niña), but in 2011, a

greater number of individual M. birostris were sighted per day in comparison to a longer aggregative season in 2015 (El Niño). For all years, season end – when 95 % of M. birostris had been sighted – was early to mid-September (Table 2.2).

2.3.2 Peak aggregation time dive logbook

Manta birostris and other megafauna were encountered on 78.2 % and 14 % of dives undertaken, respectively. Of these other megafauna, sunfish Mola ramsayi was most common, and was present on 8 % of dives. When M. birostris were present, cleaning was the most commonly observed behaviour (53.8 %), followed by cruising (31.5 %) (Fig 2.6). In general, cleaner fish attended the dorsal surface of M. birostris, with species of cleaner fish including, the black-nosed butterflyfish Johnrandallia nigrirostris, King angelfish Holacanthus passer, Cortez angelfish Pomacanthus zonipectus, and the Cortez rainbow wrasse Thalassoma lucasanum. Breaching of M. birostris was observed on 20 of 24 social interactions, and the remainder of social interactions involved courtship trains of > 3 individuals. Mean water column temperature on these dives was 22.6 °C (range, 17.9 – 25.8) and was not a significant predictor of M. birostris sightings (df = 1, F = 0.044, P = 0.8). Likewise, Lunar Phase was also not a significant predictor of M. birostris sightings (df = 2, F = 1.797, P = 0.2), thus temperature and Lunar Phase were not included in the final model.

n=82 n=140 n=24 n=14 30 25 20 identified per dive 15 10 M. birostris 5 Number of 0 Cruise Clean Social Feed

There was a higher incidence of phytoplankton and no visible plankton in the water column during 2012 (Fig 2.7). Gelatinous macroscopic zooplankton was more common during peak aggregation periods in 2014 and 2015. There was little difference in the occurrence of feeding activity among years, 2012 (n = 4), 2013 (n = 3), 2014 (n = 5), 2015 (n = 2). Cleaning and social behaviours were more commonly observed in 2012 relative to subsequent years (Fig 2.7).

1.0 1.0 Social zp 0.8 eed

F 0.8 uise r C 0.6 0.6 pp Behaviour Plankton Type 0.4 0.4 Clean

gel 0.2 0.2 ss

none 0.0 0.0 2012 2013 2014 2015 2012 2013 2014 2015 Year

Table 2.3 Summary and percentage deviance explained (% Dev. Exp) by each significant predictor of Manta birostris sightings. Variable df F % Dev. Exp P-value Year 3 15.1 9.1 <0.05 Month 1 13.3 2.7 <0.05 Plankton type 4 7.5 6.3 <0.05 Current Direction 8 3.7 8.6 <0.05 Current Strength 2 3.4 1.4 <0.05 Tidal range 1 20.9 16.7 <0.05 44.8

The GAM of M. birostris sightings per dive as the response and Year, Month, Plankton Type, Current Strength, Current Direction and Tidal Range as predictors explained 44.8 % of total variance (Table 2.3, Fig 2.8). Tidal range accounted for the highest percentage variance explained by the model (16.7 %). There were a moderate number of mantas present during weak tides, fewer mantas at intermediate tidal ranges of around 2.5 m, and a larger number of mantas as tidal range increased above 2.5 m (Fig 2.8).

Year accounted for 9.1 % total variance in the GAM with increased M. birostris sightings during August and September in 2012 compared with fewer individuals seen during these same months in 2013, 2014 and 2015. There were more sightings during dives in August than in September, but this difference in monthly sightings explained only 2.7 % of the total variance.

Plankton type accounted for 6.3 % of the total GAM variance. Increased sightings of M. birostris were associated with an absence of visible phytoplankton in the water column and when zooplankton was present (Table 2.3). Levels of Plankton type-predictor that resulted in significantly fewer M. birostris sighted were gelatinous zooplankton, suspended sediment or phytoplankton (df = 4, P <0.05) (Fig 2.8). When feeding behaviour was observed (n = 14), zooplankton was always the predominant Plankton type in the water column. The levels of Current Direction predictor that correlated with increased M. birostris sightings were NW or No Current. There were fewer manta rays observed in Strong Currents and sightings in these conditions were significantly different from Weak and Moderate Current strengths (df = 2, P <0.05) (Fig 2.8).

0.5 0.5 ear Y or f or Month f 0.0 0.0 tial r tial r a a P P −0.5 −0.5

2012 2013 2014 2015 8 9 Year Month 0.5 0.5 0.0 0.0 or Current Strength f or Current Direction f tial tial r r a a P P −0.5 −0.5

None N NE E SE S SW W NW Weak Moderate Strong Current Direction Current Strength 0.6 0.5 0.4 0.0 0.2 or Plankton Type f tial 0.0 r a P −0.5 Partial for Tidal Range (df = 4) −0.2 none ss gel pp zp 1.0 1.5 2.0 2.5 3.0 Tidal Range (m) Plankton Type

2.4 Discussion

High and low seasonal sightings of Manta birostris correlated with La Niña and El Niño activity respectively, suggesting that on a broad scale, feeding opportunities may be an important driver of occurrence of this species in the region. However, surface feeding was rarely observed at Isla de la Plata, which complements current understanding of manta ray feeding ecology from this region, which suggests surface zooplankton does not constitute a large portion of dietary intake (Chapters 4 and 5). In terms of fine-scale habitat use, environmental logbook data indicated that conditions conducive to efficient cleaning interactions such as weak currents and good water clarity are the best predictors of M. birostris sightings at Isla de la Plata.

2.4.1 Regional environment effects on manta ray sightings

There was a distinct seasonal aggregation time for M. birostris off mainland Ecuador that occurred in the July – October period, but with large inter-annual variability in animal numbers seen. The occurrence of M. birostris in Brazil (Luiz Jr et al., 2009), New Zealand (Duffy and Abbott, 2003), Mexico (Rubin, 2002), and Costa Rica (White et al., 2015) is highly seasonal, whereas in Mozambique there is slight peak in sightings during April and May, but individuals are seen all year round (Rohner et al., 2013b). Inter-annual physical and biological processes are predictable in the eastern equatorial Pacific with variability between ENSO neutral years, and more pronounced variability during La Niña or El Niño events (Wallace et al., 1989). Predictable aggregative behaviour in manta rays is associated with low-latitude environments, with known aggregation sites for manta rays found exclusively in the tropics and subtropics typically during summer months (Dewar et al., 2008, Couturier et al., 2011, Rohner et al., 2013b). These observations suggest movement to, and occupation of, aggregation sites by manta rays is associated with warm waters. At Isla de La Plata, temperature was not an important predictor of sightings on a fine-scale (i.e. per dive), but did appear to influence the length of the season that M. birostris aggregated. During the 2015 El Niño event, season duration was longer with warmer sea surface temperature values, but with fewer individuals sighted in comparison to La Niña or ENSO neutral years. Conversely, sightings of M. birostris in 2011, which coincided with higher SOI values associated with La Niña conditions, were greater but concentrated within a shorter season. During El Niño, primary productivity declines leading to the collapse of marine food webs in the eastern Pacific (Barber and Chavez, 1983). In comparison, during La Niña, surface easterly winds intensify and upwelling of cooler, nutrient-enriched subsurface waters increases in the eastern Pacific leading to greater productivity (Meinen and McPhaden, 2000b). Overall, thermal preferences appear to affect the length of M. birostris aggregation period, while

resource availability influences the number of individuals sighted during aggregative periods of this species in this region.

Higher sightings of M. birostris also occurred when the prevailing ocean current was flowing in a NW direction. The Humboldt current, a shallow offshore flow eastern boundary current that flows NW from Peru (Thiel et al., 2007), maintains recently upwelled water in bright-light conditions for sufficient time to produce high primary productivity. Although M. birostris was rarely seen foraging at Isla de la Plata, it is considered likely that productive waters nearby provide foraging opportunities in the general region, and may relate to high levels of M. birostris sighted. In addition, sightings of M. birostris were low when the predominant Plankton Type was gelatinous. Although the filter- mechanism in mobulids is well described, morphologically, its mode of operation is uncertain (Misty Paig-Tran and Summers, 2014). Gelatinous organisms could pose a problem for the feeding apparatus and M. birostris may actively avoid feeding or occupying areas where gelatinous organisms dominate the water column.

2.4.2 Feed, rest and clean hypothesis

In this study, M. birostris was seen to be foraging in only 4.7 % of research dives, despite zooplankton being the dominant macroscopic constituent of the water column at Isla de la Plata. The absence of foraging behaviour by R. typus, the other large filter-feeder at this site, was also noted. Many previous studies on manta rays’ aggregative behaviour in relatively small areas around coral cays and islands has been linked to high zooplankton abundance (Dewar et al., 2008, Anderson et al., 2011, Armstrong et al., 2016). However, the lack of observed feeding activity in M. birostris (and R. typus) at Isla de la Plata suggests this habitat does not represent an energetically viable prey patch (Chapter 3), and that foraging opportunities at this site are not the primary driver of aggregative behaviour during this observation period.

Our assumption is that M. birostris forages at night, given the lack of observed foraging activity during daylight hours. For smaller pelagic planktivorous fishes that aggregate at particular physical features during daytime hours, a feed-rest hypothesis has been proposed whereby this physical feature provides a sheltered environment and a reduction in energy expenditure during the daytime non- feeding intervals (Genin, 2004). This strategy has also been documented in other large elasmobranchs, with hammerhead sharks in relatively nearby Galápagos and Malpelo archipelagos forming schools around seamounts during the day and then moving to open-water at night to forage (Ketchum et al., 2009, Hearn et al., 2010). Reproductive opportunities are also often cited as a major 29

driver of aggregative behaviour in elasmobranchs (Pratt Jr and Carrier, 2001). However, at Isla de la Plata, M. birostris courtship behaviours were only observed on 3/295 research dives. Further, a very low proportion of mature females were recorded as having mating scars (9 %) or being pregnant (1.3 %) (A. Marshall, unpubl. data). Comparatively, the percentage of pregnant M. birostris females at Isla de la Plata is far lower than those reported for M. alfredi subpopulations off eastern Australia (10 %, Couturier et al. (2014) and Mozambique (16.4 %, Marshall et al. (2011b). Therefore, the hypothesis that M. birostris seasonally aggregate at Isla de la Plata seasonally due to cleaning and foraging opportunities nearby is a much better supported theory in comparison to the idea that reproductive opportunities are a major driver M. birostris aggregative behaviour at this site.

While M. birostris was often seen Cruising at Isla de la Plata, Cleaning was the most common behaviour observed, likewise, co-occurring sunfish spent long periods of time being cleaned. Cleaner fish (host) – manta ray (client) interactions are commonplace at aggregation sites for these species worldwide (Homma et al., 1997, Dewar et al., 2008, Jaine et al., 2012). In tropical coral reef systems, client fish visit cleaning stations to have ectoparasites, fungi, algae or necrotic tissue removed (Hobson, 1971). Without removal, ectoparasites have been shown to result in significant reductions in size, growth and reproductive output in some client fish species (Poulin and Grutter, 1996). At Osprey Reef in Australia, all reef manta rays at the site engaged in cleaning activity and occupied the cleaning stations for significantly more time than other co-occurring elasmobranchs (O'Shea et al., 2010). As such, aggregation sites and cleaning stations adjacent to off-shore island, rocky- and coral reefs seem to be interchangeable terms which implies that cleaning activities may be the underlying reason for the majority of manta ray aggregative behaviour.

Further evidence to support that M. birostris comes to Isla de la Plata to clean is that lack of macroscopic plankton in the water column was a significant predictor of sightings at this site. Cleaner fish have conspicuous colour and pattern combinations that likely to help distinguish them from other co-occurring fish in the marine environment (Cheney et al., 2009). A clear and undisturbed environment could help to facilitate visual interactions and recognition between manta rays and cleaner fish. Furthermore, M. birostris sightings were also positively correlated with a Weak Current Strength and no discernible Current Direction. Weak to no Current near cleaning stations would likely benefit cleaner-client interactions, as cleaner and host fish would expend less energy in the water column during this interaction.

While there was a non-linear relationship between M. birostris sightings and tidal range, the highest number of individuals were sighted during maximum tidal ranges. At high spring tide, the shallowest 30

cleaning stations sit at about 7 m depth, and at low spring tide this would be closer to 4 m. Manta birostris can attain disc widths of up to <7 m (McClain et al., 2015), and it is possible that the reduction in available habitat use for cleaning interactions affects sighting numbers at Isla de la Plata. However, multiple other cleaning stations are located at this site in depths ranging from 10 – 35 m and it would be unlikely that sea height fluctuations caused by tidal effects would affect cleaning stations at these depths.

2.4.4 Limitations and future work

Spatial limitations exist in this study as animals were only observed near to Isla de la Plata. Seasonally enhanced biomass in continental shelf habitats is thought to be an important driver of whale shark aggregative behaviour (McKinney et al., 2012, Rohner, 2012) and it is possible that prey patches are instead located along the continental shelf edge which is ~20 km from this aggregation site. Complementary aerial support would be needed to explore whether or not M. birostris regularly forages in nearby surrounding waters. Additionally, observations in this study were temporally constrained to surface transects and recreational SCUBA during daylight hours over peak aggregation periods from 2012 – 2015. Future studies could investigate M. birostris habitat use at night and vertical water column biomass structure alongside tagging data should also be examined, as high zooplankton prey densities at this site may be located deep during the day and migrate nearer to the surface at night. Further consistent sampling of biological and physical variables at Isla de la Plata over future years would improve the inter-annual component of this study, and may offer a practical means of monitoring environmental conditions in this coastal upwelling system, given that these conditions are either poorly sampled or unpublished for other localities throughout this region (Olson et al., 2014).

With a fixed survey area that does not include the entire range of a given population, only a fraction of this population is sampled each year (Forney, 2000). Nonetheless, data from these sites can still be informative for conservation and management strategies (McMahon and Hays, 2006). Highly mobile species such as M. birostris would be likely to exhibit variable survey proportions year on year, with drivers of movement linked to a suite of underlying causes such as; behaviour linked to reproduction, physiological factors and foraging success rates. Any environmental factors that correlate with movement of aggregative behaviour may not specify a causal relationship, and ultimately, are proxies that may be directly or indirectly related to prey availability and/or abundance (Vilchis et al., 2006). Limitations notwithstanding, consistent effort in data collection throughout the year coupled with observations over multiple years increase confidence in the interpretation that M birostris sightings 31

at Isla de la Plata in relation to predictor environmental variables included in the model are a true reflection of underlying ecological relationships. Future studies could also aim to include variables that were not able to be measured in this study, such as regional prey availability (Wirsing et al., 2007), hypoxic oxygen minimum zones (Stewart et al., 2014), and proximity of thermal fronts (Afonso et al., 2014), which influence sightings of other large migratory marine species. Subsequent inclusion of additional predictor variables as well as the identification of other unknown predictors would likely improve the amount of variance explained from statistical models that are used to analyse sightings of M. birostris and other large vulnerable marine species that undertake long- distance migrations.

2.5 Conclusions

Isla de la Plata is likely to serve as a multi-purpose area that facilitates a reduction in energy expenditure after foraging at night and provides an environment for cleaning interactions to occur. In addition, it may also serve as a platform for social interactions such as courtship between M. birostris, which in general is a wide ranging and largely solitary species (Kashiwagi et al., 2011). The proposed rest-benefit hypothesis and supposed energetic gain for M. birostris along with the cost-benefit relationship between cleaner fish and their manta ray host warrant further investigation. It is currently unknown whether cleaning interactions constitute an integral part of the life history of manta rays or are simply an incidental interaction that occurs while manta rays are utilising physical features that provide a ‘rest’ environment.

Drivers of seasonal and diel movement patterns of manta rays are likely to differ between M. birostris and M. alfredi given their differences in size, habitat occupancy time and distribution ranges (Couturier et al., 2012). In addition, drivers of movement and habitat use likely differ among regions as fine-scale environmental conditions are a product of larger regional processes that differ geographically (Behrenfeld et al., 2006). A number of different regions over the last decade have reported a decline in manta ray population numbers and it is important to document natural drivers of variability in sighting trends to correctly interpret future trends in abundance (Ward-Paige et al., 2013).

Chapter 3

Sporadic feeding on small-bodied surface zooplankton during the day by the giant manta ray Manta birostris at Isla de la Plata, Ecuador.

3.1 Introduction

Manta rays are the largest members of the planktivorous Mobulidae family and currently comprise two species: the giant manta ray Manta birostris and the reef manta ray Manta alfredi. These rays have conservative life history traits such as low fecundity, late age of maturity and slow growth rates, making them particularly vulnerable to overexploitation (Dulvy et al., 2014; Croll et al., 2015). They are caught in direct and indirect fisheries (Notarbartolo-Di-Sciara, 1988; White et al., 2006; Lack & Sant, 2009; Couturier et al., 2012), and population declines have been observed at aggregation sites in Mozambique (Rohner et al., 2013b), the Philippines, Indonesia and Mexico (Marshall et al., 2011c, Marshall et al., 2011d). Basic biological knowledge is low for both manta species, making it difficult to assess and manage or conserve their regional sub-populations (Couturier et al., 2012; Pardo et al., 2016).

Manta birostris has a circumglobal distribution, but owing to its typically offshore habitat preferences (Marshall et al., 2011c), very little is known about the feeding ecology or drivers of aggregative behaviour of this species, in comparison to the more coastal M. alfredi. Until recently, for areas where sightings data for M. birostris were available, the number of individuals seen was typically low (<5 per month (Luiz Jr et al., 2009, Rohner et al., 2013b)), with many of these sites difficult to consistently access. However, an aggregation site for M. birostris off mainland Ecuador, has been shown to host the greatest number of identified individuals (>2400, Fig 1.2), and provides an exceptional opportunity to explore possible drivers of aggregative behaviour of this generally elusive species. Food availability has been often cited as an important driver in aggregative behaviour of M. alfredi (Anderson et al., 2011) and other planktivorous elasmobranchs (Wilson et al., 2001), however, it is currently unknown whether aggregative behaviour of M. birostris at daytime aggregation sites off mainland Ecuador is driven by foraging opportunities.

Planktivorous elasmobranchs were initially considered indiscriminate filter feeders that would exploit zooplankton prey in any place and at any time (Matthews and Parker, 1950). However, not all zooplankton patches will provide the same energetic benefits, and it may be the elevation of a specific prey type or size that could be more important than general plankton productivity (Hays et al., 2006). Dense prey concentrations are likely to be a critical factor in determining the feeding behaviour of large planktivores, as the energetic intake needs to exceed the energetic cost of feeding. Minimum prey density feeding thresholds have been established for planktivorous elasmobranchs, such as M. alfredi (Armstrong et al., 2016) and basking shark, Cetorhinus maximus (Sims, 1999), and also for humpback Megaptera novaeangliae, minke Balaenoptera acutorostrata, and fin Balaenoptera

physalus baleen whales (Piatt and Methven, 1992). At aggregation sites, the feeding behaviour of planktivorous elasmobranchs can be triggered by overall increases in zooplankton biomass or increases in zooplankton size, relative to zooplankton biomass and/or zooplankton size when animals are present but are not feeding. For M. alfredi, zooplankton biomass was found to be important in triggering feeding behaviour (Armstrong et al., 2016), whereas in the whale shark, Rhincodon typus, feeding behaviour was only observed during times of high biomass of large-bodied sergestid shrimps (Rohner et al., 2015). Basking sharks use both strategies of prey selectivity and preferentially forage in areas where a dominant, large-sized, calanoid copepod species, Calanus helgolandicus, was more abundant in comparison to areas where foraging activity was not observed (Sims and Quayle, 1998).

High-resolution dietary data for manta rays is limited to a M. alfredi regional sub population off east Australia (Armstrong et al., 2016, Bennett et al., 2016). For a direct, short-term, and high-resolution assessment of animal diet, access to dead specimens is usually required. Currently only two publications detail the stomach contents of manta rays: a historical Manta alfredi stomach contents sample from eastern Australia, which predominantly contained calanoid copepods from the larger end of the size spectrum of zooplankton in this region (Bennett et al., 2016), and a study that found M. birostris in the Philippines feed heavily on euphausiids and to a lesser extent, myctophid fishes (Rohner et al., 2017). Obtaining specimens of rare and internationally protected species, such as manta rays, is unethical or at best, challenging as samples have to be obtained from markets, therefore alternative approaches are needed. Aggregation sites where feeding activities are observed can present researchers with unique opportunities to gather baseline information on vulnerable and elusive species. An alternative, non-lethal technique for characterising diet in large planktivores involves the collection of plankton alongside feeding animals. This approach can give a snapshot of species’ diet, similar to stomach contents analysis, but is still considered an indirect method as determining whether the animal ingests of all or some of the prey items is not possible (Rohner et al., 2015; Armstrong et al., 2016).

The abundance of primary producers in the ocean can be resolved from remote-sensing products (Huston and Wolverton, 2009), but determining zooplankton biomass presents additional challenges. Large amounts of in situ data are required, and thus far, global spatial patterns and drivers of zooplankton biomass, especially in the tropics, are relatively understudied (Bucklin et al., 2010, Lucas et al., 2014). Temperature and nutrients are considered the most important variables influencing the structure of marine ecosystems, with dominant plankton assemblages corresponding to which species can most efficiently survive the ambient temperature and utilise the available nutrient type (Southward et al., 1995, Jennings et al., 2008, Richardson, 2008). Preceding temperature and nutrient 35

availability, wind speed is also known to affect zooplankton abundance and distribution, especially in eastern boundary current systems (EBC). IN EBCs, an increase in trade winds that blow parallel to the coast and towards the equator force (upwell) nutrient rich water to the surface, which subsequently results in an increase in primary productivity (Chavez and Messié, 2009). Prevailing current strength and direction is also likely to influence primary production and therefore zooplankton abundance. Off Ecuador, dense upwelled phosphate rich equatorial waters from the south are pushed towards the equator and light oxygen rich waters are pushed towards the equator from the north (De Szoeke et al., 2007). Isla de la Plata’s proximity to the equator means that zooplankton abundance surrounding this island likely fluctuates according to levels of available nutrients from these differing northward and southward currents, which are linked to the prevailing trade wind. Tidal currents, together with bathymetry, can also concentrate zooplankton on an ebb- to low-tide and have resulted in the formation of feeding areas for manta rays around small islands (Dewar et al., 2008, Armstrong et al., 2016). By understanding the relationships between biomass, and environmental drivers, we can begin to identify and predict habitats that may be important for large planktivore dietary intake, and how these habitats might respond to anthropogenic-mediated change.

The overall aim of this study was to investigate M. birostris feeding activity in relation to zooplankton dynamics at Isla de la Plata, Ecuador. Here, we specifically test the following a priori hypotheses relating to M. birostris foraging behaviour and zooplankton dynamics at Isla de la Plata: i) M. birostris feeding events are triggered by an increase in food availability, such as an increase in a particular prey type or overall biomass, ii) food availability is driven by environmental variables such as chlorophyll a concentration, temperature, wind speed, current direction, and tidal currents. To provide a comparative perspective between large planktivorous elasmobranch species at predictable aggregation sites, we also compared the size spectra of zooplankton collected from M. alfredi (Armstrong et al., 2016), and R. typus (Rohner et al., 2015) feeding events to determine if similarities in ‘foraging behavioural triggers’ exist among these planktivorous elasmobranch species. We also investigated the composition and size spectra of zooplankton from M. birostris surface water feeding events, and zooplankton from the stomachs of M. birostris from the Philippines to compare surface zooplankton presumably ingested versus actually ingested zooplankton of mesopelagic origin (Rohner et al., 2017).

