Drivers of coral reef composition, cryptic marine biodiversity, and coral health along the north coast of Timor-Leste
Catherine Jung Shim Kim Bachelor of Science 0000-0002-8558-6500
Photo: C. Kim A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2021 School of Biological Sciences Coral Reef Ecosystems Laboratory Abstract
Coral reefs are among the most biodiverse ecosystems on the planet. Almost a third of tropical reefs lie in the Coral Triangle (CT), the epicenter of marine biodiversity, and are substantially threatened by human activity. Timor-Leste became independent in 2002 and is the newest of six nations in the CT, which include Indonesia, Malaysia, the Philippines, Papua New Guinea, and the Solomon Islands. It is important to establish a baseline for the current composition and state of coral reefs. For this thesis, the following specific objectives related to the coral reefs along the north coast of Timor-Leste were addressed: (1) assess coral reef variability based on kilometer-scale phototransects and explore environmental drivers of differences; (2) explore how the diversity of marine cryptic invertebrates varies at local scales over the same regions; and (3) quantify the condition of corals (coral health) using coral disease surveys and measurements of water quality and temperature in the capital area of Dili, taken before and after the global bleaching event in 2016.
The benthic structure of outer reef slope communities along the north coast was analyzed using kilometer-scale phototransects (each 1.5-2 km) collected from the 2014 XL Catlin Seaview Survey. Chapter 2 analyzed over 20,000 benthic photos using machine learning techniques, which revealed significant coral cover in Timor-Leste ranging from 5.4 to 33.0%, comparable to coral cover in other regions of the Indo-Pacific and CT. Wave exposure, distance to nearest river, and human population density were explored as potential drivers of benthic composition. A linear mixed-effects model tested the same covariates plus the ratio of branching to massive corals (i.e., structural complexity) on total coral cover. All parameters significantly explained benthic composition while there was a significant positive effect from wave exposure on coral cover that interacted with structural complexity where the effect was greatest on more complex reefs. This was due to the low incidence of storms and the comparatively protected north coast.
Marine cryptic invertebrate biodiversity was explored in Chapter 3 using Autonomous Reef Monitoring Structures (ARMS) and genetic DNA barcoding of brachyuran crabs for outer reef slope sites in Timor-Leste. This was accomplished in collaboration with the US National Oceanic and Atmospheric Administration and the Smithsonian Institution National Museum of Natural History. Three size fractions (i.e., two meiofaunal and one sessile) from the ARMS were DNA metabarcoded. The results indicated several interesting trends that corroborate the biogeographic pattern of maximal diversity in the CT. For example, rarefaction curves of i crabs and metabarcodes did not reach asymptotes meaning greater sampling would likely uncover more species. There was a high degree of unique operational taxonomic units (37% for brachyuran crabs) and diversity was greater than other Indo-Pacific sites as expected. The metabarcoding resulted in 19% unclassified operational taxonomic units, indicating the cryptic biodiversity of reefs is under-sampled with biodiversity being much more than previously quantified. The relationship between cryptofaunal diversity and coral cover was inconclusive. However, there was further evidence that rubble habitats promote the greatest diversity of coral reef cryptofauna.
Reef condition in Timor-Leste was the focus of Chapter 4. The condition of four reefs was surveyed in November 2015 and July 2017. Temperature and nutrient data were also collected in 2015. Both surveys revealed disease prevalence was low, but had higher levels of compromised health (e.g., algal overgrowth). Interestingly, the isotope signature of nitrogen along the north coast did not match that expected for coastal pollution (8-30‰) but did match that expected from oceanic sources (2-6‰). Heat stress during the 2016-2017 global bleaching event was also quantified in Timor-Leste. Surveys before and after the event did not indicate high bleaching mortality as predicted by the Coral Reef Watch Timor- Leste (CRWTL) satellite sea surface temperature. CRWTL was significantly higher (> 1˚C) than in situ temperature during the austral summer. Both the temperature and nutrient data indicate seasonal oceanographic processes such as upwelling influence shallow reefs along the north coast.
As local and global conditions change along the coastline of developing nations such as Timor-Leste, understanding the condition of reefs becomes critical. This thesis undertook the largest survey of coral reefs in the history of Timor-Leste. This study revealed rich coral reefs relatively high in biodiversity that are comparatively protected from climate change- induced ocean warming. The findings emphasize Timor-Leste’s significance as one of 50 coral reef world regions that are relatively less vulnerable to recent climate change impacts from a systematic global analysis. As such, immediate conservation priorities for Timor- Leste should focus on addressing localized anthropogenic pressures including sedimentation and overfishing. The balance between communities, ecosystems, and sustainable economies, must be further investigated using the wealth of Traditional Knowledge such as tara bandu customary law to build strong and resilient socio-ecological systems. All of this must be seen in the context of strong, intergovernmental action that is needed to address global climate change. 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 included in this thesis
No publications included.
Submitted manuscripts included in this thesis
Kim CJS, Roelfsema C, Dove S, Hoegh-Guldberg O. (in review) The condition of coral reefs in Timor-Leste before and after the 2016-2017 marine heatwave. Oceans-1001163. doi:10.1101/2020.11.03.364323
Other publications during candidature Nolan MKB, Kim CJS, Hoegh-Guldberg O, Beger M. (in review). The benefits of heterogeneity in spatial planning within coral reef environments. BIOCON-D-20-00446
Rodriguez-Ramirez A, González-Rivero M, Beijbom O, Bailhache C, Bongaerts P, Brown K, Bryant DEP, Dalton P, Dove S, Ganase A, Kennedy EV, Kim CJS… (2020) A contemporary baseline record of the world’s coral reefs. Scientific Data. 7(1): 355. doi: 10.1038/s41597- 020-00698-6
Kennedy EV, Vercelloni J, Neal B, Ambariyanto, Bryant D, Ganase A, Gartell P, Brown K, Kim CJS… (2020) Coral Reef Community Changes in Karimunjawa National Park, Indonesia: Assessing the Efficacy of Management in the Face of Local and Global Stressors. J of Mar Sci Eng. 8(760): 27. doi:10.3390/jmse8100760
González-Rivero M, Beijbom O, Rodriguez-Ramirez A, Bryant DEP, Ganase A, Gonzalez- Marrero, Herrera- Reveles A, Kennedy EV, Kim CJS… (2020). Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach. Remote Sensing. 12(3): 489. doi: 10.3390/rs12030489
Kim CJS. (2016). Coral Health and Disease in Timor-Leste. Report for the Global Change Institute, The University of Queensland, St. Lucia, QLD, Australia.
iv Conference abstracts:
Kim CJS, Timmers M, Ransome E, Dove S, Hoegh-Guldberg O, Meyer C (2017). Biodiversity of Coral Reef Cryptofauna in Relation to Coral Reef Habitat. PEP talk. Australian Marine Science Association Conference, Darwin, Australia.
Kim CJS, Rodriguez-Ramirez A, Dove S, Hoegh-Guldberg O. (2017) Drivers of kilometer- scale coral reef benthic composition in Timor-Leste. Presentation. Australian Marine Science Association Conference, Darwin, Australia.
Kim CJS, Roelfsema C, Dove S, Hoegh-Guldberg O (2016). Prevalence of Disease in Dili, Timor-Leste and Potential Drivers. Poster. International Coral Reef Symposium, Honolulu, Hawai‘i, USA.
Kim CJS, Neal B, Misa P, Timmers M, Vargas-Angel B, Roelfsema C, Dove S, Hoegh- Guldberg O (2015). Benthic Composition and Marine Biodiversity of Coral Reefs along the Northern Coast of Timor-Leste. Presentation. Australian Coral Reef Society Conference, Daydream Island, Australia.
v Contributions by others to the thesis
Sophie Dove assisted in the conception and design, analysis, interpretation, and revisions of all chapters.
Ove Hoegh-Guldberg assisted with the conception and design of Chapters 2 and 4 and revisions of all chapters.
Chris Roelfsema assisted with the conception, design, and revisions of Chapters 2 and 4.
Christopher Meyer supported laboratory work and assisted with conception and design, analysis, and interpretation of Chapter 3.
Molly Timmers and Emma Ransome contributed sequencing data and bioinformatics support for Chapter 3. Molly Timmer also assisted with conception and design, analysis, and interpretation of Chapter 3
Craig Heatherington, Dominic Bryant, Peran Bray, and Fiona Ryan contributed to data collection of Chapter 4.
Benjamin Neal, Craig Heatherington, Kristen Brown, Veronica Radice, Pete Dalton, Dominic Bryant, Sara Naylor, Abbie Taylor, Susie Green, and Shari Stepanoff contributed to data collection and logistical planning of fieldwork in Chapter 2.
Alberto Rodriguez-Ramirez and Manuel Rodriguez-Rivero contributed to data management and analysis in Chapter 2.
Statement of parts of the thesis submitted to qualify for the award of another degree No works submitted toward another degree have been included in this thesis.
Research Involving Human or Animal Subjects No animal or human participants were involved in this research.
vi Acknowledgments
I owe thanks to many people who supported me in this PhD journey.
Firstly, my family. My parents raised me while attending graduate school. I have a new- found appreciation for anyone who has a doctorate degree and can imagine how much harder it can be while raising a young family. As a student, they always supported my interest in the ocean with unwavering love and frequent trips to the aquarium. To my brother Ben, for always being himself, my sister Jessica, for being my science buddy and life-long editor, and my brother-in-law Chris, for answering all my statistical questions.
My supervisors, who brought me to Australia for a great adventure in research giving me the freedom to explore a unique and special corner of the world. Sophie, your attention to all sides of an argument has made me a better researcher. Ove, your passion and dedication you have for your work are truly inspiring. Chris, I have learned much from your field organizational skills. It has been a great privilege to learn from all of you. I owe thanks also to my committee members Selina Ward and Anthony Richardson and the two thesis examiners who have ultimately made this work better.
Thank you to the Ministry of Agriculture and Fisheries and the people of Timor-Leste. To be welcomed to do research in your country has been a life-changing experience. Working in a new place is challenging and I could not have done it without the dive operators, Compass Boating and Diving and Aquatica. Special thanks to Tony Crean for providing the 2016 bleaching pictures and my volunteers for field work Dominic Bryant, Craig Heatherington, Peran Bray, and Fiona Ryan.
Thank you to the XL Catlin Seaview Survey team at the Global Change Institute who organized and managed our field expeditions to far-flung places: Pete Dalton, Susie Green, Dave Harris, Rachael Hazell, Sara Naylor, Shari Stepanoff, Abbie Taylor, and others. Field training was essential to my work and thank you to Mike Phillips and Craig Heatherington at UQ Boating and Diving for the scientific diving course and their valuable expertise.
My research would not have been possible without collaborators. Christopher Meyer, thank you for your support, willingness to chat, and arranging laboratory work at the Smithsonian. Trudiann Dale and the Conservation International team, the fieldwork in Timor-Leste would have never happened without your help. David Mills and the Worldfish team, thank you for
vii support, guidance, and a very handy office location. Emma Ransome for laboratory work. Molly Timmers, the ARMS guru and the Coral Reef Ecosystems Program team who completed all the NOAA fieldwork in Timor-Leste, thank you.
Thank you SK Whang and Shay Ledingham for your editorial eyes and professional editing by The Editing Press. Also, gratitude for the statistical consultation of Simone Blomberg.
A big thanks to the people who made the journey fun. My PhD cohort and research family, Michelle, Rene, Veronica, Kristen, Dom, Anjani, Vanessa, and Matt. The members of the Coral Reef Ecosystems lab, Manuel, Beto, Aaron, Jacob, Robert, Francisco, Doro, Andreas, Uli, Annamieke, and others. The true support system for the inevitable ups and downs of PhD life. A shout out to Hayley, finance extraordinaire. May the water always be fast flowing.
