The Distribution and Abundance of Coral at Kilometre Scales In

The distribution and abundance of coral at kilometre scales in response to local versus global disturbances in the Central Indian Ocean Mr Dominic Edmund Paul Bryant Master of Philosophy; Bachelor of Science

A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2018 School of Biological Sciences

Abstract

Coral reefs are rapidly declining in most regions of the globe. This decline is due to threats from increasing human population pressure (e.g. overfishing, destructive fishing, coastal development, and water pollution) as well as biological events (e.g. Acanthaster planci outbreaks, coral disease etc.), and climatic disturbances (e.g. increasing frequency and intensity of tropical storms, heat stress and acidification). In the past 30 years, coral reefs have lost approximately 50% of corals, with decline occurring at rates faster than coral reef scientists and managers can monitor or manage. Part of this inability to respond effectively to these challenges comes down to the scale of the problems versus our ability to spend time on coral reefs due to diving limitations and costs.

The present thesis focused on the effect of environmental factors on coral reefs in the Maldives Archipelago in the Central Indian Ocean. To gain a better understanding of the local to global threats to reefs in this vast region, we piloted and deployed recent technological advancements in underwater data collection and analysis. The first objective of this thesis explored the utility and accuracy of underwater imaging systems combined with automated image analysis for acquiring highly detailed, but large-scale (2km) benthic surveys (XL-Catlin Seaview Survey) in the Maldives and to provide quantitative measurements of coral cover at relatively large scales. These novel techniques were calibrated against conventional techniques involving 50m transects at 15 sites surveyed in the Central Maldives. The results from this comparison demonstrate that the results of novel, large scale monitoring methods were not statistically different to that of conventional techniques yet were able to make measurements across 10-15 fold larger areas of coral reef communities. Interestingly, the novel large-scale techniques used here tended to report lower levels of coral cover than previous surveys. While some this effect is not doubt due to recent large-scale heat stress and coral mortality, part of this effect is due to differences between the two techniques. In the conventional case, survey teams tend to select areas for monitoring based on high coral cover and then following the trajectory over time. The large-scale techniques used here followed changes to the benthic structure by selecting a key habitat (10m, reef slope) and then measured changes at these large kilometre scales. Consequently, the novel large-scale surveys had a greater likelihood of encountering locations less dense in coral due to their length and ability to overcome fine- scale phenomena (thereby decreasing the mean).

i The second objective of this study involved measuring and exploring the potential effects of land use, atoll population level, and reef location (inside atoll vs. atoll forereef) on coral communities in the Chagos archipelago and Central Maldives. Reef systems in the Maldives archipelago are threatened by increasing anthropogenic pressures such as sedimentation from land reclamation activities, higher reef fishing pressure from a growing tourism industry, and marine pollution from waste water outflow. Conversely, the Chagos Archipelago is a 640,000 square kilometre Marine Protected Area, with the only established human population of 4,239 people situated within the United States Military Base on the southernmost atoll, Diego Garcia. Despite the significant differences in threats from anthropogenic stress, coral composition in percent cover did not differ statistically between the two reef systems. There were differences at subsequent levels of analysis. For example, local community island had reefs having less coral cover when compared to unpopulated reefs, which was presumably a consequence of human activities such as fishing pressure and coastal development.

The third and last objective of this thesis was to explore the drivers and impact of the 2016 mass coral bleaching event. Unusually high levels of heat stress occurred in the Maldives in April- June 2016. Revisiting the 2015 survey sites allowed the exploration of the potentially synergistic effects of local stressors on climate change related heat stress in the Central Maldives. While the results revealed a significant loss of coral cover presumably due to heat stress, impacts were similar for different land use regimes and island population levels. Part of this may be due to the fact that ‘Local community island’ reefs have already reached chronically low levels such that picking up effects of other stressors within the 2016 impacts very difficult.

In conclusion, this thesis explored the use of a novel technology for understanding large- scale change to coral reefs in the central Indian Ocean. While also providing a much- needed baseline for monitoring change in the Maldives and Chagos archipelagoes, it has provided unique insights into the interaction between a large-scale global change and local stresses occurring on coral reefs in the Indian Ocean due to local human activities.

ii 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, financial support 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 higher degree by research 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 and have sought permission from co-authors for any jointly authored works included in the thesis.

iii Publications during candidature

Bryant, D.E.P., Rodriguez-Ramirez, A., Phinn, S., Gonzalez-Rivero, M., Brown, K..T., Neal, B.P., Hoegh-Guldberg, O., Dove, S. (2017) Comparison of two photographic methodologies for collecting and analysing the condition of coral reef ecosystems. Ecosphere, 8(10).

Brown K.T., Bender-Champ, D., Bryant, D.E.P., Dove, S., Heogh-Guldberg, O. (2017) Human activities influence benthic community structure and the composition of the coral- algal interactions in the central Maldives. Journal of Experimental Marine Biology and Ecology. 497.

Campbell, M.L., Bryant, D.E.P., Hewitt, C.L. (2017) Biosecurity messages are lost in translation to citizens: Implications for devolving management to citizens. PLoS ONE, 12(4). e0175439. https://doi.org/10.1371/journal.pone.0175439

Grimsditch, G., Basheer, A., Bryant, D.E.P. (2017) Extreme white colouration of frogfish Antennarius maculatus due to coral bleaching event. Coral Reefs 36(1).

Publications included in this thesis

Contributor Statement of contribution Bryant, D.E.P. (Candidate) Conception and design (70%) Analysis and interpretation (70%) Drafting and production (65%) Rodriguez-Ramirez, A. Conception and design (0%) Analysis and interpretation (5%) Drafting and production (5%) Phinn, S. Conception and design (5%) Analysis and interpretation (5%) Drafting and production (5%)

iv Gonzalez-Rivero, M. Conception and design (5%) Analysis and interpretation (5%) Drafting and production (5%) Brown, K.T. Conception and design (0%) Analysis and interpretation (5%) Drafting and production (5%) Neal, B.P. Conception and design (0%) Analysis and interpretation (0%) Drafting and production (5%) Hoegh-Guldberg, O. Conception and design (10%) Analysis and interpretation (5%) Drafting and production (5%) Dove, S. Conception and design (10%) Analysis and interpretation (5%) Drafting and production (5%)

Manuscripts included in this thesis

No manuscripts included.

v Contributions by others to the thesis

Prof Ove Hoegh-Guldberg: Joint-Principle advisor, Concept and design, analysis and interpretation of research, drafting significant parts. Assoc. Prof. Sophie Dove: Joint-Principle advisor, Concept and design, analysis and interpretation of research, drafting significant parts. Prof Stuart Phinn: Associate Advisor, Concept and design, analysis and interpretation of research. Dr Julie Vercelloni: Analysis and interpretation of research data. Dr Manual Gonzalez-Rivero: Concept and design. Dr Alberto Rodriguez-Ramirez: Non-routine technical work; analysis and interpretation of research data. Dr Chris Roelfsema: Concept and design. Mr Peter Dalton: Non-routine technical work. Mr Sebastian Lopez Marcano: Non-routine technical work. Ms Kristen Brown: Non-routine technical work. Dr Benjamin Neal: Non-routine technical work. Mr Jon Schleyer: Non-routine technical work. Ms Abbie Taylor: Non-routine technical work. Mr Nizam Ibrahim: Non-routine technical work.

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

None.

Research Involving Human or Animal Subjects

No animal or human subjects were involved in this research.

vi Acknowledgements

This project would not have been possible without the support and guidance of my two primary supervisors, Professor Ove Hoegh-Guldberg and Associate Professor Sophie Dove. I would like to thank them for the opportunity they gave me to be a part of this project and lab, as well as all the help they provided me on the way. I would also like to acknowledge my co-supervisor Professor Stuart Phinn and thank him for coming on board to help with this project.

Next I would like to thank XL-Catlin, for providing me with the XL-Catlin scholarship and funding for the majority of this thesis work. This included the partnership with The Ocean Agency/Underwater Earth (Richard Vevers, Lorna Parry, and Christophe Bailhache). Furthermore, the work of the Global Change Institute project management team (Sara Naylor, Abbie Taylor, Sussie Green, Shari Stepanhoff) for their assistance in arranging fieldwork related activities.

The friendship and teamwork displayed by my lab partners in Coral reef ecosystems lab and Global Change Institute (Peter Dalton, Anjani Ganase, Kristen Brown, Veronica Radice, Julie Vercelloni, Catherine Kim, and Benjamin Neal,) was much appreciated and this work could not have been completed without them. I would also like to acknowledge the guidance from those who gave advice on in country knowledge, including Nizam Ibrahim from the Marine Research Centre Maldives, my brother Alexander Bryant from Emperor Divers Maldives, and Jon Schleyer of the Chagos Conservation Trust who assisted with his in country knowledge for the Chagos Archipelago field trip.

The crew of vessels used for the fieldwork of this thesis provided excellent facilities to carry out our research. These include MV Emperor Voyager and MV Emperor Atoll from Emperor Divers Maldives, and the crew of the MV Pacific Marlin in the Chagos Archipelago.

Finally I would like to think all my family and friends who have supported me through the duration of this thesis. With special thanks to my parents Mary and Nicholas, and brothers Alexander and Steve.

vii Financial support

An Australian Government Research Training Program Scholarship supported this research. Data collected for Chapter 2 and Chapter 3 were part of the XL-Catlin Seaview Survey, funded by XL-Catlin Ltd. XL-Catlin Ltd, in the form of the XL-Catlin Oceans scholarship, also provided a living stipend.

viii Keywords

Coral reefs, Indian Ocean, Maldives, Chagos Archipelago, community ecology, coral mortality, anthropogenic disturbance, climate change, spatial ecology.

Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 060202, Community Ecology (excl. Invasive Species Ecology) - 30% ANZSRC code: 060205 Marine and Esturine Ecology (incl. Marine Ichthyology) – 70%

Fields of Research (FoR) Classification

FoR code: 0602, Ecology, 100%

ix Dedications

Dedicated to my father Nicholas (1946-2018) and brother Steve (1964-2015).

x Table of Contents

List of Figures & Tables ...... xii List of Abbreviations ...... xiv Chapter 1: General Introduction ...... 1 1.1 Coral Reefs and their importance ...... 1 1.2 Impacts facing coral reefs across the globe ...... 1 1.3 Environmental drivers on coral reefs ...... 2 1.4 Coral reefs of the Central Indian Ocean ...... 3 1.5 The key threat to coral reefs: Climate change and Ocean warming ...... 4 1.6. Thesis scope ...... 5 1.7. References ...... 9 Chapter 2: Comparison of methodologies for collecting and analysing the condition of coral reef ecosystems...... 18 2.1. Abstract ...... 18 2.2 Introduction ...... 19 2.3 Methodology ...... 22 2.4. Results ...... 31 2.5. Discussion ...... 38 2.6. Acknowledgements ...... 41 2.7. References ...... 42 Chapter 3: Bimodal coral communities and recent environmental stress in the Central Indian Ocean...... 47 3.1 Abstract ...... 47 3.2 Introduction ...... 48 3.3 Material and Methods ...... 52 3.4. Results ...... 58 3.5. Discussion ...... 67 3.6. Acknowledgments ...... 74 3.7. References ...... 74 Chapter 4: Broad-scale impacts of the 2016 mass-bleaching event in the Central Maldives were not associated with land use practices...... 83 4.1 Abstract ...... 83 4.2 Introduction ...... 84 4.3 Materials and methods ...... 88 4.4 Results ...... 93 4.5 Discussion ...... 101 4.6 Acknowledgments ...... 106 4.7. References ...... 107 Chapter 5: General Discussion...... 113 5.1 Thesis findings ...... 115 5.2. Future directions ...... 118 5.3 References ...... 119 Appendices ...... 124

xi List of Figures & Tables

Fig 1.1 The SeaView II (SVII) camera system diver propulsion vehicle (DPV)…………7

Fig 2.1. Coral reef survey technique differences in spatial scale and taxonomic resolution...... 20

Fig 2.2. Map of survey sites for comparison of SVII and conventional photoquadrat methods ...... 23

Fig 2.3. Methods being compared over 50m linear transect at 15 sites in the Maldives ...... 25

Fig 2.4. Cropped and color corrected SVII 1x1m photoquadrat from Emboodhoofinolhu reef ...... 26

Table 2.1. Area covered in m2 by SVII and conventional methods ...... 27

Table 4.2. Label categories and functional groups used for the image annotation ..... 29

Fig 2.5. Correlation (with 95% confidence bands) and Bland-Altman method agreement plots for percent cover ...... 32

Fig 2.6. Correlation (with 95% confidence bands) and Bland-Altman method agreement plots for percent cover of Acropora labels ...... 33

Fig 2.7. Correlation (with 95% confidence bands) and Bland-Altman method agreement plots for percent cover of Massive coral labels ...... 35

Fig 2.8. Correlation (with 95% confidence bands) and Bland-Altman method agreement plots for percent cover of thin/foliose plating corals ...... 36

Fig 2.9. Correlation (with 95% confidence bands) and Bland-Altman method agreement plots for percent cover of other invertebrates ...... 37

Table 3.1. Sampling design for sites surveyed in the Chagos and Maldives from February to April 2015...... 52

Fig 3.1. Sites surveyed in the Central Indian Ocean ...... 54

Fig 3.2. Example of a linear 2km transect from Huluhumale forereef on the outside of North Male ...... 56

Table 3.2 Kolmogorov-Smirnov test output showing differences in density distributions of hard coral and morphologies ...... 59

Table 3.3. Mean benthic cover in Chagos Archipelago and Maldives ...... 59

Fig 3.3. Violin plots of the probability distributions of percent coverage ...... 60

Table 3.4 Wald Chi-squared test for effect of covariates (reef location and land-use) 62

xii Table 3.5 Output for linear mixed effects model investigating the effects reef location in Chagos, and reef location and land-use in Maldives ...... 63

Fig 3.4. Violin plot showing the differences in the probability distribution of hard coral cover between location ...... 64

Fig 3.5. Violin plot showing the differences in the probability distribution of branching coral cover between location ...... 65

Fig 3.6. Violin plot showing the differences in the probability distribution of hard coral cover between location ...... 66

Fig 4.1. Environmental impacts occurring in the Maldives between 2015 and 2017 benthic surveys...... 86

Fig 4.2. Map of sites surveyed from 27th March to 19th April 2015 and again from the 20th of March to 1st April 2017...... 87

Table 4.1. Sampling design for different covariates used to compare absolute coral change from benthic surveys ...... 88

Table 4.2. Mean benthic cover in 2015 and 2017 for overall hard coral and morphologies ...... 94

Table 4.3 Kolmogorov-Smirnov test output showing differences in density distributions of hard coral and morphologies ...... 94

Fig 4.3. Violin plot showing probability distributions of percent a) hard coral, b) branching, c) massive/encrusting, d) bushy, e) thin/foliose/plating cover across Maldivian sub-transects in the 2015 and 2017 ...... 95

Fig 4.4 Violin plot showing probability distributions of percent a) macroalgae, b) epilithic algae matrix, and c) crustose coralline algae across Maldivian sub-transects in the 2015 and 2017...... 96

Fig 4.5. Scatter plot showing the relationship between 2015 percent hard coral cover vs. absolute percent ...... 97

Table 4.4 Wald Chi-squared test for effect of covariates (reef location and land use) 99

Table 4.5 Output for linear mixed effects model investigating the effects of reef location + land use + COTS on percent cover change of different response variables ...... 100

Table A.1. Comprehensive list of Label categories and functional groups used for the image annotation of photographic quadrats taken using SVII of Maldivian reef slopes in 2015 and 2017 ...... 124

xiii List of Abbreviations

BIOT: British Indian Ocean Territory CCA: Crustose coralline Algae DLNNA: Deep learning neural network EAM: Epilithic Algal Matrix IPCC: Intergovernmental Panel on Climate Change MASE: Massive/encrusting corals MRC: Marine Research Centre NBS: National Bureau of Statistics NOAA: National Oceanic and Atmospheric Administration SCUBA: Self-Contained Underwater Breathing Apparatus SVII: SeaView II TFP: Thin/foliose/plating XL-CSS: XL–Catlin Seaview Survey

xiv Chapter 1: General Introduction

1.1 Coral Reefs and their importance

Coral reefs are unique marine ecosystems. Unlike other marine ecosystems such as temperate rocky reefs, the majority of the hard structure is formed by living organisms, reef-building or hermatypic corals (Syms and Kingsford 2008). Shallow tropical coral reefs survive in optimum temperatures of 26-27°C but have an absolute range of 18-36°C (Hopley 2008). Reef-building corals in shallow tropical reefs also need light due to their dependence on a mutualistic symbiotic relationship with photosynthetic protists known as zooxanthellae. As a result of this dependence on light, the distribution of tropical reef- building corals is depth restricted, ranging from a depth of 2-8m in turbid environments but down to 125m in the clear waters such as those off the outer Great Barrier Reef (Hopley 2008, Englebert et al. 2014).

Coral reefs provide essential coastal resources for over 500 million people across the tropical regions of the world (Spalding and Grenfell 1997). These resources include food, livelihoods, coastal protection, cultural relevance, and a wide range of other ecosystem goods and services (Moberg and Folke 1999). Coral reefs also have great economic value, at both local and global levels. For example, recent reports by Deloitte Access Economics (2017) has valued the Great Barrier Reef at A$56 billion, supporting 64,000 jobs with an economic contribution of A$6.4 billion per year in terms of tourism, fisheries, iconic value, and many of the ecosystem services it provides. These values are most probably grossly underestimated as it is very difficult to assess invaluable benefits such as maintenance of water quality, sand production, and other benefits associated with primary production and gas exchange (Hoegh-Guldberg et al. 2015).

1.2 Impacts facing coral reefs across the globe

Despite their importance, coral reefs are under great threat from human activities that result in a range of impacts from local (e.g. pollution, overfishing) to global (e.g. ocean warming and acidification). Recent studies have shown that the abundance of corals throughout Southeast Asia, the Indo-Pacific, Caribbean, and Indian Ocean has decreased

1 by about 50% over the past 30-40 years (Gardner et al. 2003, Bruno and Selig 2007, De'ath et al. 2012). Observed declines in coral cover across reefs has been to shown to be attributed to localized human influences such as overfishing and habitat degradation from adjacent human land use activities, causing a decrease in natural herbivores and other reef fish populations (Hughes 1994, Bellwood et al. 2004, Mumby et al. 2007, Sandin et al. 2008). These impacts, as well as global stressors such as ocean acidification and ocean warming (Hughes et al. 2003, Anthony et al. 2008, Brierley and Kingsford 2009, Dove et al. 2013) have led to an increase in anthropogenic stress towards corals, thus decreasing their ability to reproduce, grow and recover from severe disturbance events that cause coral decline such as Crown of Thorn Starfish (COTS) outbreaks, cyclones and mass bleaching events (Diaz-Pulido et al. 2009, De'ath et al. 2012). Understanding this decline has become a major concern for many countries throughout the world’s tropical regions, especially given the close linkage of human well-being to the health of reef-building corals and the reefs they build (Pandolfi et al. 2003, Knowlton and Jackson 2008, De'ath et al. 2012).

1.3 Environmental drivers on coral reefs

The geomorphological and ecomorphological zonation of coral reefs is often a response to light availability, temperature, sediments and wind/wave exposure (Hopley 2008). This means coral reef assemblages can often differ between regions, leading to domination of certain species depending on latitudinal gradients and substrate type and availability (Chappell 1980, Hopley 2008, Sommer et al. 2014). Given the variability of reef ecosystems, it is unsurprising to see coral reef ecosystems respond differently in their robustness and resilience to environmental stressors.

Reef-building corals respond ecologically to both local (e.g. pollution, eutrophication, and overfishing) (Hughes et al. 2003, Halpern et al. 2007, Darling et al. 2013) and global stressors such as increased sea surface temperature (SST), greater storm intensities, and ocean acidification (De'ath et al. 2009, Mumby et al. 2011, Dove et al. 2013). The extent, to which they respond to each of these types of stress factors, varies between different corals. Branching Acropora species, for example, are vulnerable to intense pulse perturbations such as storms, mass bleaching and crown of thorns seastar (Acanthaster planci) outbreaks, yet they often have the ability to recover quickly due to their quick

2 growth life-strategy (Hughes et al. 2003, Pandolfi et al. 2011, Darling et al. 2012, De'ath et al. 2012, Grime and Pierce 2012, Darling et al. 2013). On the other hand, corals which exhibit a slower growth strategy such as massive Porites species, are often more robust to storms and stress–tolerant to temperature anomalies, but may experience challenges when it comes to competing for space with macroalgae after large disturbance events. (Hughes 1994, Darling et al. 2013, McClanahan et al. 2014).

1.4 Coral reefs of the Central Indian Ocean

Coral reefs are found in most circumstances where coastal waters are warm, alkaline and sunlit. In terms of our understanding of coral reefs, most of the research has been done in the Western Pacific Ocean and Caribbean Sea. By contrast, the coral reefs of the Indian Ocean has received less attention primarily as a result of a lower amount of people and infrastructure which is required to understand and provide knowledge of coral reefs. Despite the lack of attention, coral reefs in the Indian Ocean are facing similar challenges to those being faced by more studied reefs in the Pacific and Caribbean regions. This is, not to say that the studies that have been done over the past 50 years have been of tremendous value and importance (Sheppard 1999, Wilkinson et al. 1999, McClanahan 2000, Edwards et al. 2001, Rajasuriya et al. 2002, Sheppard 2003, Naseer and Hatcher 2004, Obura 2005, Turner and Klaus 2005, Graham et al. 2006, McClanahan et al. 2007, Lasagna et al. 2008, Zahir et al. 2010, Sheppard et al. 2012, McClanahan and Muthiga 2014, Morri et al. 2015).