3.2 Materials and methods

3.2.1 Study site

This study was conducted at Isla de la Plata, Ecuador (1.2759° S, 81.0672° W; Fig 3.1) where Manta birostris are commonly found at cleaning stations on the NW sheltered side of the island throughout austral winter. Isla de la Plata is on the continental shelf, with surrounding waters characterised by sandy bottoms and coral reefs. Approximately 30 km west of Isla de la Plata, the continental shelf starts dropping, and within 100 km, depths can reach <3000 m (General Bathymetric Chart of the Oceans, 1984) . Maximum depths around the island can reach ~80 m, but depths over the sampling sites ranged from 10 – 30 m.

81°5′W 81°4′W 81°3′W

1°15′S

1°16′S ISLA DE LA PLATA OCEAN PACIFIC

SOUTH AMERICA

1°17′S

2 km

Figure 3.1 Isla de la Plata, Ecuador with the zooplankton sampling location is indicated by the diagonally-hatched box.

3.2.2 Sample Collection

Near surface (0 – 1 m depth) horizontal zooplankton tows (n = 84) were conducted from August to October in 2013 and 2014 to the northwest of the island (Fig 3.1). The net used had a 200 µm mesh, and 50 cm diameter mouth opening fitted with a digital flowmeter to calculate the volume of water filtered. Flowmeter calibration was performed each year before the field work commencement. Where possible, tows were taken once in the morning and once in the afternoon over consecutive 37

days to sample across the full range of the tidal cycle. Samples were kept on ice before being processed on land.

Temperature was recorded in situ as the average surface temperature between 0 – 5 m depths using a conductivity, temperature depth recorder (Levelogger Junior conductivity data logger, Solinst). Tidal range was determined from the Guayaquil, Ecuador reference station (http://tidesandcurrents.noaa.gov/), and daily Wind Speed and Direction were obtained from a weather station at Manta Airport (~50 km from Isla de la Plata). To determine current strength and direction, a GPS unit (M-241, Holux) was placed inside a surface buoy surface attached to a subsurface sail and was deployed ~5 mins before each zooplankton tow. The start and end time, and GPS location was recorded and used to calculate current direction and speed. Current speed was categorised as weak (0 – 0.3 m s-1), moderate (0.31 – 0.6 m s-1) or strong > 0.6 m s-1.

Chlorophyll a was used as proxy for local primary productivity and determined from the Moderate Resolution Imaging Spectroradiometer (MODIS; modis.gsfc.nasa.gov). Chlorophyll concentration data were mapped using the ocean colour index (OCI) algorithm from monthly climatology composites at a 4 km spatial resolution for the period 2000 – 2014. These data were then mapped to the region of interest (Fig S2.1), and the pixel value for chlorophyll concentration closest to Isla de la Plata was recorded. In the event where the closest pixel value was unavailable due to cloud cover, average chlorophyll concentration was calculated from the pixels immediately surrounding the closest pixel to Isla de Plata.

3.2.3 Zooplankton Biomass

To facilitate cross-study comparisons, the wet volumetric, and dry gravimetric biomass of zooplankton was calculated. Wet biomass was determined from the displacement volume, whereby the zooplankton sample was filtered using a 100 µm mesh sieve and interstitial water was removed using blotting paper. Filtered zooplankton sample was transferred to a measuring cylinder with a known volume of sea water (collected from the same site as the tow), and the displacement volume (mL) was measured by the amount of sea water displaced by the zooplankton. Throughout, wet biomass is expressed as grams per filtered water volume (g m-3). Zooplankton samples were then fixed in 5 % buffered formalin and seawater.

In the laboratory, the fixed zooplankton samples were rinsed in freshwater. Samples were split into two equal parts using a Folsom plankton splitter (Harris et al., 2000). To determine dry biomass, one 38

half was put into a pre-weighed glass petri dish, dried for 24 h at 60 °C and reweighed. Dry biomass was expressed as mg per filtered water volume (mg m-3). The other half of the sample was retained in formalin for later size-spectrum and taxonomic analysis.

3.2.4 Zooplankton size-spectra & Zooscan wet biomass

To determine the absolute and relative wet zooplankton spherical biomass, and community size- spectrum, we processed 84 samples from 2013 and 2014 (feeding n = 5, not feeding n = 79) using a 2400 dpi resolution ZooScan (Gorsky et al., 2010). Samples were processed according to scanning methods outlined in Schultes and Lopes (2009). Zooplankton were manually separated in the scanning tray to eliminate individuals touching, and digitally separated after the scan. Plankton identifier software was used for classification of whole organisms versus parts of organisms (e.g. chaetognath heads) and scanning artefacts (e.g. bubbles, shadows) (Gorsky et al., 2010). Non-whole organisms and scanning artefacts were subsequently removed from the analysis. The pixel area measurement for individual zooplankton was converted to mm2 and used to estimate spherical biovolume (SBv; mm3). The normalised biomass size-spectra were then calculated by summing the SBvs into 50 normalised size bins. The SBv value was used to calculate biomass (in mg) assuming a 1:1 conversion factor between volume and mass, with results presented as g m-3. Maximum individual zooplankton length and wet biomass is reported alongside the mean as it represents the largest presumed ingested or available prey. A total of 60,579 intact individuals were identified, manually validated, and subsequently used for size- spectrum and taxonomic analysis.

Zooscan calculated spherical diameter (hereafter referred to as length) of zooplankton, which was used to create a prey size-spectrum for samples when M. birostris was feeding versus when they were present but were not feeding. Log-transformed SBv-values were used to compare zooplankton from this study with those collected during previously reported Manta alfredi (Armstrong et al., 2016) and Rhincodon typus feeding events (Rohner et al., 2015). To investigate how zooplankton data varied between in situ zooplankton tows and stomach contents, we compared log-transformed SBv-values for when M. birostris was feeding in surface waters at Isla de la Plata to mesopelagic mobulid stomach content prey items from the Philippines (Rohner et al., 2017).

3.2.5 Zooplankton identification

Extracted vignettes from the scanned samples were categorised into 24 broad taxonomic groups using Plankton identifier software (v1.2.6, http://www.obs-vlfr.fr), followed by visual validation. Objects 39

identified as non-zooplankton from visual validation, such as sand, fibre, detritus and scan bubbles were excluded from further analysis, and taxa comprising <1 % of total abundance were grouped as ‘Others’.

3.2.6 Environmental influences on zooplankton biomass

A generalised additive model (GAM) was built (v.3.2.3; R Core Development Team) to investigate environmental drivers of zooplankton biomass off mainland Ecuador. Three separate models, with wet (total and spherical) or dry biomass per tow used as the response variable, were built. Predictors used in the model were: Year, Month, Temperature, Time to Low Tide, Wind Direction and Speed, and Current Direction and Strength. Models were improved by using a negative binomial distribution error structure (Normal, Poisson and Gaussian distributions fitted poorly in comparison). The model of best-fit was chosen using Akaike’s Information Criterion (AIC). Following the AIC selection process, the significance of each predictor in the final model of M. birostris sightings was assessed using a χ2 test (Venables and Ripley, 2002).

3.2.7 Manta ray behaviour

Daily SCUBA and surface transects were used to observe manta ray behaviour. Feeding was recorded when individuals were seen continuous ram or barrel roll feeding on zooplankton patches, with an open mouth and unrolled cephalic fins. Zooplankton tows when manta rays were present but not feeding were used to estimate the background zooplankton community, whereas zooplankton samples collected during feeding activity were assumed to be representative of foraging events at this site. Manta ray behaviour was categorised and used in a generalised additive model (GAM) as a binomial response (not feeding = 0, feeding = 1). The same significant variables from the analysis of zooplankton biomass were used as predictors of M. birostris behaviour.

3.3 Results

Manta birostris feeding activity was observed during 6 % of encounters (5 of 84 occasions), during which rays partook in short bursts (< 5 mins) of continuous ram feeding in surface waters. Defecation was not observed throughout the entire study period for any individual M. birostris. There were significantly more M. birostris sighted during feeding activity (10.4 ± 6.4 s.d.) than when individuals were not feeding (5.1 ± 6.6 s.d.) (Mann-Whitney-Wilcoxon rank-sum test, W = 330.5, P <0.05).

3.3.1 Zooplankton biomass Zooplankton biomass during M. birostris feeding events was higher in comparison to when individuals were not feeding using all three techniques for determining biomass (Table 3.1, Fig 3.2), however, differences were all non-significant: total volumetric displacement biomass (Mann- Whitney-Wilcoxon rank-sum test, W = 219, P =0.39), volumetric spherical biomass (Mann-Whitney- Wilcoxon rank-sum test, W = 235, P =0.52), and dry biomass (Mann-Whitney-Wilcoxon rank-sum test, W = 219.5, P =0.68).

a) b) 10 c)

2.0 ) ) ) 1.5 -3 -3 -3 8 1.5 1.0 6 1.0 4 0.5 0.5 2 Zooplankton biomass (g m Zooplankton biomass (g m

0.0 Zooplankton biomass (mg m 0.0 0 Feeding Not Feeding Not Feeding Not Feeding Feeding Feeding

Table 3.3 Median and range of zooplankton biomass per unit volume of water sampled during Manta birostris feeding and non-feeding events, based on wet (displacement volume, ZooScan volume) and dry (oven-drying) techniques. Sample Wet Biomass (g m-3) Dry Biomass (mg m-3) Displacement Volume ZooScan Volume Dried at 60°C Feeding 0.39 (0.18 – 1.66) 0.25 (0.01 – 2.1) 1.9 (0.6 – 8.1) Not Feeding 0.26 (0.03 – 1.1) 0.06 (0.003 – 1.4) 1.4 (0.3 – 9.6)

The length of time the sample was fixed in formalin had no effect on the percentage mass lost between wet biomass calculated from the displacement volume and dry biomass (Mann-Whitney-Wilcoxon rank-sum test, W = 707.5, P = 0.3), with samples collected in 2013 losing 93.6 % and samples from 2014 losing 94.0 % of their total mass after drying. There was also no significant difference for biomass lost between wet biomass determined from the ZooScan and dry samples between 2013 and 2014 (99.6 %, Mann-Whitney-Wilcoxon rank-sum test, W = 956.5 P = 0.3).

There were weak but significant positive correlations between zooplankton dry biomass and wet biomass (displacement volume) (F1,74 = 32.88, P <0.05, Fig 3.3a), zooplankton dry biomass and spherical wet biomass (calculated from ZooScan) (F1,82 = 39.29, P <0.05, Fig 3.3b), and zooplankton wet biomasses calculated between the displacement volume and ZooScan (F1,74 = 27.39, P <0.05) (Fig 3.3c).

a) 2.0 b) c) 1.5 y = 0.09x -0.03 1.5 -5

) y = 5.1e x + 0.30 y = 0.09x + 0.20 ) ) -3 -3 -3 r2 = 0.31 r2 = 0.32 r2 = 0.27 1.5 1.0 1.0 1.0

0.5 0.5 0.5 Zooplankton biomass (g m Zooplankton biomass (g m Zooplankton biomass (g m 0.0 0.0 0.0 0 2 4 6 8 10 0 2 4 6 8 10 0.0 0.5 1.0 1.5 2.0 Dry zooplankton biomass (mg m-3) Dry zooplankton biomass (mg m-3) Zooplankton biomass (g m-3)

The final GAM for wet zooplankton biomass calculated from the ZooScan explained 54.5 % of the total variance, with Month, Current Direction, Wind Speed and Time to Low Tide all significant predictors (Fig 3.4). Wet zooplankton biomass calculated from the ZooScan was significantly higher in August compared to September and October (P <0.05), and was negatively correlated with a northward current. There was also a positive linear correlation between Wind Speed and wet zooplankton biomass calculated from the ZooScan. Wet zooplankton biomass derived from the

ZooScan was non-linearly related to Time to low tide, with biomass peaking at ~2 hours before the low tide. There was no significant relationship between wet zooplankton biomass calculated from the Zooscan and Year, Temperature, Wind Direction or Current Strength (P >0.05). The final model for dry zooplankton biomass explained 20.8 % of the total variance, with Month being the only significant predictor (Fig 3.4). No significant temporal or environmental predictors were found for wet zooplankton biomass calculated from displacement volumes or for M. birostris feeding behaviour.

Wet (Zooscan) and dry zooplankton biomass per month corresponded with monthly climatology chlorophyll a concentration, with August having the highest amount of chlorophyll a (1.17 mg m-3), September having the lowest (0.28 mg m-3) and October having less than August, but more than September (0.43 mg m-3) (Fig 3.4 & S3.1).

Wet zooplankton biomass (ZooScan)

4 4 2 2 or Month f 0 0 tial

r −2 −2 a or Current Direction f P −4 −4 tial r

a N E S 8 9 10 W P NE SE SW NW None Month Current Direction

1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 −0.5 −0.5 −1.0 −1.0 s(Wind Speed, 1) −1.5 −1.5

4 6 8 10 12 14 s(Time to Low Tide, 2.77) −6 −4 −2 0 2 4 6 Wind Speed (knots s-1) Time to Low Tide (hours)

Dry zooplankton biomass

0.5

0.0 or Month f −0.5 tial r a

P −1.0

8 9 10 Month

Figure 3.4 Final generalised additive models for drivers of zooplankton dry and wet biomass. Zooplankton biomass (wet) used zooplankton biomass (g m-3) as the response and Current Direction, 43

Wind Speed and Time to Low Tide as predictors (r2 = 54.5 %, n = 63). The model for Zooplankton biomass (dry) used zooplankton biomass (mg m-3) as the response and Month as the predictor (r2 = 20.8 %, n = 84). Dashed lines represent 95 % confidence intervals.

3.3.2 Zooplankton biomass

Biomass increased in the majority of zooplankton size categories when M. birostris were feeding, albeit with large confidence intervals due to low sample sizes (Fig 3.5).

Figure 3.5 Mean size structure with 95 % shaded confidence intervals of in situ zooplankton collected at Isla de la Plata, Ecuador during times where Manta birostris were feeding or not feeding.

All samples were composed predominantly of small zooplankton of less than 3.9 mm in length (Fig. 3.5), with similar mean lengths between zooplankton collected during feeding (0.8 ± 0.3 mm) and non-feeding events (0.9 ± 0.4 mm) (Table 3.2). Mean length for zooplankton collected during M. birostris feeding events at Isla de la Plata was smaller than the mean size of zooplankton from M. alfredi foraging activity (1.1 mm, (Armstrong et al., 2016); 2.1 mm (Bennett et al., 2016)) or sergestid shrimps collected from R. typus (4.6 mm, (Rohner et al., 2015)) feeding events (Table 3.2).

Tows made during M. birostris feeding events at Isla de la Plata, and M. alfredi feeding events at Lady Elliot Island (LEI) in eastern Australia contained similar abundances (per m-3 of water) of small-

bodied zooplankton (<1 mm3), but the tow samples from LEI contained a higher biomass of larger- bodied zooplankton (>2 mm3) (Fig 3.6). There was a marked contrast between the zooplankton sampled during M. birostris surface feeding events and the material recovered as stomach contents from mobulids sampled in the Philippines, the latter of which contained much larger zooplankton in higher abundance than encountered at Isla de la Plata (Fig 3.7).

Figure 3.6 Mean size structure with 95 % shaded confidence intervals of Figure 3.7 Mean size structure with 95 % shaded confidence intervals of in situ zooplankton tows collected in at Isla de la Plata, Ecuador during zooplankton tows collected during Manta birostris feeding activity (this Manta birostris feeding events. Also included are the size structure of study) in comparison to stomach content particles from mobulid species zooplankton tows during feeding events for Manta alfredi (Armstrong et collected in the Philippines (Rohner et al., 2017). al., 2016) and Rhincodon typus (Rohner et al., 2015).

Table 3.4 ZooScan length measurements of zooplankton (mean ± s.d.) collected during Manta birostris feeding or non-feeding activity. Literature values from other planktivorous elasmobranchs feeding studies are also included and type of samples are designated as stomach contents (SC) or zooplankton tows (Tow). Species Behaviour n Type Zooplankton Length Reference (mm) Mean Range Manta birostris Feeding 5 Tow 0.8 ± 0.3 0.4 – 2.2 This study Not feeding 79 Tow 0.9 ± 0.4 0.3 – 3.9 This study Cetorhinus maximus Feeding 8 Tow 2 Sims and Quayle (1998) Manta alfredi Feeding 19 Tow 1.1 ± 1.2 0.3 – 10.8 Armstrong et al. (2016) Manta alfredi Feeding 1 SC 2.1 1 – 3.5 Bennett et al. (2016) Rhincodon typus Feeding Tow 4.4 ± 4.8 0.8 – 10.7 Motta et al. (2010) Rhincodon typus Not feeding Tow 4.1 ± 4.8 0.6 – 10.7 Motta et al. (2010) Rhincodon typus Feeding 10 Tow 4.6 ± 4.8 0.3 – 23.1 Rohner et al. (2015) Manta birostris Feeding 5 SC 3.5 ± 4.2 0.3 – 14.9 Rohner et al. (2017) Mobula tarapacana Feeding 2 SC 3.4 ± 4.3 0.3 – 15.1 Rohner et al. (2017) Mobula japanica Feeding 3 SC 3.4 ± 4.2 0.4 – 14.8 Rohner et al. (2017) Mobula thurstoni Feeding 2 SC 3.4 ± 3.7 0.4 – 15.4 Rohner et al. (2017)

3.3.3 Zooplankton community composition

There was no significant difference in zooplankton composition when M. birostris was feeding and when no feeding activity was observed (ANOSIM, R = -0.03, P = 0.71). Both feeding and non-feeding samples were dominated by calanoid copepods, with individuals from the order cyclopoida present in comparatively smaller amounts. Zooplankton in the ‘Others’ category included: harpacticoida copepods, barnacles, amphipods, decapod crustaceans, zoeae, fish larvae, tunicates, and pteropods (Fig 3.8).

Feeding Not feeding n = 5 n = 79

Chaetognaths Calanoids Cyclopoids Fish Eggs Others

Figure 3.8 Zooplankton community composition at Isla de la Plata, Ecuador in relation to when Manta birostris were feeding or not feeding.

3.4 Discussion

During the day at Isla de la Plata, Manta birostris was not commonly observed feeding. This is in stark contrast to reef manta ray (Dewar et al., 2008, Armstrong et al., 2016), whale shark (Rohner et al., 2015, Marcus et al., 2016) and basking shark (Sims, 1999) activity at aggregation sites in other oceanic regions, where high density food patches and subsequently foraging activity are commonly documented. On the rare occasions where M. birostris feeding events did occur at Isla de la Plata, zooplankton biomass was higher than when M. birostris were not feeding, which suggests that an increase in zooplankton biomass is an important trigger for feeding activity in this species. There was no difference in zooplankton particle composition or length between feeding and not feeding samples indicating that prey species selectivity does not influence surface foraging behaviour in M. birostris at Isla de la Plata. Additionally, more M. birostris individuals were sighted when feeding occurred in comparison to when feeding activity was not observed. This suggests that while foraging opportunities might not be the primary driver behind M. birostris aggregative behaviour at Isla de la Plata, the sporadic availability of high biomass zooplankton can influence the number of individuals present at this site.

3.4.1 Manta birostris feeding behaviour

The short temporal duration of feeding behaviour, as well as the low frequency at which it was recorded is likely reflective of low available zooplankton biomass at Isla de la Plata. Dry zooplankton biomass at Isla de la Plata during M. birostris feeding behaviour (1.9 mg m-3) was much lower than dry biomasses reported for active ram feeding by Manta alfredi in eastern Australia (19.1 mg m-3) and Rhincodon typus in Tanzania (25.2 mg m-3). Additionally, non-feeding background samples collected at Isla de la Plata (1.4 mg m-3) were lower than non-feeding background samples for M. alfredi (9.33 mg m-3) and R. typus (2.6 mg m-3) aggregation sites in eastern Australia and Tanzania, respectively. Mean wet zooplankton biomass calculated from displacement volumes during feeding behaviour in this study were also much lower (0.75 g m-3) than those determined for continuously ram feeding basking sharks in the temperate Atlantic (0.94 to 1.92 g m-3 (Sims and Quayle, 1998)) or whale sharks off the Yucatan Peninsula, Mexico (4.5 ± 0.6 g m-3 (Motta et al., 2010)).

The mode of feeding behaviour undertaken by large planktivorous elasmobranch species is likely fine-tuned with prey patch density and distribution (Papastamatiou et al., 2012). A large number of different feeding modes are employed among and within different sub-populations of Manta spp. worldwide, with the type of mode often thought to relate to the distribution and abundance of

zooplankton in the water column (Gadig and Neto, 2014). Although not observed in this current study, M. birostris has been seen to use ‘barrel roll’ feeding in surface waters at Isla de la Plata and at the Galapagos Islands (A. Marshall pers. comm., Fig 3.9), as well as at depth off Mexico (Stewart et al., 2016b). The repetitive barrel rolling in all of these instances appeared to occur in small dense patches of zooplankton. In comparison, M. alfredi at Lady Elliot Island in eastern Australia is frequently observed to actively ram feed in long feeding chains during times of high zooplankton biomass in surface waters over ~0.5 km2 (Weeks et al., 2015, Armstrong et al., 2016). A unique mode of cyclone feeding has also been documented in the Maldives for M. alfredi (Law, 2010), where many individuals swim in a circle creating a central vortex that concentrates zooplankton within a shallow lagoon. A variety of other circle swimming patterns in surface waters in shallow localities (<15 m) have also been documented for M. alfredi in the Red Sea (Gadig and Neto, 2014), and eastern Australia (Ludgate, 2012), and are thought to increase foraging efficiency by concentrating zooplankton centrally within a small sheltered area. In this study, only active short bursts of ram feeding, in which an individual swam through surface waters with an open mouth, were observed. Although this is likely demonstrative of increased zooplankton biomass over a scale intermediate to small concentrated patches during barrel rolling, and large patches where individuals continuously ram in long feeding chains, further studies are needed to validate this relationship between manta ray feeding mode and zooplankton biomass distribution.

Figure 3.9 Manta birostris barrel roll feeding on a dense patch of near surface zooplankton.

3.4.2 Zooplankton biomass calculations

The ability to identify robust ecological response variables during M. birostris feeding activity is vital to test predictions on drivers of foraging behaviour in this species. In this study, and other published work, biomass estimates form the basis of quantitative predictions of the distribution and abundance of zooplankton at planktivorous elasmobranch aggregation sites (Sims, 1999, Armstrong et al., 2016). However, the lack of standardised methodology in terms of net mesh size, and the reporting of different metrics for zooplankton abundance, such as wet volumetric or dry biomass, hinders cross- study comparisons. Dry biomass estimation of zooplankton is considered a more robust approach compared to estimation of wet biomass using the displacement volume method, as errors can occur due to the different interstitial water content of different taxonomic groups within a sample (Hernández-León and Montero, 2006). This issue can be mitigated by using image analysis software to calculate wet biomass, where biomass estimates are indicative of the body area of organisms calculated from spherical diameter. However, equating all particles to a spherical shape may again lead to an over-estimation of biomass. Therefore, in cases where wet biomass has been used to calculate critical foraging thresholds, and/or feeding sample quantities, possible over-estimations should always be taken into account. For example, in this study, a larger proportion of fish eggs in feeding samples may have resulted in an inflated wet biomass calculation given the high interstitial water content of fish eggs in comparison to crustacean zooplankton (Hernández-León and Montero, 2006). However, determining zooplankton biomass through volumetric means is sometimes the only option, with direct measurements of zooplankton typically requiring the destruction of the sample, which is appropriate when samples need to be preserved for future taxonomic or size spectrum analysis. Conversion factors for wet to dry biomass estimates can also be used, however, general conversion factors may be unable to account for taxonomic and size spectrum heterogeneity of zooplankton between samples (Alcaraz et al., 2003, Hernández-León and Montero, 2006). Future research could aim to report both wet and dry biomass, as well as a standardised metric, such as calorific values of surface zooplankton collected during in situ feeding events, which could aid comparisons between planktivorous elasmobranch studies.