Thank you, friends, near and far: Jaramar, Jen, Nur, Lari, Spyros, Martin, Caitie, Diane, Kari, Natalie, Sara, Jenny, Sean, housemates, and more. Last, but not least to Erick for steadfastly motivating me to finish.
viii Financial support This research was funded by the XL Catlin Seaview Survey, Australian Research Council (ARC) Centre of Excellence for Coral Reef Studies CE140100020 (SD and OHG), and the ARC Laureate FL 120100066 (OHG). Tuition fees and living stipend were provided by The University of Queensland (UQ) International Scholarship and an XL Catlin Ocean Scholar Scholarship, respectively. The work was also supported by the Winifred Violet Scott Trust, Society for Conservation Biology, and UQ Graduate Student International Travel Award. Conference attendance and travel were supported by the UQ School of Biological Sciences travel award and ARC Centre of Excellence for Coral Reef Studies Student Travel Awards.
Keywords coral reef ecology, biodiversity, DNA barcoding, marine cryptofauna, coral health and disease, environmental drivers, marine conservation, multiple stressors, climate change, Coral Triangle
Australian and New Zealand Standard Research Classifications (ANZSRC) ANZSRC code: 060205, Marine and Estuarine Ecology (incl. Marine Ichthyology), 60%
ANZSRC code: 050102, Ecosystem Function, 20%
ANZSRC code: 050202, Conservation and Biodiversity, 20%
Fields of Research (FoR) Classification FoR code: 0602, Ecology, 80%
FoR code: 0501, Ecological Applications, 20%
ix To my grandparents:
the maternal side who came to America and
the paternal side who kept me connected to Korea.
All who dedicated their lives to something they believed in.
x Table of Contents
Abstract ...... i Declaration by author ...... iii Publications included in this thesis ...... iv Contributions by others to the thesis ...... vi Acknowledgments ...... vii List of Figures ...... xv List of Tables ...... xxv List of Abbreviations ...... xxviii
Introduction ...... 1 Background ...... 3 Coral reef resilience and anthropogenic impacts ...... 3 Structural diversity and complexity of coral reefs ...... 6 Cryptofaunal diversity of coral reefs and its drivers ...... 11 Coral disease and its known drivers ...... 14 Aims & Objectives ...... 17 Thesis Structure ...... 18 Limitations ...... 19 Broad-scale overview of thesis components ...... 21 Description of Datasets ...... 22 Study site ...... 24 Geology and oceanography of Timor island ...... 24 Climate ...... 26 Coastal ecosystems along the north coast ...... 27 Conservation efforts and customary law in Timor-Leste ...... 27 Survey and temperature logger locations...... 29 Collaborators ...... 30
The distribution, functional diversity, and compositional drivers of coral reef ecosystems along the north coast of Timor-Leste ...... 31 Abstract ...... 32 Introduction ...... 33 Biological baselines and environmental change ...... 33 Potential environmental drivers of reef slope coral communities ...... 34 2.2.2.1 Temperature ...... 35 2.2.2.2 Wave exposure ...... 36 2.2.2.3 Riverine inputs ...... 36
xi
Structural complexity ...... 37 Localized anthropogenic impacts ...... 37 Coral reefs of Timor-Leste ...... 38 Aims and objectives ...... 41 Materials and methods ...... 41 Study site ...... 41 Environmental parameters ...... 43 2.4.2.1 Temperature ...... 43 2.4.2.2 Wave exposure ...... 44 2.4.2.3 Riverine inputs ...... 45 2.4.2.4 Human population ...... 45 Benthic composition ...... 46 2.4.3.1 Data collection ...... 46 2.4.3.2 Data analysis ...... 47 Statistical analysis ...... 49 2.4.4.1 Environmental parameters ...... 49 2.4.4.2 Benthic composition ...... 50 2.4.4.3 Drivers of hard coral and coral morphologies ...... 51 Results ...... 51 Variability of environmental parameters along the Timorese coast ...... 52 Kilometer-scale benthic composition across the north coast of Timor-Leste ...... 55 Predictors of live coral cover along the north coast of Timor-Leste ...... 61 Discussion ...... 64 Comparison of conventional and kilometer-scale methods ...... 65 Broad trends in benthic composition ...... 69 Drivers of benthic composition and coral cover in Timor-Leste ...... 73 2.6.3.1 Wave exposure – the protected north coast ...... 73 2.6.3.2 Riverine inputs structuring Timorese coral reefs ...... 75 2.6.3.3 The role of human population density ...... 76 Structural complexity ...... 77 Conclusion ...... 77
Cryptic marine invertebrate diversity of Timor-Leste and benthic composition ...... 81 Abstract ...... 82 Introduction ...... 83 Ecological roles of cryptofauna on coral reefs ...... 83 Habitat structure, benthic composition, and species biodiversity ...... 85 3.2.2.1 Coral reef degradation framework ...... 85 3.2.2.2 Cryptofaunal species diversity, abundance, and biomass relationships to habitat...... 86
xii Novel methods of systematically quantifying coral reef cryptofauna ...... 88 3.2.3.1 Effect of artificial substrates on cryptofaunal communities ...... 88 3.2.3.2 Genetic approaches of quantifying cryptofaunal diversity ...... 90 3.2.3.3 Autonomous Reef Monitoring Structures ...... 91 Aims and objectives ...... 92 Methods ...... 93 Autonomous reef monitoring structures (ARMS) ...... 93 DNA barcoding ...... 97 DNA metabarcoding ...... 98 Benthic composition at NOAA stations ...... 99 Statistical analyses ...... 100 Results ...... 102 The > 2 mm size fraction and DNA barcoding of brachyuran crabs ...... 102 3.5.1.1 Brachyuran crab diversity...... 103 DNA metabarcoding of 500 μm–2 mm, 106–500 μm, and sessile size fractions 107 Benthic composition at the NOAA climate stations ...... 112 3.5.3.1 Cryptofaunal relationships to benthic composition ...... 114 3.5.3.2 Correlations between benthic cover and sessile cryptofaunal sequences ...... 115 3.5.3.3 Overall species richness and benthic composition ...... 118 Discussion ...... 120 Patterns associated with > 2 mm cryptofauna ...... 120 3.6.1.1 Patterns of brachyuran crab diversity ...... 121 Diversity of Timorese cryptofauna revealed by DNA metabarcoding ...... 121 Benthic composition of reefs across climate stations ...... 123 3.6.3.1 Relationships between benthic composition and > 2 mm fraction ...... 123 3.6.3.2 Correlations between benthic cover and sessile metabarcoded sequences ...... 125 3.6.3.3 Benthic composition relationships with overall metabarcoded diversity ...... 126 Conclusion ...... 127
A baseline for Timor-Leste: Community composition, disease, and mass coral bleaching ...... 129 Abstract ...... 131 Introduction ...... 132 Local threats to the coral reef of Timor-Leste ...... 132 Disease in the context of coral reef health ...... 133 Water quality and Timorese coral reefs ...... 135 Global Impacts–ocean warming and mass coral bleaching and mortality ...... 137 Aims and objectives ...... 138 Methods ...... 139 Study Site ...... 139 xiii
Coral community composition and coral health surveys ...... 141 Measurement of nutrient concentrations and stable isotope ratios ...... 144 In situ and satellite temperature data ...... 144 Statistical analyses ...... 144 Results ...... 146 Prevalence of coral disease and indicators of compromised health ...... 146 Coral cover and community composition at four sites ...... 148 Nutrients and stable isotopes ...... 152 Temperature and the prevalence of bleaching ...... 156 Discussion ...... 158 Health condition of Timorese reefs ...... 159 Coral community composition and human impacts ...... 162 Water quality and coral communities in Timor-Leste ...... 165 Elevated Temperature and the Prevalence of Bleaching ...... 168 Conclusion ...... 171
General discussion ...... 175 Thesis significance ...... 176 Ecological implications of thesis results ...... 176 Drivers of coral reef composition along the north coast ...... 176 Cryptofaunal diversity of Timorese coral reefs ...... 179 Coral health and bleaching in Timor-Leste...... 180 Management implications and recommendations ...... 182 Recommendations ...... 183 Future directions ...... 185
Supplemental materials ...... 186 Efficacy and accuracy of conventional and kilometer-scale methods ...... 194 Comparison of benthic composition across survey years-accuracy and algae ...... 196
Supplemental materials ...... 200 Supplemental materials ...... 201 References ……………………………………………………………………………… 205
xiv List of Figures
Figure 1-1 Timor-Leste and its 13 districts. The Ombai and Wetar Straits lie to the north of the island and the Timor Sea to the south and is influenced by the Indonesian ThroughFlow...... 24 Figure 1-2 Reef flat off Jaco Island in Nino Konis Santana National Park on the eastern end of Timor-Leste in 2014. Photo: C. Kim ...... 28 Figure 1-3 Location of the main datasets used in the thesis: (1) the 26 XL Catlin Seaview Survey kilometer-scale phototransects collected in July 2014; (2) eight NOAA climate stations where data from 25 ARMS, 13 temperature loggers, and phototransects were collected between 2012–2014; and (3) coral health sites at four locations around Dili where coral health and disease belt transects were conducted in November 2015 and July 2017, nutrient assays in 2015, and temperature loggers deployed between survey points. The red line indicates the direction of the Indonesian Throughflow...... 29 Figure 2-1 Locations of National Oceanic and Atmospheric Administration (NOAA) 2013, NOAA 2014, and XL Catlin Seaview Survey benthic phototransects collected in Timor-Leste. All data publicly available–see PIFSC 2017 and Rodriquez-Ramirez et al. (2020). (Bottom Inset) Close-up of Ataúro Island with the three datasets...... 40 Figure 2-2 Timor-Leste is a small island nation. (Main map) The 26 geolocated kilometer- scale phototransects that were collected by the XL Catlin Seaview Survey in 2014. Color of transect markers indicate population density of adjacent suco, or subdistrict, from the 2015 census. Between two to five transects were collected in every district. (Bottom Inset) Close-up of Jaco Island transects with red dots indicating start, middle, and end GPS points of phototransects that were used for wave exposure points. Blue dots are geolocated subtransects with overlap between the bottom two phototransects...... 42 Figure 2-3 Locations of the NOAA temperature loggers deployed across the north coast of Timor-Leste. Eleven loggers total were deployed at each of eight sites from 4–15 m depth from October 2012 through October 2014 logging every hour. Depths of temperature loggers are as follows: Dili–6.1 m and 14 m, South Ataúro–13.6 m, North Ataúro–6.1 and 13.6 m, Manatuto–4.9 m and 14.6 m, Baucau–12.8 m, Com–4.6 m, Jaco Island–14.6 m. The logger at Beacou was only incorporated into the maximum monthly mean across the two years for the whole north coast...... 44 xv