The Maldives and Chagos archipelagos are beginning to develop scientific platforms, centres and knowledge about their unique coral reefs. Coral reefs cover a total of 13,893km2 in the Maldives (4,493km2) (Naseer and Hatcher 2004) Chagos (9,400km2) (Dumbraveanu and Sheppard 1999) combined, with a series of papers being generated around them by a number of key research groups (McClanahan 2000, Edwards et al. 2001, Lasagna et al. 2008, Morri et al. 2010, McClanahan 2011, Sheppard et al. 2012, Schlager and Purkis 2013, Kench et al. 2014, Pisapia et al. 2016). These studies have explored subjects such as the geomorphology, ecology of deepwater coral communities, fish ecology as well as the impact of large-scale events such as the 1998 mass coral bleaching and mortality event that removed over 90% of coral colonies from shallow to deep locations. There have also been a number of studies have focused on the potential

3 impact of human activities, although these are relatively limited in number and scope(Brown et al. 2017, Moritz et al. 2017, Cowburn et al. 2018).

Most of this project focused on coral reefs growing throughout the Central Maldives (fig 1.1b), particularly on the role of local and global factors and their influence on the distribution and abundance of coral reefs. In particular, it asked three major questions which form the core of this research thesis.

1) Are new scalable technologies (rapid image collection, artificial intelligence) able to deliver accurate yet large-scale perspectives on coral reefs in a rapidly changing world? 2) Are there differences in the abundance and distribution of coral between the Chagos and the Maldives archipelagos? 3) Do land use practices on local community islands and resorts have a negative effect on the distribution of coral cover in the Central Maldives? 4) Are unpopulated reefs in the Maldives more resilient to environmental stress from the 2016 mass coral bleaching event in the Central Maldives?

1.5 The key threat to coral reefs: Climate change and Ocean warming

One of the greatest stressors and subsequent impacts on coral reefs occurred during the 1998 record El Niño event, which increased water temperatures to unprecedented levels across many tropical regions, including the Central Indian Ocean (Hoegh-Guldberg 1999, Wilkinson 1999, Wilkinson et al. 1999, McClanahan et al. 2007). Bleaching began in the Southern Hemisphere during the latter part of 1997 and the early part of 1998. These bleaching episodes were first reported in the eastern Pacific and Caribbean by the Coral Health and Monitoring Program (CHAMP) Network (Hendee 1998). Subsequently coral bleaching was observed across the Pacific from French Polynesia to Australia by the beginning of 1998. Bleaching was reported a few months later in the Indian Ocean, reaching South-East Asia by May 1998. As summer rolled through the Northern Hemisphere, bleaching occurred on Caribbean coral reefs, and continued until September 1998 (Hendee 1998, Hoegh-Guldberg 1999, Wilkinson et al. 1999).

4 The research associated with this thesis began in early 2015. At this point, the key focus of the thesis was the opportunity to look at relationships between human population density, island use, and the health of coral reefs in the Maldives. Unfortunately, exceptional heating in the Indian Ocean occurred in 2015/2016 triggering one of the worst periods of heat stress had caused corals throughout the Maldives to bleach and subsequently die. While catastrophic in its effect on coral reefs, this event provided an opportunity to investigate the nature and substance of the impacts of ocean warming on coral reefs in the Central Indian Ocean. Consequently, the final chapter (4) deals with the state of coral reefs in the Maldives, before and after the warming event that occurred in early 2016. This allowed a number of key insights into how reefs are likely to respond to ocean warming projected over the coming decades and centuries.

1.6. Thesis scope

The main objective of this program is to investigate the spatial patterns in the abundance of reef building coral communities and explore potential drivers of their distributions on reef-slope sites surveyed in the Maldives and Chagos. The reef-slopes of outer atoll forereefs and inner atoll reefs at a depth of 8-12m have been selected as the study area due to the species richness and habitat heterogeneity usually found at these depths and ecosystems (Huston 1985) and there are significant lower amounts of wave energy between reef inside the atoll structure than those outside (Kench et al. 2006). As described in the previous section, events of 2016 enabled an opportunity to look at the effects of a major cold bleaching and mortality event in the Maldives.

One of the key challenges facing coral reefs scientists is the difficulty in accessing benthic community information at scales that are meaningful enough cover similar spatial extents as the broad-scale influences driving their distributions (i.e. wind/wave, thermal stress, sedimentation)(Edmunds and Bruno 1996, Bruno and Valdivia 2016, González-Rivero et al. 2016). In order to overcome this challenge we use a novel coral reef monitoring camera system known as SeaView II (SVII), which is able to achieve up to 2km underwater coral reef transects (González‐Rivero et al. 2014). This is used alongside a deep learning neural network image analysis approach that rapidly analyze image repository collected by at least 200x faster than previous image analysis used (Gonzalez- Rivero M et al. in prep).

5 1.6.1 General Methodology

The technological innovations from the XL Catlin SS have allowed benthic assessments of coral reefs at reef-scape (1-2km) scales, capturing approximately 34,000 images in the central Maldives and 21,600 images in the Chagos archipelago. This allows opportunities for understanding the spatial patterns of coral distribution and abundance with greater accuracy than established photographic transects methods that are restricted in scale (i.e. only able to cover 50-100m instead of up to 2km)(González‐Rivero et al. 2014). As these are the first kilometre scale surveys performed within the remote islands nation of the Maldives and BIOT, the data extracted from these images provide an important baseline of coral cover in both areas.

The SeaView II (SVII) (Fig 1.1a) is a customised high resolution imaging system, housing three remotely triggered digital single lens reflex (DSLR) Canon 5d MkII cameras, which have been aligned in a housing designed to capture a 360° panoramic of the surrounding environment (fig 1.1b). The camera housing is attached to a diver propulsion vehicle (DPV), allowing the diver to cover up to 2km per dive, collecting approximately 900 images a dive. A Samsung galaxy tablet (fig 1.1e) is connected on top of the DPV, which allows the diver to view images during the dive and adjust the light sensitivity (ISO) based on ambient light conditions (González‐Rivero et al. 2014). Images were automatically captured every three seconds using an intervalometer triggered by the SVII driver. The altitude of each image was recorded using a Mircon Echosounder transponder by Tritech (Tritech international, Westhill, Aberdeenshire, UK) (Fig1.1d) and pressure sensor housed inside the altimeter recorded the depth (Fig 1.1c).

Fig 1.1 a) The SeaView II (SVII) camera system diver propulsion vehicle (DPV), b) 360° panoramic camera housing with 3 Canon (Canon Inc., Tokyo, Japan) MkII cameras with a maximum resolution of 5613 x 3744 mpixels inside, c) altimeter carrying pressure sensor and data logger for the transponder, d) Micron Echosounder transponder by Tritech, e) Intervalometer attached to handle and underwater housing with Samsung Galaxy tablet for viewing images and changing ISO settings with magnetic mouse.

1.6.2. Thesis outline

1.6.2.1 - Comparison of two photographic methodologies for collecting and analysing the condition of coral reefs (Chapter 2).

Being able to measure the frequency and abundance of the key organisms is essential if we are to broaden our understanding of the drivers and resulting changes to the world’s coral reefs (Knowlton and Jackson 2008). Previous attempts to assess the condition of benthic communities on coral reefs vary in spatial scale and taxonomic resolution (Andréfouët et al. 2002, Mumby and Steneck 2008, Hedley et al. 2012, González‐Rivero et al. 2014). The time, cost and effort for in-water diver assessment technique’s, however, reduces the ability to cover large spatial scales (i.e. 50-200m SCUBA dive) (Mumby et al. 1999, Hill and Wilkinson 2004). In this chapter, we measured the correlation and agreement of SVII with a more conventionally used technique for capturing photograph

7 quadrats on coral reefs. Here, we wanted to confirm that limitations associated with using SVII (i.e. use of ambient light and, varying distance/angle from the reef photographs are captured) (González‐Rivero et al. 2014) are not affecting image quality to the point we get inconsistent results compared to conventional methods after random point count image analysis (Kohler and Gill 2006, Beijbom et al. 2012). Here, it was hypothesized that the two methods will show high levels of correlation and agreement, therefore complementing each other as methods used analyzing reef condition, especially in the Central Indian Ocean.

1.6.2.2 - Measuring the effects of reef location and land use regime in the Central Indian Ocean (Chapter 3).

Broad-scale 2km coral reef transects at 10m depth were used to collect images that were analysed using a machine-learning algorithm (i.e. deep learning neural networks algorithm (DLNNA)) (Jia et al. 2014, Gonzalez-Rivero M et al. in prep) to investigate patterns in the distribution and abundance of coral communities in locations of the Central Indian Ocean. The first being, the Chagos Archipelago with almost no human population, and the Republic of Maldives with a human population of 402,017 (Maldives NBS 2015) and three distinct land use practices imposed on adjacent coral reefs (i.e. local community islands, resorts, and unpopulated reefs) (Jaleel 2013, Moritz et al. 2017). Given significant differences in human population densities and land use practices, we expect significant differences in the abundance and distributions of coral communities between both locations. We also expected the effects of land use practices in the Maldives to outweigh the effects of reef location where the significant difference in wave energy between inside the atoll structure and outside is expected to play a role in coral community structure (Kench et al. 2006).

1.6.2.3 - Measuring the effects of reef location and land use regime in the Central Maldives after the 2016 global coral bleaching event (Chapter 4).

The coral reefs of the Maldives encountered a sea surface temperature (SST) anomaly that lasted from April to June 2016, causing widespread coral bleaching in the region (Ibrahim et al. 2016). Having extensively surveyed reefs prior to these events, the present project was well positioned to explore how local stress factors (e.g. land use, reef location, presence of Ancathaster planci outbreaks) influenced coral mortality levels following the 2016 mass bleaching event. Consequently, we focus on attempting to understand the effects of the previous community ratio of thermally stress tolerant (i.e. massive/encrusting species) to more thermally sensitive (i.e. branching (Acropora sp) and bushy corals (Pocillopora sp) corals (Darling et al. 2013). As well as, investigate the effects localized stressors on the change (increase or decrease) in coral communities growing at 10m depth on reef slopes. We did so by comparing sub-sections of 2km transects surveyed in 2015, with 2km transects surveyed within the exact same longitudes and latitudes in 2017, one year after the heat stress event (which caused extensive mass coral bleaching) in the Maldives. We expect large scale declines in coral cover, and shifts in coral community composition throughout the Central Maldives between 2015 and 2017. Furthermore, the composition of thermally tolerant corals in the community structure of reefs before the disturbance will have more of an effect on overall coral cover change than localized stress placed upon them before, during, and after the 2016 mass bleaching event.

9 1.7. References

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17 Chapter 2: Comparison of methodologies for collecting and analysing the condition of coral reef ecosystems.

2.1. Abstract

Coral reefs are declining rapidly in response to unprecedented rates of environmental change. Rapid and scalable measurements of how benthic ecosystems are responding to these changes are critically important. Recent technological developments associated with the XL Catlin Seaview Survey (XL-CSS) have begun to provide high-resolution 1m2 photographic quadrats of approximately 1.8-2km of coral reefs at 10m depth by using a semi-autonomous image collection SeaView II camera system (SVII). The rapid collection of images by SVII can result in images being taken at a variable distance (1-2m) from the substrate as well as having natural light variability between images captured. This variability can affect the quality of taxonomic resolution archived from photographic quadrats captured by SVII. Conventional approaches for taking photographic quadrats of coral reefs involve taking images from a fixed distance with the use of artificial light. These methods often provide images that enable high taxonomic resolution, but are typically used to cover areas of 50m-150m. Here, we select key metrics associated with coral reef condition from a functional perspective to contrast how much is lost in terms of association and agreement from image annotations using SVII images (1m2 photoquadrats) compared to conventional methods, capturing 0.5m2 photoquadrats using a DSLR camera from a fixed height of 0.5m above the benthos. Comparisons were made over the same 50m linear transects at 15 sites in the Maldives. Photoquadrats were manually analysed using an online image annotation tool and image repository called coral net with 25 randomly placed dots per 0.5m2 of a quadrat. Our results reveal high levels of correlation and agreement between methods when measuring the abundance of hard corals as a functional group and individual labels, which make up hard corals as a functional group. These results demonstrate the two methods are comparable when measuring functional groups and coral communities on coral reefs. Therefore, involving rapid semi-automated technologies that maximize data collection for monitoring coral reefs does not necessarily imply that taxonomic resolution is compromised. This insight has important ramifications for detecting important changes in coral reef condition, such as decline in coral cover or shifts in benthic community composition.

2.2 Introduction

Coral reefs are undergoing rapid reduction in hard coral cover (Gardner et al. 2003, Pandolfi et al. 2003, Bruno and Selig 2007, Edmunds and Elahi 2007, De'ath et al. 2012) and shifts in community structure (Gleason 1993, Glynn et al. 2001, Pratchett 2010, Pratchett et al. 2011) in many regions globally. These changes are occurring as a result of climate change (Hoegh-Guldberg et al. 2007) as well as local anthropogenic stressors (Mumby 2006, Williams et al. 2015). The increased frequency and severity of disturbances such as cyclones, coral bleaching events, Acanthaster planci (Crown-of-Thorn Starfish, COTS) outbreaks, as well as other impacts from anthropogenic influences, is heralding the need for monitoring these changes in community ecology of coral reefs rapidly and at larger scales (Edmunds and Bruno 1996, Knowlton and Jackson 2008, De'ath et al. 2012, González‐Rivero et al. 2014). The XL Catlin Seaview Survey (XL CSS) was launched in 2012 with the aim of developing a broad scale assessment of the state of key coral reefs around the world (González‐Rivero et al. 2014, González-Rivero et al. 2016) and providing a much-needed standardized global baseline (Knowlton and Jackson 2008, Trygonis and Sini 2012) that is rapid, accurate, and rigorous.

Previous attempts to assess the condition of benthic communities on coral reefs vary in spatial scale and taxonomic resolution (Fig 2.1)(Andréfouët et al. 2002, Mumby and Steneck 2008, Hedley et al. 2012, González‐Rivero et al. 2014). Satellite imagery and airborne photography (Fig 2.1d&e) provide remote sensing at scales of 1-1000km of reef surveyed enabling the construction of coarse habitat boundary maps that provide information on percentage cover for simple categories of benthos (e.g. living versus dead coral cover, macroalgae, and soft sediment) (Mumby et al. 1997, Hochberg and Atkinson 2003, Mumby et al. 2004). Furthermore, in-water surveys must be performed in order to allow for object based image analysis within the photographs collected (Phinn 1998, Mumby et al. 1999, Roelfsema et al. 2010, Phinn et al. 2012). This reduction in detail and taxonomic resolution means scientists and decision makers miss out on useful information such as changes in the diversity and community structure of coral reefs. Being able to measure the frequency and abundance of the key organisms is essential if we are to broaden our understanding of the drivers and resulting changes to the world’s coral reefs (Knowlton and Jackson 2008). On the other hand, in-water diver assessment techniques (Fig 2.1a) offer the highest level of taxonomic resolution. The time, cost and effort for in-

19 water diver assessment technique’s, however, reduces the ability to cover large spatial scales (i.e. 50-200m SCUBA dive) (Mumby et al. 1999, Hill and Wilkinson 2004). Therefore, remote sensing techniques (Fig 2.1d&e), or diver surveys based on the rapid collection of imagery (e.g. video or semi-autonomous photography) (Fig 2.1b&c) offer a cost effective monitoring technique for rapidly measuring the state of coral reefs at larger scales (González‐Rivero et al. 2014).

Fig 2.1. Coral reef survey technique differences in spatial scale and taxonomic resolution. (a: in situ diver assessment, b: High resolution photoquadrats (0.25m2-1m2), c: SVII with diver propulsion vehicle, d: Airborne photography (aircraft and drone), e: Satellite imagery).

Recent advances in photography, computer learning, and image classification have increased the scale over which measurements with a high degree of precision and accuracy can be made. The XL CSS utilizes an innovative camera assemblage (SeaView II or SVII camera) which is made up of a high-resolution 360° panoramic camera system connected to a diver population vehicle (DPV). This technology has opened up new possibilities for understanding reef ecosystems at multiple kilometer scales, at a depth of 8-12 meters (González‐Rivero et al. 2014). The SVII has the ability to collect and analyses over a thousand images per 1.5-2km transect, which would otherwise take a large amount of time and cost using conventional human-based methods. Furthermore, surveys

20 performed without a DPV become nearly impossible in areas with strong currents (González‐Rivero et al. 2014, González-Rivero et al. 2016).

Semi and fully automated underwater vehicles (AUVs) are increasingly being used for the collection of large image sets that archive taxonomic information of benthic marine habitats (Durrant and Dunbabin 2011, Williams et al. 2012, González‐Rivero et al. 2014, Roelfsema et al. 2015). Although AUVs offer advantages in terms of the amount of data collected, there are limitations when using certain types of uncontrolled photography that can introduce error into the quality of the data collected due to a reduction image quality within the image set. This error can be attributed to factors such as changes in light availability, changes in the distance an image is taken from the benthos, and changes in attitude (i.e. pitch, yaw and roll) that an image was taken (González‐Rivero et al. 2014).

The research described here aimed to select key metrics associated with coral reef condition from a functional perspective and contrast how much is lost in terms of association and agreement between image annotations using a semi-autonomous image collection method (SVII camera system) and a more conventional method, which offers more consistent image quality at the cost of spatial scale. Exploring the capabilities of a semi-autonomous AUV methodology to provide similar or better association and agreement at larger spatial scales is an important step in developing the capacity to make broad-scale interpretations regarding coral reef conditions during unprecedented rates of decline (Edmunds and Bruno 1996, Balmford et al. 2005, Edmunds and Elahi 2007, Knowlton and Jackson 2008, De'ath et al. 2012, González‐Rivero et al. 2014).

21 2.3 Methodology

2.3.1. Image collection

Images were collected during the 2015 XL Catlin SS Maldives expedition from the 30th of March to the 19th of April 2015. Fifteen of the sites from the expedition were chosen to explore the agreement and performance of the two methods (Fig 2.2). A 50m transect tape was laid out at a depth of 10m at the starting point of each of the 1.5-2km SVII transects (Fig 2.4e).

2.3.2 SVII method

The SVII camera system (Fig 2.3a) is a customised high resolution imaging system (González‐Rivero et al. 2014) approximately 2m in length and 50kg in weight. The SVII houses 3 remotely triggered digital single lens reflex (DSLR) Canon 5d MkII cameras (max resolution = 5613 x 3744 mpixels), which have been aligned in a housing designed to capture a 360° panoramic of the surrounding environment every three seconds. The camera housing is attached to a diver propulsion vehicle (DPV), allowing the diver to cover up to 2km per dive. A fisheye lens with the aperture set to f8 is connected to the camera and ambient light is used in order to preserve battery power. The SVII driver can change the ISO and shutter speed manually via a Samsung Galaxy tablet connected to the top of SVII. The diver has the ability to view each image on the Samsung Galaxy tablet screen and will make adjustments to ISO according to light availability. The shutter speed is maintained at 1/320 due to the propulsion of the SVII whilst taking images. For more details regarding SVII and its use, refer to González‐Rivero et al. (2014).

Fig 2.2. Map of survey sites for comparison of SVII and conventional photoquadrat methods in the Maldives taken during the March-April-2015 XL Catlin Seaview survey expedition. a) Map of the Indian Ocean with the survey area of the Central Maldives highlighted in red. b) Survey sites in the Central Maldives.

SVII captured images over a 50m transect at 10m depth to compare with conventional methods in approximately 1-2 minutes depending on current conditions. Photoquadrats of 1m2, with an average resolution of 20 and 10 pixels/cm, were colour corrected and then cropped from the SVII images by using a time stamped altitude measurement taken from a Mircon Echosounder altimeter by Tritech, attached to the bottom of the DPV body on SVII. A pressure sensor was also used to record the depth each image was captured. Furthermore, a global positioning system (GPS), which has been time synchronized with the cameras and sensors was attached to a surface marker tethered to the SVII diver. Therefore, every image is spatially geo-referenced (González‐ Rivero et al. 2014).

Between 20 and 40 1m2 SVII photographic quadrats (Fig 2.4a) were selected from the SVII images at each site (Table 2.1). A 50m transect tape running though the center of the image was used to identify which 1m2 SVII photographic quadrats were selected for image analysis. SVII photographic quadrats (1m2) were assessed to ensure there was no overlap of area between quadrats before being manually annotated on coral net. Differences in the number of 1m2 SVII photographic quadrats are caused by the speed the DPV is travelling. Differences in DPV speed can be due to the current at the time and the diver driving the DPV.

2.3.3. Conventional method

Divers used a 0.5 x 0.5m quadrat attached to Olympus mirrorless EM-5 digital camera (max resolution = 4608 X 3456) at a fixed distance of 0.5m away from the substrate to take pictures (Fig 2.3c) during a 60 minute SCUBA dive. Two Sea and Sea YS-D2 strobes were also attached provided artificial light for each image. Between 50 and 90 photographic quadrats (Fig 2.4b) were taken using the conventional method on a single 50m x 1m linear transect (Table 2.1). On occasion, strong currents, and equipment failures caused dives using conventional methods to be abandoned before the 50m could be completed.