3.4.3 Drivers of zooplankton abundance

While the lack of observed M. birostris feeding activity hindered the identification of the environmental drivers of foraging behaviour in this species, environmental drivers were still able to be identified for zooplankton biomass in this data-poor region. Higher zooplankton biomass, in all volumetric and gravimetric estimations, corresponded to higher chlorophyll a concentration at Isla de

la Plata, with August having the highest amount of chlorophyll a and also the highest zooplankton biomass, while September had the lowest chlorophyll a concentration and lowest zooplankton biomass. At Isla de la Plata, zooplankton wet biomass determined from the ZooScan also increased with faster wind speeds. The location of this site within the Humboldt coastal upwelling system means that it is subject to trade winds that exert a strong driving influence over upwelling and consequentially primary production (Paulik, 1971, Bakun and Weeks, 2008). However, wind direction was not a significant predictor, and wind speed was measured on an hourly scale to correspond with the time of the tow, so it is unlikely that this relationship can be explained by increased upwelling and primary productivity, given that enrichment from upwelling and subsequent increases in zooplankton concentrations occur over a larger spatial and longer temporal scale (González-Gil et al., 2015). High wind speeds, therefore, may instead influence zooplankton biomass via local scale mechanisms. Higher daily wind speeds cause more surface water mixing, and during these mixing events in shallow habitats, large increases in phytoplankton have been observed (Carrick et al., 1993), which is thought to be linked to the resuspension of sediment nutrients (Maceina and Soballe, 1990). Therefore, it is possible that increased zooplankton biomass during higher wind speeds is linked to fast increases in local-scale phytoplankton abundance resulting from increased vertical mixing of sediment nutrients at Isla de la Plata.

Total wet zooplankton biomass determined from the ZooScan also increased just before low tide at Isla de la Plata. This finding was similar to observed fluctuations in zooplankton biomass at Lady Elliot Island on the Great Barrier Reef, which also increase during the ebb to low tidal phase, likely through concentration and retention mechanisms (Armstrong et al., 2016). Unlike Lady Elliot, however, where high zooplankton biomass in the ebb to low tidal phase influenced M. alfredi feeding activity, higher wet zooplankton biomass at Isla de la Plata during this time did not appear to influence M. birostris foraging behaviour. Wet zooplankton biomass calculated from the ZooScan increased when ‘eastward’ and no current’ were recorded in comparison to all other current directions. During the study period in austral winter, coastal upwelling increases (Echevin et al., 2008), and an eastward current could theoretically bring nutrient rich waters towards Isla de la Plata from the continental shelf edge, causing a rapid increase in phytoplankton abundance, and subsequent zooplankton abundance. High wet zooplankton biomass during ‘no current’, however, is less clear, with possible mechanisms ranging from nutrient input and retention from island runoff (Perissinotto et al., 2000), to the island mass effect, which can lead to biological enhancement in the vicinity and in the wake of oceanic islands (Palacios, 2002). Further investigation of fine-scale currents around the island, coupled with information on bathymetry and phytoplankton abundance would be needed to further

elucidate mechanisms underlying the relationship between zooplankton abundance and the identified abiotic drivers thought to influence phytoplankton abundance at Isla de la Plata.

3.4.4 Zooplankton composition at Isla de la Plata

Prior to this study, there was little high resolution information on the composition of surface zooplankton prey of M. birostris, which has hindered insight into prey selectivity in this species. Samples of zooplankton collected during feeding and non-feeding events at Isla de la Plata comprised predominantly calanoid and cyclopoid copepods with a mean length of 0.8 – 0.9 mm. Zooplankton tows taken during M. alfredi feeding activity in Australia were also predominantly composed of calanoid and cyclopoid copepods, albeit with slightly larger mean lengths (1.1 mm) (Armstrong et al., 2016). The putative third species of manta ray (see Marshall et al. (2009)) feeds on fish eggs (Euthynnus alletteratus) during seasonal spawning events in the Yucatan Peninsula region off Mexico (Graham et al., 2012). The limited number of prey items identified to date are, however, unlikely to be representative of prey selectivity in Manta spp., but rather are probably an artefact of the low number of published feeding studies from aggregation sites for these species. Diets of other large planktivorous marine species in surface waters are better known, and comprise both macroscopic zooplankton and higher trophic level prey. Whale sharks have been observed to feed on sergestid shrimps, fish spawn, chaetognaths, calanoid copepods, brachyuran larvae and small ‘bait fish’ (Motta et al., 2010, Fox et al., 2013, Rohner et al., 2013a). Similarly varied dietary intake has also been found for a number of baleen whale species, individuals of which have been observed to feed on a variety of macroscopic zooplankton as well as many species of schooling fishes (Nemoto and Kawamura, 1977, Haug et al., 1995, Witteveen et al., 2008).

Feeding modes that use filter pads allow for the exploitation of a large number of macroscopic zooplankton and small fishes, time and place permitting. Therefore, prey selectivity in terms of species composition would be unlikely, and instead other factors such as overall biomass and energy content of prey patches are likely to be far more influential (Rohner et al., 2015). While the gross energy content of zooplankton collected at Isla de la Plata was not determined, there are literature values available for tropical euphausiids from other manta ray feeding grounds (19.4 KJ g-1; Rohner et al., 2017) and for tropical copepods from other oceanic regions (20.8 KJ g-1; Webber and Roff, 1995 ). If we assume that surface zooplankton at Isla de la Plata falls within these two estimations (20.1 KJ g-1), and convert this to Kcal for total dry biomass of zooplankton in feeding samples, waters around Isla de la Plata contain 9.1 Kcal m-3. In aquaria, an individual M. alfredi (disc width = 450 cm) is fed 6100 Kcal per day (Rohner et al., 2017), thus for a wild M. birostris to equal this amount 52

of daily consumption, an individual would have to filter 670 m-3. Misty Paig-Tran et al. (2013) proposed a filtration rate for M. birostris of 90 m-3 h-1, assuming an average swimming speed of 0.68 m s-1, a 90 % efficiency of fluid flow through the gill rakers, and a total mouth area of 419 cm2 (average mouth width =33 cm; Marshall et al., 2009). Therefore, over a day, M. birostris would have to continuously filter feed for over 7 hours at Isla de la Plata to equal the amount of calories that a captive M. alfredi sustains itself on. However, a 6100 Kcal per day ration in aquaria is unlikely reflective of feeding behaviour of wild animals, given that reliable food sources would be difficult to consistently find in spatially and temporally patchy tropical marine environments where manta rays are found (Bucklin et al., 2010). Manta birostris stomachs can hold the equivalent of 631167 Kcal (Rohner et al., 2017), and given this capacity, M. birostris would likely target prey patches that constitute swarming zooplankton species such as euphausiids, to obtain a high net energy gain energy within a short space of time. This form of ‘boom’ feeding behaviour would not be possible at Isla de la Plata given the consistently low biomass of zooplankton at this site during M. birostris aggregation periods.

High resolution dietary information from surface foraging events is important for the characterisation and identification of foraging grounds (López-Mendilaharsu et al., 2005). However, recent molecular evidence from this regional population of M. birostris in Ecuador suggests that surface zooplankton comprises ~27 % of M. birostris diet (Chapter 4, Part I). In addition, molecular profiles of M. alfredi and whale sharks also indicate that surface zooplankton is likely not the main source of dietary intake for these species (Couturier et al., 2013b). Therefore, high resolution information on surface feeding by tropical planktivorous elasmobranchs likely pertains to only a small proportion of overall diet for these species. As such, inferences on prey selectivity from these surface ‘snacking’ events may not be applicable to overall planktivorous elasmobranch diet, where suspected demersal or mesopelagic sources are considered more important.

3.4.5 Stomach contents analysis vs. in-situ tows

In situ sampling of planktivore foraging events and stomach contents analyses both provide high resolution dietary information, but both techniques have associated limitations. In situ sampling of observed feeding activity assumes that tows are collecting the same zooplankton particles that the filter feeding animal is ingesting, whereas stomach contents analysis is representative of ingested prey items, but with samples potentially subject to digestion bias (Cortés, 1997). The simultaneous application of these methodologies can help alleviate these limitations and reduce uncertainly in dietary studies. In addition, the simultaneous application could help identify differences in dietary 53

intake between surface waters and areas where animals cannot be observed. For M. alfredi from eastern Australia, calanoid copepods comprised the majority of in situ feeding samples and an historical stomach contents sample. The size of copepods from the stomach contents however, were highly skewed towards larger individuals when compared to copepods from the in situ tows and the larger Great Barrier Reef region (Armstrong et al., 2016, Bennett et al., 2016).

The maximum length of Euphausia diomedeae (14.9 mm) found in the stomach of M. birostris from the Philippines (Rohner et al., 2017), were ~7 times larger than the maximum length of zooplankton particles from in situ measurements of M. birostris feeding events in this study. During daylight hours, most euphausiids are found at mesopelagic depths, with some species then migrating into surface waters at night (Brinton, 1962). Few studies have the capacity to observe manta rays during the night, and acoustic telemetry results indicate that manta rays are not present at some aggregations sites during the hours of darkness (Dewar et al., 2008, Couturier, 2012). Therefore, feeding events on euphausiids or other organisms that are found both offshore and at depth have not been well documented for manta rays or any other large planktivorous elasmobranchs. Swarming behaviour of euphausiids, which would concentrate energy resources within a small area (Nicol, 1984), could indicate why, despite frequent observations of manta rays feeding on copepods (Graham et al., 2012, Armstrong et al., 2016), these epipelagic non-swarming crustaceans might not comprise the majority of manta ray diet.

3.4.6 Conclusions

Critical habitats are designated according to the data available, with the effectiveness of management depending on an understanding of how and why species use a certain habitat (Davidson and Dulvy, 2017). Food availability is often an assumed driver of aggregative behaviour, however, M. birostris were predominantly observed cleaning rather than feeding at Isla de la Plata. Off mainland Ecuador, sightings of M. birostris are affected by month, with increased sightings during August and fewer individuals seen in September (Chapter 2). However, while higher monthly zooplankton biomass in August corresponds to increased M. birostris sightings, there was no observed increase in M. birostris feeding activity at Isla de la Plata.

There has been little investigation into the temporal allocation of manta ray behaviours other than foraging, and how these are influenced by environmental drivers during aggregative behaviour. At Isla de la Plata during the day, individuals predominantly partake in cleaning interactions (Chapter 2), and sporadically forage when zooplankton biomass is high, but will not always forage during 54

times of high zooplankton biomass. During the night time, individuals appear to leave the sheltered waters of Isla de la Plata (Burgess and Marshall, 2015), and presumably search for more profitable prey patches over the more productive continental shelf habitat, which is ~40 km away and drops rapidly to depths <3000 m. Future studies could investigate spatial and temporal trends in zooplankton abundance, distribution and calorific content over this continental shelf habitat and quantify the benefits of cleaning behaviour at Isla de la Plata, given its suspected major role in aggregative behaviour of M. birostris at this site.

Chapter 4 (Part I)

Manta birostris, predator of the deep? Insight into the diet of the giant manta ray through stable isotope analysis

4.1 Introduction

Manta rays and other giant planktivorous elasmobranchs can provide high socio-economic benefits through ecotourism (O’Malley et al., 2013), and may also have an important ecological role as a concentrated food drop to the deep as carcasses (Higgs et al., 2014). Manta rays are large filter feeding elasmobranchs, but despite considerable study many aspects of their biology and ecology remain enigmatic. Dietary information for Manta species is based mostly on observational data, primarily gained from near-surface feeding events during daylight. Zooplankton collected by plankton tows at the time of these events has been assumed to represent the species’ diet. Non-lethal, minimally- invasive molecular methods, such as bulk stable isotope analysis (SIA), have proved useful in the examination of dietary intake of large, mobile and difficult-to-observe elasmobranch species (Carlisle et al., 2012, McMeans et al., 2013). The ratio of heavy to light isotopes of carbon (δ13C) and nitrogen (δ15N) can provide information on dietary sources (DeNiro and Epstein, 1978) and trophic position, respectively (DeNiro and Epstein, 1981). Carbon isotopes are transferred conservatively through the food web with initial source variations in the aquatic environment dependent on the extent of mixing of inorganic carbon. For example, considerable variability in δ13C exists between benthic and surface water marine algae, and consumers of benthic carbon sources are enriched in 13C compared to pelagic surface feeders (France, 1995). As 14N is lost more rapidly than 15N during the processes of metabolism and excretion, increasing values of δ15N are found as animals attain higher trophic positions (Fry, 2006).

For diet reconstructions using SIA data, Bayesian mixing models can be used to determine prey source contributions to the isotopic composition of a consumer tissue (Phillips, 2001). Proportional contributions of n+1 different sources, where n is the number of isotopes being measured in the study, can be measured using these mixing models (Phillips and Gregg, 2003). Mixing models can provide a mean solution of dietary inputs, along with minimum and maximum estimates, where the latter are sometimes the more robust output from the model (Benstead et al., 2006). While use of mixing models comes with considerable limitations, they provide the only way to glean quantitative/semi- quantitative dietary composition data from SIA values. Although conclusions about distinct dietary contributions from prey categories cannot occur without a priori knowledge of dietary habits for a given species, SIA is a useful approach particularly for species where stomach contents analysis (which can provide high resolution dietary information) may be inappropriate or may yield unrepresentative results due to differential prey residency times in the gut (Richardson et al., 2000).

Here, using SIA, we present information on the feeding ecology of M. birostris in the eastern equatorial Pacific along with novel insights into the origin of its main dietary sources.

4.2 Methods

Muscle tissue biopsies were collected from photographically-identified manta rays with a 5 mm diameter biopsy punch mounted on a hand-spear, while on SCUBA. Sampling was conducted at Isla de la Plata (1.2786° S, 81.0686° W) and Bajo Copé (1.81706° S, 81.06362° W), Ecuador, during July – October, 2012 – 2014. Zooplankton was collected with a plankton net (200 µm mesh, 50 cm diameter) using horizontal near-surface tows. All muscle tissue biopsies and zooplankton samples were placed on ice immediately after collection and stored at -18 °C until required for SIA.

Muscle samples were soaked in deionised water for 24 – 48 h to remove urea (Burgess and Bennett, 2016). Manta ray muscle tissue and zooplankton samples were dried at 50 – 60 °C for 24 – 48 h and then each was homogenised. A known mass (≈1.5 mg) of each sample was weighed, placed in a tin capsule and pelletized. Samples were analysed for δ13C and δ15N using an isotope ratio mass spectrometer (Hydra 20-22; Sercon Ltd., UK) coupled with an elemental analyser (Europa EA-GSL; Sercon Ltd., UK).

Stable isotope ratios were measured relative to two internationally recognised standards; Vienna Pee Dee Belemnite limestone for C13/C12 and atmospheric air for N15/N14 (Peterson and Fry, 1987). Two additional internal standards of ammonium sulphate and sucrose were used in each run. Results are expressed in delta (δ) notation in parts per thousand (‰) as follows:

δH X (‰) = ((Rsample/Rstandard) - 1) x 1000

Where X is the element, H denotes the heavy isotope mass number, and R is the ratio of heavy to light isotopes. Temporal, inter-specific and intra-specific differences in bulk δ13C and δ15N values for M. birostris and surface zooplankton were assessed using two-way ANOVAs with a type I error rate of a=0.05. Throughout, results are presented as mean and standard deviation unless otherwise stated.

Lipid removal was deemed unnecessary given the majority of Manta birostris C:N ratios were <3.5 (Post et al., 2007). The zooplankton C:N ratio was 4.3 ± 0.5, thus δ13C values were normalised using an arithmetic correction for zooplankton lipids:

13 13 δ CLN = δ CBULK + 7.95 ((C:NBULK – 3.8)/C:NBULK) (Syväranta and Rautio, 2010)

13 13 Where LN is the δ C value after lipid normalisation and BULK is the non-normalised δ C or C:N value. Relative trophic positions using M. birostris isotopic data were calculated using:

15 15 TLSIA= ((δ Nconsumer - δ Nprimary)/DTDF) + 2.5 (Post, 2002)

15 Where TLSIA is the relative trophic level, δ Nconsumer is the average isotopic value for M. birostris 15 tissue. To account for spatial and temporal heterogeneity in baseline values, the δ Nprimary used (7 ‰) was the average δ15N value of surface zooplankton that was collected at Isla de la Plata during 2013-2014 and mesopelagic fish species collected from the North Pacific Subtropical Gyre (NPSG) in 2009 – 2011 (Choy et al., 2015). An integer value of 2.5 was used, as surface zooplankton tows and mesopelagic fish species comprised a mixture of primary (TL=2) and secondary consumers 15 (TL=3). Estimates of TLSIA are sensitive to assumptions about the trophic fractionation of δ N. 15 Therefore, two TLSIA estimates were generated for M. birostris using elasmobranch specific δ N DTDFs: 2.3 ‰ (Hussey et al., 2010) and 3.7 ‰ (Kim et al., 2012a).

A Bayesian mass-balance mixing model assessed the contribution of different sources to the diet of M. birostris in the R package ‘simmr’(Parnell et al., 2013, R Development Core Team, 2017). Bayesian inference was used to address natural variation and uncertainty of stable isotope data to generate probability distributions of source contributions as percentages of total diet. Source, consumer and trophic enrichment factor variability was incorporated into the model. Co-occurring turtles, yellowfin tuna and thresher sharks were not included in the mixing model as the number of source contributions needed to assess the diet of all of these species as well M. birostris would have surpassed the number of isotopes + 1.

There are no demersal, benthic or deep-sea bulk stable isotope values available for zooplankton from coastal Ecuador and, unfortunately, due to logistical constraints we could not sample mesopelagic zooplankton from the region. Instead, sources for all mixing models were constrained to surface zooplankton from Isla de la Plata and assumed representative of mesopelagic sources from other studies. There is strong isotopic similarity between mesopelagic zooplankton and mesopelagic fishes (Valls et al., 2014), therefore, small mesopelagic fish (Cyclothone alba (n = 3), Cyema atrum (n = 3) and Hygophum proximum (n = 5)) from the NPSG with equivalent trophic positions to primary and secondary copepod consumers (2.1 – 2.9 (Hannides et al., 2009)) were used as a representative offshore mesopelagic food source. Overall mean δ13C. and δ15N values from small mesopelagic fish 59

species (C. alba, C. atrum and H. proximum) were -17.6 ± 0.8 ‰ and 6.2 ± 1.5 ‰ respectively (Choy et al., 2015). Surface zooplankton were from Isla de la Plata, coastal Ecuador (n =35 net hauls) and had non normalised lipid values for δ13C and δ15N of -20.5 ± 0.6 ‰ and 7.8 ± 1 ‰, respectively. The lipid normalised value for surface zooplankton δ13C was -19.7 ± 1 ‰. There are no experimentally determined diet tissue discrimination factors for manta rays or other large planktivorous elasmobranch species. Therefore, separate mixing models were run incorporating experimentally determined DTDFs from other elasmobranch species: Triakis semifasciata (1.7 ± 0.5 for ∆13C and 3.7 ± 0.4 for ∆15N (Kim et al., 2012a)) and large pelagic sharks Carcharias taurus and Negaprion brevirostris (0.9 ± 0.33 for 13C and 2.29 ± 0.22 for 15N (Hussey et al., 2010)). To account for the uncertainty in appropriate DTDF values and lipid normalisation of surface zooplankton δ13C four separate mixing models were run. Model 1 source inputs comprised mesopelagic fishes and lipid normalised surface zooplankton δ13C values along with DTDFs from large sharks (Hussey et al., 2010). Model 2 source inputs were mesopelagic fishes and non-lipid normalised surface zooplankton δ13C with the large shark DTDF (Hussey et al., 2010). Models 3 and 4 comprised the same source inputs as model 1 and 2 respectively, but used DTDF values from T. semifasciata (Kim et al., 2012a). To determine an overall estimate of the mean contribution to the diet of M. birostris from mesopelagic and surface sources, the mean source contribution for surface and mesopelagic prey from the four mixing models was averaged.

For inferences on species-interactions and the structure of communities using molecular analyses, it is helpful to place the focus species into context with other co-occurring species (West et al., 2006). The isotopic niche can be a powerful way to investigate the ecological niche of an animal because its chemical composition is influenced by what it consumes (Newsome et al., 2007). The isotopic niche structure of other vertebrates that seasonally co-occur with M. birostris in the eastern tropical Pacific Ocean and for which isotopic information is available was compared. This was done within a Bayesian framework using 95 % credible intervals between species groups and stable isotope Bayesian ellipses in R package ‘SIBER’ (Jackson et al., 2011). These vertebrates included marine turtles (olive ridley Lepidochelys olivacea, green Chelonia mydas and loggerhead Caretta caretta (Kelez Sara, 2011)), yellowfin tuna Thunnus albacares (Olson et al., 2010), and the pelagic thresher shark Alopias pelagicus (Polo-Silva et al., 2013).

4.3 Results

For Manta birostris muscle tissue mean δ13C and δ15N values were -16.8 ± 1.1 ‰ and 10.6 ± 1.5 ‰ respectively. Male (n = 45) and female (n = 30) δ13C values were indistinguishable from each other 60

15 and across sampling years (2-way ANOVA: F3,62 = 1.924; P = 0.13) (Table 4.1). δ N values were not affected by sex (P = 0.35) but significantly differed between biopsies taken in 2014 and 2012 and between 2014 and 2013 (2-way ANOVA: F3,62 = 16.27; P <0.05) (Tukey HSD, P <0.05). The average trophic position estimates for Manta birostris elasmobranch specific δ15N DTDFs of 3.7 ‰ and 2.3 ‰ was 3.4 (range 2.5 – 4.6) and 3.7 (range 2.3 – 5.6), respectively. Table 4.1 Mean (± s.d.) δ 13C and δ 15N values for Manta birostris Sample n C:N ± s.d. δ 13C ± s.d. δ 15N ± s.d. Manta birostris (Ecuador) 75 3.3 ± 0.3 -16.8 ± 1.1 10.6 ± 1.5 Male 45 3.3 ± 0.3 -17.0 ± 1.1 10.7 ± 1.3 Female 30 3.3 ± 0.4 -16.6 ± 1.1 10.5 ± 1.7 2012 26 3.1 ± 0.2 -16.4 ± 1.2 11.2 ± 1.2 2013 27 3.1 ± 0.3 -17.1 ± 1.1 11.4 ± 1.1 2014 22 3.5 ± 0.3 -16.8 ± 0.9 9.3 ± 1.0

There was no difference in the isotopic composition of surface zooplankton when manta rays were feeding (n = 4) or not feeding (n = 31) (δ13C, P = 0.24) (δ15N, P = 0.98). As was the case for M. birostris, surface zooplankton isotopic composition differed among sampling years (δ13C, 2-way 15 ANOVA: F2,32 = 16.38; P <0.05) (δ N, 2-way ANOVA: F2,32 = 14.45; P <0.05).