Figure 2-4 Calculated radial fetch lines every 22.5˚ in a GIS-based relative wave exposure model (GREMO) from the start, mid, and endpoints of each transect collected along the north coast of Timor-Leste by the XL Catlin Seaview Survey in 2014. Directional discount was applied based on the average wind speed and percentage of windblown per radial direction...... 45 Figure 2-5 Relative wave exposure calculated in a GIS-based relative wave exposure model (GREMO) in ArcGIS at the start, middle, and endpoints of each transect averaged and plotted per transect. The x-axis is transects grouped by district ordered from west to east on from left to right. Transects in the same district are the same color. Error bars represent standard error...... 54 Figure 2-6 Distance [km] of each subtransect across the north coast of Timor-Lest to nearest river mouth calculated in ArcGIS averaged by district. The x-axis is transect grouped by district running from west to east on the north coast of Timor-Leste from left to right. Transects in the same district are the same color and error bars represent standard error...... 55 Figure 2-7 A nMDS plot of the Bray-Curtis resemblance matrix of fourth root transformed coral reef benthic composition from kilometer-scale phototransects in Timor-Leste– 2D stress = 0.07. Bubbles represent benthic composition at centroids (the center of all subtransects calculated from the resemblance matrix) of each transect to aid visualization. Gray dashed lines represent groupings from similarity profile testing (SIMPROF). Letters and numbers on bubbles represent the region and transect number. For example, S1 is South Ataúro transect 1. Letters are as follows: O– Oecusse, SA–South Ataúro, NA–North Ataúro, D–Dili, M–Manatuto, B–Baucau, C– Com, J–Jaco Island. The pie chart represents benthic composition averaged by transect. TFP–thin/foliose/plating coral morphologies ...... 56 Figure 2-8 A nMDS plot of the Bray-Curtis distance matrix of fourth root transformed coral reef benthic composition from kilometer-scale phototransects in Timor-Leste– 2D stress = 0.07. All subtransects are plotted to visualize dispersion or within transect variance. Letters represent the region as follows: B–Baucau, C–Com, D–Dili, J–Jaco Island, M–Manatuto, NA–North Ataúro, SA–South Ataúro, O–Oecusse. Shape and label numbers represent transect number...... 57 Figure 2-9 Hard coral morphological groups (branching, massive, other or free-living, and thin/foliose/plating) from image analysis of 2014 XL Catlin Seaview Survey photos averaged by transect. Error bars represent standard error. Regions on the x-axis go xvi from west to east across the north coast of Timor-Leste. The number of transects per district varies between two to five...... 59 Figure 2-10 A distance-based redundancy analysis (dbRDA) ordination plot illustrating the relationship between three predictor variables (relative wave exposure, distance to river, and human population density) and major benthic components of the reef along the north coast of Timor-Leste. Massive, Branching, TFP (thin/foliose/plating), and Free-living represent coral morphological groups and the following benthic groups are shown: Algae–macroalgae, Soft substrate, and Turf algae on hard substrate. Numbers represent transect averages of dbRDA scores colored by district to aid visualization...... 61 Figure 2-11 (a) Ratios of branching to massive corals, with influential points removed, standardized by the total coral cover per subtransect for reefs located along the north coast of Timor-Leste. Branching and massive morphologies at the subtransect level were scaled to avoid undefined ratios (dividing by zero). (b) Log ratio of branching to massive corals. Ratios were scaled before transforming to avoid undefined ratios...... 63 Figure 2-12 The partial effects plot of the significant, two-way interaction of wave exposure and the ratio of branching to massive morphologies from the linear mixed-effects model on hard coral cover with transect as a random effect. Twenty-six kilometer- scale phototransects were collected along the north coast of Timor-Leste in 2014. Covariates were adjusted from mean-centered and both parameters were back- transformed from a log transformation. The y-axis is square root back-transformed coral cover and colored bands represent 95% confidence intervals. Points represent raw data used in the model...... 64 Figure 2-13 Comparison of the district/regional mean coral cover of three different datasets collected across the north coast of Timor-Leste and Indo-Pacific meta-analyses. Phototransects were taken by NOAA in June 2013 and Sept–Oct 2014 and by the XL Catlin Seaview Survey (XLCSS) in Jul–Aug 2014. The NOAA 2013 surveys at Ataúro Island were averaged collectively because the surveys did not follow the same geographic segregation that the NOAA 2014 and XLCSS followed. For the NOAA 2014 data, the standard error (SE) represents that of a single transect collected at each of the NOAA climate stations. The XLCSS collected 26 transects approximately 1.5–2 km in length across the north coast which were clustered into subtransects. The four surveys by Wong & Chou were done as part of Reef Check surveys xvii
(www.reefcheck.com) on northeast Ataúro Island at shallow (5.8–7.1 m) and deep (12–14 m) sites in 2004. The Indo-Pacific region values for comparison were drawn from Bruno & Selig (2007) and Vercelloni et al. (2020a). SE bars are shown except for Vercelloni et al. (2020a) which are standard deviation, and Bruno & Selig (2007) which are 3rd quantile...... 68 Figure 3-1 Newly deployed Autonomous Reef Monitoring Structures (ARMS) at National Oceanic and Atmospheric Administration Station 1 Coral Gardens (Timor-Leste) in July 2014. Three weighted units were deployed at each station except Station 8 Jaco Island where four were deployed. Square plates of ARMS are 22.5 x 22.5 cm. Photo: CREP, 2018...... 94 Figure 3-2 Eight NOAA climate stations were established across the north coast of Timor- Leste in 2012. Twenty-five ARMS were deployed and recovered with three per station (except for Station 8 where four were deployed). Brachyuran crabs from the mobile, > 2 mm size fraction from all stations, minus Station 7, were DNA barcoded. DNA metabarcoding was conducted on homogenized samples of the remaining size fractions (500 μm–2mm, 106 μm–500 μm, and sessile) per ARM...... 95 Figure 3-3 Preserved brachyuran crabs for DNA barcoding collected via Autonomous Reef Monitoring Structures deployed at eight climate stations across the north coast of Timor-Leste in 2014. Photo from PIFSC (2017) report...... 96 Figure 3-4 Photographs of Autonomous Reef Monitoring Structures plates from a single unit recovered from climate Station 2 Beloi, Timor-Leste in 2014. There are nine plates per ARMS unit and the topside and underside of each plate are photographed (except for the bottom plate) for 17 total plate images. Plates with cross hatches represent semi-closed layers. Plates were scraped and homogenized as the sessile size fraction before sampling for DNA metabarcoding. Surface area is ~ 0.09 m2 per ARMS unit. Photo from PIFSC (2017) report...... 97 Figure 3-5 Visualization of the > 2 mm size class of 25 Autonomous Reef Monitoring Structures deployed along the north coast of Timor-Leste at eight climate stations from 2012–2014. A non-metric multidimensional scaling on a Bray-Curtis similarity matrix of the distance between station centroids was completed on the fourth root transformed community matrix grouped by phyla–2D stress = 0.03. Pie-charts indicate the abundances of individual organisms of the most abundant phyla. Dark blue–Annelida, red–Arthropoda, green–Chordata, light blue–Mollusca, and pink– Echinodermata...... 103 xviii Figure 3-6 Abundance of brachyuran crabs by NOAA climate station averaged by Autonomous Reef Monitoring Structures unit deployed in Timor-Leste from 2012– 2014. Units have a surface area of ~ 0.09 m2. Crab groups are as follows: Coral crabs–Tetraliidae and Trapeziidae; Other–Acidopsidae, Carpiliidae, Dynomenidae, Leucosiidae, Palicidae, Parthenopidae, Percnidae, Pilumnidae, Xanthidae; Decorator–Epialtidae, Inachidae, Majidae, Pisidae; Swimming–Portunidae. Error bars represent standard error...... 104 Figure 3-7 Rarefaction curve of individual brachyuran crabs sequenced versus the number of species per station collected via 22 Autonomous Reef Monitoring Structures (ARMS) in Timor-Leste from 2012–2014. ARMS were summed by station with three units per station except Station 8 where four were deployed. Numbered labels indicate individual climate station, see Figure 3-2...... 105 Figure 3-8 Non-metric multidimensional scaling of Bray-Curtis dissimilarity matrix of crab community composition of Autonomous Reef Monitoring Stations (ARMS) deployed in Timor-Leste from 2012–2014. 2D stress = 0.20. Colors represent different stations and arrows indicate significant environmental parameters: massive coral (r2 = 0.3790, p = 0.0190) and almost significant soft coral cover (r2 = 0.2844, p = 0.0570). Labels are climate station number (1-6,8) and ARMS unit (A-C, one D at station 8)...... 107 Figure 3-9 Individual-based rarefaction curves on the rarefied operational taxonomic unit (OTU) matrix based on (a) the number of sequenced fractions representing individual samples at each station and (b) pooled fractions across ARMS units at each station. The red curve represents the total of all OTUs combined at each station. Error bars represent standard deviation...... 108 Figure 3-10 Classified (a) sequences and (b) operational taxonomic units (OTUs) grouped by phyla, for samples collected from Autonomous Reef Monitoring Structures deployed in Timor-Leste from 2012–2014. Sequences/OTUs percentages averaged by fraction (sessile, 106–500 μm, and 500 μm–2 mm) across all ARMS units at each station. Colors indicate phyla: Annelida, Arthropoda, Bryozoa, Echinodermata, Mollusca, NonReefCnidaria–non-scleractinian cnidaria, Other–pooled invertebrate phyla with low abundance (Chaetognatha, Chordata, Echiura, Entoprocta, Gastrorticha, Hemichordata, Kinorhyncha, Nematoda, Nemertea, Platyhelminthes, Sipuncula, Xenacoelmorpha), Plantae (Chlorophyta, Rhodophyta, Ochrophyta), Porifera, and Scleractinia–reef-building hard corals...... 110
xix
Figure 3-11 Non-metric multidimensional scaling on fourth root transformed Bray-Curtis similarity matrix of rarefied operational taxonomic units classified to phyla across all Autonomous Reef Monitoring Structures at all NOAA climate stations in Timor-Leste– 2D stress = 0.14. The shape and color of points refer to the size fraction (106-500 µm, 500 µm-2mm, and sessile) and labels represent the station number...... 111 Figure 3-12 A non-metric multidimensional scaling of the station centroids of a fourth root transformed Bray-Curtis similarity matrix on the metabarcoded sequences summed by Autonomous Reef Monitoring Structure–2D stress = 0.03. Pie-charts indicate the abundances (number of sequences) of the major taxa groups...... 112 Figure 3-13 Tier 1 benthic classifications from National Oceanic and Atmospheric Administration image analysis at climate stations in Timor-Leste. Approximately 30 images were taken 1 m apart along a 15 m transect at each station in 2014. Rubble and hard substrate include turf on rubble/hard substrate and CCA on rubble/substrate...... 113 Figure 3-14 Results from tier 2 of National Oceanic and Atmospheric Administration benthic classification scheme of phototransects collected at climate stations in Timor-Leste in 2014. Tier 1 hard coral classification was divided into morphological categories which were subsequently summed into five major categories: branching (including columnar and tabulate forms), foliose, free-living, encrusting, and massive...... 114 Figure 3-15 Mean abundance of the three most common phyla collected in the > 2 mm size fraction of Autonomous Reef Monitoring Structures (ARMS) deployed along the north coast of Timor-Leste from 2012—2014. Abundances were averaged per station with three ARMS, except for Station 8 where four ARMS were deployed. Station represents climate station number as represented on Figure 2 with stations moving generally from west to east moving right across the x-axis. Error bars represent standard error...... 115 Figure 3-16 Spearman’s correlations between sequence diversity of (a) hard coral, (b) soft coral, and (c) sponge metabarcoded from Autonomous Reef Monitoring Structures in Timor-Leste and percent cover of corresponding benthic parameters derived from NOAA image analysis of 232 photoquadrats from all eight stations in 2014. The red line represents a linear regression with gray 95% confidence interval...... 116 Figure 3-17 Subsampled operational taxonomic units of hard corals classified into broad morphological categories (branching, foliose, free-living, and massive/encrusting) based on family classification averaged by station. The standard error of summed xx morphologies per site is displayed. Each station in Timor-Leste had three Autonomous Reef Monitoring Structures except for Station 8 which had four. Family classifications into morphological groups were as follows: Branching–Meandrinidae, Merulinidae, Pocilloporidae; Massive–Agariciidae, Coscinaraeidae, Euphyllidae, Poritidae (Table 3-1)...... 117 Figure 3-18 Spearman correlations plots of hard coral (a) branching and (b) massive morphologies of sequences categorized into coral morphological groups from 25 ARMS in Timor-Leste and corresponding coral cover morphologies from benthic image analysis of 232 photoquadrats at the same stations. The red line represents a linear regression with a gray 95% confidence interval...... 