24 a b

1m2

c d

0.5m2

e 50m

Fig 2.3. Methods being compared over 50m linear transect at 15 sites in the Maldives during the March-April 2015 XL Catlin Seaview Survey expedition. a) SVII surveying a Maldivian reef slope at 10m. b) Cropped and colour corrected 1m2 photoquadrat taken from Maafushi using SVII (a) with 100 randomly allocated points using coral net (https://coralnet.ucsd.edu/). c) Conventional 0.5m2 photoquadrats being captured with a 0.5m stand off the substrate over a 50m linear transect on a Maldivian reef slope. d) 0.5m2 photoquadrat taken Maafushi using conventional methods (d) with 25 randomly allocated points using coral net (https://coralnet.ucsd.edu/). e) Illustration of photoquadrat placement over 50m linear transects in the Maldives, blue squares indicate 1m2 from SVII (a), and red squares indicate 0.5m2 photoquadrats from conventional methods (b). Image a and c were used with permission from the copyright owners the Ocean Agency/Christophe Bailhache

25 a

Fig 2.4. a) Cropped and colour corrected SVII 1x1m photoquadrat from Emboodhoofinolhu reef with a full resolution of 1171 x 1171 pixels. b) Un-color corrected conventional 0.5m X 0.5m photoquadrat using two strobes taken at Emboodhoofinolhu reef with a full resolution of 4608 x 3456 pixels.

Table 2.1. Area covered in m2 by SVII and conventional methods over the same 50m linear transects at 15 sites in the Maldives, surveyed during the 2015 XL Catlin Seaview Survey expedition (www.catlinseaviewsurvey.com) Number of

Number of SVII 1m2 Conventional 0.5m2 Latitude/Longitude quadrats = area quadrats X 0.25 = covered (m2) area covered (m2) Site 3'26.11325N/73'25.327 30 x 1 = 30 m2 90 x 0.25= 22.50 m2 Anbarra 78E 3'42.80441N/72'58.882 31 x 1 = 31 m2 58 x 0.25= 14.50 m2 Diggaru Fahlu 54E 4'9.04307N/ 30 x 1 = 30 m2 54 x 0.25= 13.50 m2 Dinoluga 72'43.87115E Emboodhoofinol 4'7.12197N/ 30 x 1 = 30 m2 74 x 0.25= 18.50 m2 hu 73'28.06809E 2'17.28663N/73'16.419 30 x 1 = 30 m2 89 x 0.25= 22.25 m2 Fenfushi 37E 4'0.50388N/72'48.6864 30 x 1 = 30 m2 80 x 0.25= 20.00 m2 Fesdhoo 3E 3'9.75164N/73'29.7246 39 x 1 = 39 m2 89 x 0.25= 22.25 m2 Kuralu 3E 2'47.02307N/73'22.107 30 x 1 = 30 m2 87 x 0.25= 21.75 m2 Kureli 77E 3'56.73654N/73'30.000 30 x 1 = 30 m2 90 x 0.25= 22.50 m2 Maafushi 41E 3'28.00391N/72'50.152 30 x 1 = 30 m2 80 x 0.25= 20.00 m2 Maamigili 6E 4'11.30840N/72'48.686 22 x 1 = 22 m2 90 x 0.25= 22.50 m2 Mathiveri 43E 3'18.63745N/73.28.07 30 x 1 = 30 m2 84 x 0.25= 21.00 m2 Rakeedhoo 505E 2'12.60078N/73'9.4533 30 x 1 = 30 m2 89 x 0.25= 22.25 m2 Thimarafushi 9E 4'18.80890N/73'30.219 30 x 1 = 30 m2 90 x 0.25= 22.50 m2 Udhafushi 89E 3'13.47848N/73'25.698 36 x 1 = 36 m2 50 x 0.25= 12.50 m2 Vattaru 29E 80 x 0.25 = 20m2 31 x1 = 31 m2 (0.92) (0.95) Mean (±SE)

2.3.4. Image annotation and analysis

Images were manually annotated by an expert annotator using CoralNet (Beijbom et al. 2012), an online repository for analysing coral reef images using 100 randomly allocated points were used for analysis of each 1m2 photoquadrats captured by SVII (Fig 4b) and 25 points were used for the 0.5m2 photoquadrats collected using conventional methods (Fig 4d). The number of points was chosen in order to assess relatively the same amount of space inside the photoquadrats form both methods (i.e. 25 points per 0.5m2 of photoquadrat). Correlation and agreement between the two methods was explored when estimating label categories made up of 1) broad benthic parameters at a functional level (i.e. dead hard coral, hard coral, macroalgae and other invertebrates). 2) Morphological groups within the hard coral functional group along with genera easily identified based on color and texture from a 2D image (i.e. branching tabulate/corymbose Acropora, branching arborescent Acropora, branching Pocillopora, branching Porites, and branching other, massive/submassive/encrusting (MASE) Porites, MASE small invisible polyps, MASE large round polyps, MASE meandering, and thin/foliose/plating (TFP) and; 3) other invertebrates such as soft corals, tunicates, and sponges (Table 2.2).

28 Table 4.2. Label categories and functional groups used for the image annotation of photoquadrats taken using SVII and conventional methodologies on Maldivian reef slopes during the March-April 2015 XL Catlin Seaview Survey expedition (www.catlinseaviewsurvey.com). Functional group Label and description Dead Hard Coral (DHC) DHC covered by Epilithic algal matrix (EAM) Dead Hard Coral (DHC) Rubble covered by Epilithic algal matrix (EAM) Dead Hard Coral (DHC) DHC covered by Crustose coralline algae (CCA) Dead Hard Coral (DHC) Rubble covered by Crustose coralline algae (CCA) Dead Hard Coral (DHC) DHC covered by Cyanobacteria Dead Hard Coral (DHC) Rubble covered by Cyanobacteria Hard Coral Tabulate/corymbose/plating Acropora spp Hard Coral Digitate Acropora spp Hard Coral Arborescent Acropora spp Hard Coral Branching Pocillopora spp Hard Coral Branching Porites spp Hard Coral Branching other Hard Coral Massive/Submassive/Encrusting (MASE) Porites spp Hard Coral Massive/Submassive/Encrusting (MASE) small polyps Hard Coral Massive/Submassive/Encrusting (MASE) large round polyps Hard Coral Massive/Submassive/Encrusting (MASE) meandering Lobophyllia spp Hard Coral Massive/Submassive/Encrusting (MASE) Meandering other Hard Coral Massive/Submassive/Encrusting (MASE) bleached Hard Coral Thin/Foliose/Plating (TFP) with visible relief structures Hard Coral Thin/Foliose/Plating (TFP) with visible round corallites Macroalgae Filamentous Macroalgae >1cm in height Macroalgae Leathery Macrophytes >1cm in height Macroalgae Halimeda spp >1cm in height Other Fish Other Transect hardware Other Anthropogenic marine debris Other Unclear (i.e. shadow, overexposed, blurry) Other Invertebrates Alcyonacea spp Other Invertebrates Crinoids Other Invertebrates Hydroids Other Invertebrates Individual tunicates (Didemnum molle) Other Invertebrates Massive or encrusting sponges Other Invertebrates Branching or rope forming sponges Other Invertebrates Gorgonacea spp (sea fans and sea whips) Other Invertebrates Mobile invertebrates (Echinoderms) Other Invertebrates Other invertebrates (colonial Ascidians and Hexacorralia) Soft Sediment Sand

2.3.5. Statistical analysis

The percent cover for each quadrat was determined and then the average cover per site for each label and functional group calculated. A D’Agostino and Pearson normality test were performed to check if the percent cover of each label and functional group for both methods was normally distributed across all 15 sites. If the percent cover for each site was normally distributed, then a Pearson Correlation Coefficient was performed to measure the strength in relationship between the percent cover estimates of the two methodologies for that specific label or functional group. A Spearman’s rank correlation coefficient was carried out using GraphPad Prism version 7 (Mac OS X) on labels and functional groups that did not pass the D’Agostino and Pearson normality test.

Bland-Altman method comparison plots, commonly used to compare estimation in agreement between two methods of measurement where the true value remains unknown (Bland and Altman 1986, Bland and Altman 2003, Giavarina 2015), were constructed to assess agreement between SVII and conventional methods. These methods also give a visualisation if one method was biased at estimating coverage of one label over the other. Bland-Altman plot was done by plotting the mean percent cover between the two methods at each site on the X-axis and the ratio between the SVII and conventional methods for each site on the Y-axis. If the ratio bias (average ratio across all sites) was more than one, the methods were observed as more biased towards SVII in estimates of benthic cover associated with a specific label or functional group. Ratio bias of less than one meant there was more biased towards conventional methods. Furthermore, an equal proportion of sites scattered on either side and closer to one is deemed as suggestive of a greater method agreement, than a lopsided scatter of points more removed from one.

30 2.4. Results

2.4.1 Correlation and agreement of functional groups.

Percent cover across all six functional groups surveyed by SVII methods and conventional methods over the 50m transects at the 15 sites surveyed were significantly correlated. The Bland-Altman method comparison plots depict different ratio bias and agreement between the two methods for percent cover of different functional groups (Fig 2.5).

The amount of dead hard coral (DHC) measured by the two methodologies was significantly correlated (r=0.9401, n=15, p<0.0001), (Fig 2.5a) with a ratio bias of 1.17 and all sites exhibiting a ratio greater than 1 (Fig 2.5b). Estimates of the amount of hard coral cover from the two methodologies were also significantly correlated (Fig 2.5c) (r=0.9527, n=15, p<0.0001), with a ratio bias of 0.906, with 80% of sites having a ratio of less than one (Fig 2.5d).

Macroalgae were significantly correlated, when using a Spearman’s rank correlation coefficient (rs) (rs =0.6739, n= 15, P=0.0073) (Fig 2.5e), and had a ratio bias of 0.58 with 73% of sites having a ratio of less than one (Fig 2.5f). Three of the 15 sites (Dinoluga, Feshdhoo and Vattaru) did not pick up Macroalgae using the SVII method, one site (Diggaru Falhu) did not find Macroalgae with conventional methods. 73% of sites had a ratio of less than one. Other (mostly unclear) labels were significantly correlated (r=0.641, n=15, P=0.0101), and had a ratio bias of 1.3 with 66% of the sites having a ratio >1 (Fig 2.5h). The benthic category, Other Invertebrates, was significantly correlated between the two methods (r= 0.873, n=15, p<0.0001). 15, P<0.0073) with a 0.43 ratio bias (Fig 2.5j) and 93% of the sites having a ratio of less than 1. Similarly, the percent cover of sand was significantly correlated (rs =0.9429, n=15, p<0.0001) (Fig 2.5k), and had a ratio bias of 0.98 with 53% of sites being under 1 (Fig 2.5l).

31 Correlation Bland-Altman Correlation Bland-Altman a b c d Dead Hard Coral Dead Hard Coral Hard Coral Hard Coral 1.8 60 1.4 100

1.6 Rakeedhoo Maafushi 80 1.2 1.17 1.47 40 1.4 60 1.0 1.2 1.17 0.906 40 20 0.8 1.0 20 Ratio (SVII/CONV) Ratio (SVII/CONV) 0.86 0.64 0.8 0 0.6 0 0 20 40 60 80 0 10 20 30 40 50 0 20 40 60 80 %Hard Coral (Conventional) 0 10 20 30 40 Mean % Cover ((SVII + CONV)/2) % Hard Coral (SVII) Mean % Cover ((SVII + CONV)/2)

% Dead Hard Coral (Conventional) % Dead Hard Coral (SVII) e f g h * Macroalgae Macroalgae Other Other 2.0 60 20 3 Maamigili 1.59 2.44 1.5 15 40 2 1.0 10 1.30 20 0.58 1 0.5 5 Ratio (SVII/CONV) Ratio (SVII/CONV)

0.0 %Other (Conventional) 0.16 0 0 10 20 30 40 50 0 0 0 10 20 30 40 50 0 5 10 15 20 25 0 5 10 15 20 %Macroalgae (Conventional) Mean % Cover ((SVII + CONV)/2) %Macroalgae (SVII) %Other (SVII) Mean % Cover ((SVII+ CONV)/2)

i j k l * Other Invertebrate Other Invertebrates Soft sendiment Soft Sediment 15 1.5 60 2.0

1.63 Dinoluga 1.5 10 1.0 40 0.83 1.0 0.98 5 0.5 20 0.43 0.5 0.33 Ratio (SVII/CONV) Ratio (SVII/CONV) 0 0.0 0.024 0.0 0 2 4 6 0 0 2 4 6 8 0 10 20 30 40 0 10 20 30 40 50

% Other Invertebrate (SVII) Mean % Cover ((SVII + CONV)/2) % Soft Sediment (Conventional) Mean % Cover ((SVII + CONV)/2) %Other Invertebrate (Conventional) % Soft Sediment (SVII) Fig 2.5. Correlation (with 95% confidence bands) and Bland-Altman method agreement plots for percent cover of functional groups surveyed by SVII and conventional (CONV methods across 50m linear transects at 15 coral reef sites from the XL Catlin Seaview Survey Maldives expedition from the 29th of March until the 17th of April 2015. *Indicates Spearman’s rank correlation performed for correlation coefficient. Red line indicates 95% limit agreements and blue line represents ratio bias between SVII and conventional methods for Bland-Altman plots.

2.4.1 Correlation and agreement within hard coral functional groups

The percent cover of tabulate/corymbose corals were correlated between methods (r=0.9325, n=15, p<0.0001) (Fig 2.6a). The ratio bias for tabulate/corymbose corals was 0.91 with 60% of the sites having a ratio of less than one (Fig 2.6b). Arborescent Acropora was correlated (r=0.9004, n=13, p<0.0001) with a ratio bias of 0.91, however only 30.7% of the sites had a ratio below one (Fig 2.6d). Digitate Acropora coral cover was correlated (r=0.9063, n=15, p<0.0001) (Fig 2.6e) and had a ratio bias of 1.736 (Fig 2.6f). 73.3% of sites had a ratio of above 1 (Fig 2.7f). Percent Pocillopora cover was correlated between the two methods (r=0.954, n= 14, p<0.0001) (Fig 2.6g) and a ratio bias of 0.9229 (Fig 2.6h). 71.4% of the sites surveyed had a ration of less than one (Fig 2.6h).

Fig 2.6. Correlation (with 95% confidence bands) and Bland-Altman method agreement plots for percent cover of Acropora labels surveyed by SVII and conventional (CONV) methods across 50m linear transects at 15 coral reef sites from the XL Catlin Seaview Survey Maldives expedition from the 29th of March until the 17th of April 2015. *Indicates Spearman’s rank correlation performed for correlation coefficient. Red line indicates 95% limit agreements and blue line represents ratio bias between SVII and conventional methods for Bland-Altman plots.

The abundance of massive Porites was correlated between sites (r=0.9312, n=15, p<0.0001) (Fig 2.7a) and had a ratio bias of 0.9033 (Fig 2.7b). 60% of the sites had a ratio difference of less than one. Massive small/invisible polyps were correlated (r=0.7126, n=15, p=0.0029) (Fig 2.7c) and had a ratio bias of 1.067. At least 53% of sites had a ratio of less than one between massive small/invisible polyp labels (Fig 2.7d). Massive large round polyps corals were correlated (rs=0.9455, n=15, p<0.0001) (Fig 2.7e) and a ratio bias of 0.594 (Fig 2.7f). 80% of sites had a ratio between massive large round polyp labels of less than one (Fig 2.7f). Massive, meandering corals were correlated (r= 0.6979, n=15, p=0.0046) (Fig 2.8g) and had a ratio bias of 0.8119 (Fig 2.7h). 66.7% of sites had a ratio between massive, meandering corals labels of less than one (Fig 2.7h).

Fig 2.7. Correlation (with 95% confidence bands) and Bland-Altman method agreement plots for percent cover of Massive coral labels surveyed by SVII and conventional (CONV) methods across 50m linear transects at 15 coral reef sites from the XL Catlin Seaview Survey Maldives expedition from the 29th of March until the 17th of April 2015. *Indicates Spearman’s rank correlation performed for correlation coefficient. Red line indicates 95% limit agreements and blue line represents ratio bias between SVII and conventional methods for Bland-Altman plots.

35 Thin Plating/Foliose (TFP) label (Fig 2.8) was analyzed by adding the total percentages of TFP ridged, round polyps and Porites labels because the sums of each label alone were too small to conduct an analysis. Estimations of TFP corals between methods were correlated (rs=0.787, n=15, p=0.0009) (Fig 2.8a) and had a ratio bias of 0.81 and 63% of sites were scattered below one (Fig 2.8b).

Fig 2.8. Correlation (with 95% confidence bands) and Bland-Altman method agreement plots for percent cover of thin/foliose plating corals surveyed by SVII and conventional (CONV) methods across 50m linear transects at 15 coral reef sites from the XL Catlin Seaview Survey Maldives expedition from the 29th of March until the 17th of April 2015. *Indicates Spearman’s rank correlation performed for correlation coefficient. Red line indicates 95% limit agreements and blue line represents ratio bias between SVII and conventional methods for Bland-Altman plots.

2.4.2. Correlation and agreement amongst other invertebrates found in the Maldives

There were three labels from the functional group of ‘other invertebrates’ (i.e. soft corals, solitary tunicates and massive encrusting sponges) that were identified during annotation and analyzed for the amount of correlation and agreement (Fig 2.9). Estimations of soft corals from the Alcyonaceans were correlated (rs =0.787, n=15, p=0.009) (Fig 2.9a) and had a ratio bias of 1.09 with 55.5% of the sites ratio scattered below one (Fig 2.9b).

Individual solitary tunicates (i.e. Didemnum molle) were correlated (rs =0.7688, n=15, p=0.0013) (Fig 2.9), with a ratio bias of 0.34 and 92.3% of the sites with a ratio of less than one (Fig 2.9d). Finally, estimation of massive encrusting sponges was not correlated

36 (r=0.5231, n=a5, p=0.0549) (Fig 2.9e) and had a ratio bias of 0.68 with 100% of the sites having a ratio difference of less than one (Fig 2.9f).

Fig 2.9. Correlation (with 95% confidence bands) and Bland-Altman method agreement plots for percent cover of other invertebrates surveyed by SVII and conventional methods across 50m linear transects at 15 coral reef sites from the XL Catlin Seaview Survey Maldives expedition from the 29th of March until the 17th of April 2015. *Indicates Spearman’s rank correlation performed for correlation coefficient. Red line indicates 95% limit agreements and blue line represents ratio bias between SVII and conventional methods for Bland-Altman plots.

2.5. Discussion

The results presented here provide strong evidence that the two methods explored here are interchangeable. Broad benthic parameters and labels within the hard coral functional group were correlated between SVII and conventional methodologies. These results demonstrate that SVII is a useful tool for rapidly interpreting coral reef conditions and composition (González‐Rivero et al. 2014). Although SVII methods cause a slight reduction in taxonomic resolution, therefore reducing the ability to see organisms <5cm. identifying different types of coral categories within table 2 is not impeded when using SVII images and can be done more rapidly and at much larger spatial scale (i.e. 1.5-2km2) than more conventional in situ survey methods (González-Rivero et al. 2016).

2.5.1. Broad benthic parameters

Functional groups were correlated between methods, indicating that the two methods are estimating similar estimates of the percent cover of broad benthic parameters on coral reefs. However, the SVII method consistently estimated a higher percent cover of dead hard coral than conventional methods and a consistently lower percent cover of other invertebrates. This can be attributed to the difference in image resolution between images by SVII after post-processing (1031 x 1031 pixels) and the conventional photoquadrats (4608 x 3456 pixels). The lowered image resolution limits the annotator’s ability to identify organisms, which are too small or have smaller distinguishing features such as individual and colonial tunicates, small encrusting sponges, and hydrozoa, etc. Therefore, these organisms are often missed with lowered image resolution and labeled as one of the labels within the dead hard coral functional group. Most likely dead hard coral covered by epilithic algae matrix (EAM), which is usually defined as a multispecific assemblage of up to 1cm in height (Connell et al. 2014, Beijbom et al. 2015). Small invertebrates such as the solitary tunicate (Didemnum molle) are known to only take up a small amount of benthic cover on coral reefs (<3%). However, Didemnum molle was seen to reach nearly 12% cover at one site in the Maldives following the 1998 coral bleaching event (McClanahan 2000). Didemnum molle can quickly colonize empty spaces after disturbances and have been found to be settle on settlement plates in the Maldives after just two months (Clark 2000).

38 2.5.2. The hard coral functional group

Overall, the two methods were in significant agreement when identifying the different types of coral within the label set (Table 2), emphasizing the ability of the SVII to assess coral community composition. The two methods recorded very similar amounts of overall percent hard coral. Agreement between the two methods improved to different extents when more defined labels were assessed (e.g., tabulate/corymbose Acropora, Arborescent Acropora, Massive Porites, Massive small and invisible polyps, Massive meandering, and thin/plating foliose labels). The correlation and agreement within coral labels is an important result as the potential loss of major reef building coral such as Acropora on coral reefs can lead to declines spatial complexity and therefore eliminate habitat for many reef dwelling organisms such as reef fish and invertebrates (Bellwood et al. 2004, Alvarez-Filip et al. 2009).

Image resolution may have influenced the ability of the annotator to pick out the definition of massive large round polyps (i.e., Favia sp, Favites sp, Diploastrea heliopora, Goniopora sp). Therefore, massive corals with large round polyps may have been identified as massive corals with small or invisible polyps, furthermore explaining why SVII estimated a higher percent cover of massive small/invisible polyp colonies over conventional methods. Although, being able to differentiate between different types of massive corals can potentially provide greater insights into the diversity of coral within the community composition. The life history traits of many Massive coral are thought to be similar (e.g., stress tolerant and slow growing)(Darling et al. 2012). Shifts in community assemblages towards more ‘stress tolerant’ and ‘weedy’ (e.g. Pocillopora sp) species have been observed after bleaching events and in areas with high fishing impacts (Darling et al. 2013).