Manta birostri s 16 Surface Zooplankton Chelonia mydas Lepidochelys olivacea 14 Caretta caretta Alopias pelagicus Thunnus albacares (‰) 12 Mesopelagic sources N 5 1 δ 10

4 1 2 -21 -19 -17 -15 δ13 3 C (‰)

Figure 4.1 Mean δ15N and δ13C values for Manta birostris, surface zooplankton (δ13C lipid normalised), mesopelagic sources (Choy et al., 2015), and co-occurring turtle species (Chelonia

mydas, Lepidochelys olivacea, Caretta caretta) (Kelez Sara, 2011), thresher shark Alopias pelagicus (Polo-Silva et al., 2013), and yellowfin tuna Thunnus albacares (Olson et al., 2010) from Ecuador and the broader eastern equatorial Pacific region. Error bars represent standard deviation.

Average enrichment between M. birostris and surface zooplankton (lipid normalised) sampled off mainland Ecuador was 2.9 ‰ and 2.8 ‰ for δ13C and δ15N values, respectively (Fig. 4.1). When surface zooplankton δ13C was not normalised the enrichment between zooplankton and M. birostris δ13C was 3.7 ‰. There was a high degree of overlap in isotopic niche space between M. birostris and other co-occurring vertebrates from the eastern equatorial Pacific (Fig 4.2).

20 Manta birostris Surface Zooplankton Chelonia mydas Lepidochelys olivacea Caretta caretta Alopias pelagicus 15 Thunnus albacares ‰) N ( 15 δ 10

−26 −24 −22 −20 −18 −16 −14 −12

δ13C (‰)

From the four mixing models generated, surface zooplankton and mesopelagic sources were found on average to contribute 27 % and 73 % to the diet of M. birostris, respectively (Table 4.2). All 62

mixing models found mesopelagic sources to generate a majority contribution in comparison to surface zooplankton to the diet of M. birostris (Fig 4.3, Table S4.1). The highest estimated source contribution for surface zooplankton to the diet of M. birostris was 43 %, which was still lower than the most conservative estimate for mesopelagic source contribution (57 %) (Model 3). Model 3, which used an elasmobranch specific δ15N DTDF of 3.7 ‰ and lipid normalised surface zooplankton δ13C values, had the highest credible interval overlap of dietary contributions from mesopelagic sources (45 – 64 %) and surface zooplankton (32 – 57 %) (Fig 4.3, Table S4.1).

Table 4.2 Mean (± s.d.) source contributions of surface zooplankton and mesopelagic sources to Manta birostris diet from four mixing models. Model 1 source inputs comprised of mesopelagic fishes and lipid normalised surface zooplankton δ13C values and used DTDFs from large sharks (Hussey et al., 2010). Model 2 source inputs were mesopelagic fishes and non-lipid normalised surface zooplankton δ13C with the large shark DTDF (Hussey et al., 2010). Models 3 and 4 comprised the same source inputs as model 1 and 2 respectively, but used DTDF values from Triakis semifasciata (Kim et al., 2012a). Also shown is the mean source contribution calculated from all 4 mixing models. Source Model 1 Model 2 Model 3 Model 4 Mean Surface Zooplankton 0.21 ± 0.07 0.12 ± 0.04 0.43 ± 0.06 0.33 ± 0.04 0.27 ± 0.14 Mesopelagic Sources 0.79 ± 0.07 0.88 ± 0.04 0.57 ± 0.06 0.68 ± 0.04 0.73 ± 0.14 s.d. δ13C 0.87 ± 0.17 0.79 ± 0.16 0.80 ± 0.13 0.84 ± 0.13 0.82 ± 0.04 s.d. δ15N 2.0 ± 0.25 2.05 ± 0.27 1.06 ± 0.19 0.95 ± 0.22 1.51 ± 0.58

4.4 Discussion

4.4.1 Manta ray feeding ecology

Large differences in δ13C and δ15N values indicate there is a broad range of dietary intake and habitats occupied by individual Manta birostris off mainland Ecuador. Here, average M. birostris δ13C values were not consistent with this species feeding predominantly on surface zooplankton and suggest a larger reliance on mesopelagic food sources. Our SIA results placed M. birostris at a relative trophic position similar to that of other mobulids; Mobula mobular (3.6) (Borrell et al., 2010), Mobula thurstoni (3.3) (Jacobsen and Bennett, 2013) and M. alfredi (3) (Couturier et al., 2013a), confirming that they are all at least secondary consumers. There are no quantitative estimates of dietary composition for M. birostris and difficulties exist in assigning pre-2009 observations of manta ray feeding to the species involved due to the taxonomic revision of the genus Manta in 2009 (Marshall et al., 2009). Current knowledge of manta ray diet is based on accounts of feeding on a variety of abundant surface zooplankton by both recognised species, a small number of studies using molecular analyses of tissue samples and a description of preserved stomach contents from an individual M. alfredi collected in 1935 (Graham et al., 2012, Couturier et al., 2013a, Bennett et al., 2016, Stewart et al., 2016a).

There was large within-population isotopic niche variability of M. birostris δ13C values, which likely reflects available prey δ13C composition within occupied habitats. Planktivory as a feeding strategy has evolved independently in many vertebrate groups including; whales (Marx and Uhen, 2010), sharks and teleosts (Friedman et al., 2010). With adaptive radiation of planktivorous megafauna 64

species during the cenzoic era concurrent with changes in global climate that included increased productivity along with amplified patchiness of marine systems (Pyenson and Vermeij, 2016). While M. birostris is considered a generalist carnivore, large differences in δ13C occur among individuals. This could be indicative of individual specialists within this subpopulation, which would facilitate a reduction in con-specific competition for resources within a patchy oceanic environment.

4.4.2 δ13C enriched food source origin

Average enrichment of δ13C between M. birostris and surface zooplankton was ~3 times higher than literature values for mixed fish species (0.4 ± 1.3 ‰ (mean ± s.d.);(Post, 2002)) and large sharks (0.9 ± 0.3 ‰, (Hussey et al., 2010)). Similar results were found in a separate study for M. alfredi, which also had enriched δ13C values compared to its presumed surface zooplankton prey (Couturier et al., 2013a). Surface zooplankton has been considered a primary food source for manta rays, based on numerous observations of foraging behaviour at the surface during daylight hours (Couturier et al., 2011, Couturier et al., 2012). However, all mixing models estimated that mesopelagic sources comprised the majority of dietary intake for M. birostris. Electronic tag studies on M. birostris, M. alfredi and closely related Mobula tarapacana have shown that these rays, despite being predominantly surface dwellers, dive to depths of ~1400 m (A. Marshall, unpubl. data), 432 m and 2000 m respectively (Braun et al., 2014, Thorrold et al., 2014). The dive-profiles suggest that all of these species forage at depth in the deep scattering layers. In addition, video of M. birostris taken at depth with a submersible vehicle confirms that individuals forage on mesopelagic sources in the Mexican east Pacific region (Stewart et al., 2016b). The results from the mixing model in the current study are consistent with the idea that this submersible footage may be indicative of a common event.

Large overlap in isotopic niche between M. birostris and all other marine vertebrates sampled from the eastern equatorial Pacific was found, and although isotopic niche is not the same as ecological niche, it can be used to infer characteristics of community structure and niche breadth of community members (Jackson et al., 2011). Apart from the loggerhead turtle, individuals of which transverse the entirety of the tropical Pacific in their lifetime (Bowen et al., 1995), M. birostris has the broadest isotopic niche compared to other co-occurring marine vertebrates examined in the eastern equatorial Pacific Ocean. Additionally, expected enrichments in ∆13C and ∆15N for large shark species (Hussey et al., 2010) occurred between M. birostris, a secondary consumer, and the thresher shark; a tertiary consumer that typically inhabits the upper regions of the water column but obtains the majority of its diet from the mesopelagic environment (Polo-Silva et al., 2013). These expected enrichments indicate that both of these elasmobranch species are feeding within the same mesopelagic food web. 65

Mesopelagic prey occurs in much cooler temperatures than those in surface waters. It is thus expected that an ectotherm, such as manta ray, would exhibit compensatory behaviour for body heat loss after foraging on those prey in cold waters. Studies on large ectothermic planktivores such as the sunfish and the whale shark showed behavioural indications of deep feeding, with time spent within mesopelagic depth involving body temperature decrease always followed by a recovery time in warmer surface waters (Thums et al., 2012, Nakamura et al., 2015). Manta rays are commonly seen in surface waters or cleaning in shallow coral reef habitats typically in tropical or sub-tropical regions (Couturier et al., 2012, Rohner et al., 2013b). Off mainland Ecuador, M. birostris aggregate around cleaning stations at Isla de la Plata, which is situated <40 km from a continental shelf edge that descends to ~3000 m (Dumont et al., 2014). It is possible that a driver behind aggregative behaviour of M. birostris at this site relates to individuals undergoing thermal recovery in warm surface waters after foraging at depth nearby.

4.4.3 Variability in isotopic baselines

Inter-annual variability in SI-values (13C and 15N) was found for both manta rays and surface zooplankton, with a large range between individual M. birostris δ15N values (7.3 ‰). Natural variation in consumer δ15N can be a consequence of a change in dominant primary producers at the base of a food web (Lorrain et al., 2015). Such baseline shifts can influence food-chain length, which affects relative trophic position estimates for wide-ranging predators that forage in isotopically different oceanic regions (Young et al., 2015). Yellowfin tuna occurring at higher latitudes had higher δ15N values, which is consistent with δ15N values spatial variation of particulate organic matter within the eastern equatorial Pacific region (Lorrain et al., 2015). Therefore, it is possible that high δ15N values of some M. birostris reflect large home ranges that comprise Ecuadorian, and higher latitude waters that are characterised by higher primary producer and subsequent zooplankton δ15N values (Lorrain et al., 2015). However, these recorded baseline changes occurred in surface waters, and surface zooplankton are suspected not to comprise a majority of dietary intake for M. birostris in this region. It is currently unknown if vertical baseline changes at depth are similar to or coincide with horizontal baseline changes in surface waters in the Eastern equatorial Pacific.

While primary producer baseline changes likely account for some of the variation in δ15N values and trophic position of M. birostris, the targeting of higher trophic level prey both in surface waters and at depth may also be a factor in high δ15N values seen in this study. Manta birostris has distinctive filtering pads and this species could capture and consume relatively large prey via a dead-end sieving mechanism (Sibanda et al., 2001, Misty Paig-Tran and Summers, 2014). While the diets of Manta 66

spp. are poorly characterised, there are reports of individuals feeding on zooplankton and small to moderate sized fish (Compagno and Last, 1999); however it is uncertain to which species these observations relate. Diets of other large marine planktivores are better known and typically comprise both macroscopic zooplankton and higher trophic level prey. Whale sharks have been observed to feed on mysid and sergestid shrimps and ‘bait fish’ when in continuous ram-feeding mode, which is analogous to manta ray feeding mode (Fox et al., 2013, Rohner et al., 2013a). Additionally, baleen whales (fin Balaenoptera physalus, common minke Balaenoptera acutorostrata and humpback Megaptera novaeangliae) eat a variety of macroscopic zooplankton along with many species of schooling fishes (Nemoto and Kawamura, 1977, Haug et al., 1995, Witteveen et al., 2015). To differentiate between the various mechanisms contributing to isotopic variation in M. birostris in the eastern tropical Pacific, more data are needed on the isotopic composition of available prey across larger horizontal and vertical areas where individuals are likely to feed.

4.4.4 Limitations

A potential limitation of this study is the assumption that mesopelagic isotope values from the NPSG are representative of mesopelagic sources, offshore of mainland Ecuador and the broader Eastern Equatorial Pacific region, which M. birostris of this sub-population feeds. There might be expected differences in isotopic values as the NPSG is less productive than the coastal upwelling Eastern Boundary Current (EBC) system off Ecuador, where a shallow thermocline facilitates enhanced nutrient supply (Chavez et al., 2003). Small average differences in δ13C (0.7 ‰) were been found between mesopelagic fishes collected in the NPSG and another EBC (California), with larger differences occurring in δ15N (8.5 ‰) (Choy et al., 2012). However, different species were collected between these two sites and there were considerable differences in maximum sizes of mesopelagic fish sampled from California (490 mm) compared to those collected from the NSPG (277 mm). Additionally, mean bulk trophic position estimates for mesopelagic fish collected from the NSPG (2.2) were much lower those for California (3.8) and this could have attributed to the overserved large difference in δ15N values between the two regions (Choy et al., 2012). In another study, there was no predictable difference in δ15N values of northern fur seals that forage inshore (California) or offshore (140–180° W, NPSG), however, northern fur seals are not mesopelagic feeders (Graham et al., 2010). There is no current consensus whether isotopic values would differ between mesopelagic sources in the NSPG and off mainland Ecuador given that there is no data for the latter.

4.4.5 Future Work

While stomach contents analysis (SCA) can provide good quantitative descriptions of diet (Bennett et al., 2016), in the case of manta rays (listed as Vulnerable on the IUCN’s Red List), such approaches should be restricted to situations where rays have suffered natural mortality or have been landed in commercial and artisanal fisheries. In such circumstances, SCA should be used to help validate and test the findings of SIA. However, the low resolution of bulk SIA precludes detailed dietary assessment from this technique alone, and critical values such as isotopic incorporation rate and diet tissue discrimination factors need to be determined experimentally for planktivorous elasmobranchs to aid interpretation of SIA results. In addition, better molecular characterisation of potential prey sources, such as mesopelagic and demersal zooplankton, over various spatio-temporal scales would assist in interpretations of unexpected molecular profiles of consumers. However, collecting information on low- and mid-trophic prey communities is challenging and current direct sampling methods are not adequate to provide enough representative samples. Instead, we can use suitable values gleaned from the literature and focus on marine top predators to monitor the health of pelagic food webs as their feeding ecologies and spatial distributions provide a direct insight into food web dynamics, oceanic productivity and critical megafauna habitats (Graham et al., 2010).

4.5 Conclusions

This study, along with others, suggests that manta rays and other large planktivorous elasmobranchs that live in these low latitude patchy marine systems, need to be energetically subsidized by mesopelagic resources (Rohner et al., 2013a). The mesopelagic zone is the next frontier for open ocean fisheries (Haedrich et al., 2001), and it is concerning that we still do not fully understand the reliance on this zone by marine megafauna that already face threats in well characterised surface habitats (Rohner et al., 2013b).

Chapter 4 (Part II)

The use of epidermal mucus in elasmobranch stable isotope studies: a pilot study using the giant manta ray Manta birostris

4.6 Introduction

Non-lethal and minimally-invasive molecular methods are useful ecological tools in the examination of diet and movement of large, highly-mobile and difficult-to-observe marine species (Carlisle et al., 2012, McMeans et al., 2013). Determination of the ratio of heavy to light isotopes of carbon and nitrogen through stable isotope analysis (SIA) can provide information on dietary sources [δ13C (‰), DeNiro and Epstein (1978)] and the relative trophic position at which an organism feeds [δ15N (‰), DeNiro and Epstein (1981)]. This approach has been used successfully to examine aspects of the biology and ecology of several planktivorous elasmobranch species based on muscle tissue (Borrell et al., 2010, Couturier et al., 2013a).

Tissues can exhibit different diet-tissue discrimination values and turnover rates due to molecular composition and isotopic routing (Brenna, 2001, McCutchan et al., 2003, Kim et al., 2012a). Isotopic incorporation rate and protein turnover of a tissue are linked via synthesis and catabolism processes and evidence suggests the rate of protein turnover affects isotopic incorporation times in different tissues (Carleton and Del Rio, 2005). Structural components such as muscle and cartilage have slower rates of isotopic incorporation in comparison to more metabolically active visceral components (e.g. liver and plasma proteins) (Martínez del Rio et al., 2009, Buchheister and Latour, 2010, Heady and Moore, 2013).

Tissues with relatively slow turnover rates provide an integrated (‘time-averaged’) signal of overall dietary intake, whereas faster turnover rate tissues have the potential to identify recent resource switches. Changes in feeding habits occur, with seasonal and ontogenetic shifts in resource-use being primary drivers behind differences in prey selection (Wetherbee et al., 2012). Stable isotope analysis studies that adopt a multiple tissue approach have the potential to reconstruct a wide temporal representation of dietary profiles due to these different tissue turnover rates (MacNeil et al., 2006). In teleost fishes, birds and elasmobranchs, blood plasma and liver tissue have faster turnover rates than skeletal muscle tissue (MacNeil et al., 2006, Bauchinger and McWilliams, 2009, Kim et al., 2012b, Heady and Moore, 2013).

Internal tissues that have fast turnover rates are difficult or impossible to obtain from free-living elasmobranchs as opposed to skin and muscle tissue, which is relatively easy to biopsy (Couturier et al., 2013a, Couturier et al., 2013b). However, muscle tissue in elasmobranchs can take over a year to reach isotopic equilibrium (MacNeil et al., 2006), and biopsied tissue will be representative of ecological interactions at the sampling site only if the study organism has fed there for this entire

period of time. Migratory animals with relatively large home ranges feed across multiple regions and isoscapes (Olson et al., 2010, Carlisle et al., 2012). Foraging behaviour at the time of sampling is an important occurrence to document, however, if only a slow turnover tissue (e.g. muscle) is sampled, then this observed activity and any other short-lived changes in diet will not be represented in this tissue type.

For planktivorous elasmobranchs, foraging activity is commonly seen in inshore surface waters, but stomach contents analysis and results from molecular analyses of slow turnover muscle tissue indicate that inshore surface zooplankton is probably not the primary source of dietary intake for these species (Couturier et al., 2013a, Couturier et al., 2013b, Bennett et al., 2016, Marcus et al., 2016). Manta birostris is the largest filter feeding ray species and is listed as Vulnerable to extinction on the IUCN’s Red List (Marshall et al., 2011a). To date, characterisation of M. birostris diet has been limited due to practical difficulties in both accessing aggregation sites and examining stomach contents from this species.

At present, there is no validated fast-turnover tissue in elasmobranch SIA studies that can be collected without invasive sampling. Epidermal mucus serves as a barrier against infection or injury, reduces friction with water and plays a role in osmotic regulation (Shephard, 1981, Harnish et al., 2011). Furthermore, mucus collected from free-swimming manta rays and basking sharks, is suitable for PCR-based genetic studies (Lieber et al., 2013, Kashiwagi et al., 2015) and controlled feeding studies show that teleost fish mucus is a reliable fast turnover ‘tissue’ for dietary studies using SIA (Church et al., 2009, Heady and Moore, 2013). However, the viability of mucus in elasmobranch SIA studies is currently unknown.

The largest identified population of giant mantas seasonally aggregate off mainland Ecuador around inshore islands and seamounts. In this region, M. birostris spends the majority of its time offshore (A. Marshall unpubl. data), and drivers of movements inshore are not yet known. In the current study, the first aim was to test the viability of mucus in a dietary SIA study. We then examined isotopic profiles, gained from SIA of epidermal mucus and muscle tissue, to explore the short-term and long- term dietary intake of M. birostris at Isla de la Plata, Ecuador.

4.7 Materials and methods

Mucus samples were collected from 24 individual Manta birostris at the Isla de la Plata, Ecuador (1°15 29.62 S, 81°4 25.96 W) in August – October, 2013 and August – September, 2014. Samples 71

were obtained while using SCUBA, by brushing the dorsal surface of individual rays in a circular motion with a clean toothbrush. Once the bristles were coated with mucus, the brush was placed in a 50 mL tube and capped to prevent cross-contamination or loss of the sample. Using a hand spear with a modified biopsy punch, a small muscle sample from 18 of the 24 rays sampled for mucus was also taken. Mucus and muscle biopsies were placed on ice after collection, and each subsequently transferred into a 1.5 mL Eppendorf tube prior to storage at −18 °C until required for SIA. Primary consumers (zooplankton) were collected every 2 − 3 days using horizontal near-surface tows (200 µm mesh plankton net of 50 cm diameter) from Isla de la Plata in August−October, 2013 (n = 19) and August−September, 2014 (n = 16) during M. birostris feeding and non-feeding activity.

Manta ray muscle samples were placed in distilled water for 24 – 48 h to remove urea (Burgess and Bennett, 2016). Mucus was examined underneath a stereo-microscope to screen for any unwanted phyto or zooplankton particles. Manta ray muscle, mucus and zooplankton samples were then air- dried at 50 – 60 °C for 24 – 48 h and each was then separately homogenised. A known mass (~1.5 mg) of muscle, mucus or zooplankton was weighed, placed in a tin capsule and pelletized. Samples were then combusted and separated chromatographically in an elemental analyser (Europa EA-GSL, Sercon Ltd; Crewe, UK) attached to an isotope-ratio mass spectrometer (Hydra 20-22, Sercon Ltd). Stable isotope ratios were measured relative to internationally recognised standards; potassium hydrogen phthalate for C13/C12 and atmospheric air for N15/N14. Additional internal standards of ammonium sulphate (IAEA-N1 and IAEA-N2) and sucrose (IAEA-CH-6) were also used in each run. Results are expressed in delta (δ) notation in parts per thousand (‰) as follows:

δH X (‰) = ((Rsample/Rstandard)-1) x 1000

Where X is the element, H denotes the heavy isotope mass number, and R is the ratio of heavy to light isotopes. Results are presented as mean and standard deviation unless otherwise stated.

Differences in bulk δ13C and δ15N values between M. birostris muscle and epidermal mucus were assessed using paired t-tests when both tissues were available for an individual. High C:N ratios are often used as justification for mathematical correction of δ13C values as higher carbon content in lipids (dominated by light 12C) could bias results (Post et al., 2007). Where C:N ratios were >3.5, δ13C values were normalised for M. birostris muscle tissue and surface zooplankton using the relevant equations:

13 13 Fish δ CLE = δ CBULK – 3.32 + 0.99 (C:NBULK) (Post et al., 2007) 72

13 13 Zooplankton δ CLE = δ CBULK + 7.95 ((C:NBULK – 3.8)/C:NBULK) (Syväranta and Rautio, 2010)

13 13 Where LE is the lipid extracted δ C value and BULK is the non-normalised δ C or C:N value. As no arithmetic correction exists for fish mucus that exhibits a C:N ratio >3.5, results for this secretory product are reported in a non-normalised form.

To evaluate the contribution of surface and mesopelagic sources to the isotopic profile of M. birostris mucus and muscle tissue, expected isotopic profiles of prey were plotted against mean M. birostris epidermal mucus and muscle values. Diet tissue discrimination factors (DTDFs) have not been determined for manta rays, and mucus use in elasmobranch dietary studies using SIA has not been explored previously. Therefore, for calculating expected prey values, appropriate DTDFs were sourced from the literature. For M. birostris muscle samples, DTDFs from large shark species were used [∆13C = 1.9 ± 0.7 and ∆15N = 2.29 ± 0.22 (Hussey et al., 2010)] and DTDFs for teleost epidermal mucus were used for M. birostris epidermal mucus [∆13C = -0.6 ± 0.4 and ∆15N = 0.9 ± 0.33 (Church et al., 2009)]. These enrichment factors were deducted from individual M. birostris mucus and muscle values and the resulting expected prey value was then averaged. Differences in expected prey from M. birostris muscle and mucus was determined with an ANOVA using R statistical software v0.98.953 (R Development Core Team, 2017).