118 Figure 3-19 Operational taxonomic unit richness from Autonomous Reef Monitoring Structures with units containing sequences from all fractions in Timor-Leste plotted with (a) coral cover and (b) rubble cover from image analysis of 232 photoquadrats averaged by station. The red line is a locally estimated scatterplot smoother (LOESS) with gray representing a 95% confidence interval...... 119 Figure 3-20 Autonomous Reef Monitoring Structure (ARMS) unit at Station 6 in Timor-Leste. Upon retrieval in 2014, unit was covered with Xenia spp. octocoral. The red cable tie is on the bottom corner of the ARMS unit...... 126 Figure 4-1 Survey sites in Timor-Leste around the capital of Dili. Rural-N in the island in the channel, Rural-E 40 km east of Dili, and Urban-W and Urban-E flanking Dili. The highly seasonal Comoro river can be seen just east of Urban-W. The four sites were sampled at two points in November 2015 and June 2017...... 140 Figure 4-2 Examples of disease documented during the surveys undertaken in Timor-Leste between November 15-27th, 2015. (a) WS–White Syndrome band of distinct tissue loss on tabulate Acroporids with white skeleton abutting live tissue with exposed skeleton gradually colonized by turf algae; (b) GA–growth anomaly on an Acroporid; (c) TRE–Trematodiasis bright pink nodules (unconfirmed for the presence of trematode) with burrowing worms also present. Red boxes indicate a close-up. See Table S4-3 for more information...... 142 Figure 4-3 Examples of compromised health documented during the surveys undertaken in Timor-Leste between November 15-27th, 2015. (a) bleaching–loss of symbionts from coral tissue; (b) burrowing barnacles and worms; (c) flatworm infestation likely Waminoa spp.; (d) predation by Drupella spp. snails; (e) unexplained tissue loss; (f) CCA overgrowth; (g) cyanobacteria overgrowth; (h) Colonial tunicate overgrowth; (i) xxi
pigmentation; j) filamentous turf algae overgrowth. Red boxes indicate a close-up. See Table S4-3 for more information...... 143 Figure 4-4 Prevalence of disease and indicator of compromised coral health from 15 x 2 m belt transect surveys at four sites in Timor-Leste from November 15-27th, 2015 and June 15-29th, 2017. WS–White Syndrome, AllAlgae–combined macroalgae, turf, and cyanobacteria overgrowth, BL–Bleaching, BUR–Burrowing invertebrates (Vermetid worms, barnacles, etc.), CCA–Crustose coralline algae overgrowth, OTH–combined pigmentation, predation, invertebrate infestation/overgrowth, TL–Unexplained tissue loss...... 147 Figure 4-5 Benthic composition cover from 15 m line intercept transects by site and depth for 2015 and 2017 survey periods in Timor-Leste (north coast). Major categories include Hard Coral, CCA–crustose coralline algae, Invert–mobile invertebrates, Macroalgae, Soft Coral, Substrate/Sand, Turf Algae...... 149 Figure 4-6 Shannon Diversity Index calculated per site and depth on genera present per belt transect in Timor-Leste. Top bars are at 5 m depth with 10 m below. Sites on the x- axis are split into the 2015 and 2017 survey periods...... 150 Figure 4-7 Principal Coordinate Analysis biplot of coral genera diversity from belt transects. Shapes indicate site with empty and solid markers indicating 5 and 10 m depths, respectively. Color indicates survey year: blue–2015 and green–2017. Abbreviations are coral abundance as follows: ACRtab–Acropora tabulate, FUN–Fungiids, GAL– Galaxea, MONTI–Montipora, GONIO–Goniopora, PLA–Platygyra, POC–Pocillopora, PORmass–Porites massive, and STY–Stylophora...... 151 + - - 3- Figure 4-8 Seawater nutrient concentrations (top to bottom DIN, NH4 , NO3 + NO2 , PO4 ) sampled in triplicate on each transect at four sites (Urban-W, Urban-E, Rural-N, Rural-E), two depths (5 m and 10 m), and three transects per depth in Timor-Leste in 2015. Bold line is the median, box ends are the first and third quartile, lines are 95% confidence interval of the median, and points are Tukey’s outliers...... 153 Figure 4-9 Mean temperature by month for remotely sensed sea surface temperature (CRWTL–Coral Reef Watch Timor-Leste) and in situ temperature loggers (Rural-N, Urban-E, Urban-W at 5 and 10 m depth) between sampling periods in 2015 and 2017. The dashed blue line is the maximum monthly mean (MMM) from CRWTL data and the solid blue line is MMM + 1˚C. Error bars represent 2 standard error units...... 157 Figure 4-10 Plot of the significant season × method interaction between the Coral Reef Watch Timor-Leste virtual station (CRWTL) remotely sensed sea surface xxii
temperature and in situ temperature logger data collected between Nov 2015 and Jul 2017. The dashed blue line is the maximum monthly mean (MMM, 29.5˚C) from the CRWTL data and the solid blue line is MMM + 1˚C (30.5 ˚C), the bleaching threshold for the accumulation of degree heating weeks. Error bars represent 95% confidence intervals...... 158 Figure 4-11 Gleaning and recreational activity at Urban-W site in Timor-Leste during 2015 surveys. Urban-W is in the Dom Alexio subdistrict of Dili which had a population density of 5017.9 people per km2 in the 2015 Timor-Leste census and had more activity compared to other sites surveyed. Women can be seen in the foreground gleaning and children playing on the reef flat further offshore...... 164 Figure 4-12 Photos by Tony Crean, a local dive operator, emailed on May 31, 2016. (a) Bleached Goniopora spp. at Adara on the west coast of Ataúro Island, primarily steep wall environments. An estimated 90% of Goniopora spp. was bleached with no other hard corals affected on Ataúro Island reefs. Multiple other genera of coral were visibly unbleached. Depth of photo unknown. (b) Image of Jaco Island, the easternmost point of Timor-Leste with bleached massive Porites spp. corals. Similar percent of corals bleached from 5–18 m...... 170
xxiii
Figure S2-1 Comparison of benthic composition at three NOAA climate stations surveys in Timor-Leste in 2013 and 2014: Baucau, Com, and Jaco Island. Phototransects were 30 m in both years and analyzed according to the NOAA benthic image analysis protocol. See supplemental Figure S2-2 and Figure S2-3, and Figure S2-4 for representative benthic images...... 190 Figure S2-2 Representative photos of NOAA surveys at Jaco Island (a) LAU-63 in 2013 at latitude and longitude coordinates -8.410837, 127.3122, and (b) LAU-05 in 2014 at - 8.4108, 127.3122. Coral reefs were dominated by thin/foliose/plating coral colonies and Halimeda sp. algae were common. File names are LAU-63_2013_A_19.jpg and LAU-05_2014_A_19.jpg respectively and can be found at http://accession.nodc.noaa.gov/0166378...... 191 Figure S2-3 Representative photos of NOAA surveys at Com (a) LAU-26 in 2013 at latitude and longitude coordinates -8.346345, 127.1610, and (b) LAU-01 in 2014 at -8.34638, 127.1610. This site was dominated by massive corals, free-living corals, and soft coral. File names are LAU-26_2013_A_23.jpg and LAU-01_2014_A_13.jpg respectively and can be found at http://accession.nodc.noaa.gov/0166378...... 192 Figure S2-4 Representative photos of NOAA surveys at Baucau (a) BAU-09 in 2013 at latitude and longitude coordinates -8.419592, 126.4271, and (b) BAU-04 in 2014 at - 8.4196, 126.4271. The site was dominated by soft coral. The file names are BAU- 09_2013_A_22.jpg and BAU-04_2014_A_15.jpg respectively and can be found at http://accession.nodc.noaa.gov/0166378...... 193 Figure S3-1 Mean abundance of the three most common phyla collected in the > 2 mm size fraction of Autonomous Reef Monitoring Structures (ARMS) deployed along the north coast of Timor-Leste from 2012—2014: arthropods, echinoderms, and molluscs. Abundances were averaged per station with three ARMS, except for Station 8 where four ARMS were deployed. Station represents NOAA climate station number as represented on Figure 2 with stations moving generally from west to east moving right across the x-axis. Error bars represent standard error...... 200
xxiv List of Tables
Table 1-1 Column Headings: Data—data stream; Method—method of data collection; Collection—parties involved in data collection; Processing—parties involved in data processing; Description—further description of data and which chapter data is utilized in. Acronyms: XL Catlin—XL Catlin Seaview Survey; CK—Catherine Kim; NOAA— United States National Oceanic and Atmospheric Administration; SI—Smithsonian Institute ...... 22 Table 2-1 Population density from of sucos, or subdistricts, adjacent to the benthic phototransects taken from the Timor-Leste 2015 census...... 46 Table 2-2 The five major benthic functional groups for benthic composition used in the automated image analysis of phototransects collected in Timor-Leste...... 48 Table 2-3 Number of kilometer-scale transects per district collected by the XL Catlin Seaview Survey in Timor-Leste in 2014. Each transect was hierarchically grouped into subtransects, with a maximum of 100 m length, to create replication within each kilometer-scale transect...... 49 Table 2-4 Temperature average, standard error, maximum, minimum, and range of ten temperature loggers deployed from 5–10 m depth at eight NOAA climate stations from October 2012 through October 2014...... 53 Table 2-5 Significant ANOVA results for testing differences between transects collected in Timor-Leste in 2014 on the following benthic parameters. Massive corals included encrusting morphologies and the branching group included tabulate morphologies. All but turf on substrate benthic categories were square root transformed and soft substrate was log-transformed, scaled from 0. df – degrees of freedom...... 58 Table 2-6 Model coefficients for a linear mixed-effects model of coral cover. The response variable was hard coral cover with fixed effects wave exposure, river distance, and the ratio of branching to massive coral morphologies with transect as the random effect. All covariates were mean-centered. Coral cover was square root transformed and E’ is the back-transformed estimates. Wave exposure, the ratio of branching to massive corals, and population density were also log-transformed...... 62 Table 2-7 Comparison of methods of coral reef benthic composition datasets in Timor-Leste shown in Figure 2-13...... 66 Table 3-1 Morphological assignments of coral families from classified operational taxonomic units...... 101 xxv
Table 3-2 Brachyuran crab community Bray-Curtis similarity index (BCI) as collected by 22 Autonomous Reef Monitoring Structures deployed at eight climate stations from 2012–2014. Station 7 is omitted. The BCI values ranging from 0-1 (0 meaning no commonalities between communities and 1 being identical) are under the gray diagonal. Table above the diagonal represents number of shared species between stations. The station is climate station number as indicated in Figure 3-2...... 106 Table 3-3 PERMANOVA table of results testing main effects of station and size fraction, random and fixed effects respectively, on the fourth root transformed Bray-Curtis similarity matrix of classified operational taxonomic units sequenced from Autonomous Reef Monitoring Structure samples deployed in Timor-Leste. df– degrees of freedom ...... 109 Table 3-4 Pairwise PERMANOVA comparison of size fraction on the fourth root transformed Bray-Curtis dissimilarity matrix of classified operational taxonomic units sequenced from Autonomous Reef Monitoring Structures deployed in Timor-Leste from 2012– 2014...... 111 Table 3-5 Timor-Leste OTU richness compared to Autonomous Reef Monitoring Structures cryptofaunal biodiversity studies in Florida, Mo’orea, and the Red Sea. The ratio is the total OTUs divided by total OTUS in Timor-Leste...... 122 Table 4-1 An overview of coral disease prevalence within the Coral Triangle. Disease abbreviations are as follows: AtrN–Atramentous necrosis, BrB–Brown band disease, BBD–Black band disease, GAs–Growth anomalies, N–Necrosis, SATL–Subacute tissue loss, SEB–Skeletal eroding band, PR–Pigmentation Response, PUWS– Porites Ulcerative White Spot, UWS–Ulcerative white spot, WP–White Plague, WS– White syndrome, YBD–Yellow band disease...... 135 Table 4-2 Delta 15N stable isotope, N%, and C:N ratio ANOVA results of two genera of algae sampled in replicates at the four sites (Urban-W, Urban-E, Rural-N, Rural-E), two depths (5 m and 10 m), and three transects per depth in Timor-Leste in 2015. Bolded values are significant results with mean, standard error, and post hoc groupings presented per site. No Chlorodesmis spp. was sampled at Rural-N or Rural-E at 10 m and the three samples collected from a single transect at Rural-E 5 m were removed for the ANOVAs...... 155 Table 4-3 PERMANOVA analyses testing the effect of site, depth, and seawater nutrient + - 3- concentrations (NH4 , NO3 , and PO4 ) on the of the prevalence of coral disease and