Quantifying the coral community composition on coral reefs is extremely important and more valuable to management than knowing hard coral cover alone (Bellwood et al. 2004). Disturbance events can affect some coral morphologies more than others. Cyclones, for examples, can often lead to a much greater reduction in branching Acropora than other massive or encrusting corals (Brown 1997). Furthermore, it is important to identify shifts in coral community composition through time, after major disturbance events, in order to explore the reef recovery process. Frequent disturbance events in Moorea, French Polynesia between 1979 and 2003 caused taxonomic shifts in coral composition

39 from tabulate Acropora and Monitpora dominated reefs to reefs with a relative abundance of Pocillopora and massive Porites (Pratchett et al. 2011).

The Maldives went through an extreme underwater heatwave in April 2016, which lead to the mass die-off of reef-building corals and Crown of Thorn Starfish (Ancanthaster planci) in some sites caused a major decline in Acropora at various sites in 2015 (Pisapia et al. 2016). Being able to detect change in coral composition at larger scales on coral reefs will provide valuable information in terms of initial mortality and recovery from both the coral bleaching event and Acanthaster planci outbreaks.

2.5.3. Other invertebrates

There was a significant correlation and relative agreement between the two methods when identifying soft corals (Alcyonaceans). This is important to know because soft corals in the family Alcyonacea are a major sessile invertebrate on indo-pacific reefs, often dominating disturbed reefs when they are present (Dai 1991). However, they can also be affected by disturbances such as bleaching events (Wilkinson 1999). Also, Didemnum molle (solitary tunicate) estimations were correlated between methods, but were consistently showing a higher percent cover in the conventional photoquadrats. As mentioned above this is mostly probably due to image resolution making it difficult to identify small solitary tunicates (<2cm) that are not dominating to compositions of the reef. Massive encrusting sponges were not correlated between methods. This is most likely due to image resolution and rarity of encrusting sponges found at the sites visited in the Maldives during the 2015 XL Catlin Seaview Survey expedition, or the cryptic nature of encrusting species in general. Encrusting sponges have key roles on coral reefs such as bio-erosion and competition for space (Diaz and Rützler 2001, Bell 2008, González-Rivero et al. 2013). Detecting changes in rare or exotic species requires different survey techniques than the two used for this study, such as those used to detect marine bioinvasions (Campbell et al. 2007).

2.5.4. Conclusions

Our results demonstrate that the SVII provides an important tool for rapidly, accurately and rigorously survey coral reefs at multi-kilometer scales, while at the same time maintaining taxonomic resolution that is comparable to conventional methods at much greater scales.

40 The larger spatial scale (1.5-2km per dive) that the SVII was able to achieve provides an important bridge to remote sensing techniques (Mumby et al. 2004, Roelfsema and Phinn 2010, Phinn et al. 2012). Furthermore, the scale provided by SVII also provides a link with satellite imagery that is used to measure and relate biophysical parameters (e.g. SST, chlorophyll-a, wind/wave exposure, and light attenuation) to the spatial patterns observed using the SVII (Mumby et al. 2011, Chollett and Mumby 2012, Yamano 2013, González- Rivero et al. 2016). These linkages are likely to become of increasing importance as large-scale changes to the environment from climate change impact reefs more and more. This type of information will be critical as the scientific and management communities seek to understand the resilience of coral reefs in a changing world. In the latter case, technologies such as the SVII are likely to offer the ability to rapidly detect more robust (or indeed, less robust) sections of coral reefs for conservation action. Technologies such as the SVII have the potential for providing a global series of baseline measurements estimating the state of coral reefs, which is essential for understanding how coral reefs are likely to change over the coming decade and century.

2.6. Acknowledgements

This study was undertaken as part of the XL Catlin Seaview Survey, designed and undertaken by the Global Change Institute, and funded by XL Catlin Pty Ltd in partnership with The Ocean Agency and The University of Queensland. DEPB was supported by The Australian Government Research Training Program Scholarship through The University of Queensland and OHG was supported by an ARC Laureate Fellowship. The SVII camera system was invented by Richard Ververs, Arron Spence and Christophe Bailhache. The authors would like to also acknowledge the XL Catlin Seaview Survey project management and field team at the Global Change Institute for their tireless efforts in arranging all associated field activities and also acknowledge the technical assistance with image processing from Panedia. We would also like to thank the Maldives government and members of the Marine Research Centre, Ministry of Fisheries and Agriculture, and Environmental Protection Agency for their assistance with permitting and in-country knowledge. Finally, we would like to recognize the excellent efforts of the boat crew and administration team from MV Emperor Voyager for proving an excellent research vessel with no delays occurring during our expedition.

41 2.7. References

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44 Mumby, P. J., I. A. Elliott, C. M. Eakin, W. Skirving, C. B. Paris, H. J. Edwards, S. Enríquez, R. Iglesias‐Prieto, L. M. Cherubin, and J. R. Stevens. 2011. Reserve design for uncertain responses of coral reefs to climate change. Ecology Letters 14:132-140. Mumby, P. J., E. P. Green, A. J. Edwards, and C. D. Clark. 1999. The cost-effectiveness of remote sensing for tropical coastal resources assessment and management. Journal of Environmental Management 55:157-166. Mumby, P. J., W. Skirving, A. E. Strong, J. T. Hardy, E. F. LeDrew, E. J. Hochberg, R. P. Stumpf, and L. T. David. 2004. Remote sensing of coral reefs and their physical environment. Marine Pollution Bulletin 48:219-228. Mumby, P. J., and R. S. Steneck. 2008. Coral reef management and conservation in light of rapidly evolving ecological paradigms. Trends in Ecology & Evolution 23:555- 563. Pandolfi, J. M., R. H. Bradbury, E. Sala, T. P. Hughes, K. A. Bjorndal, R. G. Cooke, D. McArdle, L. McClenachan, M. J. H. Newman, G. Paredes, R. R. Warner, and J. B. C. Jackson. 2003. Global trajectories of the long-term decline of coral reef ecosystems. Science 301:955-958. Phinn, S. R. 1998. A framework for selecting appropriate remotely sensed data dimensions for environmental monitoring and management. International Journal of Remote Sensing 19:3457-3463. Phinn, S. R., C. M. Roelfsema, and P. J. Mumby. 2012. Multi-scale, object-based image analysis for mapping geomorphic and ecological zones on coral reefs. International Journal of Remote Sensing 33:3768-3797. Pisapia, C., D. Burn, R. Yoosuf, A. Najeeb, K. Anderson, and M. Pratchett. 2016. Coral recovery in the central Maldives archipelago since the last major mass-bleaching, in 1998. Scientific Reports 6:34720. Pratchett, M. 2010. Changes in coral assemblages during an outbreak of Acanthaster planci at Lizard Island, northern Great Barrier Reef (1995–1999). Coral Reefs 29:717-725. Pratchett, M. S., M. Trapon, M. L. Berumen, and K. Chong-Seng. 2011. Recent disturbances augment community shifts in coral assemblages in Moorea, French Polynesia. Coral Reefs 30:183-193. Roelfsema, C., M. Lyons, M. Dunbabin, E. M. Kovacs, and S. Phinn. 2015. Integrating field survey data with satellite image data to improve shallow water seagrass maps: the role of AUV and snorkeler surveys? Remote Sensing Letters 6:135-144.

45 Roelfsema, C., and S. Phinn. 2010. Integrating field data with high spatial resolution multispectral satellite imagery for calibration and validation of coral reef benthic community maps. Journal of Applied Remote Sensing 4:043527-043527-043528. Roelfsema, C., S. Phinn, S. Jupiter, J. Comley, M. Beger, and E. Paterson. 2010. The application of Object Based Image Analysis of high spatial resolution imagery for mapping large coral reef systems in the West Pacific at geomorphic and benthic community spatial scales. Pages 4346-4349 2010 Ieee International Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, USA. Trygonis, V., and M. Sini. 2012. photoQuad: A dedicated seabed image processing software, and a comparative error analysis of four photoquadrat methods. Journal of Experimental Marine Biology and Ecology 424:99-108. Wilkinson, C. 1999. The 1997-1998 mass bleaching event around the world. Australian Institute of Marine Science, Townsville, Queensland, Australia. Williams, G. J., J. M. Gove, Y. Eynaud, B. J. Zgliczynski, and S. A. Sandin. 2015. Local human impacts decouple natural biophysical relationships on Pacific coral reefs. Ecography. Williams, S. B., O. R. Pizarro, M. V. Jakuba, C. R. Johnson, N. S. Barrett, R. C. Babcock, G. A. Kendrick, P. D. Steinberg, A. J. Heyward, and P. J. Doherty. 2012. Monitoring of benthic reference sites: using an autonomous underwater vehicle. IEEE Robotics & Automation Magazine 19:73-84. Yamano, H. 2013. Multispectral applications. Pages 51-78 in J. Goodman, S. J. Purkis, and S. R. Phinn, editors. Coral Reef Remote Sensing. Springer + Business Media, Dordrecht, Netherlands

Chapter 3: Bimodal coral communities and recent environmental stress in the Central Indian Ocean.

3.1 Abstract

Coral reefs around the world have been in decline for at least the last 30-40 years from a combination of local anthropogenic factors (e.g. pollution, over-fishing) and global factors such as ocean warming and acidification. The coral communities in archipelagos of the Maldives and the Chagos were both detrimentally affected by the 1998 thermal bleaching event. The Chagos, however, has been more or less protected from the differential land- use practices that occur in the Maldives. Coral cover can also be influenced by non- anthropogenic yet nonetheless local factors associated with differential exposure to wave energy. Here, we explore coral communities in the Central Indian Ocean and investigate the relative importance of local factors on two archipelagos recovering from past global impacts. To do so, we use linear mixed effects models based on detailed yet, kilometre scale (2km) coral reef surveys, conducted at 10-12m, in reef slope environments, to explore the effects of reef location (i.e. inside versus outside atoll) and land use (i.e. islands that are either unpopulated, host a resort, or are used by the local community for dwelling) on coral cover. We found that the mean percent coral cover, on reef slopes at 10-12 m, in the Maldives (17.8% ±0.25) was significantly less than the same habitats in the Chagos (20.9% ±0.25). Linear mixed effects models, suggested that the location of the reef (inside vs. outside of atolls) had a different impact for Chagos, then that observed for the Maldives. In the Chagos, there was 5% less coral cover outside than inside, but the community structure of corals remained similar. In the Maldives, the coral cover was the same in both locations, however, the community structure was enriched by 7.3% Branching coral cover inside, but enriched by 8.3% Massive/Encrusting cover outside the atoll. Wave energy and differential drag on corals of different morphology can explain both results. Over-time, however, coral communities tend to become more even following multiple disturbances, suggesting that recovery in the Maldives has been impeded relative to that in the Chagos. In this regard, there is evidence to suggest reefs in the Maldives were impacted by different types and intensities of land use. Reefs adjacent to local

47 community islands had 7.3% less coral cover, and 2.35% less Massive/Encrusting than those adjacent to unpopulated sites, controlling for the effect of location. Examining the distribution of coral cover on the two archipelagos revealed that coral populations in the Chagos were normally distributed. In the Maldives, however, they had a skewed, but bimodal distribution, with a high proportion of sub-transects having low coral cover while a smaller proportion of sub-transects had 50% or higher coral cover. The differences in distribution support the notion that there are drivers at play in the Maldives that are not present Chagos. Drivers, such as different land-use that can either inhibited the recovery or be directly responsible for coral loss. But clearly, there are also factors that have the potentially to stimulate recovery, or locally protect pockets of reef from all disturbance. These areas are of particular interest especially in terms of their ability to survive the often- intense global and local stresses typifying recent decades. Following up on these resilient reef systems may enable us to better understand why they have resisted stress, perhaps enabling new insights into how coral reefs might be managed, as well as supporting the identification and targeting of future conservation and management efforts.

3.2 Introduction

3.2.1 Impacts challenging coral reefs

Coral reefs are a major reservoir of biological diversity within the ocean, with as much as 25% of all marine species living in and around coral reefs (Reaka-Kudla 1997). They are also very important to humanity as a coastal resource with over 500 million people worldwide using coral reefs for food, livelihoods, recreation, coastal protection, and cultural services (Spalding and Grenfell 1997, Moberg and Folke 1999, Worm et al. 2006). Recent estimates of the direct economic benefits of coral reefs put them at hundreds of billions per year as a minimum (Hoegh-Guldberg et al. 2015).

Despite their importance, however, the abundance of corals is declining as a result of a combination anthropogenic risk factors (e.g. coastal development, coastal pollution, and overfishing), and biological disturbances such as Crown of Thorn Starfish (COTS; Acanthaster planci) outbreaks (Hughes 1994, Brown 1997, Sheppard 1999, Gardner et al. 2003, Pandolfi et al. 2003, Bruno et al. 2007, Edmunds and Elahi 2007, Hoegh-Guldberg et al. 2007, Carpenter et al. 2008, Knowlton and Jackson 2008, De'ath et al. 2012). More

48 recently, disturbances such as ocean warming have begun to drive significant increases in heat stress and coral bleaching, as well as increasing impacts from intensifying tropical storms (Hoegh-Guldberg 1999, Hoegh-Guldberg et al. 2014). The distribution of coral reefs plus the anthropogenic risks and environmental perturbations that affect them vary at regional scales (Veron 1995). These drivers include those operating spatially (i.e. region, atoll, reef, site), temporally (i.e. hours, days-weeks, months-years, multiple years) and through high intensity (i.e. moderate, severe, catastrophic) and short-lived events (Holling 1992, Bryant et al. 1998, Halpern et al. 2008, De'ath et al. 2012).

The complex dynamic relationship between anthropogenic risks associated with human population use and the environmental factors driving coral reef assemblages are not well understood or completely mapped (Sandin et al. 2008, González‐Rivero et al. 2014, Bruno and Valdivia 2016, Crane et al. 2017). Quantifying the effects of human activities on coral distributions can be challenging but is necessary over multiple scales in order to fully understand and mitigate ecological impacts caused by damaging human activities (e.g. land reclamation, effluent water outflow, overfishing, coastal hardening, etc. (Côté and Darling 2010, Bruno and Valdivia 2016, Crane et al. 2017). While there are many studies that strongly implicate anthropogenic risk factors and population pressure in the degradation of coral reefs (Hughes et al. 2003, Carpenter et al. 2008, Knowlton and Jackson 2008, Sandin et al. 2008). A recent quantitative analysis of 3581 surveys on 1851 coral reefs from 1996 to 2006 (Bruno et al. 2009) by Bruno and Valdivia (2016) revealed coral reef declines are not correlated to the local effects of human population density, and any impacts of localized anthropogenic stressors were undetectable at broad geographic scales.

3.2.2 Current coral reef monitoring methods

The inconsistency in whether the relationship between anthropogenic risks and environmental factors is synergistic or antagonistic is at least partly due to the different precision and accuracy of coral reef monitoring data across different regions of the world. Efforts vary in the level of replication, depth of taxonomic resolution, and actual depths at which information was collected (Wilkinson 1998, Houk and Van Woesik 2013, Flower et al. 2017). Currently, most quantitative surveys of coral abundance (coral cover) rely on a range of small scale in water diver techniques (10-150m) including line intercept transects (LIT), point intercept transect (PIT), photographic quadrats, or video transects. These

49 surveys are often limited by depth and time due to the SCUBA diving limitations involved with increasing depth (Hill and Wilkinson 2004, van Woesik et al. 2009). Recent technological developments are beginning to change this. The recent use of autonomous underwater vehicles (AUVs), the rapid collection of digital images and their analysis using image recognition and machine learning, is opening up underwater surveys to be carried out at larger scales (2 km or more)(González‐Rivero et al. 2014, González-Rivero et al. 2016, Bryant et al. 2017). These are revealing interesting differences based on the limitations of conventional techniques.

3.2.3 Anthropogenic influences towards coral reefs

Understanding the relationship between humans and coral reefs is very important in terms of developing ways to reduce the rate of loss of coral reef ecosystems. Most of our understanding has come from studies of coral reef ecosystems in the Caribbean and Pacific Ocean (Gardner et al. 2003, Mumby et al. 2007, Bocos et al. 2011, De'ath et al. 2013, Williams et al. 2015, Crane et al. 2017). Coral reefs of the Indian Ocean are, however, under similar pressures and are no less vulnerable to rapid ocean warming than their counterparts in the Pacific and Caribbean regions (Maina et al. 2008). The relationship between humans and coral reefs in Central Indian Ocean (CIO), however, is the least understood. Given the diversity of reefs in terms of human pressure, there are significant opportunities for improving the knowledge of human impacts on coral reefs and their solutions (Rajasuriya et al. 2002, Sheppard et al. 2008, MFT 2015, Pisapia et al. 2016).

3.2.4 Coral reefs of the Central Indian Ocean

Recent studies on coral reefs associated with atoll nations in the Pacific (Sandin et al. 2008, Williams et al. 2015, Smith et al. 2016, Crane et al. 2017) have attempted to unravel the influence of anthropogenic risk factors and human populations in driving the state of coral reefs. Here, we investigate the effects of reef location and land use on coral cover in the Central Indian Ocean (Chagos Archipelago and Republic of Maldives) using data generated from kilometre scale measurements and artificial learning. Our study began with an initial expedition from 12th of February 2015 to the 17th of April 2015, which

50 occurred well before the 2016 global mass coral bleaching and mortality event affected the region (Ibrahim et al. 2016). Both regions suffered from the mass mortality of corals losing approximate 90% of corals in 1998 from heat stress as a result of a positive anomaly in ocean temperature driven by exceptionally strong 1997-98 El Nino weather pattern riding on the back of increased background temperatures as a result of ocean warming (Sheppard 1999, Wilkinson 1999, McClanahan 2000, Zahir 2000). There have been multiple minor bleaching episodes since the 1998 event, but recovery in the Chagos appeared to be more rapid than in the Maldives. The Chagos apparently recovering to pre- 1998 conditions of 35-50% at 10m by 2006 (Sheppard et al. 2008), whilst the Maldives returned to the average of 40% coral cover by 2013 (Morri et al. 2015, Pisapia et al. 2016).

The Chagos and Maldives archipelagos are separated by approximately 500km. As a result, they experience considerably different weather patterns and oceanographic influences, although both experience reversing dominant winds in the two halves of the year (Kench et al. 2006, Sheppard et al. 2008). The reefs of the Chagos Archipelago and Maldives differ with regard to the intensity of human pressures, with reef systems in the Maldives Archipelago being highly threatened in many areas by land reclamation activities, fishing pressure from a growing tourism industry, and marine pollution from waste water outflow and anthropogenic marine debris (Jaleel 2013, Moritz et al. 2017). Chagos, on the other hand, has a 640,000 square kilometre Marine Protected Area, with the only established human population of 4,239 people situated within the United States Military Base on the southernmost atoll, Diego Garcia (Sheppard 2000). The distinct differences in human population pressure within and between the two atoll systems offer a major opportunity to understand the impact of local (natural and anthropogenic) stressors on coral reefs. The proximity to the equator (-7°S-7°N) means both countries rare experience typhoons/cyclones and if they do, are usually developing and weak (McClanahan et al. 2000).

There are four key questions at the outset of this study: (1) Do reef slopes (10-12m depth) in the Maldives and the Chagos, differ in terms of coral cover and community composition? (2) How do physical differences (e.g. wave energy) explain differences in the distribution and abundance of coral reef communities in the Central Indian Ocean? (3) Do land use practices within the Maldives play a significance role in explaining the extent and structure of coral reef communities in the Maldives? Finally, (4) Is there evidence of reef

51 communities that are showing exceptional resilience in terms of survival or recovery from prior environmental stress?

3.3 Material and Methods

3.3.1 Sites and data collection

A total of 57 transects (1.5-2km in length) were measured and analysed as part of the global XL-Catlin Seaview Survey. This global survey concentrated on coral communities on reef slopes (10-12m), in the Central Indian Ocean. Similarly, 22 sites were surveyed in the Chagos Archipelago between the 12th -24th of February 2015. As there are no populated islands, other than the British Naval Base on Diego Garcia, in the Chagos Archipelago, all survey transects were placed in the category, ‘unpopulated reefs’. In the Central region of the Republic of the Maldives, 35 sites were surveyed between the 29th of March – 17th of April 2015. There was a significant human activity in the Maldives. Of these sites, eleven were categorised as being associated with ‘local community islands’ (LCIs), all of which were outside the atoll structure. Seven sites were associated with the category, resort reefs, where four sites were inside and three were outside the atoll structure. There was a total of sixteen sites in the category ‘unpopulated reefs’, twelve of which were outside the atoll and four were inside the atoll structure.