4.8 Results and discussion

This is the first time that paired, muscle and epidermal mucus samples from individual elasmobranchs (manta rays) have been compared isotopically to explore their viability for use in short-term and long- term dietary intake studies. Pair-wise comparisons revealed significant differences between Manta birostris epidermal mucus and muscle for δ13C (t-test, P < 0.05), but not for δ15N (t-test, P = 0.22). 13 2 There was no correlation between muscle and epidermal mucus δ C (F1,16 = 1.22, P = 0.28, r = 15 2 0.0129) and δ N values (F1,16 = 0.08, P = 0.9, r = 0.005). Significant differences were detected 13 between lipid normalised and non-lipid normalised muscle tissue δ C values (ANOVA, F1,16=124.5, P < 0.5). Epidermal mucus appeared homogeneous in structure and did not contain any phyto or zooplankton particles.

The δ13C values for mucus, an assumed fast turn-over tissue, were consistent with individuals foraging on a wide range of prey from surface zooplankton (light in 13C) to organisms associated with mesopelagic, demersal or benthic habitats (heavy in 13C) (France, 1995). The lower range of δ13C 73

values from muscle tissue in comparison to epidermal mucus indicates that the long-term integrated diet is more conservative (Table 4.3). Assuming epidermal mucus is representative of more recent dietary intake, results here indicate that M. birostris opportunistically feeds during seasonal aggregation time at Isla de la Plata, as well as engaging in cleaning and social interactions. Observed near-surface feeding events at Isla de la Plata may simply act as a ‘subsistence diet’ for when M. birostris is temporarily away from its optimal foraging sites, as likely occurs for many planktivores in a landscape where food resources are patchily-distributed (Piontkovski and Williams, 1995). Flexible and opportunistic trophic interactions have also been demonstrated using SIA in the bull shark Carcharhinus leucas (Matich and Heithaus, 2014). Here, stable isotope values from muscle tissue indicated long-term specialisation of prey from a marine or freshwater/estuarine food web and blood plasma identified short-term seasonal predation on prey originating from marsh habitats (Matich and Heithaus, 2014).

There was large intra-tissue variation in M. birostris epidermal mucus and muscle δ15N values. Baseline shifts in δ15N can influence food-chain length, which affects relative trophic position estimates for wide-ranging predators that forage in isotopically different oceanic regions (Young et al., 2015). Manta rays and other large filter-feeding elasmobranch species are considered to have a generalist lifestyle and have the potential to exploit a range of zooplankton prey types, and possibly small vertebrates (Compagno and Last, 1999, Burgess et al., 2016). These prey types fluctuate in space and time, and it is likely that these baseline shifts coupled with a generalist lifestyle is reflected in the range of muscle and mucus δ15N values seen here. This interpretation however, assumes that individuals behave in an identical manner. Alternatively, the broad isotopic niche seen in both tissue types could be indicative of a population made up of individuals with different dietary preferences or sources.

Table 4.3 Carbon to nitrogen ratios and isotopic values (means ± standard deviations) for surface zooplankton and Manta birostris muscle and epidermal mucus. Lipid-normalised (LN) δ13C values for M. birostris muscle and surface zooplankton are also included. 13 15 Sample n C:N δ C(‰) δ N(‰) Mean Min Max Mean Min Max M. birostris Muscle 18 3.4 ± 0.4 −16.5 ± 1.1 −17.9 −14.5 9.7 ± 1.7 5.3 12.2 M. birostris Muscle (LN) 18 3.4 ± 0.4 −16.6 ± 1.1 −17.7 −14.1 9.7 ± 1.7 5.3 12.2 M. birostris Mucus 24 4.1 ± 0.7 −18.7 ± 2.6 −24.7 −16.5 8.6 ± 1.8 6.4 13.7 Zooplankton 35 4.3 ± 0.4 −20.5 ± 0.6 −22.1 −18.6 7.8 ± 1 5.5 9.7 Zooplankton (LN) 35 4.3 ± 0.4 −20 ± 1.4 −22.4 −17.9 7.8 ± 1 5.5 9.7

In comparison to the surface zooplankton primary consumer baseline, M. birostris muscle and epidermal mucus were elevated in δ13C by 3.6 ‰ and 1.2 ‰ and in δ15N by 1.9 ‰ and 0.8 ‰, respectively (Fig. 4.4). Muscle trophic level enrichment was lower than expected for either general predator-prey interactions [∆15N, 3.4 ± 1.0 ‰, (Post, 2002)] or large elasmobranch-prey interactions [∆15N, 2.29 ± 0.22 ‰, (Hussey et al., 2010)]. There are no experimentally determined DTDFs for muscle or mucus of large planktivorous elasmobranchs, and it is possible that enrichment in δ15N between planktivorous elasmobranch consumers and their prey is lower than has been reported for large sharks (Hussey et al., 2010). Alternatively, nitrogen discrimination factors can also decease with protein quantity (Robbins et al., 2010). Zooplankton is an umbrella term that includes an enormous taxonomic diversity (~7000 different species in 15 different genera) (Bucklin et al., 2010), with protein content varying among taxonomic groups and depending on life stage (Limbourn and Nichols, 2009). Therefore, it is possible that opportunistic foraging by M. birostris in this region or unsampled prey types with low protein quality or quantity could have caused this observed low nitrogen enrichment between prey and M. birostris muscle tissue.

There was no difference detected between M. birostris muscle and mucus expected prey δ15N 13 (ANOVA, F1,16 = 0.033, P = 0.86) or δ C values (ANOVA, F1,16 = 0.459, P = 0.51), indicating that long-term, and assumed short-term dietary intake comprised prey of a similar trophic level and primary producer source (Fig 4.4). Expected prey profiles for M. birostris muscle and mucus were enriched in 13C in comparison to surface zooplankton, indicating that this dietary source is not a large component of the diet in the long or short-term for M. birostris in this region. In Ecuador, M. birostris have been observed surface feeding at Isla de la Plata and the Galapagos Islands. Surface feeding events are also commonly documented for manta rays and other large planktivorous elasmobranch species at aggregation sites in different oceanic regions (Rohner et al., 2015, Armstrong et al., 2016, Marcus et al., 2016). The delay in incorporation of isotopic signatures into muscle tissue has made the relevance of these observed, possible ‘snacking events’ at aggregation sites, difficult to interpret. Experimentally-determined DTDFs and turnover time for elasmobranch epidermal mucus would enable the quantification of the contribution of such events to dietary intake and whether these surface feeding opportunities constitute a major driver for aggregative behaviour of these species.

Epidermal mucus had a significantly higher C:N ratio (4.1 ± 0.7) than that of muscle (3.4 ± 0.4; t- test, P <0.05) (Table 4.3), reflecting that 7 of 18 muscle samples and 14 of 24 epidermal mucus samples had a C:N ratio of >3.5. The C:N ratio is a predictor of lipid content in sampled tissues, and relies on the assumption that an increase in lipid content will correlate with an increase in C:N ratios given that lipids contain mostly carbon and little nitrogen (Barnes et al., 2007). Information on lipid content from this ratio can then be used to quantify the condition of the sampled organism whereby individuals in better condition (higher lipid content) exhibit higher C:N ratios (Fagan et al., 2011). Occupation of different habitat types influences lipids in salmonids (Dempson et al., 2004) and there are seasonal declines in lipid content during times of low dietary intake in some fishes (Jonsson and Jonsson, 2003, Wagner et al., 2010). Results here from epidermal mucus suggest that Isla de la Plata could serve as place where M. birostris can find food, clean and socialise, leading to a better physical body condition. Alternatively, M. birostris arrives at Isla de la Plata in good physical condition and that the primary driver for aggregative behaviour for M. birostris here may be linked to reproduction rather than foraging opportunities. Some studies, however, have suggested that there is no predictable relationship between bulk C:N ratios and lipid content (Fagan et al., 2011). There is a need for validation of C:N ratio use in elasmobranch SIA studies and should initially include the quantitative assessment of lipid content in the sample in comparison to C:N. Were a relationship found, further validation would be required in the form of repeat samples from the same individual across a wide temporal scale to account for seasonal fluctuations in body condition.

Growth is an important determinant of isotopic turnover rates in various tissues (Carleton and Del Rio, 2010, Vander Zanden et al., 2015). In teleost fishes, epidermal mucus has an estimated turnover half-life of 30 – 200 days depending on the size of the fish (Church et al., 2009, Heady and Moore, 2013). Large elasmobranch species have slow growth rates so turnover times for mucus in elasmobranchs could be slower than in teleosts (Frisk et al., 2001). In the freshwater stingray, Potamotrygon motoro, liver tissue (considered a fast turnover tissue) took 166 days to stabilise isotopically after a diet switch. Comparatively, liver reached this isotopic steady-state ~2.5 times faster than muscle. In leopard sharks, Triakis semifasciata, plasma carbon took up to 100 days to reach isotopic equilibrium after a dietary switch, but with large differences identifiable after 50 days (Kim et al., 2012b). Similar to other currently recognised fast turnover tissues in elasmobranchs, epidermal mucus could be representative of diet over a monthly or seasonal temporal scale.

Studies using SIA are the most powerful when laboratory, theory and field-based insights are linked. To validate suspected drivers of aggregative behaviour, large planktivorous elasmobranch SIA studies need to also collect information on animal movements and seasonal changes in prey availability throughout the year at aggregation sites. Satellite tagging data for M. birostris from this region shows that this is predominantly an offshore species, although individuals aggregate in coastal areas for short periods (Hearn et al., 2014). However, there is no available data on seasonal changes in zooplankton biomass throughout the year at Isla de la Plata (Burgess et al., 2016). A follow-up study would be needed to reveal whether increases in regional upwelling during austral winter (Montecino and Lange, 2009) translate into local increased biological productivity and subsequent availability of zooplankton biomass at this aggregation site.

4.9 Conclusions

Preliminary findings from this work suggest that mucus could be useful as a material in elasmobranch dietary studies using SIA. Particular benefits of mucus use relate to the minimally-invasive method of collection, its potential in exploring seasonal shifts in diet, and even its use in molecular genetics studies (Kashiwagi et al. 2015). The use of mucus may prove particularly valuable for elasmobranch species for which stomach content data are unavailable. Captive feeding experiments on elasmobranch species under controlled conditions in aquaria are needed to fully evaluate the use of mucus as a material for dietary SIA studies.

Chapter 5

Novel signature fatty acid profile of the giant manta ray Manta birostris suggests reliance on an uncharacterised mesopelagic food source low in polyunsaturated fatty acids

5.1 Introduction

The genus Manta, comprises two recognised species; the reef manta ray Manta alfredi, and the giant manta ray Manta. birostris. Both species of manta rays occur in mid- to low latitudes, although M. alfredi has a narrower latitudinal range compared to M. birostris (Couturier et al., 2015). There is a relatively large body of information on the biology and ecology of M. alfredi, but relatively little is known about M. birostris due mainly to difficulties associated with this species’ preference for offshore waters (Marshall et al., 2011a). While both species have been shown to move over hundreds of kilometres (Couturier et al., 2011, Stewart et al., 2016a), they often aggregate at particular sites throughout their respective home ranges (Dewar et al., 2008, Luiz Jr et al., 2009). The traditionally held view is that Manta spp. feed on surface zooplankton during daylight hours at these aggregation sites, and that aggregative behaviour therefore is linked with local-scale food availability (Dewar et al., 2008, Armstrong et al., 2016). Both manta ray species have been seen to feed in surface waters (Duffy and Abbott, 2003, Jaine et al., 2012), however, surface foraging behaviour is more commonly documented at M. alfredi aggregation sites (Armstrong et al., 2016), in comparison to M. birostris aggregation sites where it is more unusual (Luiz Jr et al., 2009).

While stomach contents analysis has provided some useful dietary information for M. alfredi (Bennett et al., 2016), these data provide a ‘snap-shot’ of recent feeding behaviour and may or may not be representative of feeding ecology more generally. In addition, obtaining specimens of rare and internationally protected species, such as manta rays, is unethical or at best, challenging as samples have to be obtained from markets, therefore, alternative approaches are needed. The application of non-lethal molecular methods, wherein small muscle tissue biopsies can be taken and analysed, has the ability to provide information on assimilated dietary intake over long time-frames (Carlisle et al., 2012). The recent application of such molecular methods for reef manta rays and whale sharks has provided insight into their feeding ecology (Couturier et al., 2013b, Marcus et al., 2016). These studies suggest that surface zooplankton may comprise a small proportion of dietary intake, with the majority of their diet sourced from deeper (Chapter 4), or demersal sources (Couturier et al., 2013a, Marcus et al., 2016).

Signature fatty acid (FA) analysis provides a way to assess trophic interactions as marine primary producers can usually be identified via the presence, absence or combination of certain FAs (Dalsgaard et al., 2003). Omega-3 and omega-6 polyunsaturated FA (PUFA) are considered physiologically vital in marine fishes as they are constituents of complex lipids and can influence growth, reproduction and survival (Koven et al., 1990; Arts et al., 2001). PUFAs are thought to be

79 transferred and concentrated throughout the food web (Parrish, 2009), inducing a potential bottomup control on animal population (i.e., influence of lower trophic forms on the higher ones) (Litzow et al., 2006, Werbrouck et al., 2016). Important PUFAs include: docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and arachidonic acid (ARA). DHA influences cell membrane characteristics such as fluidity or permeability, while EPA and ARA are precursors of antagonist eicosanoids, i.e., hormone-like compounds, produced in response to external stimuli (Tocher, 2003, Hixson et al., 2014). Most marine fishes are unable to synthesise PUFAs due to the lack of elongation and desaturation enzyme systems, and need to acquire them from their diet (Tocher, 2003). As these PUFAs are a direct consequence of diet, they can be used as a chemical biotracer of food origin.

Lipids play an essential role in many metabolic and biological processes and are grouped according to molecular properties, with triacylglycerols (TAG) and wax esters (WE) important for the storage of metabolic energy, while phospholipids (PL) and sterols (ST) are structural components of cellular membranes (Sargent, 1989). The simultaneous determination of lipid class (LC) composition and FA profile for any given marine species, can therefore, reveal important information on biological and physiological parameters (Sargent, 1989), as well as dietary insights for these species (Pethybridge et al., 2010).

Isla de la Plata, a newly discovered aggregation site off mainland Ecuador, seasonally hosts what appears to be the largest identified regional population of M. birostris (Chapter 1). The accessibility of this site affords a unique opportunity for observations of behaviour and sample collection for molecular analysis from this largely elusive species. The aim of this study was to provide insight into the long-term assimilated diet of M. birostris by comparing the FA profiles of this species and surface zooplankton from Isla de la Plata to investigate whether M. birostris predominantly feed on surface zooplankton, or if they instead target an alternative food source. A secondary aim was to explore the LC composition of M. birostris muscle and surface zooplankton to investigate energy storage capabilities between these two sample types. The FA and LC profiles of M. birostris and surface zooplankton presented here are the first reported for any organisms sampled off mainland Ecuador.

5.2 Methods

5.2.1 Sample collection

Manta birostris muscle samples and surface zooplankton were collected at Isla de la Plata (1.2786° S, 81.0686° W) and Bajo Copé (1.81706° S, 81.06362° W), Ecuador. A total of 49 M. birostris

80 biopsies, each from a different, visually identified individual, were collected on SCUBA during August – October over several years (2012, n = 11; 2013, n = 9; 2014, n = 29) by use of a modified hand spear. The sex of M. birostris individuals was determined through the presence (male) or absence (female) of claspers (Marshall and Bennett, 2010b). Feeding behaviour was recorded when M. birostris were engaged in continuous ram-feeding on zooplankton patches, with an open mouth, visible gill rakers and unrolled cephalic fins.

Surface zooplankton was collected via horizontal surface tows with a 200 µm mesh plankton net (50 cm mouth diameter) for 5 minutes at an average speed of 0.5 – 1 m s-1. Samples were collected during manta ray feeding (n = 3) and non-feeding events (n = 29), and at regular intervals throughout the day in order to cover the full tidal cycle. All M. birostris and zooplankton samples were placed on ice immediately after collection and then stored at -18 °C, or on dry ice, until required for FA analyses. This study was conducted in accordance with the University of Queensland Animal Ethics approval number SBS/319/14/ARC/EA/LEIER. Field work was done with approval from the Minesterio del Ambiente and Machalilla National Park authorities, with Proyecto Mantas Ecuador and the NAZCA Institute for Marine Research under permits: 008 RM-DPM-MA, 011 AT-DPAM-MAE and 009 AT- DPAM-MAE.

5.2.2 Lipid extraction

Muscle biopsies from M. birostris (n = 11) were freeze-dried and lipid extracted following a 1-day modified Folch method, or were processed wet and lipid extracted using a 3-day Folch method (n = 37) (Appendix, Chapter 5). The total lipid extract from both methods was dried under a stream of nitrogen gas, weighed, and stored at -18 °C until further analysis.

5.2.3 Fatty acid signature analysis

An aliquot of the total lipid extract (LE) was trans-methylated to produce fatty acid methyl esters (FAME) following the protocol from Coutteau et al. (1995) (Appendix, Chapter 5). For gas chromatography (GC) analysis, an Agilent Technologies 7890B GC equipped with a non-polar Equity™-1 fused silica capillary column (15 m × 0.1 mm internal diameter, 0.1 µm film thickness), a digital electrometer, a split/splitless injector and an Agilent Technologies 7683B Series auto- sampler was used. The carrier gas was hydrogen with the generator set at 620 kPa. Samples were injected in split-less mode at an oven inlet temperature of 250 °C. After injection, samples were held at 50 °C for 1 min, then oven temperature was raised to 230 °C at 2.5 °C min−1. Gas

Chromatography/Mass Selective Detector ChemStation software (Agilent Technologies, Palo Alto, California) was used to quantify FAME peaks. Peak identities were then confirmed with a Thermo Finnigan GCQ GC-MS system (Finnigan, San Jose, California). Fatty acids were expressed as area percentage of total FA (% FA).

5.2.4 Lipid class profiles

Lipid class (LC) composition was determined for a representative subset of M. birostris (n = 10) and zooplankton (n = 6) samples. The total LE from each sample along with a standard solution containing known quantities of common lipid classes; wax esters (WE), triacylglycerols (TAG), free fatty acids (FFA), sterols (ST) and phospholipids (PL) was spotted in duplicate on chromarods. The chromarods were then developed for 25 min in a polar solvent (hexane:diethyl-ether:acetic acid, 60:17:0.1, by volume). Chromarods were oven-dried at 100 °C for 10 min and then immediately analysed with an Iatroscan Mark V TH10 thin layer chromatograph with a flame ionization detector, which had been previously calibrated for each lipid class. Peak identification was by comparison with sample retention times in relation to the standards and peak areas quantified using SIC-480II Iatroscan™Integrating Software v.7.0-E (System Instruments, Mitsubishi Chemical Medicine). Predetermined linear regressions were used to identify peak areas that were then transformed to mass per µl spotted.

5.2.5 Data processing and statistical analyses

For comparisons among M. birostris and surface zooplankton FA profiles, only FAs in concentrations of >1 % of the total FA profile were used in analyses. Non-parametric multi-dimensional scaling (nMDS) was used to visually determine relationships of groupings within and between M. birostris and surface zooplankton clusters. Similarity percentage analysis (SIMPER) was used to identify the contribution of each FA to the observed similarity between and within groups. Classical hierarchal cluster analysis applied to the nMDS plot to show the clustering and % similarity of identified groups. All analyses used PAST v3.12 software (Hammer et al., 2001) on untransformed data with a non- parametric Bray-Curtis similarity matrix. Unless otherwise stated, all data are reported as mean and standard deviation.

5.3 Results

When Manta birostris were present during the sampling periods of August to October in 2013, and August to September 2014, surface feeding was observed on 8 of 190 research dives (Chapter 2).

5.3.1 Manta ray fatty acid profile

There was a significant difference between the FA profiles of M. birostris and surface zooplankton (ANOSIM, R value = 0.82, P <0.05) and an overall average similarity of 39.8 %. Largest contributions to dissimilarities were DHA (23.8 %,), 16:0 (14 %), 18:0 (13.2 %) and 18:1ω9c (11.3 %) (SIMPER) (Fig 5.1).

A total of 34 FAs were detected in M. birostris samples. There was no significant difference between FA profiles of M. birostris samples that were freeze-dried and extracted with the 1-day method in comparison to wet samples extracted with the 3-day method (ANOSIM, R value = 0.11, P = 0.10). The FA profiles of M. birostris did not significantly differ among years (ANOSIM, R value = 0.05,

P = 0.27) (Fig 5.2, Appendix Table S5.1) or months (ANOSIM, R = 0.001, P = 0.45). The average similarity based on the Bray-Curtis similarity measure between M. birostris samples collected in 2012 and 2013 was 77.6 % (SIMPER). There was less similarity between M. birostris samples collected in 2012 and 2014 (62.5 %) than those in 2013 and 2014 (68.1 %) (SIMPER).

The FA profiles of M. birostris were dominated by saturated fatty acids (SFA) (61.4 ± 11.2 %) followed by long chain monounsaturated fatty acids (LCMUFA) (32 ± 11.4 %) of which 18:1ω9 isomers were most abundant (Table 5.1). The principal PUFA was ARA accounting for ~2 % of total FA profile (Table 5.2).

22:1ω9 Similarity 70%

A 2012 2013 2014 16:0 Stress: 0.1586 18:1ω9t 18:1ω9c

At 70 % similarity, cluster analysis revealed two distinct FA profile groups (A and B) for M. birostris (Fig 5.1), with significant differences between the FA profiles of individuals from each group (ANOSIM, R value = 0.91, P <0.05) (Table 5.2). There were low levels of PUFA detected in both M. birostris clusters, but of the PUFAs present, ω6 dominated. Cluster A and B both had ω3/ω6 ratios of 0.1 (Table 5.2). Cluster A comprised of male and female samples from 2014, whereas Cluster B comprised males and females across all sampling years (2012 – 2014).

Table 5.1 Fatty acid (FA) profiles, reported as percentage proportion of total FA, for Manta birostris and surface zooplankton sampled off mainland Ecuador (this study), and Orcinus orca from California (Herman et al., 2005) with a similarly low percentage proportion of polyunsaturated fatty acids Fatty acid Surface Manta Orca Orca Orca zooplankton birostris (offshore) (resident) (transient) ΣSFA 36.6 ± 12.5 58.6 ± 11 17.1 ± 2.5 14.5 ± 2 16.7 ± 3.78 ΣSCMUFA 5.1 ± 1.9 2.1 ± 2 25.8 ± 2.7 31.8 ± 4 39.4 ± 6.4 ΣLCMUFA 13.3 ± 3 34.8 ± 11.8 49.3 ± 2.4 47 ± 4.2 39.1 ± 7.6 ΣPUFA 44.9 ± 12.6 4.5 ± 6.2 7.8 ± 1.9 6.7 ± 1.4 4.8 ± 1.8 ΣW3 40.9 ± 11.4 1.4 ± 4.7 4.5 ± 2 3.6 ± 1.2 2.1 ± 1 ΣW6 4 ± 1.3 3.1 ± 3.4 2.3 ± 0.4 2 ± 0.3 1.6 ± 0.6 W3:W6 9.7 ± 3.1 0.2 ± 0.4 1.9 1.8 1.3 Functional groupings of FA compositions are shown as: total saturated fatty acids (ΣSFA); total short- chain monounsaturated fatty acids (≤C16) (ΣSCMUFA); total long-chain monounsaturated fatty acids (>C16) (LCMUFA); total polyunsaturated fatty acids (ΣPUFA); total omega-3 fatty acids (ΣW3) and total omega-6 fatty acids (ΣW6).