xxvi signs of compromised coral health from surveys in Timor-Leste in 2015. Bolded values are significant results with significance at p = 0.05...... 156 Table 4-4 Nutrients from water samples collected in the Laclo River and Metinaro in 2006 as reported in Alongi et al. (2012a). Values from Rural-E (5 m depth) in 2015 approximately half-way between the Laclo and Metinaro in Timor-Leste are also shown (marked with a *)...... 166
Table S2-1 Full labelset used for automated image analysis...... 186 Table S2-2 Model selection coefficients with square root transformed coral cover as the response variable with the addition of covariates relative wave exposure, distance to river, the ratio of branching to massive corals, and human population density. All covariates were mean-centered and wave exposure, and the ratio of branching to massive corals was log-transformed...... 189 Table S3-1 Raw sequences per sample from Autonomous Reef Monitoring Structures by the US National Oceanic and Atmospheric Administration deployed in Timor-Leste from 2012–2014...... 200 Table S4-1 Summary of Indo-Pacific studies assessing nutrients on reefs...... 201 Table S4-2 Description of coral diseases and compromised health found during surveys in Timor-Leste and references citing negative impacts to corals. Superscript letters correspond to images–first three in Figure 4-2 and remaining in Figure 4-3...... 202 Table S4-3 Average (± standard error) percent coral cover, diseased corals, and corals exhibiting other signs of compromised health, average number of genera, density of hard corals (colonies/m2), and total number of colonies surveyed per site and depth. % D–percent disease, % Comp–percent compromised health, # Gen–number of genera, Total # Col–Total number of colonies ...... 204 Table S4-4 Average (± standard error) seawater nutrient values from samples collected in + triplicate from transects at four sites in Timor-Leste in 2015. DIN is the sum of NH4 , - - NO2 , and NO3 . All units are in μM...... 204
xxvii
List of Abbreviations
ANOVA analysis of variance ARMS Autonomous Reef Monitoring Structures BCI Bray-Curtis index CCA crustose coralline algae CI Conservation International; confidence interval COI cytochrome oxidase subunit I COTS crown of thorns seastar CREP Coral Reef Ecosystems Program, Pacific Islands Fisheries Science Center of the United States National Oceanic and Atmospheric Administration CRW Coral Reef Watch CRWTL Coral Reef Watch Timor-Leste regional virtual station CT Coral Triangle dbRDA distance-based redundancy analysis df degrees of freedom DHW degree heating week DIN dissolved inorganic nitrogen DMSO dimethyl sulfoxide ENSO El Niño Southern Oscillation GA growth anomaly GBR Great Barrier Reef ITF Indonesian Throughflow LOESS locally estimated scatterplot smoothing MMM maximum monthly mean MPA marine protected area NGBR Northern Great Barrier Reef NGO non-governmental organization NKSNP Nino Konis Santana National Park nMDS non-metric multidimensional scaling NOAA United States National Oceanic and Atmospheric Administration OTU operational taxonomic unit PCO principal coordinates analysis xxviii PCR polymerase chain reaction PNG Papua New Guinea PVC polyvinyl chloride PERMANOVA permutational of multivariate analysis of variance RAP Rapid Marine Assessment SD standard deviation SE standard error SI Smithsonian Institution SIMPER similarity percentages SIMPROF similarity profile testing SST sea surface temperature Sv Sverdrup, 106 m3/s TFP thin, foliose, plating corals TukeyHSD Tukey’s honestly significant difference USAID United States Agency for International Development WS white syndrome XLCSS XL Catlin Seaview Survey
xxix
Introduction
Photo: C. Kim
1
Humanity is inextricably connected and supported by the ocean. It provides half of the respired oxygen (IPCC, 2013) and 20% of the consumed protein required to sustain three billion people (FAO, 2014). Recent attempts at ocean evaluation suggest at least 24 trillion USD when the direct benefits, outputs, services enabled, trade, and transportation are considered. The global annual gross marine product, akin to a country’s gross domestic product, is 2.5 trillion USD, making the world’s oceans the seventh-largest economy in the world (Hoegh-Guldberg et al., 2015). The intrinsic value of the ocean is not only monetary but also lies in its critical importance to life on Earth through its major influence on the weather, atmosphere, and planetary temperature.
Tropical countries, like those of Southeast Asia, are particularly dependent on coastal ecosystems such as coral reefs. However, coral reefs are among the most threatened marine ecosystems from climatic and local anthropogenic impacts (Burke et al., 2012; Hoegh-Guldberg et al., 2007). The rapid and accelerating decline of these unique systems (Bruno and Selig, 2007; De’ath et al., 2009; Gardner et al., 2003; Hughes et al., 2017a) represents a loss of resources, ecosystems services, biodiversity, and ultimately human livelihoods and life (Barbier et al., 2011; Moberg and Folke, 1999; Smith, 1978; Temmerman et al., 2013). These critical interdependencies are especially evident in the Coral Triangle (CT), a geographical region that spans six countries in the Indo-Pacific: Indonesia, the Philippines, the Solomon Islands, Malaysia, Papua New Guinea, and Timor-Leste. The CT is known for containing the world’s highest diversity of marine life (Allen, 2008; Burke et al., 2012; Veron et al., 2009). With the marine resources of the CT provide socio-economic benefits to the 360 million people in the region (ADB, 2014). The importance of this region is highlighted by the unanimous push by CT countries for the formation of the Coral Triangle Initiative on Coral Reefs, Fisheries, and Food Security in 2009. It was the first multi-lateral cooperation of its kind to sustainably manage marine and coastal resources by addressing critical issues such as climate change, marine biodiversity, and food security (Clifton, 2009).
Timor-Leste lies on the southern edge of the CT and became an independent state in 2002 after a long period of conflict with Indonesia, which occupied Timor-Leste from 1975 to 1999 (de Sousa, 2001). The north coast of the country is approximately 300 km in length and houses significant coral reef environments (Boggs et al., 2012). Recent work has begun to reveal the status of Timorese marine resources in this developing nation including the 2012 Rapid Marine Biological Assessment (RAP) and United States Agency for International Development (USAID)-funded marine surveys by the National Oceanic and Atmospheric 2
Administration (NOAA) from 2012 through 2014 (Erdmann and Mohan, 2013; PIFSC, 2017). Notably, this work aims to investigate the coral reefs of the northern coast. Specifically, this thesis explores these valuable ecosystems, comparing north coast reef resources to other reefs within the CT where appropriate, and further contextualizing with related global findings. As such, this thesis set out to:
1) Describe the functional diversity and heterogeneity of coral reefs across the north coast and their relationship to potential abiotic drivers such as wave action;
2) Explore the diversity and abundance of marine cryptofauna as a measure of marine biodiversity, and relate this biodiversity to coral cover; and,
3) Establish baselines for the state of Timorese reefs by measuring the abundance of coral disease and other measures suggestive of reduced health in corals, at locations and temporal periods potentially affected by nutrient inputs and thermal stress.
Background
Coral reef resilience and anthropogenic impacts Coral reefs, like most ecosystems, are under a myriad of local and global stressors including sedimentation, coastal pollution, ocean warming, and acidification (Burke et al., 2012). In the face of all these impacts, understanding resilience – how ecosystems resist and recover from disturbances – is critical to sustainable management, especially in an era of change. Holling (1973) defines resilience as a “measure of the persistence of a system and of their ability to absorb change and disturbance and still maintain the same relationships between populations or state variables.” However, resilience is typically more complex and difficult to quantify than other factors that community metrics and monitoring generally track. Measuring resilience therefore typically requires quantifying processes that underpin healthy ecosystem functioning such as herbivory, larval dispersion, and larval recruitment. As such, resilience-based monitoring, which targets quantifying these processes, has been developing since the emergence of large-scale monitoring in the mid-1990s. This quantification of processes necessarily relies on proxies (Lam et al., 2017).
One such proxy is a functional group approach which is a practical way of operationalizing the quantification of resilience. The role of functional groups and how they relate to ecological processes has been gaining increasing attention (Bellwood et al., 2004; Nyström, 3 2006; Nyström and Folke, 2001; Steneck and Watling, 1982). Functional groups, in contrast to taxonomic groups, are assemblages of species that perform a similar ecological function. Despite various levels of contribution between species, several species contributing to the same functional group supports functional redundancy. For example, several fish species (i.e., certain surgeonfish or damselfish) and other invertebrate taxa like gastropods contribute to the denuding herbivore functional group in reducing fleshy algal biomass. These groups have differing grazing rates and require different densities to achieve the same grazing effect (Bellwood et al., 2004; Nyström, 2006; Steneck, 2001). Functional groupings can be made based on any characteristic shared by organisms, such as morphology, physiology, or behavior (Steneck, 2001).
Furthermore, information on the functional diversity of coral reefs has important implications for the overall health and resilience of these ecosystems (Bellwood et al., 2004; Nyström, 2006). Many scleractinian corals are reef-builders and are often referred to as ecosystem engineers that provide a structurally-complex habitat for reef-associated fauna (Burkepile and Hay, 2008; Wild et al., 2011). Different morphologies of corals provide different degrees of structural complexity within these environments. For instance, corals that have massive morphologies create reefs that are less structurally complex than corals that have branching morphologies (Sebens, 1991). Coral morphological zonation has been demonstrated across depth and has been attributed to physical (water movement, temperature, light), biological (predation by fishes), and competitive (overgrowth) factors (Smith et al., 2006; Stoddart, 1969; Storlazzi et al., 2005; Wellington, 1982). A similar combination of physical, biological, ecological, and anthropogenic factors is likely to influence coral morphological functional diversity across large, horizontal spatial scales (González-Rivero et al., 2014). Two measures of ecological functionality are functional redundancy—the number of species filling a particular functional role, and response diversity—the range of responses to environmental change among species contributing toward an ecological function.