Table 3.1. Sampling design for sites surveyed in the Chagos and Maldives from February to April 2015. Treatment Number of sites Number of sub-transects Chagos 29 1263 Inside 10 390 Outside 19 873 Maldives 35 1568 Unpopulated inside 4 175 Unpopulated outside 13 591 Resort inside 4 175 Resort outside 3 119 LCI inside 0 0 LCI outside 11 508

Approximately 900 images (22 megapixel) taken 1.5m above the substrate (at 10-12m) were captured from each transects (site) using the SeaView II (SVII) camera system. This camera system comprised a 360° camera (Fig1.1a) attached to a diver propulsion vehicle

52 (DPV) (Fig 1.1a). Images were automatically captured every three seconds using an intervalometer triggered by the SVII driver. The altitude of each image was recorded using a Mircon Echosounder transponder by Tritech (Tritech international, Westhill, Aberdeenshire, UK) (Fig1.1c) and pressure sensor housed inside the altimeter recorded the depth (Fig 1.1b). Each Image was geo-referenced using a Global Positioning System (GPS), and time stamped (Phinn et al. 2012). (to camera, transponder, and pressure sensor) attached to a surface buoy towed by the SVII driver. Post dive, the first suitable image with correct ISO settings adjusted for ambient light conditions was selected as the first image of the transect. As there is no artificial light source attached to SVII, the driver can view images being captured and manually change the ISO depending on light conditions using a Samsung galaxy tablet (Samsung group, Seoul, South Korea) inside an underwater housing (Fig 1.1e). Images were cropped into 1m2 photographic quadrats using Matlab R2013a (Mathworks, inc., Natick, Massachusetts, USA) by calculating a fixed resolution of 10 pixels per centimetre from the dimensions of the camera lens and the altitude of SVII recorded by the transponder (González‐Rivero et al. 2014, González- Rivero et al. 2016). All images were flattened and colour corrected using batch processing on Adobe Bridge (Adobe Systems, San Jose, California, USA) in preparation for automated image analysis. Further details in the survey method used by the XL-Catlin Seaview survey and the present study can be found in González‐Rivero et al. (2014). Comparison with conventional methods revealed that the methods used here are accurate yet 10-20 fold larger in terms of the area covered each dive (Bryant et al. 2017).

Fig 3.1. Sites surveyed in the Central Indian Ocean (a) from 12th of February 2015 until the 17th of April 2015. b) Chagos Archipelago, and c) The Maldives archipelago, Blue dots indicate unpopulated reefs, green dots indicate resort reefs, red dots indicate local community islands. For a break down of inside vs. outside site refer to Table 3.1. The blue dot at the top of South Male (i.e. Emboodhoo Finolhu) and on the oceanic faro between Meemu and Vaavu (Vattaru) were both surveyed on the leeward side of the atoll faro rim. Data for map outline was downloaded from the Millennium Coral Reef Mapping Project (IMaRS-USF 2005).

54 3.2.2 Automated image analysis

The CAFFE deep-learning framework for image analysis (Jia et al. 2014) was used to rapidly annotate 50 randomly placed points per image from a repository of 26,100 1m2 2 photographic quadrats from the Chagos Archipelago and 54,716 1m photographic quadrats from the Central Maldives. CAFFE is a deep learning neural network framework that classifies points within images based on colour and texture information within multiple layers of an image and calculates the probability of pre-determined benthic categories (Table 3.1) from a training set of images which have been annotated by an expert (Beijbom et al. 2012, Jia et al. 2014, Beijbom et al. 2015, Gonzalez-Rivero M et al. in prep). Labels for benthic categories (Table A.1)were made up of broad benthic parameters and functional groups along with genera identified based on colour and image (Bryant et al. 2017). Training for automated annotation was carried out from a subset of 360 1m2 photographic quadrats from the Chagos Archipelago and 1171 1m2 photographic quadrats from the Central Maldives. An online repository for image annotation and analysis called CoralNet was used for training, with 100 randomly positioned point on each photographic quadrat annotated by experts (Kohler and Gill 2006, Beijbom et al. 2012). An independent test set of 331 images from Chagos Archipelago and 331 from the Central Maldives was annotated with 50 points per 1m2 photographic quadrat to establish the accuracy and error of the automated annotation before the final deployment of the neural network using Python (Python Software Foundation, Delaware, USA).

3.3.3 Aggregation of 100m sub-transects

A sub-sampling unit size of 100m (e.g. aggregating 100 one m2 quadrats) was selected to limit the impact of small scale variability on the outcomes if only images were used (Wiens 1989, Levin 1992). Images were aggregated into 100m sub-transects, at each site, using hierarchical clustering, where images are clustered into different groups based on their distance from each other using time-stamped GPS coordinates. The clustering was aimed at preventing images from different areas of the reef being clustered together on particularly non-linear reefs (Hoegh-Guldberg et al. in prep). One hundred metre sub- transects were created by making the maximum distance an image can be from another 100m. Sub-transects with less than ten images were subsequently removed before statistical analysis took place to reduce the chance of large standard error for each sub- transect resulting from a small sample size.

Fig 3.2. Example of a) a linear 2km transect from Huluhumale forereef on the outside of North Male with different colours representing different 100m(max) sub-transects and b) A nonlinear 2km transect from Fesdu Island house reef inside North Ari Atoll where different colour represents different 100m (max) sub-transects. By using hierarchical clustering based on nearest distance, we avoid clustering images into sub-transects which represent different areas of the reef at the same time (Hoegh-Guldberg et al. in prep).

56 3.3.4 Statistical analysis

3.3.4.1 Overall differences in benthic cover between Chagos and Maldives

A Kolmogorov–Smirnov test was used to identify Archipelago differences in the probability distribution of each untransformed response variable. Differences in mean benthic cover for overall hard coral, branching coral, massive/encrusting coral (MASE), bushy corals (e.g. Pocillopora sp and Stylophora sp) and thin/foliose/plating (TFP) corals between Chagos and Maldives were analysed using unpaired two-tailed t-tests. Percent cover of all the response variables was transformed using a cube root transformation to address violation of assumptions (normality) for this parametric test.

3.3.4.2 The effects of reef location and land use on benthic cover

Linear mixed effects models (LME) were used with the ‘lme4’ package (Bates et al. 2015) in R (R Core Team 2017) to analyse the effect of reef location (inside atoll structure vs. outside atoll structure) on the response variables of hard coral cover, branching cover, MASE cover, bushy cover, and TFP cover of hard for Chagos. In the Maldives, the effects of reef condition were assessed with different land use practices as a potentially co- founding factor. Sub-transects were nested within reef sites (i.e. 2km transects) as random factors in the model. A Wald Chi-squared test was used to analyses the significance of covariates on the response variable before running model selection. A likelihood ratio test was used to compare the Maldives models with and without land use included. The model with the lowest AIC, was considered the most parsimonious and therefore selected for analysis. If the model with land use had a higher AIC, but the likelihood ratio test showed it was not significantly different from the proceeding model it was still used in the LME model analysis(Warren and Seifert 2011). Standardised residuals were inspected to check for obvious deviances from normality or homoscedasticity. The predicted values of the response variable from the model and the actual benthic cover of the response variable were plotted to check model fit (Nobre and Singer 2007) (e.g. it is not over/under predicting coral cover). Beta coefficients were backed transformed in order to interpret the output the LME.

57 3.4. Results

3.4.1 Chagos vs. Maldives

The Kolmogorov-Simonov test detected significant differences between the two archipelagos in terms of overall hard coral (D= 0.78, p<0.005), as well as for the coral categories of branching (D=0.149, p<0.005), bushy (D=0.06, p<0.005), and TFP percent cover (D=0.427, P<0.005; Table 3.3; Fig 3.3). Distributions of hard coral cover amongst sub-transects in the Maldives were relatively bimodal with a small subset of sub-transects having very high coral cover (∼50%), but a much larger subset having relatively low coral cover (Fig 3.3a). MASE corals also had different density distributions among sub-transects for the coral populations surveys at the two archipelagos (D= 0.1, p<0.005, Fig 3.3c), despite having similar mean percent cover (Table 3.2). The differences in the probability distributions are illustrated in the violin plots (Fig 3.3), that show that sub-transects in Maldives tend to be less normally distributed with a greater proportion of sub-transects with coral cover in the lower quartile range.

Results from unpaired two-tailed t-tests revealed that the percent hard coral cover analysed per sub-transect was significantly greater in the Chagos Archipelago (20.9%) compared to the Maldives (17.8%) (t=-6.47, df =2512.6, p<0.005) (Table 3.3). The same pattern was observed for the different morphologies within the hard coral functional groups (Table 3.2) including branching corals (t=-8.72, df =2594.4, p<0.005) and bushy corals (fig3.3d) (Pocillopora sp & Stylophora sp). There was however, more thin/foliose/plating (TFP) found in the Maldives than in the Chagos (t=-3.656, df =2427.4, p<0.005) (Table 3.3).

58 Table 3.2 Kolmogorov-Smirnov test output showing differences in density distributions of hard coral and morphologies that fall within the hard functional coral group for sub-transect in the Maldives vs. Chagos Archipelago. Response variable D-statistic p-value %Hard coral 0.78 2.20E-16 %Branching 0.149 2.20E-16 %Massive/encrusting 0.1 1.69E-07 %Bushy 0.06 0.009059 %Thin/foliose/plating 0.78 2.20E-16

Table 3.3. Mean benthic cover in Chagos Archipelago (n= 1276) and Maldives (n=1568) for overall hard coral and morphologies that fall within the hard functional coral group. * denotes significantly different at p<0.05. %Cover Maldives %Cover Region (±SE) Chagos (±SE) t df p-value Coral* 17.8(±0.25) 20.9(±0.25) -6.47 2512.6 1.15e-10 Branching* 5.45(±0.15) 7.17(±0.25) -8.72 2594.4 2.20E-16 Massive/encrusting 9.53(±0.25) 9.67(±0.25) -1.06 2696.2 0.288600 Bushy* 2.45(±0.55) 3.03(±0.25) 16.33 1478.0 2.20e-16 Thin/foliose/plating* 2.45(±0.19) 0.427(±0.25) -3.656 2427.4 0.000261

Fig 3.3. Violin plots of the probability distributions of percent coverage by a) hard coral, b) branching, c) massive/encrusting, d) bushy, and e) thin/foliose/plating cover across sub- transects in the Chagos (blue) and Maldives (red). The outer margins of the black box plot inside the violin represents the interquartile range, with median (horizontal black line) and mean (small black square). Vertical black line (not always discernible) inside the violin plot represent 95% confidence intervals, with black dots representing sub-transects outliers (outside 95% CI range). Edges of the violin plots represent kernel density estimations with wider sections showing higher probability that the % hard coral cover of a sub-transect will fall within that section. Blue shaded area represents 90-95% quantile range for Chagos, and red shaded area represents 90%-95% quantile range for Maldives range.

4.3.2 Effects of reef location and land use on percent coral cover in the Central Indian Ocean.

4.3.2.1. Hard coral

There was a significant effect of reef location on percent hard coral cover within the Chagos Archipelago (Fig 3.4a) (χ2=23.4, df =1 P<0.0001) (Table 3.4.). Based on the LME output, outside reefs in Chagos are estimated to have 5.09% (t=-4.82, df = 1132, p<0001) less hard coral cover than reefs inside (Table 3.5). In the Maldives, reef location (Fig 3.4b) did not have a significant effect on overall hard coral cover, while land use did (χ2=0.51, df =2, p=0.006) (Table 3.4). Taking into account the confounding effects of reef location, reef sites adjacent to LCI’s were estimated to have 7.32% less absolute hard coral cover than unpopulated reef sites (t=-3.10, df=31, p=0.004) (Table 3.5). This would mean a 42.2% loss in the reduction in hard coral cover from the overall average in the Maldives of 17.8% (Table 3.3).

4.3.2.2 Branching coral

There was a significant effect reef location on percent branching coral cover in the Chagos Archipelago (Fig 3.5a) (χ2=11.24, df=1, p<0.001) (Table 3.4.). Based on the LME output outside reefs in Chagos are estimated to have 1.73% (t=-3.35, df = 1132, p=0.0004) less branching coral cover than reefs inside (Table 3.5). In the Maldives, reef location had a significant effect on overall branching coral cover (Fig 3.5b) (χ2=14.87, df=1, p=0.0001), but land use did not (Table 3.4). Outside reef sites were estimated to have 7.21% less branching coral cover than reefs located inside the atoll lagoon (t=-3.85, p=0.0006) (Table 3.5).

4.3.3.3 Massive/encrusting coral

There was a significant effect reef location on percent massive/encrusting coral cover in the Chagos Archipelago (Fig 3.5a) (χ2=10.59, df=1, p<0.001) (Table 3.4.). Based on the LME output, outside reefs in Chagos are estimated to have 1.75% (t=-3.25, df = 1132, p=0.0004) less massive/encrusting coral cover than reefs inside (Table 3.5). In the Maldives, there was a significant effect on massive/encrusting coral cover from reef

61 location (χ2=20.6, df =1, p=0.0022), and land use (χ2=11.9 df =2, p=0.0026) (Fig 3.6c). Reefs on the outside were estimated to have 8.31% more massive encrusting coral than those inside (t=4.54, df=31, p<0.0001), and LCI’s were estimated to have 2.65% less coral cover than unpopulated reefs (t=-3.40, df=32, p=0.0019), (Table 3.5).

Table 3.4 Wald Chi-squared test for effect of covariates (reef location and land-use) as explicators of the response variable.* Indicates significant p-value. χ2 DF Pr(>χ2) % Hard coral Reef location (Chagos)* 23.4 1 1.35e-06 Reef location (Maldives) 0.51 1 0.475 Land use (Maldives)* 10.2 2 0.006 %Branching Reef location (Chagos)* 11.24 1 <0.001 Reef location (Maldives)* 14.87 1 0.0001 Land use (Maldives) 4.059 2 0.1314 %Massive Reef location (Chagos)* 10.59 1 <0.001 Reef location (Maldives)* 20.63 1 0.0022 Land use (Maldives)* 11.884 2 0.0026

62 Table 3.5 Output for linear mixed effects model investigating the effects reef location in Chagos, and reef location and land-use in Maldives, on percent hard coral (HC), branching (B) and massive (M) cover. The reference group of reefs selected for this analyse are those occurring inside the atolls of the Chagos archipelago and Maldives. For land-use in the Maldives, the reference group was unpopulated reefs. Random effects specified the nesting of sub-transects within reefs sites. *denotes significant difference at p<0.05. St Response variable β variables β coefficient Error DF t-value p-value

Chagos(HC) β0(intercept)* 22.7 (2.28) 0.07 1132 39.5 <0.001

β1(outside)* -5.09 (-0.23) 0.05 1132 -4.82 0.0008

Maldives (HC) β0(intercept) 17.8 (2.18) 0.144 1392 18.0 <0.001

β1(outside) 2.34 (0.11) 0.15 31 0.71 0.4815

Β2 (Resort) -4.12 (-0.22) 0.16 31 -1.41 0.1686

Β2(LCI)* -7.32 (-0.42) 0.13 31 -3.10 0.0041

Chagos (B) β0(intercept)* 6.64 (1.88) 0.09 1132 20.12 <0.001

β1(outside)* -1.73 (-0.18) 0.06 1132 -3.35 0.0004

Maldives(B) β0(intercept) 10.8 (2.22) 0.16 1392 13.6 <0.001

β1(outside)* -7..21(-0.67) 0.17 31 -3.85 0.0006

Β2 (Resort) -3.06 (-0.23) 0.18 31 -1.24 0.2238

Β2(LCI) -3.64 (-0.28) 0.15 31 -1.81 0.0786

Chagos(M) β0(intercept) 9.39 (2.116 0.08 31 27.0 <0.001

β1(outside)* -1.75 (-0.14) 0.04 31 -3.25 0.001

Maldives(M) β0(intercept) 4.17 (1.61) 0.14 1392 11.1 <0.001

β1(outside)* 8.31 (0.71) 0.16 31 4.54 <0.001

Β2 (Resort) -1.37 (-0.20) 0.16 31 -1.26 0.2170

Β2(LCI)* -2.65 (-0.46) 0.14 31 -3.40 0.0019

Fig 3.4. Violin plot showing the differences in the probability distribution of hard coral cover between location a) inside (red) vs. outside (blue) in Chagos, b) inside vs. outside reefs in Maldives; and between land-uses c) Unpopulated (blue), resort (green) vs. local community islands (red) in the Maldives. The outer margins of the black box plot inside the violin represents the interquartile range, with median (horizontal black line) and mean (small black square). Vertical black line (not always discernible) inside the violin plot represent 95% confidence intervals, with black dots representing sub-transects outliers (outside 95% CI range). Edges of the violin plots represent kernel density estimations with wider sections showing higher probability that the % hard coral cover of a sub-transect will fall within that section. Red shaded area represents 90-96% quantile range for inside reefs in Chagos (a), Maldives (b) and local community islands in the Maldives(c). Blue shaded area represents 90%-95% quantile range for outside atoll reefs in Chagos (a), Maldives (b), and unpopulated reefs in the Maldives (c). Green shaded area represents 90-96% quantile range for resort reefs in the Maldives.

Fig 3.5. Violin plot showing the differences in the probability distribution of branching coral cover between location a) inside (red) vs. outside (blue) in Chagos, b) inside vs. outside reefs in Maldives; and between land-uses c) Unpopulated (blue), resort(green) vs. local community islands (red) in the Maldives. The outer margins of the black box plot inside the violin represents the interquartile range, with median (horizontal black line) and mean (small black square). Vertical black line (not always discernible) inside the violin plot represent 95% confidence intervals, with black dots representing sub-transects outliers (outside 95% CI range). Edges of the violin plots represent kernel density estimations with wider sections showing higher probability that the % hard coral cover of a sub-transect will fall within that section. Red shaded area represents 90-96% quantile range for inside reefs in Chagos (a), Maldives (b) and local community islands in the Maldives (c). Blue shaded area represents 90%-95% quantile range for outside atoll reefs in Chagos (a) Maldives (b), and unpopulated reefs in the Maldives (c). Green shaded area represents 90-96% quantile range for resort reefs in the Maldives.

Fig 3.6. Violin plot showing the differences in the probability distribution of hard coral cover between location a) inside (red) vs. outside (blue) in Chagos, b) inside vs. outside reefs in Maldives; and between land-uses c) Unpopulated (blue), resort (green) vs. local community islands (red) in the Maldives. The outer margins of the black box plot inside the violin represents the interquartile range, with median (horizontal black line) and mean (small black square). Vertical black line (not always discernible) inside the violin plot represent 95% confidence intervals, with black dots representing sub-transects outliers (outside 95% CI range). Edges of the violin plots represent kernel density estimations with wider sections showing higher probability that the % hard coral cover of a sub-transect will fall within that section. Red shaded area represents 90-96% quantile range for inside reefs in Chagos (a), Maldives (b) and local community islands in the Maldives(c). Blue shaded area represents 90%-95% quantile range for outside atoll reefs in Chagos (a) Maldives (b), and unpopulated reefs in the Maldives (c). Green shaded area represents 90-96% quantile range for resort reefs in the Maldives.

66 3.5. Discussion

Coral reefs of the Central Indian Ocean are relatively understudied when it comes to the influence of local human activities as compared to those of the Pacific and Caribbean (Sheppard 2000, Rajasuriya et al. 2002). A lack of river systems and arable land on the small islands associated with Maldivian and Chagos atolls eliminates the range of natural and human impacts associated with the interaction of catchments with reef systems (Sheppard 2000, Schlager and Purkis 2013). Furthermore, both the Maldives and Chagos have avoided coral-algal phase shifts with low levels of fleshy macroalgae, which can be attributed to large populations of herbivorous fish species (e.g. Acanthuridae, Scaridae) supressing macroalgal overgrowth of coral reefs (McClanahan, 2011, Brown et al. 2017).

By contrast, the lack of available land, and the distance between land masses (Harborne et al. 2017), creates unique problems for housing, management, and providing jobs for a growing population. The Maldivian Government has tended to solve these problems by grouping the population on small land masses that it has enlarged (LCIs), whilst establishing resorts in mostly dispersed wind protected locations to fuel the country’s economy (Sovacool 2012, Jaleel 2013). The building works associated with both of these activities has the potential to undermine adjacent coral communities. Differentiating these potential human impacts from previous global impacts associated with past disturbances such as the 1998 Global Bleaching Event (Sheppard 1999, Wilkinson 1999, McClanahan 2000), or from natural scouring and modification of reef communities due to monsoonal weather patterns that drive high winds for much of the year (Kench et al. 2009, Gischler et al. 2014) is non-trivial. The unpopulated Chagos Archipelago, which were also significantly impacted by the 1998 bleaching event (Sheppard 1999), offer a rough comparison that may assist in teasing out the human impacts on coral abundance and community structure of corals across the two archipelagos.

3.5.1 The role of physical differences in the benthic communities of the Maldives and Chagos.

The overarching question of the present study was: What is the state of key reef slope communities across the two archipelagos after a history of disturbances and recovery in the face of local pressures and global events such as the 1998 heat stress event (McClanahan et al. 2007, Sheppard et al. 2008)? The expectation was that the 2015 coral

67 cover would, on average, be much greater in the Chagos than in the Maldives, with human activity on some Maldivian reefs either impeding recovery or directly contributing to loss of coral cover. The results of the study for the most part support this expectation. Although, coral cover was significantly less across both archipelagos than the current literature would suggest (Sheppard et al. 2008, Morri et al. 2015, Pisapia et al. 2016). In the Chagos, reefs within the atoll structure tended to have higher coral covers than those outside, although the potential scouring associated with greater wave energy did not appear to change the relative composition of broad functional hard coral communities. By contrast, there was no change in total hard coral cover in the Maldives, for inner and outer locations. Alternatively, community composition oscillated with greater branching cover within the atolls, and greater massive cover outside the protection of the atolls. The LCIs in the Maldives, that tended to be adjacent to outer reef sites, were reduced in total coral cover, especially massive coral, compared to unpopulated reefs. Ignoring the results obtained from the Chagos, it would be tempting to argue that (i) differences in the community composition between inner and outer reefs in the Central Maldives are driven by the ability of massive forms to tolerate wave stress more than branching forms (Darling et al. 2013) and that, (ii) massive corals are, over the long term, more impacted than branching corals by sedimentation and other issues associated with land reclamation (Jaleel 2013)). Such an interpretation, however, excludes the potential impact of past disturbances on these reefs, an impact that is potentially captured in (i) the bimodal probability distributions of unpopulated reefs in the Maldives; and, (ii) the fact that community structure is not, apparently, impacted by differences in the wave energy expected amongst locations (inner vs. outer) in the Chagos.