The overall similarity between M. birostris cluster A and B was 64.1 % (SIMPER) and major differences between A and B were due to variations in 18:1ω9 isomers (Table 5.2, Appendix Table S5.2). Cluster A had higher mean percentage of 18:1ω9t, whereas 18:1ω9c was higher in cluster B (Table 5.2). Other FAs that contributed to major differences between M. birostris cluster groups were 22:0 (10.3 %), 16:0 (9.1 %), 18:1ω7 (8.9 %), 22:1ω9 (7.5 %) and 18:0 (5 %) (Appendix Table S5.2).

5.3.2 Surface zooplankton fatty acid profile

The FA profiles of surface zooplankton collected during ‘feeding’ and ‘non-feeding’ events at Isla de la Plata did not significantly differ (ANOSIM, R value = 0.01, P = 0.43). There was no significant difference in the FA profiles of zooplankton sampled among different years (ANOSIM, R value = 0.03, P = 0.25) or collection sites (ANOSIM, R value = 0.08, P = 0.18), while there was a significant difference among zooplankton collected in different months (ANOSIM, R value = 0.26, P <0.05) (Appendix Table S5.3). For all years, pairwise comparisons revealed a significant difference between August and September, and September and October (P <0.05), but no significant differences between August and October (P = 0.14). However, upon ordination, no discrete clusters were formed and there was a high degree of overlap between each month (Appendix Fig S5.1).

Table 5.2 Fatty acid (FA) composition (% of total FA ± s.d.) of Manta birostris muscle and whole surface zooplankton collected from Isla de la Plata, Ecuador, alongside FA profiles of Manta alfredi and Rhincodon typus from different oceanic regions. Fatty acid Surface Surface M. birostris M. birostris M. alfredi M. alfredi R. typus R. typus R. typus zooplankton zooplankton 2013 2014 Cluster 1 Cluster 2 Cluster A Cluster B E. Aust Mozambique Mozambique W. Aust W. Aust 14:0 7.5 ± 1.6 7 ± 3.2 1.3 ± 1.1 3.1 ± 5.4 0.6 ± 0.1 0.1 ± 0.1 0.2 ± 0.0 15:0 1.3 ± 0.2 1.2 ± 0.6 0.1 ± 0.3 0.1 ± 0.4 0.4 0.1 ± 0.0 0.2 ± 0.0 16:0 0.2±11.5 23.1 ± 8.6 26 ± 3.2 31 ± 5.9 14.7 ± 0.4 13.8 ± 0.5 12.2 ± 0.4 11.9 ± 1.4 9.7 ± 0.5 17:0 2 ± 0.4 1.6 ± 0.8 0.1 ± 0.4 0.1 ± 0.3 1.6 ± 0.1 1.5 ± 0.1 0.8 ± 0.1 1.0 ± 0.0 18:0 8.7 ± 1.2 7 ± 4.8 24.1 ± 2.2 23.9 ± 4.1 16.8 ± 0.4 17.8 ± 0.5 17.7 ± 0.3 32.0 ± 3.4 18.0 ± 0.5 20:0 0.9 ± 0.3 0.6 ± 1.1 1.1 ± 1.1 0.5 ± 1.2 0.3 ± 0 0.8 ± 0.1 0.8 ± 0.4 22:0 0.8 ± 0.2 0.5 ± 1.6 7.8 ± 5.8 0.8 ± 1.9 0.9 ± 0.1 0.8 ± 0.2 0.5 ± 0.0 23:0 0.7 ± 0.2 0.5 ± 3.6 0.6 ± 0 ΣSFA 22.5 ± 9.7 41.7 ± 10.5 60.7 ± 7.3 59.6 ± 7.7 35.1 ± 0.7 39.1 ± 0.7 37.4 ± 0.1 48.5 ± 3.2 33.2 ± 0.8 16:1ω7 6.1 ± 0.9 4.4 ± 2.2 2 ± 1.6 1.7 ± 1.5 2.7 ± 0.3 2.1 ± 0.3 1.9 ± 0.2 0.9 ± 0.3 1.3 ± 0.1 18:1ω9t 0.3 ± 0.2 0.2 ± 7.1 17.2 ± 7.5 0.1 ± 0.4 18:1ω9c 7 ± 1.7 5.8 ± 2.4 8.2 ± 2 22.7 ± 5 15.7 ± 0.4 16.7 ± 0.7 16 ± 0.5 13.1 ± 1.9 15.6 ± 0.7 18:1ω7 3.1 ± 0.6 2.4 ± 1.4 0 6.2 ± 3.1 6.1 ± 0.2 4.6 ± 0.5 4.3 ± 0.3 3.5 ± 0.5 4.1 ± 0.4 20:1ω9 0.7 ± 0.3 0.7 ± 0.5 0.4 ± 0.7 1.1 ± 1.5 1.0 ± 0.03 0.7 ± 0.02 0.7 1.2 ± 0.4 1.4 ± 0.2 22:1ω9 0.4 ± 0.8 1.1 ± 2 4.8 ± 4.2 4.8 ± 6.5 0.2 0.7 ± 0.2 0.6 ± 0.3 24:1ω9 1.9 ± 0.4 1.3 ± 0.6 0.2 ± 0.6 0.7 ± 1.2 1.1 ± 0.1 1.9 ± 0.1 2.3 ± 0.1 0.8 ± 0.1 1.6 ± 0.1 ΣMUFA 20.2 ± 3.1 16.3 ± 7 32.9 ± 4.4 37.5 ± 7.6 29.9 ± 0.7 31.0 ± 0.9 30.2 ± 0.1 25.4 ± 2.6 30.7 ± 1.2 18:2ω6c 1.7 ± 0.2 1.4 ± 1.5 2.4 ± 2.2 0.3 ± 0.8 0.7 ± 0.0 1.0 ± 0.1 18:3ω6 0.2 ± 0.2 0.2 ± 0.1 1.5 ± 4.4 0.1 ± 0.3 18:3ω3 1.2 ± 0.3 1 ± 0.8 0.2 ± 0.8 0.1 ± 0.0 0.6 ± 0.3 20:4ω6 (ARA) 2.4 ± 0.4 1.6 ± 0.7 2 ± 1.8 1.9 ± 2.2 11.7 ± 0.8 16.9 ± 0.6 17.8 ± 0.4 12.5 ± 1.7 16.4 ± 1.0 20:5ω3 (EPA) 13.9 ± 3 9.4 ± 4.2 1.2 ± 0.1 1.1 ± 0.1 1.2 ± 0.1 1.7 ± 0.2 2.0 ± 0.3 22:5ω3 1.8 ± 1.3 1.7 ± 1 0.2 ± 0.5 2.0 ± 0.1 2.1 ± 0.1 2.5 ± 0.1 0.1 ± 0.0 0.1 ± 0.0 22:6ω3 (DHA) 34.6 ± 4.9 25.9 ± 9.7 0.1 ± 0.4 0.3 ± 0.8 10.0 ± 0.5 2.5 ± 0.2 2.8 ± 0.2 2.4 ± 0.2 3.5 ± 0.3 ΣPUFA 57.3 ± 8 42 ± 15.2 6.3 ± 5 2.9 ± 3.1 34.9 ± 1.2 29.9 ± 0.9 32.4 ± 0.1 26.1 ± 2.9 36.2 ± 0.9 ΣΩ3 52.1 ± 7.5 38.3 ± 15 0.3 ± 1 0.6 ± 1.3 13.4 ± 0.6 6.1 ± 0.3 6.5 ± 0.1 ΣΩ6 5.1 ± 0.7 3.6 ± 1.9 5.9 ± 4.8 2.3 ± 2.4 21.0 ± 1.4 23.8 ± 0.8 25.9 ± 0.1

Ω3/Ω6 10.4 ± 1.6 10.7 ± 4.8 0.1 ± 0.2 0.1 ± 0.3 0.7 0.3 0.3 0.3 ± 0.1 0.5 ± 0.1 Others* 2.2 ± 0.1 1.4 ± 0.1 0.6 ± 0.1 0.4 ± 0.1 18:1ω7/18:1ω9 0.4 0.9 0.03 0.3 0.4 0.3 0.3 0.3 EPA/DHA 0.4 0.4 0.1 0 0.1 0.4 0.7 0.6 16:1ω7/16:0 30.5 0.2 0.1 0.1 0.2 0.2 0.1 0.1 Reference This study This study This study This study (Couturier et al., (Couturier et al., (Rohner et al., (Marcus et al., (Marcus et al., 2013a) 2013a) 2013a) 2016) 2016) Clusters A and B, and clusters 1 and 2 designate distinct FA profiles groups of M. birostris and surface zooplankton, respectively, as revealed by cluster analysis. Also included are the FA compositions of Manta alfredi from eastern Australia (± s.e.) (E. Aust) and Mozambique (Couturier et al., 2013a), and Rhincodon typus from Mozambique (± s.e.) (Rohner et al., 2013a) and western Australia (W. Aust) (± s.e.) (Marcus et al., 2016). Functional groupings of FA compositions are shown as: total saturated fatty acids (ΣSFA); total monounsaturated fatty acids (ΣMUFA); total polyunsaturated fatty acids (ΣPUFA); total omega-3 fatty acids (ΣW3); total omega-6 fatty acids (ΣW6); Arachidonic acid (ARA); Eicosapentaenoic acid (EPA); docosahexaenoic acid (DHA). *Fatty acids comprising <1% of FA profile; 18:2ω6 trans, 22:0, 15:1, 14:15, 19:0, 24:0, 17:1, 20:17, 18:43, 20:36, 20:33, 22:46, 20:26

Surface zooplankton from Isla de la Plata predominantly comprised PUFAs (44.9 ± 12.6 %) (Table 5.2). Cluster analysis at 80 % similarity revealed there to be 2 discrete clusters (1 & 2) of surface zooplankton collected and 4 outlier samples (Fig. 5.3, Appendix Table S5.4).

22:6ω3 Similarity 80% 2013 2014

14:0 Stress: 0.08 20:5ω3 16:0

The major FA for both zooplankton clusters was DHA followed by EPA for cluster 1 and 16:0 for cluster 2 (Table 5.2). Both surface zooplankton clusters had similar levels of ARA to M. birostris and clusters 1 and 2 had mean ω3/ω6 ratios of 10.4 and 10.7, respectively (Table 5.2). While both cluster 1 and 2 were predominantly comprised of calanoid copepods, cluster 1 samples contained a slightly higher abundance of chaetognaths in comparison to cluster 2. The EPA/DHA ratio for both surface zooplankton groups was 0.4. The FA profiles of surface zooplankton collected from Isla de la Plata were dominated by DHA and had a high ω3/ω6 ratio (11.8) in comparison to M. birostris (0.6).

5.3.3 Lipid class profiles

Manta birostris LC profiles were dominated by PL (82.9 ± 6.3 %), followed by ST (9.5 ± 5 %), and FFA (4.3 ± 3.4 %) (Table 5.3). Lipids from surface zooplankton collected from coastal Ecuador were dominated by TAG/WE (38.5 ± 13.9 %) followed by PL (30.1 ± 9.1 %) and then FFA (19.3 ± 16.2 %) (Table 5.4).

Table 5.3 Lipid class (LC) profiles, reported as % of total lipid, for Manta birostris from Ecuador (this study), alongside Manta alfredi from Mozambique and eastern Australia (Couturier et al., 2013a), and Rhincodon typus from western Australia (Marcus et al., 2016) and Mozambique (Rohner et al., 2013a). Lipid classes shown; wax esters (WE), triacylglycerols (TAG), free fatty acids (FFA), sterols (ST) and phospholipids (PL). Lipid Class M. birostris M. alfredi M. alfredi R. typus R. typus Ecuador E. Australia Mozambique W. Australia Mozambique (n = 10) (n = 14 ) (n = 6) (n = ) (n = 24) TAG/WE 3.7 ± 2.2 4.5 5.1 11.4 5.1 FFA 4.3 ± 3.4 2.4 ± 0.5 3.0 ± 0.4 2.0 ± 0.6 5.4 ± 0.7 ST 9.5 ± 5 8.0 ± 0.7 13.4 ± 2.2 14.6 ± 1.3 21.4 ± 0.7 PL 82.9 ± 6.3 85.2 ± 0.8 78.5 ± 2.5 71.9 ± \ 3.0 68.1 ± 2.2 Reference This study (Couturier et al., (Couturier et al., (Marcus et al., (Rohner et al., 2013a) 2013a) 2016) 2013a)

Table 5.4 Average lipid class (LC) profiles of surface zooplankton from Manta birostris aggregation sites in Ecuador (this study), and from Manta alfredi aggregation sites in eastern Australia and Mozambique (Couturier et al., 2013a). Lipids are reported as % of total lipid with lipid classes shown; wax esters (WE), triacylglycerols (TAG), free fatty acids (FFA), sterols (ST) and phospholipids (PL). Lipid Class Surface Zooplankton Ecuador E. Australia Mozambique (n = 6) (n = 38) (n = 8) TAG/WE 38.5 ± 13.9 24 6.2 FFA 19.3 ± 16.2 17.1 ± 0.9 57.2 ± 2.1 ST 7.5 ± 7.3 5.5 ± 0.2 6.4 ± 0.5 PL 30.1 ± 9.1 53.5 ± 2.1 30.2 ± 1.5 Reference This study (Couturier et al., 2013a) (Couturier et al., 2013a)

5.4 Discussion

The FA profiles of Manta birostris and surface zooplankton were markedly different apart from similar proportions of ARA, which suggests a small reliance on surface zooplankton for dietary intake in M. birostris. This complements previous stable isotope data that estimated surface zooplankton sources on average contributed a small portion of dietary intake (27 %) in comparison to mesopelagic sources (73 %) (Chapter 4, Part I) . The principal PUFA for M. birostris was ARA, which is also the principal PUFA for Manta alfredi and Rhincodon typus from Australia and Mozambique (Couturier et al., 2013b, Marcus et al., 2016). However, the proportion of ARA was much lower in the FA profile of M. birostris from the eastern Pacific than in FA profiles of planktivorous elasmobranchs from other study regions.

The relative lack of PUFA in the FA profile of M. birostris is an atypical and novel finding among all planktivorous elasmobranch fatty acid studies to date. This could be attributed to degradation of M. birostris samples, however, this was deemed minimal as average FFA values were <5 %. In comparison, M. alfredi had similar FFA amounts (2.4 – 3 %), but had FA profiles dominated by PUFAs (Couturier et al., 2013a). A lack of PUFAs in the FA profile of M. birostris muscle could indicate their long-term diet comprises an uncharacterised, ‘PUFA-poor’ food source and that there is an absent capacity for endogenous biosynthesis of long-chain PUFA in this species. Alternatively, muscle tissue in elasmobranchs is known to be relatively lipid poor in comparison to the liver (Pethybridge et al., 2014). Here, the ‘PUFA-poor’ FA profile of M. birostris muscle could be representative of preferential breakdown of PUFA, and/or designated routing of PUFA to the liver, or a function of the geographic zone that this species inhabits (Colombo et al., 2016).

5.4.1 PUFA-poor fatty acid profiles of Manta birostris

In stable isotope studies, elasmobranch muscle tissue can take over a year to represent an ingested prey item (MacNeil et al., 2006), and biopsied muscle is only representative of ecological interactions at the sampling site if the study organism has fed there for this entire period of time. The occurrence of M. birostris off mainland Ecuador is highly seasonal and biopsied muscle therefore, is likely representative of M. birostris feeding activity within a PUFA-poor environment before arrival to the aggregation site in Ecuador. Presuming surface zooplankton at Isla de la Plata are representative of surface zooplankton in the surrounding region, surface waters in the eastern equatorial Pacific are seemingly not PUFA-poor environments. Although this is a broad assumption, surface zooplankton FA profiles off mainland Ecuador were not markedly different to those for surface zooplankton collected from other tropical oceanic regions, such as The Great Barrier Reef (eastern Australia) and southern Mozambique (Couturier et al., 2013a). Surface zooplankton FA profiles from all three of these regions were dominated by omega-3 FAs, in the order of DHA, EPA and ARA. Omega-3-to omega-6 ratios were also similar among these regions, with Mozambique having a slightly higher ratio (12.8) compared to eastern Australia (9.5) and Ecuador (10.4).

Variations in essential fatty acid requirements for different fish species reflects different dietary and metabolic adaptations to different habitats (Sargent. Et al 1999). There is no information on how manta rays allocate FA in their tissue, although previous studies show that muscle from the reef manta ray in eastern Australia and Mozambique has a relatively standard composition (Couturier et al., 2013a). The inner blubber of killer whales from the NE Pacific also contained low proportions of PUFA (~4.8 – 7.8 % of total FA) and high proportions of MUFA (Herman et al., 2005). Blubber FA

90 compositions are likely to be altered relative to ingested FAs due to selective metabolism of certain FAs prior to their disposition in the blubber (Iverson et al., 2004). However, the FA profile of inner blubber from Arctic Bowhead whales and Mediterranean Fin whales all contained >20 % PUFA (Ruchonnet et al., 2006, Budge et al., 2008). Killer whales and M. birostris are the only two vertebrate species with published FA profiles in the temperate to tropical eastern Pacific, and there are no FA profiles for killer whales or M. birostris outside of this region. Therefore, it is difficult to assign the lack of PUFA seen in these two species as species specific, or a metabolic adaptation to this particular region (Colombo et al., 2016). Further sampling of prey baselines at different depths and during night time hours, where surface assemblages change dramatically due to diurnal vertical migrating species (Bollens et al., 1992), is required to establish the origin of the PUFA-poor food source reflected in the FA profile of M. birostris.

Mesopelagic copepods (0 – 300 m) from the Costa Rica Dome contain low to moderate proportions of PUFA (3.5 – 22 % total FA), with one particular species, Rhinocalunus rostifrons shown to have on average 5 % PUFA over two collection years. This is in contrast to mesopelagic zooplankton from Hawaii (0 – 1000 m), which contained high proportions of PUFA (Wilson et al., 2010). There are no published FA profiles for mesopelagic zooplankton off mainland Ecuador, or the wider Peru Humboldt region where some tagged M. birostris have been shown to frequent (Hearn et al., 2014). The dominant zooplankton in the Peru upwelling region is Euphausia macronata (Wakeham et al., 1983), which during the day is typically found at mesopelagic depths ~300 m, with daytime net tows completely devoid of this species (Mauchline and Fisher, 1969, Staresinic et al., 1978). However, at night E. macronata undertakes large vertical migrations and can be found feeding near surface waters at ~15 m (Staresinic et al., 1978). Euphausiids are the dominant prey type for other planktivorous mobulid rays: Mobula thurstoni and Mo. japanica from the Gulf of California (Notarbartolo-di- Sciara, 1988), and Mo. thurstoni from the Western Atlantic (Gadig et al., 2003). They are also a common prey item for other planktivorous species including baleen whales (Dolphin, 1987), whale sharks Rhincodon typus (Jarman and Wilson, 2004) and megamouth sharks Megachasma pelagios (Sawamoto and Matsumoto, 2012). Given the ubiquity of krill-feeding by large planktivores and the large abundance of E. macronata in close proximity to Isla de la Plata, it is possible that this mesopelagic zooplankton is a major prey source for M. birostris. However, a caveat still remains in that if E. macronata migrate to surface waters to feed, these surface waters during the day are not PUFA-poor environments. Further sampling and the determination of FA profiles of night-time surface zooplankton assemblages over continental shelf habitat where large abundances of E. macronata reside during the day are required to resolve whether this krill species is a major dietary item for M. birostris.

5.4.2 Dietary derived fatty acids in giant manta rays

Arachidonic acid (ARA) was the PUFA with the highest proportion in the FA profile of M. birostris, but still comprised very little of the total FA profile (~2 %). In fishes, DHA and EPA, but not ARA, are typically the major PUFAs of cell membranes (Sargent et al., 1999). Although ARA is thought of as a minor component in fish cell membranes, its role has been largely unassessed. Here, ARA was the dominant PUFA in the profile of M. birostris, with only trace amounts of DHA found and no EPA. ARA is a major precursor of eicosanoids in fishes, which are produced in response to stressful situations at both a cellular and body level (Schreck et al., 2016). EPA is also a major precursor to eicosanoids, albeit less biologically active ones than those formed from ARA. EPA competitively inhibits the formation and actions of eicosanoids from ARA, therefore, high tissue ratios of ARA: EPA result in enhanced eicosanoid actions (Sargent et al., 1999). Higher proportions of ARA than EPA seen here could be linked to an environmentally induced stressor such as low food availability or changes in dissolved oxygen concentrations throughout the water column in the eastern tropical Pacific; a region of near constant hypoxia in surface waters and with suboxic conditions at depths of 300 – 500 m (Sargent et al., 1999, Bertrand et al., 2004, Stramma et al., 2008). Alternatively, FA profiles are species-specific (Ben-Amotz et al., 1985), and given there are no published FA profiles for M. birostris from other regions, the lack of PUFA might be a consequence of species-specific energy storage and cell mechanisms as opposed to being a result of conditions in the eastern equatorial Pacific.

Typically, photosynthetic organisms are the only organisms that can biosynthesize 18:2ω6 and 18:3ω3 de novo (Dalsgaard et al., 2003), with the derivatives from these PUFAs (ARA, EPA and DHA) deemed essential constituents in heterotrophic organisms. The presence of these derivatives in higher quantities in planktivorous elasmobranchs from eastern and western Australia, and southern Mozambique indicate a higher reliance on surface food sources. At M. alfredi aggregation sites in eastern Australia, surface foraging activity is commonly observed all year round (Jaine et al., 2012). In comparison, M. birostris were only observed feeding on 4 % of encounters during peak aggregation time off mainland Ecuador (S2 Appendix), and likely get the majority of their diet from offshore mesopelagic sources (Chapter 4) .