Coral morphology is often considered a measure of response diversity (Nyström, 2006). For example, massive and encrusting corals are generally more resilient to disturbances such as storms, disease, and sedimentation, compared to branching morphologies that are more sensitive to thermally induced bleaching and storms (Goreau et al., 2000; Loya et al., 2001). Additionally, branching corals are known to be rapid colonizers during reestablishment after disturbance events (Hughes and Connell, 1999). For example, the coral communities of the Caribbean Sea are less functionally diverse than the Indo-Pacific and comparatively more 4 vulnerable to prolonged human exploitation and impacts. Additionally, the loss of dominant species within the habitat building (i.e., corals such as Acropora palmata and Acropora. cervicornis) and herbivore functional groups (i.e., the sea urchin Diadema antillarum) in the Caribbean pushed the functional redundancy to the point of no return, resulting in a rapid phase shift from coral-dominated to macroalgal-dominated habitat (Bellwood et al., 2004; Hughes, 1994; Nyström, 2006).
Studies of ecosystem function within systems on relatively small scales indicate that systems with high species richness and functional diversity are more resilient to disturbances than less taxonomically and functionally diverse systems (McCann, 2000). However, the opposite has also been shown, where greater diversity within a taxonomic group does not necessarily equate to greater resilience to anthropogenic pressures and disturbances (Bellwood et al., 2004; Mora et al., 2011). Quantifying coral functional diversity using morphological traits versus taxonomic diversity allows for a more ecologically meaningful comparison between areas with large disparities in taxonomic species richness (Steneck, 2001).
Recent work using a variety of methods at a variety of scales has laid a good foundation for exploring the benthic composition of coral reefs within Timor-Leste (Ayling et al., 2009; Erdmann and Mohan, 2013; PIFSC, 2017; Wong and Chou, 2004). Like other coral reef regions, coral cover is variable in Timor-Leste. Wong and Chou (2004), for example, found a range of 18.42–45.68% in northeast Ataúro Island, while semi-quantitative surveys by the RAP had coral cover that ranged from 5–70%. NOAA surveys reported coral cover from 0.0–42.3% in 2013, and 6.4–38.1% in 2014. Country averages of live coral cover are similar to the RAP surveys, estimating 28%, with NOAA 2013 surveys quantifying 15.6 ± 0.8%, and NOAA 2014 surveys at 27.4 ± 4.7%. Although low, these coral cover estimates are similar to Indo-Pacific estimates from meta-analyses from Bruno and Selig (2007) at 22.1% (95% confidence interval [CI]: 20.7, 23.4) and global estimates (excluding the Atlantic) of 18.8– 26.5% hard coral cover (Vercelloni et al., 2020a).
Determining coral functional diversity along the north coast of Timor-Leste is especially relevant at this time given the extensive infrastructure and tourism development planned over the next decade (RDTL, 2011). The data used in the present study was collected as part of the XL Catlin Seaview Survey (XLCSS), which has collected an image dataset that adds to previous biodiversity surveys. While previous surveys collected information over 5 tens of meters, the surveys of the XLCSS collected measurements of benthic ecosystems at kilometer-scales. Coral morphological functional diversity can also be compared across the other CT sites surveyed by the XLCSS. With planned development and a likely increase in human impacts in Timor-Leste, understanding Timorese coral reefs on a functional level will have important implications for conservation and management.
The research described in this work explored the morphological functional diversity of corals to better understand heterogeneity within a reef, between reefs, and to thereby assess, to the fullest extent possible, the impact of potential drivers of heterogeneity across the north coast. In this regard, human and environmental parameters shape both coral reef structure and function and are difficult to tease apart. Initial assessment of the functional diversity of the benthos offers ecologically valuable information important to exploring ecosystem resilience and functional diversity that will be comparable to other regions within the CT. Additionally, assessing the functionality of coral reefs could provide another metric to assess their cryptic invertebrate communities.
Structural diversity and complexity of coral reefs Relationships between habitat and fauna are a fundamental ecological theme (reviewed in McCoy and Bell, 1991), where ecosystems tend to have well-defined species-area relationships (Coleman, 1981; reviewed in Lomolino, 2000; Williams, 1943). There are other ecological links between species and structural complexity that are important and often more nuanced. Habitat structural complexity is defined as the physical, three-dimensional structure of an ecosystem (reviewed in Graham and Nash, 2013) and is affected by several factors such as surface area and habitat patch size (Matias et al., 2010; McCoy and Bell, 1991). Three factors are important in assessing habitat structure: (1) heterogeneity of different structural components, (2) complexity or the abundances of individual structural components, and (3) the scale of the area or volume used to measure heterogeneity and complexity (McCoy and Bell, 1991). The degree of heterogeneity across habitats varies and habitat structure in any given ecosystem is generally produced by key ecosystem engineers such as trees, kelp, and corals (Bruno and Bertness, 2001; Jones et al., 1996) with contributions from other organisms and non-living habitat components such as geological features and rubble (Kleypas et al., 2001). The complexity factor speaks to the variation within each key ecosystem engineer such as size and morphology. Heterogeneity and complexity can often be confounding factors in studies assessing structural complexity;
6 while they can be experimentally manipulated, they are harder to independently assess in the field. Complexity can be assessed at a variety of scales from seascapes to individual coral heads (Noonan et al., 2012; Tokeshi and Arakaki, 2012; Tscharntke et al., 2005). Matching the scale of structural complexity with the organism or parameter of interest is an important consideration that may limit comparisons between studies (McCoy and Bell, 1991).
Complex habitats tend to provide greater ecosystem services, alter ecological interactions, and increase biodiversity (Lee et al., 2014; MacArthur and MacArthur, 1961; Sheppard et al., 2005; Tscharntke et al., 2005). Coral reefs and mangroves, for example, provide important shoreline protection from waves and storms (Sheppard et al., 2005). Structurally complex mangrove habitats also provide shade and facilitate highly turbid waters with fine sediment that reduces the rate of predator-prey interactions (Lee, 2008). MacArthur and MacArthur (1961) first demonstrated a positive relationship between habitat complexity as measured by foliage height diversity and species richness in birds. A diversity of microhabitats is thought to enhance diversity and abundance of inhabitants (Crowder and Cooper, 1982). This effect has been demonstrated in many ecosystems such as forests (Spies, 1982), seagrass (Heck and Wetstone, 1977), and kelp beds (Russell, 1977). On coral reefs, reef-building corals are the main ecosystem engineers that contribute to rugosity, a term for structural complexity (Luckhurst and Luckhurst, 1978; Risk, 1972). Overall, the habitat structure of coral reefs is a balance of several processes including bioerosion, competition and predation, and cohabitation. Live, hard coral are important drivers for coral reef structural complexity, but local and global impacts increasingly threaten corals. This can lead in turn, to a further loss of habitat complexity (Graham et al., 2015).
Obtaining scalable measures of habitat complexity is central to understanding drivers of biodiversity. Rugosity on reefs can be measured at small scales using the chain and link rugosity index, visual assessments, photogrammetry, fractal dimensions, and functional approaches (Ferrari et al., 2016; Luckhurst and Luckhurst, 1978; Nash et al., 2013; Wilson et al., 2007). Earlier work has assessed relationships of rugosity and reef fishes and, as the most studied group, the most detailed relationships have been derived (Graham and Nash, 2013). A positive relationship has been reported for many coral reefs between fish species diversity, abundance, and/or biomass with structural complexity (Bejarano et al., 2011; Cinner et al., 2009; Emslie et al., 2008; Friedlander et al., 2003; Luckhurst and Luckhurst, 1978; Rogers et al., 2014). Variability between the outcome of different methods, differing 7 scales, and a lack of mechanistic measures, however, confound many studies (Bradbury and Reichelt, 1983; Kovalenko et al., 2012; Robson et al., 2005; Tokeshi and Arakaki, 2012).
Numerous studies have also found scale-dependent relationships with organisms such as fish, where refuges correlate with body size (Friedlander and Parrish, 1998; Hixon and Beets, 1993; Luckhurst and Luckhurst, 1978). There are also links between the multimodality of fish abundance and body depth, with the prevalence of particular habitat structure is predicted by the textural discontinuity hypothesis. This hypothesis states that the biological and physical characteristics of associated animals exploit environmental texture at particular scales (Holling, 1992; Nash et al., 2013). Finer scale studies on individual corals and juvenile reef fish survival demonstrate the complexity of coral microhabitats also mediates predation success (Beukers and Jones, 1998; Noonan et al., 2012). Impacts of declining structural complexity have also been assessed with potentially devastating implications such as a three-fold decrease in fisheries production, substantially reduced species diversity, and local extirpation (Emslie et al., 2014; Garpe et al., 2006; Graham et al., 2006; Rogers et al., 2014). Although much further work is needed to continue to elucidate relationships between habitat structure and reef fishes, there have been many significant advances in our understanding.
The relationships between structural complexity and other coral reef groups (corals, algae, and mobile invertebrates) have been documented to a much lesser degree than reef fishes (reviewed in Graham and Nash, 2013). A positive relationship has been identified between rugosity and mobile invertebrates (Fraser and Sedberry, 2008; Idjadi and Edmunds, 2006; Vytopil and Willis, 2001). Sea urchins specifically have been assessed with structural complexity, the results of which demonstrate both positive (Lee, 2006) and negative relationships (McClanahan and Shafir, 1990; Weil et al., 2005). Positive effects of complexity on urchin densities could be linked to the increased number of refugia from predators (Ogden, 1976) and, conversely, negative impacts could be associated with sea urchins’ role as bioeroders on reefs (Birkeland et al., 1981; Ogden, 1977). Graham and Nash (2013) found an overall strong negative relationship with algal cover, which was not significant if refined into macroalgal versus turf algae groups or when assessing the Caribbean and Indo- Pacific biogeographic regions separately.
The relationship between live coral and structural complexity is not as clear (reviewed in Graham and Nash, 2013) although live coral cover tends to be positively correlated with 8 structural complexity (Bergman et al., 2000; Friedlander et al., 2003; Mangi and Roberts, 2007; McClanahan, 1999; McClanahan and Shafir, 1990). A meta-analysis indicated that total coral cover had a significant positive correlation with reef complexity for the Indo- Pacific; however, this relationship was weaker than other parameters tested such as echinoid abundance, fish density and biomass, and total algal cover (Graham and Nash, 2013). Darling et al. (2017) found that hard coral cover characteristics such as total coral cover in addition to reef zone (e.g., slope, crest, flat) strongly influenced structural complexity; this supports the importance of both geologic features and coral metrics in structural complexity. Cross-scale fractal dimension assessments further support this point, with complexity varying significantly between reefs of carbonate versus granitic substrata (Nash et al., 2013). Functional morphology of corals, particularly branching corals, have documented both positive and negative relationships with overall structural complexity (Darling et al., 2017; Graham and Nash, 2013). Branching corals could provide greater habitat complexity, offering a variety of microhabitats, or create a homogenous environment when in large monotypic fields. Two different methods for quantifying reef structural complexity were used in establishing these correlations; chain and link (Graham and Nash, 2013), and a qualitative ranking including geologic features such as caves and overhangs (Darling et al., 2017).
Structural complexity is vulnerable to global climate change owing to the influence of both ocean warming and acidification. Warmer, longer underwater heatwaves and intensifying storms, combined with weaker calcification, undermine and ultimately destroy habitat complexity and therefore resident biodiversity (Hoegh-Guldberg et al., 2007; Hughes et al., 2017a). The Caribbean has experienced a drastic reduction of rugosity in association with several events impacting coral communities since 1969: loss of structurally dominant Acroporid corals, large-scale bleaching events, and hurricanes (Alvarez-Filip et al., 2009; Aronson and Precht, 2001; Gardner et al., 2005; McWilliams et al., 2005). Disturbances have heterogeneous effects on coral assemblages taxonomically and spatially, which drive complex interactions. For instance, a bleaching event was found to increase the structural complexity of a virtual quadrat from a photomosaic more than the control quadrat. This was driven by the loss of a plating Montipora sp., a species more susceptible to bleaching and was subsequently overgrown by branching coral and algae (Ferrari et al., 2016). There is a lag between coral death and loss of reef structure (Pratchett et al., 2008). Sublethal impacts from disturbances such as bleaching reduce fecundity, growth, and skeletal density (Dove
9 et al., 2013). These effects could have impacts on the maintenance of structural complexity on coral reefs as a ~ 10% live coral cover threshold was found to be critical to maintaining net positive reef accretion (Perry et al., 2013).