3.5.2 Estimates of hard coral cover and differences in probability distributions across regions, land-uses and functional groups.

One of the interesting observations of the current project is the observation that the abundance of coral measured on forereefs at 10-12 m in 2016 was approximately 50% of that measured by other research groups (Sheppard et al. 2008, Morri et al. 2015, Pisapia et al. 2016). In this case, there may be a bias towards higher coral cover as a result of the way sites were selected. For example, previous studies have set out to measure the change in coral communities, and therefore were likely to have selected areas with high coral cover, which was subsequently monitored. In the case of the present study, the selection of sites was done independently of the amount of coral cover on them. That is, a

68 starting point for each 2 km transect in the present study was defined by the fact that it was a site on a reef slope at 10-12 m depth. This means that transects included areas that might have only been covered by sand and bare rock surfaces, in addition to coral communities with low through high density patches of coral cover. As a result, the distribution and abundance of coral communities measured here is probably more accurate than the much more limited studies that have pre-selected coral communities to follow. This observation is important to the measurement and understanding of change on coral reefs, especially in the current period of rapid global change.

There was an expectation, however, that the impact of human pressures in the Maldives (i.e. land use change, tourist pressure; (Jaleel 2013, Moritz et al. 2017, Cowburn et al. 2018), would result in significantly less coral cover overall when compared with the Chagos Archipelago, using the same large scale and repeatable methods described here (Sheppard 1999, Sheppard et al. 2008). By using broad-scale kilometre survey methods and machine learning image annotation applied in this survey (Beijbom et al. 2012, Beijbom et al. 2015, González-Rivero et al. 2016, Bryant et al. 2017, Gonzalez-Rivero M et al. in prep, Hoegh-Guldberg et al. in prep), we showed that Chagos not only had significantly higher coral cover than the Maldives but also had a significantly different distributions of percent hard coral cover – with Chagos following a normal distribution, whilst the distributions in Maldives were bimodal, with a cluster of sub-transects creating a small mode with coral cover above the 5% quantile. Finally, we showed that in the Maldives, land-use practices associated with the development of local community islands, but not with the building of resorts, lead to significant declines in coral cover, especially the cover of massive/encrusting species. LCI development involves land reclamation, an activity that significantly increases sedimentation with detrimental impacts on proximal coral communities (Dodge and Vaisnys 1977, Bak 1978, Jones et al. 2015, Jones et al. 2016, Duckworth et al. 2017). Sedimentation can impact corals by reducing light levels (Bessell-Browne et al. 2017) by increasing the amount of energy they must expend to clear their surface of sediments and prevent burial (Junjie et al. 2014), and by impeding settlement of larvae (Babcock and Davies 1991) or growth of small fragments (Dodge and Vaisnys 1977, Rodgers et al. 2012). Smothering is perhaps, the greatest risk faced by corals exposed to large volumes of sediment. Smothering leads to lesions and mortality within a few days (Duckworth et al. 2017). In this respect, studies suggest that corals with branching morphologies are more adept at clearing themselves of sediments, than those

69 with massive or foliose morphologies (Duckworth et al. 2017) perhaps explaining the detected sensitivity of massive/encrusting forms to LCIs in the Maldives.

The patterns associated in the distribution and abundance of corals (Fig 3.5) presented interesting differences between archipelagos. In the case of the Maldives, there is essentially a larger gap between the 95 percentile and the median when compared to coral communities of reefs in Chagos. Furthermore, the distribution in the Maldives is relatively bimodal with a smaller subset of reefs clearly performing better in terms of coral cover (>50%) than other reefs (<20%). These high coral cover reefs were, for the most part, restricted to the unpopulated reefs of the Maldives, whose probability distributions followed a very similar bimodal pattern that was not replicated for other land-uses (Fig 3.4 &b Fig. 3.6). The inequity in coral cover within unpopulated reefs of the Maldives suggests the presence of drivers that are not presently accounted for in the model. One such driver could be human impacts that exert their pressure on reefs away from local population centres; for example, motorised fishing practices (Rajasuriya et al. 2002, Jaleel 2013). In this regard, a local feature may impede (e.g. fishing) or benefit the colonisation of one functional group of coral over another (Nyström et al. 2000, Bellwood et al. 2004, Darling et al. 2013). The violin plot of massive corals on the unpopulated reefs of the Maldives has a very similar bimodal distribution to the overall coral cover of unpopulated reefs in Maldives, suggesting that massive corals appear more resilient to stress or are present in pockets of reefs that have escaped more localised disturbances in the Maldives (Kench et al. 2006, Kench et al. 2008, Darling et al. 2013),

In the case of the present study, there is evidence for, potentially more resilient corals, MASE coral to favourably re-establish on outer, heavily wave impacted, reefs; and, for, perhaps less resilient, branching communities to favourably re-establish on inner, relatively wave protected, reefs following significant perturbations to the reefs of the Maldives (Sheppard 1999, Wilkinson 1999, McClanahan 2000, Zahir 2000). After a disturbance, it is not unusual, for an environment to favour colonisation by one type of organisms over another (Sheppard 1982, Sousa 1984, Rogers 1993). Massive/encrusting species experience lower drag forces than branching species, that extend out into the water column (Rogers 1990). As a result, branching morphologies may require time (and repeated loss of branches through fragmentation) to establish a sufficient bulk density in lower anchoring branches to provide greater resistance to the forces that act upon them. Alternatively, in an area protected from waves, were skeletal density may not be so

70 important, these corals potentially have a much greater capacity to expand in space, potentially out-competing massive corals, or relegating them to an under canopy hidden from the view of cameras. The alternative answer is that massive corals survived or recovered the previous thermal disturbance better than branching forms (Loya et al. 2001, Sheppard et al. 2008, McClanahan and Muthiga 2014). Such an answer would, however, require an argument that the disturbance was greater on the outer compared to the inner reefs in the Maldives. This happened in Chagos after the 1998 beaching event, with massive Porites sp, showing more resistance to mortality from bleaching (Sheppard 1999). Importantly, reefs of the Chagos that are reported to have re-established coral cover prior to the reefs of the Maldives following the 1998 thermal event (Sheppard et al. 2002, Sheppard et al. 2008). The present study demonstrated that they have attained the same community structure inside and outside of atolls. This is also a classic feature associated with community succession following disturbances, as with time, organisms modify the habitat providing niches for more diverse organisms to occupy resulting in a more diverse and even community structure (Rogers 1993).

3.5.3 Evidence of exceptional coral reef resilience in the Central Indian Ocean

The final question that was posed at the beginning of this chapter was: Is there evidence of reef communities that show exceptional resilience in terms of survival or recovery from prior environmental stress? This question is a consequence of the ability to survey large amounts of seascape to be surveyed with relatively fine detail. The skewness of the distribution of coral cover among sub-transects, as illustrated by the violin plots, reveal that there are sub-transects (though a small proportion) which have maintained coral cover significantly above that of the average (some to 50% or more). The fact that each of these many subs-transects are GIS located, facilitates a further project that might aim to understand why these particular patches of coral reef have persisted or re-established themselves through disturbance when most others have not. These sub-transects or patches of reef may prove to be of high conservation value and deserve further study.

71 3.5.4 Conclusions

The research described here has identified the utility of the fine scale measurement of ecological structures at kilometre scales for coral reefs like those of the Central Indian Ocean. These large-scale technologies have identified key differences between the coral reefs of the Maldives versus the Chagos archipelagos. These measurements reveal that coral cover in these extensive regions are being driven by the influence of human activities via land-use and coastal development, as well as through more diffuse human influences. In either archipelago, there continues to be an urgency to deal with local impacts on the health of coral reefs, which underpin the economic opportunities for people in this region in terms of tourism and related livelihoods.

The research also indicates a number of future directions in terms of understanding and managing the threats that face marine ecosystems within the Maldives and Chagos archipelagoes. One of the most important is to understand how local human activities affect the impact of global events such as the extensive mass coral bleaching and mortality event of 1998. This question is at the heart of the next chapter in this thesis. While understanding the implications of global change are important, there continue to be a number of key questions arising from the present chapter, including:

(1) What are the underlying reasons for reef sites that have a high abundance of corals? While the present study has identified these important and unique areas, our understanding of why they have persisted despite the often-high levels of human influence?

(2) What are the potential management steps that need to be taken to encourage the regeneration of damaged coral reefs within the Maldives particularly? While land use practices associated with LCIs is negatively correlated with coral distributions, understanding what aspect of human activity is contributing to these problems is relatively unknown, yet stands at the centre of any meaningful management response aiming to preserve and encourage the regrowth of affected areas.

(3) Can the potential reefs that appear to be resilient to environmental stress, persist through future climatic impacts such as coral bleaching events? With future predictions of thermal stress events being expected to be the norm in future (Hoegh-

72 Guldberg 1999, Sheppard 2003), it is important understand how reefs under direct pressure from human land use activities react to future disturbances when compared to unpopulated reefs that have shown signs of resilience to environmental stress in the past.

73 3.6. Acknowledgments

This study was undertaken as part of the XL Catlin Seaview Survey and undertaken by the Global Change Institute (GCI). The 2015 survey was in part funded by CL Catlin Pty Ltd in partnership (to Hoegh-Guldberg) with the Ocean Agency and the 2017 expedition was funded by the Hoegh-Guldberg and Dove laboratories at the University of Queensland (UQ). DEPB was supported by the Australian Government Research Training Program Scholarship through UQ and XL-Catlin oceans scholarship stipend. OHG were both supported by an ARC Laureate Fellowship and by the Centre for Excellence in coral reef studies (to OHG and SD). The SVII camera was invented by Richard Ververs, Aaron Spence, and Christophe Bailhache. I would like to acknowledge Jon Schleyer and the Chagos conservation trust, along with the field team at GCI for their time and assistance in arranging field activities. The Maldives government and members of the Marine Research Centre, Ministry of Fisheries and Agriculture, and Environmental Protection Agency for their assistance with permitting and in-country knowledge. Finally I would like to the crew of MV Emperor Voyager and MV Pacific Marlin for their efforts in providing excellent facilities for our research goals.

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82 Chapter 4: Broad-scale impacts of the 2016 mass-bleaching event in the Central Maldives were not associated with land use practices.

4. 1 Abstract

The coral reefs of the Maldives encountered an elevated sea surface temperature (SST) anomaly that lasted from April to June 2016. In 1998, a similar SST anomaly resulted in mass coral bleaching and subsequent broad-scale reductions in coral cover throughout the Maldives. Between events, reefs can recover to some extent although local factors (e.g. elevated coastal development, pollution, fishing pressure) can potentially influence the damage and recovery associated with these types of global events. The Maldivian Government has increasing realised that managing local factors is important in the face of rapid climate change. In this regard, government initiatives have also aimed to consolidate human populations around local community islands (LCI), while planning to protect other locations through limiting the establishment of human populations on other islands. LCIs as a result have include increased land reclamation activities and localised waste water effluent while other islands have little or none. These differences in land use regimes suggest that differential local land use, may not only affect the status, or recovery of reefs, following bleaching events that occurred two decades before (Chapter 3); but they may also influence the mortality and loss of corals from the 2016 mass coral bleaching and mortality event. Repeated measures test were first used to determine if there were declines in coral cover that could potentially be attributed the 2016 thermal anomaly. The average percent cover lost per unit initial cover was then estimated using linear regressions. Finally, we used linear mixed effects models (LME), and included the random effects of 100m sub-transects nested within 2km transects surveyed across 26 reefs. Models investigated the fixed effect of the reef location (inside or outside the atoll), land- use regime (unpopulated, resort, LCI), and the presence/absence of Acanthaster planci (COTS) outbreaks between 2014 and 2017, on the absolute change in coral cover between 2015 and 2017. Results confirmed an overall reduction in coral cover from 18.6% (±0.34%SE) in 2015 to 13.0% (±0.21%SE) in 2017. The initial cover, one year before the bleaching event was found to have a high correlation with the subsequent coral decline. On average there was a 0.68% reduction in coral cover per unit increase of initial coral cover in 2015. Against the hypotheses, the LMEs found that land-use had no effect on coral loss associated with the thermal event; and reef location was only significant for

83 Branching coral cover, with a lesser reduction on the outer compared to the inner reefs. This latter feature however, could just as easily be explained by the lower initial branching cover observed in outer reefs compared to inner reefs. The results indicate that the 2016 bleaching event affected the healthiest corals, in the most unpopulated reefs in the Maldives, just as much as those already damaged by land use practices and severe though localised COTS outbreaks.

4. 2 Introduction

Understanding the coral communities of the central Indian Ocean is a major focus of this thesis. The original focus of this research concentrated on stresses originating from local factors such as land use and reef location. In 2016, however, elevated sea temperatures triggered a major bleaching event which result in mortality (Ibrahim et al. 2016). Having extensively surveyed reefs prior to these events, the present project was well positioned to explore how local stress factors (e.g. land use, reef location, presence of Acanthaster planci) influenced coral mortality levels between 2015 and 2017, a period that included the 2016 mass bleaching event (Ibrahim et al. 2016). This chapter, consequently, focuses on improving our understanding of the effects of localised stressors on the ability coral communities to survive a large scale bleaching event.

The first records of heat stress in the Maldives occurred during the extraordinary warm conditions of 1998. The 1998 event highlighted the very significant impacts of increased sea surface temperature (SST) on coral reef ecosystems, where temperatures reached 3- 5oC above average of 29-30oC from May to June 1998 (Wilkinson 1998, Hoegh-Guldberg 1999, McPhaden and Yu 1999, McClanahan 2000, McClanahan et al. 2007), causing the death of 60-100% of corals depending on the location (Wilkinson et al. 1999, Zahir 2000). As a result of this mortality, the average hard coral cover dropped to less than 10% across the reefs of the Maldives (McClanahan 2000, Zahir 2000, Morri et al. 2015). Since that time, significant recovery of coral reefs began, with substantial populations of large plating Acropora coral colonies returning as early as 2004 (Lasagna et al. 2010a) (Lasagna et al. 2010b) and coral communities in general showing signs of recovery to thermal stress seven years after 1998 (McClanahan and Muthiga 2014). Average coral cover at monitoring sites in the Maldives returned to the pre-1998 coral cover by 2013 despite the events such as the 2004 tsunami, and sporadic and milder temperature anomaly events

84 occurring in 2003, 2005, 2007, and 2010 (Zahir et al. 2010, Morri et al. 2015, Pisapia et al. 2016).

The first signs of coral bleaching in the Maldives in 2016 were reported to the Marine research Centre Maldives in the most southern Atoll, Adduu by sea plane pilots on the 13th of April 2016 (Ibrahim et al. 2016). By the end of the April 2016, SSTs had risen to 0.4- 1.8°C above the average maximum monthly mean (NOAA Coral Reef Watch 2017), and bleaching was widespread throughout the country and a regional monitoring effort to measure the extent and severity of coral bleaching was underway (Fig 4.1). Bleaching surveys revealed 77% of corals had bleached across 71 sites at depths of 7-13m with no significant difference in bleaching severity between exposed and sheltered sites (Ibrahim et al. 2016). Furthermore, Poritidae corals experienced the least amount of coral bleaching, while Acroporidae corals experienced the most (Ibrahim et al. 2016). This matched the patterns associated with the coral communities and thermal stress in the Maldives in 1998, as well as more broadly in 2016 (Loya et al. 2001, Hughes et al. 2017, Hughes et al. 2018).

One of the objectives of the present study was to assess the potential coral mortality that was triggered by the 2016 mass bleaching event by estimating coral cover and community structure one year on from the 2016 SST anomaly event. It is important to note, at the outset of this project, that heat stress related coral bleaching was not the only disturbance that could have caused mass coral mortality on reefs in the Central Maldives between 2015-2017. Concentrated and localised impacts due to the Acanthaster planci (Crown-of- thorns starfish (COTS) (Fig 4.2) as well as increased land reclamation for island developments such as resorts and airports on Maldivian reefs also potentially affected coral cover. While these impacts were large, they were also highly restricted geographically. COTS outbreaks were first observed in 2014 on patches of reefs in North Male atoll. By 2016, they had moved west towards North Aril Atoll, where reports of over 3000-6000 COTS were being removed from resort reefs of Kandholhudu and Fesdu (Ibrahim et al. 2016, Pisapia et al. 2016). Adjacent Land reclamation also appeared to have a negative effect on coral cover due to increased sedimentation (Erftemeijer et al. 2012, Ford 2012, Mora et al. 2016). These activities have been on the rise with highly probable linkages to economic development strategies such as those to boost tourism, as well as the population consolidation plan (PCP) and attempts to strengthen coastal protection (Sovacool 2012, Jaleel 2013).

Fig 4.1. Environmental impacts occurring in the Maldives between 2015 and 2017 benthic surveys. a) Mass coral bleaching on Velidhoo house reef in the Maldives during the 2016 bleaching event on 19/05/2016. B) Acanthaster Planci eating a Favidae sp coral colony at Gathafushi house reef, adjacent to Fesdu island during the 2016 bleaching event on 25/05/2015. Both photographs taken by the author Dominic Bryant.

The present project surveyed 26 reef slope sites (Fig 4.3) at 10-12m in the Central Maldives region, in March/April 2015 and March/April 2017. The field teams used camera systems attached to a diver propulsion vehicle (DPV), to undertake large-scale surveys of coral communities before and after the impact of the 2016 thermal anomaly event in the Maldives (one year either side). The first objective was to determine if cover (abundance)

86 of coral and algae groups changed between 2015 and 2017. Given this, we then investigated if the changes correlated with the initial coral cover in 2015. Finally, we explored the potential role of localised stressors such as being adjacent to local community islands (LCI) and resorts; or exposed to more frequent and stronger waves outside versus inside atolls, and/or presence of a COTS could explain the changes in observed changes in benthic communities.

Fig 4.2. Map of sites surveyed from 27th March to 19th April 2015 and again from the 20th of March to 1st April 2017. Blue dots indicate unpopulated sites, green dots indicate resorts sites, red dots indicate local community islands, and pink stars indicate sites surveys where presence of COTS outbreaks were recorded before the 2016 mass bleaching event in the Maldives (Ibrahim et al. 2016). Data for map outline was downloaded from the Millennium Coral Reef Mapping Project (IMaRS-USF 2005)

87 4.3 Materials and methods

4.3.1 Study sites A total of 26 sites in the Central Maldives (Fig 4.2) were surveyed from 27th March to 19th April 2015 and again from the 20th of March to 1st April 2017 (Table 4.1). Crown-of-Thorns Starfish (COTS) data was provided by the Maldives Ministry of Fisheries and Agriculture (MOFA) Marine Research Centre (MRC), which indicated that four of the 26 sites surveyed had experienced COTS since the first observations of the current COTS outbreak in 2014 (Fig 4.2). There was a slight unbalance in sites outside versus inside – given that unpopulated islands tended to be more difficult to access and survey than islands with resorts on them. Sites on the inside of reefs associated with unpopulated islands had relative low amounts of 10-12 m habitat for undertaking 2km surveys. Islands with significant human populations tended to be on the edge of the atoll rim with well-developed harbours on the leeward facing lagoon for the fishing communities living on LCIs (Jaleel 2013). Sub-transects were aggregated using the same hierarchical cluster method from the previous chapter, however each image was given a unique code for its year. Images from both year were clustered into 100m sub-transects based on their nearest distance. Any sub-transects that’s only contained images from one year were subsequently removed (Hoegh-Guldberg et al. in prep).

Table 4.1. Sampling design for different covariates used to compare absolute coral change from benthic surveys in the Maldives between April/March 2015 and April/March/2017 Treatment Number of sites Number of 100m sub-transects Unpopulated outside 10 419 Unpopulated inside 3 115 Resort outside 1 40 Resort inside 4 159 Local community island outside 8 332 Local community island inside 0 0 Total 26 1065

4.3.2 Measurement of coral cover and benthic composition Survey data was collected using the SVII camera system (Fig 1.1) which allows dive teams to take multiple high resolution (22 megapixels) every three seconds across a 1.5- 2km linear transect at 10m depth and 1.5m above the substrate. Each image is geo-

88 referenced by synchronizing the camera system to a global positioning system (GPS) device, which is attached to a surface buoy towed behind the diver operating SVII (González‐Rivero et al. 2014). The start and end coordinates from the 2015 surveys were used to determine the start and compass heading for the 2017 surveys, which was dependent on current. Images that were collected from both surveys were then aggregated into 100m sub-transects from the 2015 survey coordinates using a hierarchical clustering approach. In 2015, the distance above the benthos of each image was taken and time stamped Tritech Micron Echosounder altimeter attached to the bottom of the DPV body of SVII (González‐Rivero et al. 2014). However, due to technical problems, this equipment was not available for the 2017 survey. In this case, divers took measurements approximately 1.5 m above the substrate with each image being cropped with the assumption it was taken from an altitude of 1.5m off the substrate.

4.3.3. Image processing Images were captured using Canon 5D MkII digital single lens reflex (SLR) camera and Nikon fisheye lens inside the SVII camera housing. Images were colour corrected and flattened using Adobe Photoshop and Adobe Bridge batch processing software. The 2015 image set was cropped into 1m2 photoquadrats by using the altitude SVII was above the benthos at the time each image was captured. Image cropping was batch processed using MatlLab R2013a (González-Rivero et al. 2016). The 2017 1m2 photoquadrats were cropped using the assumption images were taken approximately 1.5m above the benthos. As the project was exploring percentage cover estimates (and not absolute measures of abundance), approximate measurements were suitable for the before and after comparisons.