5.4.3 Other fatty acids potentially derived from diet

Essential fatty acids are defined as those that cannot be biosynthesised in sufficient quantities for normal physiological function. At present, only omega-3 and omega-6 FAs are considered essential FAs in sharks and rays. Low PUFAs here, across all M. birostris individuals sampled over multiple

92 years and months suggests that FAs other than PUFAs are acting in an ‘essential’ capacity. Although not explicitly considered dietary FAs, the MUFA profile of M. birostris was dominated by 18:1w9 isomers. Deep-sea organisms are richer in C18:1 FAs compared to those from shallow waters and in the liver oils of deep-sea sharks, PUFAs constitute only minor components (1 – 13 %) (Bakes and Nichols, 1995). Specifically, oleic acid (18:1w9c) increases with depth and is common in bathypelagic crustaceans and fishes (Lewis, 1967), along with upper mesopelagic copepods (0 – 300m) (Cass et al., 2011) and upper and lower mesopelagic (500 – 1000m) zooplankton (Wilson et al., 2010). Additionally, the most common mesopelagic zooplankton off neighbouring Peru, E. macronata, is known to produce major amounts of C18 fatty acids, with oleic acid being the most abundant (Wakeham et al., 1983). In deep sea sharks, the FA composition in all tissues is dominated by 18:1ω9, with content of this FA reported at 21 – 43 % (Remme et al., 2006). As well as energy storage, it has also been proposed that high proportions of C18:1 FAs might be an adaptive response to high pressure in deep waters (Hazel and Williams, 1990). Satellite tagging of M. alfredi and closely related Mobula tarapacana have shown that these predominantly surface dwelling rays can dive to depths of 432 m and 2000 m, respectively (Braun et al., 2014, Thorrold et al., 2014). High proportions of oleic acid in M. birostris muscle tissue are likely mesopelagic in origin, however, another important consideration is that vertically migrating mesopelagic zooplankton, such as euphausiids and myctophid fishes, are hugely abundant (Hays, 2003, Irigoien et al., 2014). These species undertake extensive diel vertical migrations from below 200 – 1000 m to surface waters during night time hours to feed (Staresinic et al., 1978, Watanabe et al., 1999). Complementary electronic tagging studies are needed to validate whether this M. birostris is targeting mesopelagic prey in shallow or deeper waters, or a combination of both.

Overall, the MUFA profile of M. birostris contained high amounts of 18:1w9 isomers, with proportions of the trans-isomer (elaidic acid) higher in Group A in comparison to group B where the cis-isomer (oleic acid) dominated. In killer whales, FA profiles can distinguish between ‘transient’ ‘offshore’ and ‘resident’ populations (Herman et al., 2008), and in bowhead whales the variability of FA profiles within populations has been linked with differences in phytoplankton-derived FA (Budge et al., 2008). Cis- and trans-isomers often have different physical properties and in this case, elaidic acid has a more symmetrical shape. This symmetry enables a larger number of trans-molecules to pack tightly into a space in comparison to the unsymmetrical cis-molecules. The increase in elaidic acid composition could therefore be linked with a more efficient energy storage mechanism in the subset of individuals from 2014 (Group A) and demonstrative of these individuals occupying more nutrient depleted offshore environment.

5.4.4 Lipid content and class composition

Lipid content (LC) profiles of M. birostris muscle contained low proportions of triacylglycerols (TAG). In chondrichthyan species, TAG is usually stored in the liver (Pethybridge et al., 2011) and muscle used here may be an inappropriate proxy for energy storage capabilities. The LC profile of M. birostris from the eastern Pacific was dominated by phospholipids (PL) and comparable to the LC profiles of M. alfredi from eastern Australia and Mozambique (Couturier et al., 2013a). The observed LC profiles are comparable to those of deep water and demersal sharks, which have relatively high levels of structural components (PL) and low or zero of energy storage lipids (TAG/WE) (Pethybridge et al., 2010, Pethybridge et al., 2011).

Some zooplankton samples had relatively high amounts of FFAs. However, the same samples all contained high proportions of PUFA, indicating that if there was degradation, this was solely restricted to lipid class composition (Young et al., 2010a). Surface zooplankton from Isla de la Plata, Ecuador had similar levels of FFA and ST to surface zooplankton from eastern Australia (Couturier et al., 2013a). However, these east and west Pacific sampling sites differed in their primary constituents with TAG/WE being dominant in the east Pacific (this study) while PL were the dominant lipid class constituent in the W. Pacific (Couturier et al., 2013a). TAG is usually associated with energy storage in marine organisms (Lee et al., 2006), and it is possible that this is an adaptation of zooplankton in the eastern Pacific which is as a region is subject to El Niño–Southern Oscillation mediated weather events, and subsequent fluctuations in productivity (Bakun and Broad, 2003). Further sampling of surface zooplankton from more sites in the tropical eastern and western Pacific would be needed to more comprehensively investigate these difference in LC profiles.

5.4.5 Limitations of molecular tracers

The allure of non-lethal and cost effective techniques often means molecular approaches are being more readily used to reconstruct diets for migratory and threatened species. However, the robust applicability of FA analysis in higher trophic level organisms is highly dependent on prior knowledge of how FA profiles are altered through de novo biosynthesis, metabolisation and breakdown of dietary FAs. These processes are regulated by life history strategies, the environment and type of lipid storage (Dalsgaard et al., 2003). Molecular approaches are most robust when there is a priori knowledge on diet through more traditional approaches such as stomach contents analysis (Dale et al., 2011).

In the case of elusive and threatened species, stomach contents data and faeces are often unavailable, and prior knowledge of feeding ecology is instead based on observational accounts of foraging

94 activity. When migratory species feed away from sight, on a variety of different prey, and in areas with poorly characterised molecular baseline values, it is difficult to make robust inferences on diet from molecular profiles alone. The interpretation of FA analyses remains hindered by our limited knowledge of lipid metabolism and FA biosynthesis in elasmobranchs, along with unknown turnover times of different tissues. However, certain scenarios can be discounted given that FA profiles of prey are assimilated relatively intact into consumer elasmobranch muscle tissue (McMeans et al., 2012). This study is an example of such a scenario, where the suspected surface zooplankton prey source was PUFA rich and theoretically, these PUFA would be assimilated and apparent in the M. birostris consumer if surface zooplankton collected during the day comprised part of their diet. Ultimately, to support the study of wild populations, aquaria studies would be needed to investigate de novo biosynthesis, metabolisation and breakdown of dietary FAs in planktivorous elasmobranchs.

5.4.6 Conclusions

Better management of fishing activity requires information on areas where foraging activity and fisheries overlap (Pikitch et al., 2004) and the combination of results from dietary molecular analyses with movement and fisheries data will enable more robust estimations on habitat and prey preferences of vulnerable elasmobranch species (Rohner et al., 2017). Manta birostris is a species that has the ability to move into deep and physiologically inhospitable environments (Stewart et al., 2016b), and their molecular profile (stable isotopes and fatty acids) indicates this vertical habitat use adaptation could be crucial for long-term dietary intake (Chapter 4, Part I). Climate change is predicted to reduce the amount of available deep sea habitat for yellow fin tuna in the nearby Galapagos region (Del Raye and Weng, 2015), and lower concentrations of important w3 fatty acids are associated with tropical latitudes in comparison to more temperate latitudes, suggesting that ‘tropicalization’ could adversely affect dietary intake of marine predators should their home ranges expand into higher latitudes as a consequence of the expansion of warmer and less productive waters at the equator (Polovina et al., 2008, Cheung et al., 2009, Parrish et al., 2015). Thus, a climate change mediated potential reduction of usable vertical habitat as well as a subsequent poleward range expansion in this region for M. birostris could negatively impact this species, which already has little capacity to withstand directed fishery pressure (Dulvy et al., 2014) via a reduction in quality and quantity of important dietary resources.

Chapter 6

General Discussion

6.1 Overview

This thesis has provided a comprehensive overview of M. birostris feeding ecology using non- invasive techniques, and identified the most likely drivers behind the largest worldwide aggregation of this species at Isla de la Plata, Ecuador. The first major finding was that variation in sightings among years was related to the El Niño Southern Oscillation. Fewer individuals were recorded during in the year that had experienced a very strong El Niño event, which is associated with decreased productivity and subsequent food availability in the eastern equatorial Pacific, suggesting that overall food availability in this region is likely to be important for the seasonal aggregation of this species. In terms of local food availability, the second major finding was that M. birostris rarely forage but primarily clean at Isla de la Plata during the day, with environmental parameters conducive to cleaning interactions the best predictors of M. birostris occurrence at this site. Taken together, these two findings suggest that while regional occurrence of M. birostris is related to food availability, Isla de la Plata itself is not an important foraging hotspot for this species. The third major finding was that the SI and FA profiles of M. birostris were not characteristic of a species that derives a large proportion of diet from near-surface zooplankton that are common during the day, and instead were more typical for an elasmobranch that predominantly feeds on mesopelagic prey. Overall, M. birostris likely aggregate at Isla de la Plata because it provides a suitable shallow habitat for cleaning and social interactions during the day, however, during the night, unpublished acoustic telemetry results indicate that individuals leave this site (Burgess and Marshall, 2015). These findings coupled with the rarity of M. birostris foraging activity during the day Isla de la Plata and this site’s proximity to the shelf edge (<40 km), means that M. birostris likely move offshore to shelf edge habitats during the night where they feed on vertically migrating mesopelagic prey assemblages such as euphausiids.

6.2 Manta birostris aggregations

6.2.1 Temporal trends in the tropical eastern Pacific

Prior to this thesis, there was little information on how regional climatic conditions affect M. birostris sightings. The primary study site, Isla de la Plata, is situated within the northern part of the Peru- Humboldt Current system, where physical processes and biological production are subject to inter- annual variability (Feely et al., 1987), with the El Niño-Southern Oscillation (ENSO) the primary source of this variability (Chavez et al., 1999). El Niño is the negative phase of the Southern Oscillation, and is associated with warmer sea surface temperatures, a deeper thermocline and a decline in primary production (Chavez et al., 1999). During the 2015 El Niño event, fewer M. birostris were sighted in comparison to the positive ENSO phase (La Niña), or ENSO neutral years (Chapter

2). In the eastern Pacific, during the extreme 1997 – 1998 El Niño event, some elasmobranchs extended their range, with species shifting away from the pool of warm water at the equator (Lea and Rosenblatt, 2000, White et al., 2015). Therefore, during the weak to moderate El Niño event in 2015, it is possible that M. birostris moved away from the equatorial warm water pool, which resulted in fewer individuals seen at Isla de la Plata in comparison to ENSO neutral or La Niña conditions. At Cocos Island, 1000 km north of Isla de la Plata, increases in M. birostris abundance during weak to moderate El Niño activity in comparison to La Niña and ENSO neutral conditions have been recorded over the course of two decades (White et al., 2015). In addition, historical accounts of M. birostris occurrence off Peru indicate that during the extreme El Niño in 1942, individuals were commonly seen as far as 16° S when water temperatures at this latitude were above 21 °C (Lobell, 1942). Possible range extensions for M. birostris off Ecuador during El Niño events, therefore, could incorporate habitats both at more northern and more southern latitudes.

During La Niña episodes, trade winds intensify and upwelling of cooler, nutrient-enriched subsurface waters increases in the eastern Pacific leading to greater productivity (Meinen and McPhaden, 2000b). In 2011, where high SOI values were consistent with La Niña conditions, a higher number of M. birostris were recorded in comparison to El Niño conditions, but sightings were concentrated within a shorter aggregation season in comparison to El Niño and ENSO neutral years (Chapter 2) Indicating that while increased food availability may increase sightings of M. birostris in the region, cooler surface waters produced during La Nina events may comprise a sub-optimal thermal choice for this species. In the west Indian Ocean, higher SOI values, associated along-shelf winds and increased upwelling rates were positively correlated with higher whale shark Rhincodon typus abundance (Sleeman et al., 2010). The association between M. birostris and other planktivorous elasmobranch sightings and ENSO activity indicates that large scale regional processes exert an influence over aggregative behaviour, presumably due to the combination of increased regional foraging opportunities, directional cues provided by the prevailing current, and thermal preferences of these species.

Sightings of M. birostris varied annually, but remained relatively stable throughout the study period, with no apparent decline or increase in abundance. Similar patterns have also been observed in M. birostris sightings off Brazil and Mozambique, albeit with seemingly much smaller populations (Luiz Jr et al., 2009, Rohner et al., 2013b). To the north of Isla de la Plata, M. birostris can be occasionally found at Cocos Island off Costa Rica, with small numbers of this species present on 4 % of all dives at the island (White et al., 2015). At Cocos Island, the relative abundance of M. birostris has declined 89 % over a period of 20 years (White et al., 2015), however, relative abundance of M. birostris over

98 the same temporal scale as examined in this thesis (2010 – 2015), while lower in comparison to the previous decade, appeared stable. At Cocos Island, there is visible and illegal fishing activity of elasmobranch species within National Park boundaries, and a reduction in sightings could be linked to fishing mortality. However, reasons behind a reduction in sightings in Costa Rica are ambiguous, and catch data for M. birostris is required to assess whether fishing is likely to significantly influence population numbers of this species at this site. Alternatively, declines in M. birostris sightings off Costa Rica could be a consequence of their avoidance of areas that have experienced an increase in ecotourism operations, with associated disturbance of the rays (Rohner et al., 2013b), or a natural change in distribution of prey species leading to a shift in distribution of consumers (Sims and Reid, 2002).

It is unknown whether M. birostris sighted at Cocos Island and Isla de la Plata comprise the same population, but the relative proximity of both islands (~1000 km apart) means this is a possibility. The smaller reef manta ray Manta alfredi has recorded movements across a latitudinal range of 1035 km off eastern Australia (Jaine et al., 2014), and M. birostris tagged off Ecuador have shown movements of up to ~550 km from the tagging site (A. Marshall, unpubl. data). Large-scale movements are also common for other planktivorous elasmobranchs in the eastern Pacific, with whale sharks off Mexico travelling on average 1812 km over 5 months (Eckert and Stewart, 2001), and whale sharks in the Galapagos conducting a 3300 km round trip from their tagging site within four months (Hearn et al., 2013). In contrast, M. birostris off Mexico in the eastern Pacific, and off Indonesia in the western Pacific, seemingly only conduct relatively localised movements within about a 150 km radius (Stewart et al., 2016a). However, juvenile M. birostris are generally found in offshore habitats in both Mexico and southeast Asia (Stewart et al., 2016a), and were not observed in this study by Stewart et al. (2016a). This suggests that despite relatively localised movements of a small number of adult M. birostris off Mexico and Indonesia, long range movements would presumably have to occur from inshore to offshore habitats for females to pup, or from offshore to inshore habitats for when juvenile M. birostris reached sexual maturity.

Should M. birostris off Costa Rica and Ecuador comprise the same population, this could mean the 6-year data set in this thesis may present a misleadingly stable snapshot of a M. birostris population that over 20 years that has suffered a decline. However, the large number of identified M. birostris (>2400) off mainland Ecuador compared to the number of identified individuals from other areas of this species’ distribution (Fig 1.2), suggests that eastern equatorial Pacific M. birostris sub- populations are not an immediate conservation concern. Further genetic analyses and tagging data for M. birostris off both Ecuador and Costa Rica would be needed to resolve whether individuals sighted

99 at both localities in the eastern equatorial Pacific comprise the same sub- population. Additional information on population size through photo identification based population modelling (Couturier et al., 2014), or effective population size estimates from genetic markers (Blower et al., 2012), would then be needed to assess whether M. birostris sub-populations off Ecuador and Costa Rica, or a combined eastern equatorial Pacific sub-population, are at risk.

6.2.3 Aggregative behaviour of Manta rays

Typically, aggregative behaviour of marine vertebrates is viewed as evolutionarily advantageous, whereby individuals derive certain benefits such as an increase in survivorship or reproductive success (Parrish and Edelstein-Keshet, 1999). An obvious aspect of survivorship in M. birostris, a large marine species that is assumed to have few predators, is food availability. Optimal foraging theory states that a prey resource will be easier to find at high density, and therefore, high density resource patches are favoured by consumers (Charnov, 1973). Dense prey concentrations are likely to be a critical factor in determining the feeding behaviour of large planktivores, as the energetic intake needs to exceed the energetic cost of feeding (Parker and Boeseman, 1954, Bone and Moore, 2008). Minimum prey density feeding thresholds have been determined for M. alfredi (Armstrong et al., 2016), whale sharks (Nelson and Eckert, 2007), basking sharks Cetorhinus maximus (Sims, 1999), and humpback Megaptera novaeangliae, minke Balaenoptera acutorostrata and fin Balaenoptera physalus baleen whale species (Piatt and Methven, 1992). Unfortunately, the low incidence of feeding activity at Isla de la Plata meant that a robust critical feeding threshold could not be established for M. birostris (Chapter 3). However, surface zooplankton biomass during M. birostris feeding events (1.9 mg m-3) was considerably lower than the calculated prey density threshold for M. alfredi (11.2 mg m-3) (Armstrong et al., 2016), indicating that availability of surface zooplankton biomass is not a major driver for M. birostris aggregative behaviour at Isla de la Plata during austral winter.

An understudied potential driver of M. birostris aggregative behaviour is the provision of cleaning habitat, in the form of shallow coral or rocky reefs that support species of cleaner fishes. Available cleaning habitat appears to be a common factor among the majority of sites worldwide where manta rays are predictably sighted in large numbers (O'Shea et al., 2010, Marshall et al., 2011c, Jaine et al., 2012). Cleaning behaviour is common and widespread among reef fishes (Losey Jr, 1972), with interactions generally occurring within a defined area or ‘station’ (Youngbluth, 1968). At Osprey Reef off eastern Australia, M. alfredi spend anywhere from five minutes to five hours at cleaning stations. In comparison, other co-occurring elasmobranchs had cleaning interactions that lasted for a matter of seconds (O'Shea et al., 2010). Despite being located on different sides of the Pacific, and

100 hosting different species of manta ray, there are a lot of similarities between observations at Osprey Reef and Isla de la Plata. Both sites appear to almost exclusively be used for cleaning, with tidal phase affecting the number of individual manta rays participating in cleaning interactions (Chapter 2). In addition, large numbers of solitary individuals were noted in both studies, which suggests that while social interactions can be an important influence over aggregative behaviour (Mourier et al., 2012), there are other drivers of manta ray presence at these sites.

While there is little information on manta ray cleaning behaviour, the theoretical advantage conferred on manta rays that engage in cleaning behaviours could be well supported by the current literature on teleost species. Cleaning interactions can remove mucus, dead or diseased tissue (Grutter and Bshary, 2003), as well as facilitating a reduction in ectoparasite load of the animal being cleaned (Poulin and Grutter, 1996). However, the current literature on teleosts does not readily explain the long periods of time that some manta rays spend at cleaning stations in comparison to shark species, or how cleaning behaviour in manta rays is influenced by tidal phase (O'Shea et al., 2010). Manta rays have a dorso-ventrally compressed body form, and therefore a high surface area-to-body volume ratio. (Gray, 1953). In coral reef fishes, the mean parasite load increases exponentially with the increase of mean surface area of the fish (Grutter, 1995), parasite abundance is positively correlated with fish standard length (Grutter, 1994, Oliva et al., 1996), and cleaner fish are also more likely to initiate a cleaning interaction with a larger client fish (Grutter et al., 2005). It remains to be seen, however, whether similarities exist between elasmobranchs and teleost cleaning interactions given that the majority of published information on cleaning behaviour between fishes involves cleaner fish in the genus Labroides and teleost clients (Grutter, 1999, Bshary and Schäffer, 2002, Cheney et al., 2009). Labroides cleaner wrasse partake in cleaning interactions with M. alfredi off Mozambique (Marshall, 2008) and Micronesia (Krajewski et al., 2017), but at Isla de la Plata, M. birostris are predominantly cleaned by black-nosed butterflyfish Johnrandallia nigrirostris (Chapter 2), a species of cleaner fish that had the largest number of clients and client species out of five species of cleaner fish that were identified at Malpelo Island in the eastern tropical Pacific (Quimbayo et al., 2017). Future studies could determine ectoparasite species and abundance on manta rays to try and elucidate why manta rays spend longer amount of time at cleaning stations than smaller shark species (O'Shea et al., 2010). In addition, future studies could investigate whether cleaner fish show preference to a specific area, as is seen in cleaner fish-thresher shark interactions (Oliver et al., 2011), to infer ectoparasite distribution on manta rays. Individual fitness benefits from cleaning interactions also need to be quantified to reconcile whether cleaning behaviour, or the need to be cleaned, can be integrated into classical evolutionary theories on resource use. In addition, while the prevalence of cleaning behaviour appears to be ubiquitous among the majority of worldwide manta ray aggregation sites

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(Dewar et al., 2008, Jaine et al., 2012), current observations on manta ray habitat use are likely slightly skewed towards cleaning habitat, which are sheltered, shallow, and have diverse fish assemblages, which make them prime locations for ecotourism and research activities.

6.2.4 Mesopelagic feeding

The synergistic application of SIA and FA on muscle tissue determined that daytime surface zooplankton does not comprise the majority of long term diet for M. birostris in the eastern equatorial Pacific (Chapters 4, Part I & 5). These findings suggest that in this region, M. birostris gets the majority of its energetic intake from another source, and is in agreement with multiple other studies that indicate daytime surface zooplankton does not comprise the majority of diet for planktivorous elasmobranch species (Couturier et al., 2013b, Marcus et al., 2016, Rohner et al., 2017). Given the molecular profile of M. birostris indicated a reliance on mesopelagic dietary sources, the rarity of M. birostris foraging activity during the day at Isla de la Plata, and the night time absence of this species at this site (Burgess and Marshall, 2015), it is likely that M. birostris feed at depth during the day, or over nearby productive continental shelf habitats at night when there is a high abundance of vertically migrating mesopelagic prey in surface waters (Hays, 2003).

The exploitation of mesopelagic sources by M. birostris has been confirmed from submersible footage from Mexico of M. birostris feeding at depth, and inferred from seasonal vertical movement patterns of this species associated with thermocline depth, where individuals were recorded in water temperatures as low as 9 °C (Stewart et al., 2016b). Furthermore, M. birostris stomach contents from the Philippines were dominated by Euphausia diomedeae, a krill species commonly found at 75 – 300 m (Mauchline and Fisher, 1969, Rohner et al., 2017), and mesopelagic euphausiid species also dominate the stomach contents of other closely related mobulid species (Notarbartolo-di-Sciara, 1988, Gadig et al., 2003). Animal daily routines are typically a trade-off between maximising foraging success and optimising physiological performance (Young et al., 2010b, Houston and McNamara, 2014). The occupancy of M. birostris at cleaning stations in warm surface waters during the day, could be a form of behavioural thermoregulation after recent foraging trips to cooler deep scattering layers, similar to ocean sunfish where individuals forage in deep waters and then recover their body temperature during surface periods (Nakamura et al., 2015). Future work could investigate whether M. birostris body temperature decreases during diving excursions and recovers during subsequent surface periods, and whether there is a relationship between time spent warming in surface waters and time foraging in cooler waters.

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6.3 Study limitations and future work

6.3.1 Habitat use

The identification of natural variability M. birostris sightings is vital for the robust interpretation of future trends in this species’ abundance. At Isla de la Plata, photo identification (photo ID) was used as a non-invasive tool to document M. birostris occurrence and subsequently to infer habitat use patterns over time (Marshall and Pierce, 2012). Photo ID, however, does have a number of limitations associated with spatio-temporal sampling logistics. Photos are only able to be taken by snorkelers or divers in relatively shallow waters, generally during daylight hours, and usually in areas where there is physical structure (e.g. reefs, off-shores islands). At Isla de la Plata, photo ID surveys were constrained to sites off the northern part of the island, with daily surface transects comprised of M. birostris counts conducted along almost the entirety of the east coast of the island (Chapter 2). The reliance on a commercial diver operator to conduct field work for this thesis meant we could not investigate the south and west coasts of Isla de la Plata for M. birostris occurrence. However, the south and west coasts of Isla de la Plata are exposed to the prevailing north-easterly trade winds, which create rough conditions and large swells on these sides of the island (Fig 6.1). Reef manta rays predominantly use the more sheltered sides of islands that are protected from prevailing winds, with this choice in sheltered habitat thought to minimise disturbances to cleaning and feeding activities (Dewar et al., 2008, Couturier, 2012). It is possible that M. birostris also uses habitat off the west and south coasts of Isla de la Plata and that daily sightings of this species on the northern sites are underestimates of the total number of individuals using the island. However, given the prevalence of cleaning behaviour, and the positive correlation between the number of individuals sighted with clear and undisturbed conditions (Chapter 2), it is likely that individuals are specifically targeting these northern sites as they provide the most suitable environment for cleaning interactions. A broader photo identification survey area, as well as the establishment of an acoustic array around the entirety of Isla de la Plata would be needed to determine whether M. birostris predominately uses cleaning stations in the northern and most sheltered waters off this island. In addition, Isla de la Plata is not the only known aggregation site for M. birostris in this region. Future work off mainland Ecuador would benefit from aerial survey data, which would allow a much larger area to examined and enable the identification of other aggregation sites where surface foraging behaviour during daytime hours may be common.