Coral reefs in the CT are known for having the highest coral diversity globally, with structurally-complex and biodiverse reefs. Rugosity of Timorese reefs has not been explicitly measured. The NOAA surveys used a benthic substrate ratio (e.g., total hard coral, soft coral, and crustose coralline algae [CCA] divided by total turf algae and macroalgae) on phototransects at 139 sites along the north coast of Timor-Leste in 2013. The benthic substrate ratio district averages ranged from 0.7 to 1.4 where a value > 1 indicates greater reef-accreting organisms than algae. District averages of hard coral (range 10.4–20.5%) and soft coral (6–24%) were comparable, while overall districts were dominated by turf algae (35.4–54.5%). Benthic substrate ratio values in 2014 ranged from 0.3–2.7, however, these were calculated off a single phototransect at each NOAA climate station (PIFSC, 2017). In 2012, the RAP outlined four main coral community types along the north coast of Timor- Leste: digitate Acroporid, Pectiniid, Fungiid, and Diploastrea communities (Erdmann and Mohan, 2013). Based on these dominant coral types, a general degree of structural complexity could be inferred.
There are also localized impacts in the CT that greatly reduce structural complexity, such as gleaning and bomb fishing. Gleaning, or the harvesting of invertebrates from the reef flat at low tide, is an important means of subsistence in Timor-Leste (Tilley et al., 2020). It has also been attributed to extensive coral loss over decades on reef flats in Africa (Andréfouët et al., 2013). Furthermore, destructive fishing, including blast fishing by deploying handmade bombs on reefs and collecting stunned fishes, is highly destructive and prevalent throughout the CT. Over 85% of reefs in the region are estimated to be threatened by destructive fishing practices (Burke et al., 2012). While the recovery of isolated craters from blast fishing has been documented, rubble fields from bombing over large spatial and temporal scale showed little recovery (Fox and Caldwell, 2006). In Timor-Leste, there is comparatively little destructive fishing as compared to neighboring countries (Burke et al., 2012). There is some evidence of blast fishing thought to have been more prevalent during the Indonesian occupation in Timor-Leste (Erdmann and Mohan, 2013). More recent usage of destructive practices has been dissuaded by government incentives such as impounding boats (R. Grantham, personal communication, September 9th, 2019).
10
Cryptofaunal diversity of coral reefs and its drivers High levels of biodiversity are a critically important characteristic of tropical coastal ecosystems. The term biodiversity was first introduced in 1986 at the National Forum on BioDiversity in Washington, D.C. This national forum was one of the first to begin to assess and quantify biodiversity globally. Currently, approximately 250,000 marine species have been described (Bouchet, 2006; Reaka-Kudla, 1997) with many different methods utilized to extrapolate total marine biodiversity (the number of unique species) ranging from 5 x 105 (May, 1994) to 108 (Grassle and Maciolek, 1992).
While coral reefs only make up 0.2% of the ocean area, they are the most diverse marine ecosystems on a per-area basis along with deep-sea environments (Sala and Knowlton, 2006). Reaka-Kulda (1997) describes three main components of coral reef biodiversity: fishes, reef-building organisms, and cryptofauna. The fishes and reef-building corals are well-studied, with a well-developed understanding of their patterns of diversity and endemism; however, most of the marine biodiversity lies outside of these groups and is largely unquantified (Reaka-Kudla, 1997). The cryptofaunal component is estimated as equivalent to or exceeding, the associated reef biomass (Choi and Ginsburg, 1983; Richter et al., 2001) and is, therefore an important contributor to reef processes such as reef consolidation (Gischler and Ginsburg, 1996; Warme, 1977) and nutrient cycling (Choi and Ginsburg, 1983; Richter et al., 2001; Richter and Wunsch, 1999; Szmant-Froelich, 1983).
Drivers of cryptofaunal communities include large-scale reef parameters such as light availability and water movement. There is some evidence of light structuring cryptofauna communities, with photosynthetic activity reducing with distance from cavity opening (Logan et al., 1984). However, it is difficult to disentangle the interactive effects of the presence of algae and predation (Cinelli et al., 1977; Navas et al., 1998; Wunsch et al., 2003). Water movement of cryptic reef habitats is important as many invertebrate faunae are filter feeders (Buss and Jackson, 1981). Flow dynamics depend on the magnitude and direction of surface flow in addition to the structure of the habitat, such as porosity (reviewed in Wolanski, 1994). Generally, contact with the benthos slows down water flow, leading to the deposition of sediments (Nowell and Jumars, 1984). However, fast-flow with higher turbidity and storm events can be detrimental (Gischler and Ginsburg, 1996; Scheffers et al., 2010). Sheltered environments have been associated with greater biomass of cryptic species (Hutchings and
11
Weate, 1977), and mid-shelf versus outer and inner-shelf environments were associated with greater numbers of cryptic fish species (Depczynski and Bellwood, 2005).
Habitat such as rubble, dead substrate, or living coral is another important parameter to consider. Moreover, cryptic habitat features often have interactive effects with biophysical parameters such as water flow. Hard corals are undoubtedly an important feature in supporting cryptofaunal communities. There is a whole class of coral obligate invertebrates (Knudsen, 1967) with different coral morphologies sustaining different communities
(Shirayama and Horikoshi, 1982). In branching corals, a higher density of coral branches harbors higher densities of motile cryptofauna, and the degree of openness between branches was a significant factor in epifaunal communities within branches (Vytopil and Willis, 2001). Coral mucus and other metabolite products may be an important food source in non-living coral habitats adjacent to live corals (McCloskey, 1970; Wild et al., 2004). Other groups of invertebrates such as bioeroders are deterred by coral cnidae and mucus (Fonseca et al., 2006; Hutchings, 1985). Even so, Idjadi and Edmunds (2006) found that reef-associated invertebrates were positively correlated with structural complexity, but not live coral cover. Subsequently, non-living substrates such as rubble are important habitats for cryptofauna. As with the density of branching corals, the porosity of rubble was an important factor in structuring cryptofaunal communities. High porosity (bigger spaces) was associated with greater diversity, but less total biomass (Enochs et al., 2011). Larger open spaces could provide less protection from predation but may also provide more diversity in microhabitats, in turn, supporting a more diverse community.
Estimating the cryptic biodiversity of coral reefs is especially difficult as these organisms by definition are living in habitats that are not easily observed within the reef structure. Past studies have collected coral rubble, live, or dead coral heads (Enochs, 2012; Enochs and Manzello, 2012a; Plaisance et al., 2009, 2011a) and created artificial frameworks using coral rubble (Enochs et al., 2011) to sample cryptofauna on coral reef habitats. Autonomous Reef Monitoring Structures (ARMS) are a novel method of systematically assessing marine cryptofauna (PIFSC, 2017; Zimmerman and Martin, 2004). Created by a partnership between NOAA, the Australian Institute of Marine Science, and the Smithsonian Institution’s (SI) Census of Marine Life project, these artificial units of constant volume address the difficulties in quantifying this fauna. Recent ARMS studies have determined that distance between units is negatively correlated with the similarity of the invertebrate communities. Leray and Knowlton (2015) analyzed ARMS communities deployed at sites a few meters, 12 hundreds of meters, and kilometers apart, and found that the units which were closer together were more similar. The NOAA ARMS data and benthic imagery provide an opportunity to investigate how marine biodiversity, as measured by cryptic invertebrate groups, correlates with benthic composition along the north coast of Timor-Leste.
Taxonomic work requires intensive resources, time, and expertise. The advent of cheaper and faster genetic techniques, however, has meant that greater quantities of reef biodiversity can be assessed using methods such as DNA barcoding and metabarcoding. This shift toward genetic methods is not without criticism, namely that a one-off genetic approach is insufficient to delineate new species (Will et al., 2005). However, barcoding also allows for systematic quantification of groups, such as cryptofauna, that have been difficult to sample and identify with conventional taxonomic methods (Blaxter et al., 2005; Knowlton, 1993). Best practices point towards using genetic techniques such as DNA barcoding in conjunction with more traditional morphological, ecological, and taxonomic methods (Knowlton, 2000; Will et al., 2005).
Understanding the diversity of coral reefs is important to gain a sense of overall reef resilience, yet is poorly described for many coral reef ecosystems, including those of Timor- Leste and the larger CT region (Bertzky and Stoll-Kleemann, 2009; Clifton, 2009; Veron et al., 2009). Semi-quantitative biodiversity assessments of corals and reef fish were conducted in the 2012 RAP, with over 367 and 741 species documented respectively. A follow-up RAP survey in 2016, focusing on ten sites on Ataúro Island averaged 253 species of fishes per site. This diversity was on par with Raja Ampat in Indonesia, the epicenter of marine biodiversity (Slezak, 2016). Species point count surveys in 2013 conducted by NOAA produced an average of 57 fish species per site. Although much lower than the species richness recorded on the Ataúro Island, the 2012 RAP utilized semi-quantitative swims versus stationary point counts, and the 2013 NOAA surveys are an average of 150 sites across the north coast (PIFSC, 2017). Like most biodiversity assessments, those of Timor-Leste so far have focused on the conspicuous groups of hard corals and fishes. In Chapter 3, Timorese biodiversity will be further explored, with an analysis of the cryptic marine invertebrate fauna collected from ARMS. This measure of cryptic marine invertebrates will be assessed with the benthic community data to elucidate relationships between two major components of coral reefs.
13 Coral disease and its known drivers The effects of global and localized anthropogenic impacts can be assessed at an ecosystem level through biodiversity assessments, and through measuring of the health of ecosystem engineers such as corals. Notably then, the distribution and abundance of coral-dominated reef ecosystems are declining at an unprecedented rate (Bruno and Selig, 2007; De’ath et al., 2009; Hoegh-Guldberg, 1999; Hughes et al., 2003; McClanahan et al., 2002), and coral disease is recognized as a significant driver of the global deterioration of coral reefs (Harvell et al., 1999; Lamb et al., 2018; Porter et al., 2001; Weil et al., 2006). However, many of the fundamental parameters associated with the ecology of disease, such as etiology and mode of transmission, are not well known for most diseases in marine environments. Coral epidemiology is also inherently difficult as there remain many challenges to identifying, describing, and defining causative agents in the marine environment (Mera and Bourne, 2018; Work and Aeby, 2006). Additionally, corals display limited immune responses, which leads to visually similar disease lesions for multiple diseases with different etiologies, and different pathogens can cause similar symptoms (Beurmann et al., 2017; Ushijima et al., 2014; Work et al., 2012). Pathogens can also change over time (Sutherland et al., 2016). Currently, many marine diseases are thought to occur through dysbiosis—over-proliferation of commensal microorganisms on the host—in response to an environmental stressor such as warming temperatures (Bourne et al., 2016; Lesser et al., 2007). There is likely interplay between host microbial communities, pathogens, reservoirs, and vectors in outbreaks of marine disease that are not well understood (Dinsdale et al., 2008; Harvell et al., 2007).