4.3.4 Annotation of images in preparation for analysis using machine learning. Photoquadrats were annotated using a deep learning neural network algorithm (DLNNA), which is similar to the Caffe deep-learning framework for image analysis (Jia et al. 2014). The DLNNA algorithm was developed and deployed using Python (Python Software Foundation, Delaware, United States). This form of annotation uses data in a digital image (i.e. colour, texture, contrast etc.) within a fixed spatial resolution of 10 pixels per centimetre to identify a human created classification label around a randomly designated point. The final deployment for the DLNNA algorithm was done using 1,310 trained images, which were originally annotated manual by an expert using coral net

89 (https://coralnet.ucsd.edu) an online image repository for annotation of coral reef images (Beijbom et al. 2012).

Labels were created to represent benthic fauna found on coral reefs and were split into 5 functional groups (hard coral, algae, other invertebrates, seagrass, soft sediment and miscellaneous abiotic). The labels from the ‘hard coral’ functional group were designated to provide as much taxonomic and morphological resolution possible (i.e. branching, massive/sun-massive/encrusting, “bushy” (Pocillopora sp) (Tanner 1995), and thin/foliose/plating when dealing 2D images and automated image annotation. Algae were split up into three functional groups, including macroalgae (e.g. Halimeda spp, Dictyota spp,), the epilithic algae matrix (EAM), and crustose coralline algae (CCA) (Table A.1). To increase the predictability of the computer to identify the image labels that were rare in the training set, rare image labels were targeted using MatLab and the information provided by targeted points were included in the DLNNA.

4.3.5 Statistical analysis

4.3.5.1 Coral cover change in the paired sub-transects between 2015 and 2017

Differences in mean benthic cover for overall hard coral, branching coral, massive/encrusting coral (MASE), bushy corals (e.g. Pocillopora sp) (Tanner 1995), and thin/foliose/plating (TFP) corals in the Central Maldives between 2015 and 2017 were analysed using paired two-tailed t-tests with subs-transect as subject. Data were cube-root transformed to meet the assumptions of normality and homogeneity of variance.

As some untransformed distributions appeared to be non-normal. A non-parametric two sample Kolmogorov-Smirnov tests (KS-test) was done using R to compare the probability distributions of percent overall hard coral cover, branching coral cover, massive/encrusting (MASE), bushy, thin/folios/plating (TFP), macroalgae, epilithic algae matrix (EAM), and crustose coralline algae (CCA) cover between 2015 and 2017. The closer the D statistic is to 0 the more likely the two sample (i.e. coral cover in 2015 vs. coral cover form 2017 were drawn from the same distribution (Massey 1951)

90 4.3.5.2 Response variable: Absolute percent change vs. relative percent change

Absolute percent cover change was calculated by subtracting the percent cover in 2017 from that in 2015. Absolute change in cover was used as the response variable, instead of relative percent change from the initial change, because relative percent change is less ecologically relevant. Relative change accords the same impact to a reef that increases from 1% to 2% cover, as it does to a reef that increases from 30% to 60% cover. Clearly, however, from an ecosystem service perspective, these two reefs would function quite differently (Perry et al. 2013). Furthermore, very small changes in percent cover between years are more likely to be artefacts of the methodology (below detection limits) as opposed to real. Relative percent change potentially equate artificial and real changes.

The model sought to explain the roles of different reef types, and/or dominant coral morphologies, on cover reductions after 2015. It was noted that there was a high probability for a strong correlation between initial (2015) cover and the response variable (absolute percent change, see untransformed probability distribution for different years). Ideally, we would like to deal with this correlation by including initial cover as a covariate in the model. Unfortunately, we already know that different land-uses gave rise to different coral cover in 2015 (Chapter 3). The covariate cannot be introduced because the initial cover within the different levels of a fixed variable are known not to be random (Miller and Chapman 2001). For this reason, correlations between initial cover and absolute percent change in cover were determined by linear regression analysis, although initial cover was excluded from the linear mixed model developed.

The initial ratio of massive/encrusting to other coral has the potential to impact the absolute change in coral cover, given observations in the literature that corals with this morphology tend to be more resistant to thermal anomalies (McClanahan et al. 2007, Darling et al. 2013). Unfortunately, the balance of massive/encrusting to other corals influenced the fixed variable “reef location” in our analysis of the 2015 coral cover (Chapter 3). Correlations between the initial ratio of massive/ encrusting coral were therefore also separately analyses via regression analysis. Additionally, separate models for the impact of the fixed variables, “reef location” and “land-use” were performed on the specific cover changes of branching, MASE, and bushy corals to determine whether their impact varied for corals with different functional morphologies.

91 4.3.5.3 Construction of optimal models for the analysis of the influence of reef location, reef land-use practice, and COTS on absolute change in benthic cover.

Linear mixed effects models (LME) were used with the ‘lme4’ package (Bates et al. 2015) in R (R Core Team 2017) to analyse the fixed effect of reef location, different land use practices and recorded presence of COTS outbreaks on absolute percent cover change of the response variable (i.e. hard coral, branching, massive/encrusting, bushy coral, cover etc.) between 2015 and 2017 surveys. Sub-transects were nested within reef sites (i.e. 2km transects) as random factors in the model.

A Wald Chi-squared test was used to analyze the significance of covariates on the response variable before running model selection. Likelihood ratio tests for model selection were built on the inclusion of fixed variables in the following order: reef location, land use and presence of COTS outbreak (table 4.4). The model with the lowest Akaike Information Criterion (AIC) was generally picked for analysis. In the case that the inclusion of a fixed effect provided a higher AIC value, they were only included if the likelihood ratio test showed the model with their inclusion was not significantly different from the model with the lowest AIC. Standardised residuals were inspected to check for obvious deviances from normality or homoscedasticity. Transformations were used to achieve normality where necessary. The predicted values of the response variable from the model and the actual benthic cover of the response variable were plotted to check model fit (i.e. it is not over/under predicting coral cover (Bates et al. 2015).

92 4.4. Results

4.4.1 Pre (2015) vs. post (2016) mass coral bleaching.

Results from the paired two sample t-tests showed percent hard coral cover analysed per sub-transect was significantly greater in 2015 (18.6%) compared to 2017 (13.0%) (Table 4.12 (Fig, 4.3a). The same pattern was observed for the different morphologies within the hard coral functional groups (Table 4.2), including branching corals and bushy corals (Pocillopora sp)(Fig. 4.3). There was however, more thin/foliose/plating (TFP), and more massive/encrusting coral cover found in the 2017 than in 2015 (Table 4.2, Fig 4.3); as well as more Macroalgae, EAM, and CCA (Table 4.1, Fig 4.4).

The Kolmogorov-Simonov test showed that untransformed overall hard coral (Fig 4.3a) (D= 0.216, p<0.005), branching (Fig 4.3b) (D=0.454, p<0.005), bushy (Fig 4.3d) (D=0.330, p<0.005), and TFP (Fig 4.3e) (D=0.363, P<0.005), all had significantly different percent cover density distributions between 2015 and 2017 (Table 3.3; Fig 4.3). Massive/encrusting coral cover (Fig 4.3c) also had different density distributions between the two years (D= 0.145, p<0.005), despite having virtually the same mean percent cover in 2015 as there was in 2017 (Table 4.3). The differences in the probability distributions are revealed in the violin plots (Fig 4.3), that show that 2017 sub-transects tend to be skewed more to the bottom of the violins, indicating more sub-transects were in the lower quartile range in 2017 compared to 2015 (Fig.4.3)(Table 4.2). By contrast, the proportion of sub-transect with very high Macroalgae (Fig 4.4a) and CCA (Fig 4.4b) content increased in 2017 relative to 2015. EAM distributions were normal in 2015, but skewed with a higher proportion of EAM with high percent cover in 2017 (Fig 4.4b). The changes in shape of the distributions, together with the loss of reefs with high coral cover suggests that initial cover is an important covariate of absolute change in coral cover.

93 Table 4.2. Mean benthic cover in 2015 (n= 1065) and 2017 (n=1065) for overall hard coral and morphologies that fall within the hard functional coral group. %Cover %Cover Response variable 2015 (±SE) 2017 (±SE) t df p-value Hard Coral 18.6 (±0.34) 13.0 (±0.21) -13.60 1064 <2.2e-16 Branching 6.29 (±0.20) 1.31 (±0.05) -42.06 1064 <2.2e-16 Massive/encrusting 9.34 (±0.27) 9.74 (±0.18) -7.082 1064 2.2e-16 Bushy 2.55 (±0.08) 1.18 (±0.03) -26.54 1064 <2.2e-16 Thin/foliose/plating 0.41 (±0.08) 0.79 (±0.03) 24.02 1064 < 2.2e-16 Macroalgae 1.80 (±0.20) 3.28 (±0.33) 15.29 1064 0.000145 EAM 58.6 (±040) 60.5 (±0.39) 4.126 1064 0.000040 CCA 2.31 (±0.20) 3.87 (±0.20) 13.77 1064 < 2.2e-16

Table 4.3 Kolmogorov-Smirnov test output showing differences in density distributions of hard coral and morphologies that fall within the hard functional coral group for sub-transect from the 2015 and 2017 Maldivian surveys. Response variable D-statistic p-value %Hard coral 0.216 <2.2e-16 %Branching 0.454 <2.2e-16 %Massive/encrusting 0.145 1.69e-07 %Bushy 0.330 <2.2e-16 %Thin/foliose/plating 0.363 <2.2e-16 %Macroalgae 0.198 < 2.2e-16 %EAM 0.089 0.000417 %CCA 0.223 < 2.2e-16

Fig 4.3. Violin plot showing probability distributions of percent a) hard coral, b) branching, c) massive/encrusting (MASE), d) Bushy, e) thin/foliose/plating (TFP) cover across Maldivian sub-transects in the 2015 (red) and 2017 (blue). The outer margins of the black box plot inside the violin represents the interquartile range, with median (horizontal black line) and mean (small black square). Vertical black line (not always discernible) inside the violin plot represent 95% confidence intervals, with black dots representing outliers (outside 95% CI range). Edges of the violin plots represent kernel density estimations with wider sections showing higher probability that the percent coral cover of a sub-transect will fall within that section. Red shaded area represents 90-95% quantile range for 2015 sub- transects, and blue shaded area represents 90%-95% quantile range for 2017 sub- transects.

Fig 4.4 Violin plot showing probability distributions of percent a) macroalgae, b) epilithic algae matrix (EAM), and c) crustose coralline algae (CCA) across Maldivian sub-transects in the 2015 (red) and 2017 (blue). The outer margins of the black box plot inside the violin represents the interquartile range, with median (horizontal black line) and mean (small black square). Vertical black line (not always discernible) inside the violin plot represent 95% confidence intervals, with black dots representing outliers (outside 95% CI range). Edges of the violin plots represent kernel density estimations with wider sections showing higher probability that the percent algae cover of a sub-transect will fall within that section. Red shaded area represents 90-95% quantile range for 2015 sub-transects, and blue shaded area represents 90%-95% quantile range for 2017 sub-transects.

96 4.4.2. Initial cover vs. absolute change.

There was a significant relationship between initial cover and absolute change for hard coral cover (R2 = 0.599, P< 0.005) (Fig 4.5a), branching coral cover (R2 = 0.945, P< 0.005) (Fig 4.5b), massive/encrusting coral cover (R2 = 0.544, P< 0.005) (Fig 4.4c), and bushy coral cover (R2 = 0.800, P< 0.005) (Fig 4.5d). On average, absolute change in total coral cover decreased by 0.68% per percent increase in initial coral cover (Fig 4.5a). Whilst, absolute change in branching, massive/encrusting, and bushy cover decreased by 0.86%, 0.59%, and 0.85% per increase in initial cover, respectively (Fig 4.5b-d).

Fig 4.5. Scatter plot showing the relationship between 2015 percent hard coral cover vs. absolute percent, a) hard coral, b) branching, c), massive/encrusting, and d) bushy coral cover which occurred between 2015 and 2015 in the Central Maldives. Red dots indicate local community island, green dots indicate resorts, and blue dots indicate unpopulated reefs. Black line represents the linear regression line

97 4.3.2 Fixed variables to include in an optimal model for describing absolute percent loss in coral cover between 2015 and 2017

Based on protocol describe for model selection, the model with all fixed factors included showed the lowest AIC for all the response variables. A Wald Chi-Squared test revealed reef location was the only fixed effect that caused a significant response in the absolute change in branching coral cover (χ2= 5.567, df =1, p= 0.0183). All other covariates were shown to not have an effect (Table 4.4).

4.3.2 Effects of reef location and land use and COTS on absolute percent coral cover change in the Maldives from 2015 to 2017.

Output from the LME’s showed percent change in cover between 2015 and 2017 associated with the intercept (e.g. reference groups - Unpopulated inside reefs that have not been impacted by COTS) were not significantly different from zero for total hard cover, massive/encrusting cover and bushy cover respectively (Table 4.5). Neither reef location, nor land use, nor crown-of-thorns had any effect on these values, suggesting that these means apply to all reefs. The intercept values, however, that are conducting at the level of reef site (transect), are not dissimilar to the values obtained by the pairing of all sub- transect within simple t-tests. The LME intercepts apply to only a subset of the total data and are a product of nesting sub-transect within transect (reef site). Only for branching coral cover was mean cover loss, for unpopulated inner reefs that lacked only observed COTs impact, significantly different from zero. These reefs had an absolute loss in branching forms of 10.6% between 2015 and 2017 (Table 4.5). A loss that was 5.5% less for outer reefs relative to inner reefs (Table 4.5). In chapter 3, however, outer reefs had 7.3% corals with branching morphology than inner reefs. A difference in initial cover that equates to a 5-6% difference in absolute change based on the linear regressions (Fig. 4.5)

98 Table 4.4 Wald Chi-squared test for effect of covariates (reef location and land use) on the effect of the percent cover change of the responsible variable between 2015-2017.* Indicated significant p-value. Response variable Covariate χ2 DF Pr(>χ2) % Change hard coral Reef location -0.821 1 0.7742 Land use 1.721 2 0.4229 COTS 0.075 1 0.7842 % Change branching Reef location* 5.567 1 0.0183 Land use 2.551 2 0.2793 COTS 1.330 1 0.2448 % Change MASE Reef location 3.175 1 0.7374 Land use 2.217 2 0.3299 COTS 0.070 1 0.7908 % Change Bushy Reef location 0.1124 1 0.7374 Land use 1.9462 2 0.3779 COTS 0.0054 1 0.9413

99 Table 4.5 Output for linear mixed effects model investigating the effects of reef location + land use + COTS on percent cover change of different response variables (Hard Coral, HC; Branching Coral, B; Massive Coral, M) between 2015 and 2017 in the Maldives. The reference group of reefs selected for this analyse are those occurring inside the atolls, which are unpopulated and without a recorded presence of a recent COTS outbreak in before 2017 data collection. Random effects specified the nesting of sub-transects within reefs sites. * Indicates significant p values.

Response β St. variable β variables coefficient error DF t p-value

% Change (HC) β0(intercept) -5.313 4.423 1065 -1.201 0.230

β3(outside) -1.447 5.046 21 -0.287 0.777

β4 (Resort) -4.116 5.952 21 -0.691 0.497

β5(LCI) 4.492 4.029 21 1.115 0.278

β6(COTS) 1.671 6.103 21 0.274 0.787

% Change (B) β0(intercept)* -10.56 2.03 1065 -5.191 0.000

β2(outside)* 5.47 2.32 21 2.359 0.028

β 3 (Resort) 1.13 2.74 21 0.412 0.684

β 3(LCI) 2.86 1.85 21 1.543 0.138

Β4(COTS) 3.24 2.81 21 1.153 0.262

% Change (M) β0(intercept) 5.44 3.28 1065 1.661 0.097

β2(outside) -6.66 3.74 21 -1.782 0.089

β 3 (Resort) -4.69 4.41 21 -1.064 0.299

β 3(LCI) 3.11 2.99 21 1.042 0.309

β 4(COTS) -1.20 4.52 21 -0.265 0.793

% Change (Bu) β0(intercept) -0.51 1.10 1065 -0.460 0.646

β2(outside) -0.42 1.26 21 -0.335 0.741

β 3 (Resort) -0.58 1.48 21 -0.389 0.701

β 3(LCI) -1.34 1.00 21 -1.340 0.195

Β4(COTS) -0.11 1.52 21 -0.074 0.942

100 4.5 Discussion

4.5.1 Coral bleaching in the Central Maldives

The 2015/2016 heat stress event (Heron et al. 2016) triggered mass coral bleaching and mortality at many locations in the Pacific (Couch et al. 2017, Hughes et al. 2017) and Indian Ocean (Ibrahim et al. 2016, Perry and Morgan 2017, Sheppard et al. 2017). Coral bleaching in the Maldives lasted from April to June 2016 where 75% of corals experienced bleaching along reef slopes at 10m throughout the country (Fig4.1a) (Ibrahim et al. 2016). The present project conducted coral reef surveys from March to April 2015, approximately one year prior to the beginning of the mass-bleaching event. Therefore, the present project was well positioned to measure the mortality impact of heat stress and bleaching on coral communities in the Central Maldives (Fig4.2) from March to April 2017, approximately one year after the beginning of the 2016 coral bleaching. Importantly, heat stress was not the only variable to influence coral mortality in the Central Maldives in the period 2015-2017. Prior to the 2016 warming, there were reports of outbreaks of COTS (Fig 4.1b), which dominated reef slopes on Fesdu and Kandolholhudhoo in North Ari atoll, and had been recorded during the 2015 surveys on Rasfari and Reethi Rah on the Easter edge of North Male Atoll (Fig4.2) (Ibrahim et al. 2016, Pisapia et al. 2016).

In addition to heat stress, Maldivian reefs are also being affected by the influence of increasing human populations and activities associated with land use change and tourism (Jaleel 2013, Brown et al. 2017, Moritz et al. 2017, Cowburn et al. 2018). Significantly, some impacts on coral reefs reported in other regions such the influence of rivers and agriculture on water quality are not present in the Maldives, simplifying the questions regarding human impacts to some extent (Sovacool 2012). Results from the 2015 surveys indicated coral communities in the Central Maldives, however, were negatively affected by LCIs when compared to unpopulated reefs. The 2015 surveys also identified reefs that had persisted though periods of environmental stress prior to 2015, with relative high coral cover above 50% (Chapter 3). The present project set out to answer the specific question regarding the dynamics of the 2016 coral-bleaching event on coral communities living on these unique coral reefs that are nonetheless impacted by localised stresses from activities associated with a growing human population and tourism industry (Jaleel 2013, Brown et al. 2017, Moritz et al. 2017, Cowburn et al. 2018).

101 4.5.2. Coral mortality across the Central Maldives between 2015 and 2017.

The use of rigorous and repeatable kilometre-scale survey methods with machine learning in both surveys provided robust evidence that the percent of total hard coral cover of the Maldives significantly declined from 2015 to 2017 from 18.6% to 13.0% (t13.914, df =1812, p<0.005; Table 4.1). One of the most interesting observations was that there was a very strong impact of the initial level of coral cover on loss (Fig 4.5). In this case, sites with high levels of coral cover experience proportionally greater decreases in coral cover. This highlights the magnitude of the 2016 coral bleaching event. Reefs with the highest coral cover that seemed to be less affected by human land use practices, than other reefs, lost the most.

Mass coral bleaching in 2016 also changed the community structure of Maldivian coral reefs. For example, branching and bushy corals were disproportionately impacted as compared with MASE corals which showed a subtle increase in cover between 2015 and 2017. Interestingly, they showed different distributions with the cluster of corals that appeared resilient to long-term environmental stress disappearing. TFP coral increased in cover, however they were so low in percent cover to begin with, that a doubling of cover from 0.41% (±0.008%SE) to 0.79%(±0.03%SE) is unlikely to be ecologically relevant.

MASE coral communities did show decreases in cover in a cluster of sub-transects that had high coral cover reefs in 2015, but had lost these by 2017. This is interesting given that MASE corals are generally considered being relatively thermally tolerant (e.g. (Hoegh- Guldberg and Salvat 1995, Loya et al. 2001, Darling et al. 2013) and the pattern presented here would appear to contradict this, at least for a subset of reefs that were dominated by MASE prior to the event. The general trend, however, supported an increase in MASE in response to the bleaching event. There are a couple of potential explanations: Firstly, benthic surveys during the 2016 bleaching event revealed that encrusting species from Coscinarea sp, Leptastrea sp, and Pavona sp, were found to be amongst the most resilient taxa to bleaching (Ibrahim et al. 2016). And, secondly, the increase may be due to cryptic MASE being revealed to the camera as the canopy of branching corals dies back. McClanahan et al. (2007) reported that following the 1998 thermal anomaly, the coral communities in the central Maldives became dominated by usually cryptic MASE species e.g. Pavona sp and Coscinarea sp. Shaded corals tend to be more resistant to thermal

102 insult (Obura and Grimsditch 2009), and cryptic remnants of corals can often recolonise reefs rapidly following large scale mortality events (Diaz-Pulido et al. 2009).