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6.3.2 Manta birostris at Isla de la Plata

Robust interpretation of dietary data for any animal requires the identification of how representative a data set is for the population being studied. For example, were sample sizes for sex and body size sufficient to allow for the description of diet across all demographics, and were data collected adequately over space and time to account for seasonal and regional differences? For large planktivores, rarely are all of these criteria met, and inferences about a species’ diet are usually constrained to a specific demographic at an aggregation site during a particular time of the year. All individuals sighted at Isla de la Plata, except one, were adults. Therefore, conclusions on dietary preferences for this species off mainland Ecuador gained through molecular analyses of tissues relate to mature individuals, but of unknown ages. A similar situation exists for M. alfredi, as the majority of studies have been conducted on individuals, that appear to be mature, based on body size and clasper length (in males), and the presence of mating scars and visible pregnancies (in females) (Marshall et al., 2011b, Couturier et al., 2014). Little is known about juvenile manta rays and they are rarely observed at day-time cleaning habitats where adult Manta spp. are commonly found (Marshall et al., 2011b, Couturier et al., 2014). In addition, juvenile M. birostris are more commonly caught than adult M. birostris in night-time pelagic gill-net fisheries off Sri Lanka (Stewart et al., 2016a), whereas in the Philippines, predominantly adult and few juvenile M. birostris are caught in night-time coastal gill-net fisheries (Rohner et al., 2017). Day-time observations and night-time fisheries catch indicate that manta rays exhibit size-based habitat segregation, however, molecular analyses of juvenile manta ray tissue and analysis of their stomach contents is needed to resolve whether size-based habitat segregation results in differences in dietary intake between juvenile and adult manta rays.

Regional circulation patterns in the ocean are known to influence mortality in marine fishes with early planktonic stages (Bograd et al., 1994, Hinckley et al., 1996), but are less likely to affect large, long- lived viviparous species like M. birostris. However, it is possible that environmental variation experienced by individuals in different locations can lead to within-population feeding history variation. Feeding history is known to affect habitat choices in turtles (Heithaus et al., 2007), marine mammals (Kovacs et al., 2011), and birds (Frederiksen et al., 2006), and the biophysical predictor variables identified in the present study (Chapter 2), might only be important determinants for a particular subset of the overall population. For example, the M. birostris aggregation at Isla de la Plata may comprise individuals that had been feeding over nearby productive shelf habitats prior to their arrival at the study site, and this could account for the low incidence of daytime feeding activity at this site (Chapters 2 and 3). Feeding history and associated body condition is difficult to measure in large, free swimming animals and visual observations alone can be highly subjective. Relative

104 levels of energy stores to other structural components can be a good indicator of body condition (Green, 2001), however, elasmobranch energy stores are typically in the liver (Pethybridge et al., 2014), which is not possible to sample in free-swimming animals. Other non-lethal techniques to assess body condition could be investigated, such as the measurement of steroid hormones that can be used to determine seasonal reproductive patterns (Awruch et al., 2008), or responses to natural and human-induced stressors (Romero and Wikelski, 2001), to facilitate cross study comparisons.

Figure 6.1. Aerial drone photo of the north and east side of Isla de la Plata, Ecuador (bottom panel). The top two panels show rough conditions on the south coast of the island where diving and boat surveys could not be conducted.

6.3.3 Designation of ‘critical’ foraging habitats

The Endangered Species Act defines critical areas as habitats that comprise features ‘essential for the survival and recovery of a listed species, and which may require special management considerations or protections’ (U.S Fish and Wildlife Service) . Critical foraging areas can, therefore, be easy to identify for marine species that have a predictable habitat occupancy in relation to reproductive

105 behaviours and that feed within a distinct foraging area in close proximity to sites associated with these reproductive behaviours (Eckert et al., 1999, López-Mendilaharsu et al., 2005). While information on reproductive behaviour and potential nursery habitats for M. birostris is unavailable, molecular analyses here suggested that mesopelagic sources make up the majority of diet for mature individuals of this species (Chapters 4 and 5). However, potential mesopelagic prey species such as euphausiids, vertically migrate, staying deep during the day and moving to shallower waters at night (Hays, 2003). At present, molecular analyses are unable to resolve what depth putative mesopelagic dietary items are consumed, thereby limiting inferences on whether M. birostris predominantly target prey at a certain depth of the water column. To resolve this issue, zooplankton sampling throughout the diel cycle at aggregation sites and other suspected foraging locations such as shelf edge habitats is required. While this is difficult for projects constrained to commercial dive operators with modest research budgets, there is potential for collaborations with larger research institutions that have these sampling capabilities. Baseline molecular profiles of prey also need to be combined with known extent of consumer movements, to validate that availability of certain mesopelagic prey types coincides with reasonable spatial and temporal overlap of the predator species. The addition of accelerometers on animals, which can identify swimming speeds assumed to be associated with foraging behaviour, alongside deep sea cameras that record foraging events, would also help corroborate assumptions on foraging behaviour from tracking and molecular studies.

6.3.4 Trophic role and broader ecological context

Feeding ecology studies and the characterisation of a species’ diet can provide useful insights into its role within an ecosystem (Wetherbee et al., 2012). All elasmobranchs are carnivores and usually have apex or meso-predator roles; making them chief components in regulation and maintenance of ecosystems they inhabit (Myers et al., 2007, Heithaus et al., 2008). In general, manta rays occupy a lower trophic level (3.4 – 3.7, Chapter 4, Part I), compared to similarly sized non-planktivorous shark species (3.8 – 4.5 (Estrada et al., 2003)), and it is unknown what consequences their loss would have, if any, on ecosystem function. Small planktivorous fish that comprise massive populations and occupy a similar intermediate secondary consumer trophic role to manta rays, can play an important role in marine ecosystems via the reallocation of biomass of higher trophic level organisms that feed on these small planktivores (Cury et al., 2000, Duarte and Garcıa, 2004). Predators of Manta spp. include large shark species (Marshall and Bennett, 2010a), and orcas (Alava and Merlen, 2009). Manta ray presence at aggregation sites, however, is not usually accompanied by an increase in these predator species (Marshall and Bennett, 2010a), indicating that manta rays are unlikely to play a role in influencing the distribution of apex predators. However, the carcasses of large planktivores such

106 as manta rays (Higgs et al., 2014) and baleen whales (Smith and Baco, 2003), may instead constitute an important trophic role as concentrated food drop to the deep, which would provide a positive feedback loop in recycling vital components and nutrients. Therefore, the removal of manta rays and other large planktivorous elasmobranchs could have a similar effect to industrial whaling on deep sea communities, whereby deep sea biodiversity is altered due to the diminishment of an important source of organic matter (Butman et al., 1995).

6.4 Conclusion

Prior to this study, little information was available on the largest identified population of M. birostris off mainland Ecuador. Daytime surface zooplankton availability was shown not to be an important driver of M. birostris aggregative behaviour at Isla de la Plata, however, there was a link between the number of individuals aggregating at this site, and the physical and biological oceanography of the broader eastern equatorial Pacific region (Chapter 2). While not an important foraging hotspot, Isla de la Plata likely serves as multi-purpose area for M. birostris that facilitates a reduction in energy expenditure after night time foraging, provides an environment for cleaning, and as a platform for social interactions such as courtship in this species (Chapter 2), which is typically solitary (Kashiwagi et al., 2011). The cleaning habitat at Isla de la Plata is present all year round and hosts a number of client species. However, an important feature of this cleaning site in terms of M. birostris utilisation is likely related to its proximity to seasonally productive continental shelf edges (Chapter 2), where individuals may feed during night time hours when they are absent from daytime aggregation sites (Deakos et al., 2011, Couturier, 2012, Jaine et al., 2014). The molecular profiles of M. birostris were demonstrative of a species that does not gain the majority of its diet from daytime surface zooplankton sources (Chapters 4 and 5), which also supports possible nocturnal foraging behaviour in this species related to high and low biomass in marine environments between night and day time (Hays, 2003), respectively. However, it is still uncertain whether manta rays would aggregate at certain sites if there was a provision of cleaning habitat but no offshore food resources, or vice versa. The effect of El Niño on the number of M. birostris sighted off mainland Ecuador during this study suggests that while the provision of regional food resources influences the occurrence of this species, M. birostris still aggregate at Isla de la Plata to clean even in times of low biological production, albeit in smaller numbers than in times of higher regional productivity (Chapter 2).

An increase in scientific interest in Manta spp. over the past decade has a provided a large amount of important biological and ecological information that can subsequently be used for effective protection measures (Couturier et al., 2012). Future research on M. birostris within the eastern Pacific, and

107 throughout other oceanic regions, should aim to ask questions that have been constructed alongside a priori information on sampling location parameters. Thereby allowing research at aggregation sites to incorporate the most common behaviour observed alongside theoretical arguments for aggregative behaviour. Additional information on drivers of M. birostris aggregative behaviour and habitat use is vital to assess whether current conservation protection measures are adequate to prevent further declines in regional populations, and to advance our understanding of the biology and ecology of this largely elusive species.

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Appendices

Chapter 3

3ºN

0 Ecaudor 3ºS

3ºN

0 Ecaudor 3ºS

3ºN

0 Ecaudor 3ºS

90ºW 80ºW

Chlorophyll a concentration (mg m3)

0.01 0.06 0.32 1.78 10

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Chapter 4 (Part I)

Table S4.1 Mixing model summary statistics on the mean credible interval source contributions of surface zooplankton and mesopelagic sources to Manta birostris diet. Model 1 source inputs comprised of mesopelagic fishes and lipid normalised surface zooplankton δ13C values and used DTDFs from large sharks (Hussey et al., 2010). Model 2 source inputs were mesopelagic fishes and non-lipid normalised surface zooplankton δ13C with the large shark DTDF (Hussey et al., 2010). Models 3 and 4 comprised the same source inputs as model 1 and 2, respectively, but used DTDF values from Triakis semifasciata (Kim et al., 2012a). Model 5 used the highest δ13C and δ15N values of surface zooplankton and mesopelagic fishes to assess whether extreme surface zooplankton values could account for the discrepancy in stable isotope profiles between surface zooplankton and M. birostris. Model Source 2.5% 25% 50% 75% 97.5% 1 Surface Zooplankton 0.037 0.086 0.115 0.147 0.215 Mesopelagic fishes 0.785 0.853 0.885 0.914 0.963 s.d. δ13C 0.468 0.684 0.787 0.893 1.109 s.d. δ15N 1.578 1.875 2.042 2.219 2.593

2 Surface Zooplankton 0.077 0.154 0.204 0.254 0.363 Mesopelagic fishes 0.637 0.746 0.796 0.846 0.923 s.d. δ13C 0.564 0.764 0.866 0.976 1.209 s.d. δ15N 1.548 1.821 1.977 2.152 2.527

3 Surface Zooplankton 0.321 0.393 0.431 0.468 0.541 Mesopelagic fishes 0.459 0.532 0.569 0.607 0.679 s.d. δ13C 0.558 0.713 0.798 0.88 1.064 s.d. δ15N 0.701 0.93 1.055 1.177 1.434 4 Surface Zooplankton 0.241 0.298 0.327 0.357 0.411 Mesopelagic fishes 0.589 0.643 0.673 0.702 0.759 s.d. δ13C 0.598 0.753 0.833 0.925 1.104 s.d. δ15N 0.495 0.814 0.96 1.092 1.358

5 Surface Zooplankton 13C* 0.050 0.197 0.314 0.417 0.567 Surface Zooplankton 15N** 0.026 0.081 0.137 0.204 0.303 Mesopelagic fishes*** 0.373 0.489 0.549 0.608 0.695 s.d. δ13C 0.785 0.927 1 1.076 1.237 s.d. δ15N 0.304 0.819 1.008 1.162 1.456 *δ13C = -18.6, δ15N = 8.8, **δ13C = -19.9, δ15N = 9.7, ***δ13C = -17, δ15N = 7

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Chapter 5

Lipid extraction

For the one-day method, 10 – 40 mg of freeze dried Manta birostris muscle was weighed out then 0.24 mL distilled water and 6 mL of 2:1 chloroform:methanol (2:1) was added. The mixture was stirred for ~1 minute, left to rest for 10 minutes, and then stirred again. The mixture was then poured into a syringe with a GF/C filter paper at the bottom and was filtered into a centrifuge tube. The tube was washed by the addition of 6 mL chloroform:methanol (2:1) solution and remixed for 30 seconds. This wash solution was then poured again through the syringe and filter paper. 3 mL of 0.9 % NaCl was added to the centrifuge tube and contents were thoroughly mixed through inversion and centrifuged (5 min, 2500 rpm). The top layer was then discarded and 2.2 mL 50 % MeOH was added to each sample. Contents were again mixed by inversion and centrifuged (5 min, 2500 rpm). The top layer was discarded again and the mixture was filtered through Na2SO4 filter (20 mL syringe, 3 mL

Na2SO4) into a 25 mL cylinder. The screw cap centrifuge tube and the syringe were washed with a small amount of pure chloroform to remove any residual lipids.

For the three-day (Folch et al., 1957) method, a homogenized wet M. birostris sample (15 – 100 mg) was weighed out and 10 mL of chloroform:methanol (2:1) was added. The contents were mixed and left to settle for 1 hour. Contents were then filtered into a separating funnel ensuring the removal of all solids. Another 10 mL of chloroform:methanol (2:1) was added to the filtered sample to wash any residual lipids into the separating funnel. 5 mL of 0.9 % NaCl was then added to the solvent and the funnel was closed and shaken to mix the NaCl and solvent solution. The funnel was then left at 4 °C for 6 hours to separate into water and solvent layers. The lower solvent phase was drained off and the upper layer was discarded. The lower solvent phase was added to the separating funnel again and 5 mL of 1 % NaCl:methanol (1:1) was added. The funnel was closed and shaken again to mix the contents. The funnel was then left at 4 °C for 12 hours to separate into water and solvent layers. The lower phase was again drained off into an evaporating flask and the upper layer was discarded. The total lipid extract (LE) from both methods was dried under a stream of inert nitrogen gas and weighed, and was then stored at −18 °C.

Fatty acid signature analysis

Fatty acid methyl esters (FAME) were obtained by transferring the LE into a pyrex screw cap test tube, this was then dried under a stream of inert nitrogen gas. To the dried LE, 1.0 mL of MeOH:Toluene (3:2) and 1.0 mL of Acetyl chloride: methanol (1:20) (freshly prepared on ice) was

142 added. The tube was then flushed with nitrogen and the stopper was tightly replaced and shaken. Test tubes were then placed on a heating block at 100 °C for 1 hour. Tubes were allowed to cool, then 0.3 mg of internal standards methyl heneicosanoic acid (Me C21:0) and tricosanoic acid (C23:0) were added. 1.5 mL hexane and 1 mL water were then washed into the tube and the tube was flushed with nitrogen and shaken. Tubes were centrifuged at 1500 rpm for 2 mins and the upper layer was pipetted off and filtered through Na2SO4 into a 4.5 mL HPLC vial. The hexane extraction was then repeated 2 more times and the collected upper layers were mixed and collected into a 1.5 mL vial for gas chromatography (GC) analysis.

August September October

Stress: 0.08

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Table S5.1 Signature fatty acid (FA) profiles (% of total FA ± s.d.) of Manta birostris among sample collection years. The FA profiles of M. birostris were not significantly different among years (ANOSIM, R = 0.05, P = 0.27). Fatty Acid M. birostris M. birostris M. birostris 2012 (n = 9) 2013 (n = 11) 2014 (n = 29) 14:0 6.9 ± 8.7 2.2 ± 2.5 2.1 ± 3.3 16:0 36.9 ± 6 29.6 ± 4.7 25.9 ± 7.3 18:0 21 ± 5.8 25.7 ± 3.7 23 ± 6 20:0 1 ± 0.9 0 1.5 ± 4.2 22:0 0.6 ± 1 0 3.4 ± 5.5 ΣSFA 67.2 ± 6.1 57.5 ± 4.9 56.1 ± 11.6 15:1 0.4 ± 0.5 0.7 ± 1 0 16:1ω7 2 ± 0.9 1.9 ± 1.7 1.8 ± 2 18:1ω9t 0.4 ± 0.8 0 5.2 ± 8.9 18:1ω9c 21.6 ± 4 27.9 ± 4.6 16.4 ± 8.8 18:1ω7 4 ± 1.3 7.6 ± 1.9 4.8 ± 4.3 20:1ω9 0.5 ± 0.6 1.4 ± 1.3 1.1 ± 1.7 22:1ω9 0.4 ± 0.7 0 ± 0 8.7 ± 13.7 24:1ω9 1.6 ± 1.4 0.4 ± 1 0.3 ± 0.9 ΣMUFA 30.9 ± 5.5 40 ± 5.3 38.3 ± 13.1 18:2ω6c 0.3 ± 0.4 0.3 ± 0.7 1.2 ± 2.3 18:3ω3 0 0 0.6 ± 1.9 20:4ω6 1 ± 1.1 1.6 ± 2.4 2.1 ± 2.3 22:5ω3 0 0 0.6 ± 2.4 22:6ω3 0.1 ± 0.4 0.6 ± 1.4 0.7 ± 2.7 ΣPUFA 1.9 ± 1.6 2.5 ± 4.4 5.6 ± 7 ΣΩ3 0.4 ± 0.6 0.6 ± 1.4 1.9 ± 5.6 ΣΩ6 1.5 ± 1.5 1.9 ± 3.1 3.7 ± 3.8 Ω3/ Ω6 0.1 ± 0.2 0.1 ± 0.2 0.2 ± 0.5 Others* 1.1 ± 0.1 0 0.7 ± 0.1 *15:0, 17:0, 19:0, 22:0, 24:0, 14:1ω5, 17:1, 20:1ω7, 18:2ω6t, 18:3ω6, 18:4ω3, 20:2ω6, 20:3ω6, 20:3ω3, 20:5ω3, 22:4ω6. Functional groupings of FAs are shown as: total saturated fatty acids (ΣSFA); total monounsaturated fatty acids (ΣMUFA); total polyunsaturated fatty acids (ΣPUFA); total omega-3 fatty acids (ΣW3); total omega-6 fatty acids (ΣW6).

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Table S5.2 Similarity percentage analysis (SIMPER) results of Manta birostris fatty acid profiles for cluster groups A and B (70 % similarity). Fatty acids with an average contribution >5 % are included and data was not transformed prior to analysis. Fatty Acid Contribution to Cumulative Cluster A Cluster B similarity (%) contribution to (% of total FA) (% of total FA) similarity (%) 18:1ω9t 24.8 24.8 17.2 0.1 18:1ω9c 21 45.86 8.2 22.7 22:0 10.3 56.2 7.8 0.8 16:0 9.1 65.3 26 31 18:1ω7 8.9 74.3 0 6.2 22:1ω9 7.5 81.8 4.8 4.8 18:0 5 86.8 24.1 24

Table S5.3 Signature fatty acid (FA) profiles (% of total FA ± s.d.) of surface zooplankton collected from Isla de la Plata, Ecuador among sampling months. Fatty Acid Zooplankton Zooplankton Zooplankton August (n = 8) September (n = 16) October (n = 8) 14:0 8.7 ± 2.2 7.6 ± 2.4 6.8 ± 1.4 15:0 1.2 ± 0.3 1.2 ± 0.4 1.1 ± 0.5 16:0 10.8 ± 11.4 21.4 ± 9.7 8.4 ± 15.5 17:0 1.7 ± 0.4 1.5 ± 0.5 2 ± 0.8 18:0 7.8 ± 2.1 7.8 ± 1.9 9.9 ± 2.9 19:0 0.2 ± 0.2 0.2 ± 0.1 0.4 ± 0.2 20:0 0.6 ± 0.2 0.8 ± 1 1 ± 0.5 22:0 0.5 ± 0.3 0.8 ± 1 0.7 ± 0.3 ΣSFA 32 ± 8.9 41.8 ± 10.7 30.9 ± 15.8 16:1ω7 6.2 ± 1.9 4.7 ± 1.7 5 ± 2.1 18:1ω9c 6.9 ± 1.9 5.9 ± 1.4 8 ± 2.5 18:1ω7 3.3 ± 1.1 2.4 ± 0.6 2.6 ± 1.1 20:1ω9 0.9 ± 0.3 0.6 ± 0.3 0.6 ± 0.4 22:1ω9 0.9 ± 0.9 1.4 ± 1.3 1.4 ± 2.6 24:1ω9 1.4 ± 0.4 1.3 ± 0.4 1.8 ± 0.9 ΣMUFA 20.1 ± 4.8 16.8 ± 3.3 20.1 ± 1.7 18:2ω6c 1.6 ± 0.4 1.5 ± 0.6 1.3 ± 0.6 18:3ω3 1.2 ± 0.4 1.2 ± 0.6 0.9 ± 0.4 20:4ω6 2.2 ± 0.5 1.4 ± 0.5 2 ± 0.9 20:5ω3 11.8 ± 3.4 9.2 ± 3.2 10.9 ± 4.8 22:5ω3 2.2 ± 1.5 1.5 ± 0.8 1.5 ± 0.6 22:6ω3 27.6 ± 5.8 25.4 ± 7.2 31.3 ± 8.9 ΣPUFA 47.8 ± 9.2 41.4 ± 11.8 49 ± 16.1 ΣΩ3 43.2 ± 8.6 37.8 ± 10.8 44.9 ± 14.4 ΣΩ6 4.6 ± 1 3.6 ± 1.2 4.1 ± 1.8 Ω3/ Ω6 9.7 ± 2.2 9.9 ± 3.1 9.3 ± 4 145

Others* 1.8 ± 0.1 1.8 ± 0.2 1.9 ± 0.1 *17:1, 18:1ω9t, 20:1ω, 18:2ω6t, 18:3ω6, 18:4ω3, 20:2ω6, 20:3ω6, 20:3ω3, 22:4ω6. Functional groupings of FAs are shown as: total saturated fatty acids (ΣSFA); total monounsaturated fatty acids (ΣMUFA); total polyunsaturated fatty acids (ΣPUFA); total omega-3 fatty acids (ΣW3); total omega-6 fatty acids (ΣW6).

Table S5.4 Similarity percentage analysis (SIMPER) of surface zooplankton fatty acid profiles among cluster groups 1 and 2 (80 % similarity). Fatty acids with an average contribution >5 % are included and data was not transformed prior to analysis. Fatty Acid Contribution to Cumulative Cluster 1 Cluster 2 similarity contribution to (% of total FA) (% of total FA) (%) similarity (%) 16:0 46.9 46.9 0.164 23.1 22:6ω3 18 64.9 34.6 25.9 20:5ω3 9.2 74.1 13.9 9.41

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