In line with previous studies, a number of sites experienced an increase in macroalgal and calcareous coralline algae. The interaction of coral and macroalgae is a critical part of the biology of both types of organisms, and shift towards systems dominated by macroalgae, whether permanent or temporary, are common following major disturbances (Hughes 1994, Mumby et al. 2007, Bruno et al. 2009). Brown et al. (2017) showed coral have a greater competitive advantage against Halimeda sp (by far the most common macroalgae on Maldivian reefs from these surveys). It is unsurprising that increases in macroalgae only took place where Halimeda sp were already present and coral cover was already low. These sites tended to be outside atoll reefs adjacent to LCI’s where there are likely impacts from excessive nutrients caused by wastewater effluent (Moritz et al. 2017)

4.5.3. The effects of initial percent cover, reef location, land use and COTS on absolute coral cover change.

There were no significant effects of land-use or COTS on the loss of coral from the Maldives from 2015 to 2017 (Table 4.3). Location had an effect for branching corals, but this effect could equally be explained by the greater branching cover observed within reefs prior to this latest bleaching event (Chapter3). The absence of local impact is potentially interesting given that reducing the effect of other local stresses (like land use impacts and COTS is widely seen as an opportunity for improving the recovery of reef systems (Sandin et al. 2008, De'ath et al. 2012, Williams et al. 2015). The lack of local pressures was also associated with rapid recovery (within 1 year) of Acropora dominated reefs to a relatively small scale and localised thermal events, observed for the Keppels in Great Barrier Reef of the Pacific (Diaz-Pulido et al. 2009). Much longer recovery timescales, are however common in the literature, especially when the community structure is complex (Lasagna et al. 2010a). Our results suggest, therefore, that corals in the Maldives are not made more sensitive to the thermal event by differential land use practices, and that returning within a year of the event does not provide sufficient time to evaluate the effects of local impacts on recovery. Interestingly, there is the potential that sedimentation caused by land reclamation may be beneficial, because it reduces the penetration of light to the corals during a stress event (Anthony et al. 2007). The benefit however, needs to be weighed

103 against lost carbon acquisition, because it is important to realise that corals can die without bleaching (Hughes et al. 2018).

In the investigation of the effects of local factors on coral cover, over an interval that included a major thermal event, it is easy to lose site of the major finding of this study. The thermal event led to an 0.68% reduction in coral cover for every 1% initial cover is equivalent to a 32% relative loss in cover from a reef with an average initial cover of 18%, (18x0.68 = 12.24% end cover), which would equate to a relative loss of 31% ((18- 12.24)/18=0.31). The number is significant because it is roughly the same as that obtained for the GBR in 2016 (Hughes et al. 2017). The GBR anomaly persisted for a subsequent summer, increasing the lost coral cover to 50% (Hoegh-Guldberg et al. in prep), acting as a reminder that whilst accumulating greenhouse gases warm the planet, we cannot be assured of another 18 years, before the next large scale thermal event impacts the Maldives (Sheppard 2003, Hoegh-Guldberg et al. in prep).

4.5.4. Conclusion and next steps: The future of coral reefs in the Central Indian Ocean.

The results of this chapter have illustrated the complex relationships between coral reefs and stress factors which originate from both local and global sources. While we have shown major changes in the distribution, abundance and community structure of coral communities as a result of the 2016 heat stress event, we have revealed that the management of coastal land use and related stresses such COTS outbreaks do little to reduce the mortality arising from heat stress events and the corresponding mass coral bleaching. This does not mean, however, that managing land use and other local stresses is of no value. Quite the contrary, the recovery of reefs after global disturbances such as that experienced in 2016 is likely to be strongly dependent on the careful management and protection of coral stocks that remain (Nyström et al. 2000, Hughes et al. 2003, Bellwood et al. 2004, Hughes et al. 2011).

With this in mind, it will be important to focus on what we can learn from the recovery of reefs from the 2016 event and continuing to explore how land use will influence recovery of coral reefs communities in the central Maldives. Meanwhile, steps must be taken to understand best management practices for aiding the recovery of reefs adjacent to LCI’s and resorts. A current ecosystem service assessment valued the estimated annual profits from coral reefs in the North Ari Atoll (Fig 4.1) alone to be 28.56 million US dollars (Agardy

104 et al. 2017). Therefore, careful management of coral reefs in the Maldives is especially important, in a time human where human population growth and economic development call for activities that have potential reef degrading effects via increased waste water effluent bringing excess nutrients, and increased sand dredging and land reclamation runoff causing sedimentation (Jaleel 2013, Agardy et al. 2017).

There will also be an increasing role for developing and implementing technologies that increase our ability to understand the broad scale patterns, especially as coral reefs are degrading faster than our collective ability to monitor their change (Knowlton and Jackson 2008). Data and analysed using technologies like the rapid and rigorous techniques will almost certainly play a role in, (1) understanding coral recovery from the 2016 bleaching event in the Central Maldives. This can be achieved by using the 2017 data and associated imagery as a baseline and regularly monitoring reefs to investigate for signs of recovery or further decline (Hoegh-Guldberg et al. in prep). As long as GPS coordinates are use appropriately, this can be achieved with or without the SVII camera system as shown by Bryant et al. (2017). And, (2) Use the current data and potential data collected in future to select high priority sites for management, which have shown signs of resilience and recovery from disturbance events over broad-scales (reef) or fine-scale (i.e. 100m sub-transects)(González‐Rivero et al. 2014). In this regard, the identification and study of the cluster of sites that have very high coral cover despite the environmental changes going on around them is likely to yield important management insights for prioritising effective conservation interventions as large scales.

105 4.6 Acknowledgments

This study was undertaken as part of the XL Catlin Seaview Survey and undertaken by the Global Change Institute (GCI). The 2015 survey was in part funded by CL Catlin Pty Ltd in partnership (to Hoegh-Guldberg) with the Ocean Agency and the 2017 expedition was funded by the Hoegh-Guldberg and Dove laboratories at the University of Queensland (UQ). DEPB was supported by the Australian Government Research Training Program Scholarship through UQ and XL-Catlin oceans scholarship stipend. OHG were both supported by an ARC Laureate Fellowship and by the Centre for Excellence in coral reef studies (to OHG and SD). The SVII camera was invented by Richard Ververs, Aaron Spence, and Christophe Bailhache. I would like to acknowledge the field team at GCI for their time and assistance in arranging field activities. The Maldives government and members of the Marine Research Centre, Ministry of Fisheries and Agriculture, and Environmental Protection Agency for their assistance with permitting and in-country knowledge. Finally I would like to Emperor Divers Maldives and the crew of MV Emperor Atoll for marvelous efforts in providing an excellent vessel for our research goals.

106 4.7. References

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112 Chapter 5: General Discussion.

Coral reefs are fundamentally important coastal resources, supporting approximately 500 million people across the tropical regions of the world (Spalding and Grenfell 1997). These resources provide ecosystem services, including food, livelihoods, coastal protection, tourism, cultural relevance, and a wide range of other ecosystem goods and services (Moberg and Folke 1999). Furthermore, the value of ecosystem services is likely to be substantially undervalued given the challenges of putting a dollar value on intangible benefits on services such as the maintenance of water quality, sand production, and other benefits associated with primary production and gas exchange (Hoegh-Guldberg et al. 2015). Despite their importance, coral reefs are under great threat from human activities that result in a range of impacts from local (e.g. pollution, overfishing) to global stress factors (e.g. ocean warming and acidification). Corals throughout Southeast Asia, the Indo- Pacific, Caribbean, and Indian Ocean have decreased by about 50% over the past 30-40 years (Gardner et al. 2003, Bruno and Selig 2007, De'ath et al. 2012). Understanding the cause and effect associated with this decline has become a major concern for a large number of countries throughout the world’s tropical regions, especially given the close linkage of human well-being to the health of reef-building corals and the reefs they build (Pandolfi et al. 2003, Knowlton and Jackson 2008, De'ath et al. 2012).

The convoluted relationship between anthropogenic threats associated with human population use and the environmental factors driving coral reef assemblages, consequently, is not fully understood or mapped (Sandin et al. 2008, González‐Rivero et al. 2014, Bruno and Valdivia 2016, Crane et al. 2017). Quantifying the effects of human activities on coral distributions can be challenging, but necessary over multiple scales (e.g., fine to broad) in order to fully understand and mitigate ecological impacts caused by damaging human activities (e.g. land reclamation, effluent water outflow, overfishing, coastal hardening, etc.) (Côté and Darling 2010, Bruno and Valdivia 2016, Crane et al. 2017). While there have been considerable efforts to understand the biology, ecology, and human impacts of Pacific and Caribbean reef systems, coral reefs in the Central Indian Ocean have been relatively less studied. This is despite the fact that they represent enormous useful natural systems, which is ideal for investigating the direct and indirect influence of human activities at a range of different scales and time frames that are otherwise not influenced by the broad issues such as coasts and river systems (Sovacool

113 2012, Jaleel 2013). We do however, know in more detail, the impact and recovery of large- scale events such as the 1998 mass coral bleaching and mortality event that removed over 90% of coral colonies from shallow to deep locations (Sheppard 1999, McClanahan 2000, Sheppard 2003, Sheppard et al. 2008, Lasagna et al. 2010, Sheppard et al. 2012, Pisapia et al. 2016, Sheppard et al. 2017).

This thesis set out to investigate the localized and global threats to the abundance and distribution of coral cover in the Central Indian Ocean (both the Chagos Archipelago and Republic of Maldives) using data generated using a novel technology (rapid image collection and analysis) that delivers a new large scale perspective on the ecology of coral (Beijbom et al. 2012, González‐Rivero et al. 2014, Jia et al. 2014, Beijbom et al. 2015, Gonzalez-Rivero M et al. in prep).

In doing so the research described here was able to a four key questions of key questions:

1) Are new scalable technologies (rapid image collection, artificial intelligence) able to deliver accurate yet large scale perspectives on coral reefs in a rapidly changing world? 2) Are there differences in the abundance and distribution of coral between the Chagos and the Maldives archipelagos? 3) Do land use practices on local community islands and resorts have a negative effect on the distribution of coral cover in the Central Maldives? 4) Are unpopulated reefs in the Maldives more resilient to environmental stress from the 2016 mass coral bleaching event in the Central Maldives?

In answering these questions, the results illuminate the current pace of change in one of the largest coral reefs in the world, while at the same time demonstrating that reducing local scale threats to coral reefs does not reduce the mortality of coral communities. These actions, however, may play an important role in the recovery from major disturbances such as coral bleaching and mortality events.

114

5.1 Thesis findings

5.1.1 Chapter 2- Comparison of two photographic methodologies for collecting and analyzing the condition of coral reefs

The ability to measure the frequency and abundance of the key organisms is essential if we are to broaden our understanding of the drivers and resulting changes to the world’s coral reefs (Knowlton and Jackson 2008). Previous attempts to assess the condition of benthic communities on coral reefs vary in spatial scale and taxonomic resolution (Andréfouët et al. 2002, Mumby and Steneck 2008, Hedley et al. 2012, González‐Rivero et al. 2014). The time, cost and effort for in-water diver assessment technique’s, however, reduces the ability to cover large spatial scales (i.e. 50-200m SCUBA dive) (Mumby et al. 1999, Hill and Wilkinson 2004).

We aimed to select key metrics associated with coral reef condition from a functional perspective and contrast how much is lost in terms of association and agreement between image annotations using a semi-autonomous image collection method (SVII camera system) and a conventional method based of divers taking individual photos from short (usually < 50m) transects and then analysing them individually. We chose the Maldives for all the research done in this thesis based on the fact that very little was known at the start of the project about the patterns of coral communities across the archipelago, and due to the opportunity to join the XL Catlin Seaview survey thereby have access to a very large number of reef sites across the Maldives.

Our results showed high levels of correlation and agreement between methods when measuring the abundance of hard corals as a functional group and individual labels, which make up hard corals as a functional group (Fig2.5-Fig2.9). The findings of this chapter demonstrated that our semi-autonomous photographic monitoring technique (SVII) provides an important tool for rapidly, accurately and rigorously survey coral reefs at multi- kilometre scales, while at the same time maintaining taxonomic resolution and is closely comparable to conventional methods done at much smaller scales.

115 The link between broad and fine scale measurements is likely to become of increasing importance as large-scale changes to the environment from climate change impact reefs more and more. Broad-scale monitoring data will be critical as the scientific and management communities seek to understand the resilience of coral reefs in a changing world. Therefore, tools such as the SVII have the ability to provide a global series of baseline measurements estimating the state of coral reefs, at scales necessary for understanding how coral reefs are likely to change over the coming decade and centre.

5.1.2 Chapter 3 - Measuring the effects of reef location and land use regime on reef slope coral populations across the Central Indian Ocean.

The complex dynamic relationship between anthropogenic risks associated with human population use and the environmental factors driving coral reef assemblages, consequently, are also not fully understood or mapped (Sandin et al. 2008, González‐ Rivero et al. 2014, Bruno and Valdivia 2016, Crane et al. 2017). Quantifying the effects of human activities on coral distributions can be challenging, but necessary over multiple scales in order to fully understand and mitigate ecological impacts caused by damaging human activities (e.g. land reclamation, sewage effluent outflow, overfishing, coastal hardening, etc. (Côté and Darling 2010, Bruno and Valdivia 2016, Crane et al. 2017). The relationship between humans and coral reefs in Central Indian Ocean (CIO), however, is probably the least understood (Rajasuriya et al. 2002, Sheppard et al. 2008, MFT 2015, Pisapia et al. 2016). In Chapter 3, my research investigated the effects of reef location and land use on coral cover in the Central Indian Ocean (both the Chagos Archipelago and Republic of Maldives) using broad-scale data generated from 2km linear transects at 10-12m depth, and using automated image analysis based on deep learning neural network algorithms (DLNNA) (Beijbom et al. 2012, González‐Rivero et al. 2014, Jia et al. 2014, Beijbom et al. 2015, González-Rivero et al. 2016, Gonzalez-Rivero M et al. in prep). Linear mixed effects models based on to 100m sub-transects nested with 2km transects of reef slope environments in the Maldives and Chagos archipelagos, were used to explore the effects of reef location (i.e. inside versus outside atoll) and land use (i.e. islands that are either unpopulated, host a resort, or are used by the local community for dwelling - LCI) on coral cover. We found that the mean percent coral cover on reef slopes at 10-12 m were in the Maldives significantly less than similar habitats in the Chagos (20.9% (±0.25), with the distribution of coral abundance in sub-transects being significantly associated between the two archipelagos. Investigating the abundance and distribution of

116 coral cover in these regions, we were able to demonstrate that the abundance of corals on sub-transects (each 100 m2) was normally distributed in Chagos than those from the Maldives, where a bimodal pattern dominated (Fig 3.3). The bimodal distribution of Maldivian reefs highlights the existence of a small number of sub-transects that have high (>50%) coral cover, which for the most part, were restricted to the unpopulated reefs of the Maldives. This bimodal patterns was not observed for the other two land use types where substantial numbers of people live and where unsustainable practices and other impacts are often commonplace (LCI and resort islands; Fig 3.4).

These findings revealed that coral cover in the Central Maldives regions is being driven by the influence of human activities via land-use and coastal development (Brown et al. 2017), as well as through more diffuse human influences. In either archipelago, there continues to be an urgency to deal with local impacts on the health of coral reefs, which underpin the economic opportunities for people in this region in terms of tourism and related livelihoods.

5.1.3 Chapter 4 – Analysis of the influence of reef location and land use regime on patterns associated with 2016 heat stress in the Central Maldives.

Coral reefs of the Maldives encountered an elevated sea surface temperature (SST) anomaly that lasted from April to June 2016. In 1998, a similar SST anomaly resulted in mass coral bleaching and subsequent broad-scale reductions in coral cover throughout the Maldives. Between events, reefs can recover to a certain extent (McClanahan et al. 2007, Sheppard et al. 2008, Morri et al. 2015) although local factors (e.g. elevated coastal development, pollution, fishing pressure) can potentially influence the damage and recovery associated with these types of global events (Brown et al. 2017), as was found in Chapter 3 when local community islands showed less coral cover than uninhabited islands. The differences in land uses in the Central Maldives presented an opportunity to explore the influence of local land use (unpopulated to ICI island reefs) on the mortality and loss of corals from the 2016 mass coral bleaching and mortality event associated with the exceptional temperatures of 2016. We first set out to determine the rate of decline of coral cover on reefs of the Central Maldives from 2015 to 2017, with the 2016 mass coral bleaching vent in-between.

117 The resulting research and analysis revealed a large scale decrease in the abundance and distribution of corals in the Central Maldives from 2015 to 2016 (Fig 4.3). Investigations into how different coral morphologies reacted to the bleaching event also showed that thermally sensitive branching and bushy corals declined the most, whereas the abundance of massive/encrusting coral stayed the same between the two years. There was however a large reduction in branching and massive/encrusting coral cover from Anbarra and Madhiggaru Falhu, which were identified as high coral reef in 2015 (see Chapter 3). This provided evidence that these relatively pristine areas identified in 2015 were not immune to the effects of the 2016-bleaching vent. In all these results showed, over a large-scale, the coral bleaching event affected all reefs in the Maldives regardless of land use practice and reef location.

5.2. Future directions

The investigation of broad-scale change in the coral reefs growing along the edge of the Chagos and Maldivian archipelagos in the Central Indian Ocean, provided an opportunity to unravel the complexities of the impacts of threats that are arising in this region from local (e.g. land use, pollution and overfishing) and global (e.g. ocean warming and acidification). By focusing on an area with little arable land, almost no rivers, and a relatively small human population the Central Indian Ocean. We have been able to show that different land use practices provide a statistically defendable explanation of the differences in the abundance reef-building during periods between large-scale disturbance events such as the 2016 heat stress event (i.e. mass mortality). Secondly the results also show, that regards of anthropogenic stress our healthiest reefs still face threats from climate change related disturbances such as increased intensity and frequency of sea surface temperature anomaly events (Hoegh-Guldberg 1999, Sheppard 2003) and therefore tackling the problem of human induced climate change is essential if we are to prevent the complete loss of coral reefs in the in the coming decades (Gatusso et al. 2014, Hoegh-Guldberg et al. 2014). It is important however, to allow reefs the best chance of recovery from large-scale disturbances in order to preserve important ecosystem functions they currently provide (Moberg and Folke 1999). As our results have shown, degraded reefs are less likely to recover under direct human pressure. New technologies in the form of broad-scale monitoring techniques and improvements in artificial intelligence to automate the image analysis process (González‐Rivero et al. 2014, Jia et al. 2014, Gonzalez-Rivero M et al. in prep), means we can get a better understanding on the

118 baseline of recovery and decline between broad scale disturbance events and localized impacts (Knowlton and Jackson 2008), at a faster rate that previously achievable. These technologies also provide the facility to rapidly identify areas of reef that may not be in as much decline when it comes to reef-building corals. Thereby allowing more refined management information and decision-making in terms of managing these delicate systems. These ‘green shoots’ of coral reef recovery may play a very important role as we stabilize ocean conditions and achieve the Paris climate goals. These rare and important places may be critical to the re-ignition of the world's most diverse marine ecosystem and all its benefits for humanity.

5.3 References

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123 Appendices

Table A.1. Comprehensive list of Label categories and functional groups used for the image annotation of photographic quadrats taken using SVII of Maldivian reef slopes in 2015 and 2017

Functional Label and description used for training Binned label for statistical group automated image analysis procedure. analysis Dead Hard Coral DHC covered by Epilithic algal matrix (EAM) EAM (DHC) Dead Hard Coral Rubble covered by Epilithic algal matrix EAM (DHC) (EAM) Dead Hard Coral DHC covered by Crustose coralline algae CCA (DHC) (CCA) Dead Hard Coral Rubble covered by Crustose coralline algae CCA (DHC) (CCA) Dead Hard Coral DHC covered by Cyanobacteria EAM (DHC) Dead Hard Coral Rubble covered by Cyanobacteria EAM (DHC) Hard Coral Tabulate/corymbose/plating Acropora spp Branching Hard Coral Digitate Acropora spp Branching Hard Coral Arborescent Acropora spp Branching Hard Coral Branching Porites spp Branching Hard Coral Branching other Branching Hard Coral Branching Pocillopora spp, Stylophora sp Bushy Hard Coral Massive/Submassive/Encrusting (MASE) Massive/encrusting Porites spp Hard Coral Massive/Submassive/Encrusting (MASE) Massive/encrusting small polyps Hard Coral Massive/Submassive/Encrusting (MASE) Massive/encrusting large round polyps Hard Coral Massive/Submassive/Encrusting (MASE) Massive/encrusting meandering Lobophyllia spp Hard Coral Massive/Submassive/Encrusting (MASE) Massive/encrusting Meandering other

124 Hard Coral Massive/Submassive/Encrusting (MASE) Massive/encrusting bleached Hard Coral Thin/Foliose/Plating (TFP) with visible relief TFP structures Hard Coral Thin/Foliose/Plating (TFP) with visible round TFP corallites Macroalgae Filamentous Macroalgae >1cm in height Macroalgae

Macroalgae Leathery Macrophytes >1cm in height Macroalgae

Macroalgae Halimeda spp >1cm in height Macroalgae

Other Fish Other Other Transect hardware Other Other Anthropogenic marine debris Other Other Unclear (i.e. shadow, overexposed, blurry) Other Other Alcyonacea spp Other Invertebrates Invertebrates Other Crinoids Other Invertebrates Invertebrates Other Hydroids Other Invertebrates Invertebrates Other Individual tunicates (Didemnum molle) Other Invertebrates Invertebrates Other Massive or encrusting sponges Other Invertebrates Invertebrates Other Branching or rope forming sponges Other Invertebrates Invertebrates Other Gorgonacea spp (sea fans and sea whips) Other Invertebrates Invertebrates Other Mobile invertebrates (Echinoderms) Other Invertebrates Invertebrates Other Other invertebrates (colonial Ascidians and Other Invertebrates Invertebrates Hexacorralia) Soft Sediment Sand Sand

125