Although the epidemiology of marine disease is challenging, evidence indicates several factors, such as ocean warming and nutrient pollution, contribute to the increase in coral diseases (Bruno et al., 2007; Harvell et al., 2007; Vega Thurber et al., 2014). Therefore, under a regime of warming and acidifying oceans, the incidence of coral diseases is expected to rise (Harvell et al., 2002; Rosenberg and Ben Haim, 2002). For example, elevated temperatures have repeatedly triggered outbreaks across a variety of host species and geography. Underlying mechanisms for temperature-driven disease outbreaks in corals include shifts in pathogen virulence, microbial communities, and host defenses (McDevitt- Irwin et al., 2017; Ward et al., 2007). Coral disease related to thermal stress events is also linked to coral bleaching, which puts corals into a reduced state of fitness and increases susceptibility to other threats such as disease. Additionally, localized impacts such as nutrient pollution have been shown to affect the prevalence and incidence of marine disease 14
(Bruno et al., 2003; Harvell et al., 2007; Kaczmarsky and Richardson, 2010; Vega Thurber et al., 2014; Voss and Richardson, 2006). Greater nutrient concentrations could improve the survival of pathogens and decrease the fitness of corals through decreased water quality impeding photosynthetic efficiency. Increased coral disease has also been positively correlated with plastic pollution on reefs, which may serve as extra substrates harboring pathogens (Lamb et al., 2018).
Thermal stress is a primary threat to coral reefs. It also influences disease prevalence, which has shaped reefs over the last several decades. Caribbean coral reefs, for example, have been decimated by white band disease and coral bleaching (i.e., disruption of the symbiosis of dinoflagellates and corals). Together these phenomena have decreased the size of populations of A. cervicornis and A. palmata to the point where these reef-building corals are now on the US endangered species list (Acropora Recovery Team, 2015; Aronson and Precht, 2001). These two corals were once the dominant, habitat-forming species responsible for most of the spatial heterogeneity in the Caribbean. Their disappearance had significant ecological effects and reduced biomass and biodiversity of associated reef fauna (Weil, 2004). During the 2016–2017 global bleaching, there was a strong correlation of white syndrome (WS) on a tabulate Acropora in on the Great Barrier Reef (GBR) and bleached colonies, which exacerbated mortality (Brodnicke et al., 2019). With the likelihood of increasing bleaching events in the future, this relationship between coral bleaching and disease is important to understand.
Disease epidemics have also affected other important functional groups, such as the unprecedented die-off of the D. antillarum black sea urchin, a prominent grazer (Knowlton, 2001). These disease events are thought to have played a major role in the phase shift from coral-dominated to macroalgal-dominated communities throughout the Caribbean (Hughes, 1994) and degraded its coral reefs (Aronson and Precht, 2001; Bruno, 2015; Hughes, 1994). Coral disease dynamics in the Indo-Pacific are less understood although the region contains approximately 75% of the world’s coral reefs (Burke et al., 2011). Although the proportion of hard corals impacted by disease in the Indo-Pacific is lower than in the Caribbean (Sutherland et al., 2004), there is a lack of spatial-temporal quantitative data on the prevalence of coral diseases, distribution patterns, or impacts on many of these reefs (Raymundo et al., 2003; Sutherland et al., 2004; Weil et al., 2006; Willis et al., 2004). The Indo-Pacific is not considered a coral disease hotspot like the Caribbean (Weil, 2004); however, recent work indicates coral disease is a significant factor that must be addressed 15 (Harvell et al., 2007; Kaczmarsky, 2006; Weil et al., 2012; Willis et al., 2004). In Timor-Leste, biodiversity assessment surveys found that the prevalence of disease or bleaching on Timorese reefs was low (Turak and DeVantier, 2012). These reefs tended to suffer more from Drupella sp. snail and crown-of-thorns seastar (COTS) predation (Ayling et al., 2009; Turak and Devantier, 2013).
Gaining a baseline understanding of coral disease and its incidence in Timor-Leste is important on several fronts including resource management and monitoring interventions. Upwelling in the coastal regions of Timor-Leste potentially offers protection for coral reefs from stress related to climate-induced sea temperature increases (Boggs et al., 2012; Erdmann and Mohan, 2013). In the context of the CT, Timor-Leste has the potential to serve as a thermal refuge for coral reefs in this region, due to cooler waters of the Indonesian Throughflow (ITF) to the north and south (Beyer et al., 2018; Hoegh-Guldberg et al., 2018). During the recent RAP survey described above, Timorese water temperatures of 25–27 C were consistently 3–4°C cooler than neighboring locations. Coral disease has been⁰ observed in conjunction with bleaching events (Brodnicke et al., 2019; Miller et al., 2009), which are primarily caused by environmental stressors such as thermal anomalies and increased solar radiation. These stressors affect the underlying biological and physiological properties of corals (Brown, 1997), such as the ability to fight infections, thus increasing the risk of disease (Rosenberg and Ben Haim, 2002).
In addition to these environmental factors, other localized impacts such as sedimentation, nutrient pollution, fishing, and marine tourism also have negative impacts on coral reef health (Bellwood et al., 2004; Bruno et al., 2003; Hoegh-Guldberg et al., 2007; Hughes et al., 2003; Hughes and Connell, 1999; Lamb and Willis, 2011). The incidence of coral disease is affected by a combination of both environmental and anthropogenic factors with sewage likely a significant factor in Timor-Leste as much of the country’s water and sanitation infrastructure was destroyed during recent conflicts (1975–1999). The capital region of Dili houses over 200,000 people, with minimal wastewater treatment and the resulting effluent draining into the ocean (RDTL, 2011). Rainfall is highly seasonal, and the extent of the impact of pollution will to some extent be dependent on the flow dynamics at the mouth of rivers, with the potential for large dilution if nearshore currents are strong. The Timor-Leste Strategic Development Plan aims to have adequate sewage systems in all districts and sub- districts by 2030.
16
Several proximal stressors are also having increasing impacts. Timor-Leste, for example, has also been heavily affected by deforestation, which has been occurring at a rate of 1.1% per year, more than four times the global average, from 1972–1999 (Sandlund et al., 2001). The 2010 Global Forest Resources Assessment estimated 1.22-1.44% annual deforestation from 1990-2010 in Timor-Leste, comparative to 1.75-0.71% in Indonesia (FAO, 2010). Logging continued during the United Nations administration (Bouma and Kobryn, 2004) and remains a substantial issue in the present day. Rural mountain communities have little else to rely on for sustenance and thus use wood for fuel while also clearing land for subsistence agriculture and livestock (Alongi et al., 2012a; Cardinoza, 2005; JICA, 2010; Sandlund et al., 2001). Coupled with the steep, mountainous terrain—roughly 40% of the country has slopes of 40% incline—erosion, flooding, and landslides are major environmental concerns (Sandlund et al., 2001).
The 2012 RAP measured marine diversity and a few metrics of coral injury and disease present along the northern coast. Expanding upon these previous surveys by conducting additional coral health assessments would improve the resolution of the current status of Timorese reefs and inform marine resource management and conservation. The role of coral disease and the impact of stressors on the prevalence and severity of disease is of increasing importance, especially in the face of climate change (Harvell et al., 1999). Timor- Leste provides a backdrop of a young nation with a relatively low population and associated subsistence level impacts along most of the coasts. The country is on the verge of developing rapidly, with large investments in infrastructure planned over the next few decades (COVID19 allowing). Understanding its current condition of coral reefs consequently has the potential to assist in understanding change and planning for a sustainable, and hopefully rich and equitable, future.
Aims & Objectives
The proceeding information points to interesting questions on a little-studied country and its coral reefs. Given Timor-Leste’s rapid development and threats at both global and local scales, the aims and objectives of this thesis were to: assess the benthic composition and coral morphological functional diversity of coral reefs of the north coast of Timor-Leste; quantify reef-associated biodiversity and relate it to benthic community composition; and conduct coral health and disease surveys in conjunction with nutrient surveys and temperature data. 17 To achieve these aims, the following objectives were addressed:
Objective 1: Measure the structure and community composition of reef slope coral reefs in Timor-Leste while exploring coral functional diversity, and potential drivers across biophysical and population gradients in north Timor-Leste.
Objective 2: Explore the cryptic biodiversity of signature invertebrate taxa collected via ARMS, especially how it relates to the benthic coral community composition, using NOAA phototransects taken of the same site.
Objective 3: Quantify the prevalence and severity of coral disease and other signs of compromised health (bleaching, macroalgal overgrowth, etc.) as related to anthropogenic eutrophication, as well as heat stress before and after the 2016-2017 global bleaching event in Timor-Leste.
Thesis Structure
The work presented here used conventional, and novel techniques, for understanding the ecology of coral reefs in Timor-Leste at a variety of scales. Chapter 2 begins by providing a broadscale perspective of coral community structure benthic composition. This was done using a conventional image analysis approach along 30 m transects combined with a novel technology involving kilometer-scale surveys plus image recognition that provided a multi- kilometer-scale perspective. The project used additional phototransect datasets at a conventional scale of tens of meters, which was collected by collaborators at NOAA around the same period as a point of comparison. The kilometer-scale dataset allowed a more robust analysis of potential drivers that shape coral community structure along the outer reef slope corals of Timor-Leste’s north coast.
In Chapter 3, the thesis uses new techniques to quantify the biodiversity of coral reefs along the north coast of Timor-Leste by exploring invertebrate cryptofauna. With DNA barcoding and metabarcoding, the project systematically surveyed cryptic coral reef invertebrate communities with ARMS in this region. This comprehensive assessment of cryptofaunal biodiversity was analyzed with benthic composition derived from conventional methods to further elucidate habitat and cryptic community structure relationships. In Chapter 4, coral health was investigated at four sites, with different sources of nutrients (e.g., fertilizer versus sewage) also being identified using stable isotope and seawater nutrient analyses. The
18 effect of heat stress was quantified by surveying before and after the heat stress associated with the 2016–2017 global bleaching event. Additionally, in situ temperature loggers and remotely sensed sea surface temperature (SST) data were collected in Timor-Leste to quantify the heat stress in-country during the global bleaching event.
The information generated by this project on the little-known coral reefs of Timor-Leste will help guide sustainable Timorese policy and management of its valuable coral reef resources. Importantly, this monitoring of coastal marine resources underpins informed management decisions, implementing policies, and documenting change over time. However, monitoring requires significant financial, time, and technical resources that are barriers to data collection, storage, and analysis. Unfortunately, management decisions surrounding key issues impacting coastal resources, such as food security and development, will move forward regardless of the availability of data on these systems. Currently, science and monitoring efforts are supported by international aid agencies and foreign researchers who provide critical data. Ultimately, sustainable management of the country should be championed by the Timorese and training the next generation in environmental management and monitoring efforts is a key step. Capacity building across all sectors is a priority and still in its infancy in the resource management sector. The National University of Timor-Leste only recently implemented a marine program in 2018.
Significant conservation efforts have been achieved (e.g., the establishment of Nino Konis Santa National Park [NKSNP]) and continue to move forward. However, considering environmental impacts outside of conservation such as tourism and development, conservation is imperative. For example, many countries conduct Environmental Impact Assessments (EIA) before development projects are approved and begun. Wever (2008) reports that draft legislation for EIAs was being discussed in the Timorese parliament, but currently, there is still no legislation concerning EIAs. Successfully implementing this type of legislation and enforcing the outcomes of the EIAs is critical to sustainable development and management of natural resources.
Limitations Despite increasing the scale of benthic phototransects in Chapter 2, scale is still a limitation of the work presented in the remaining chapters. The 26 kilometer-scale transects were clump grouped together along the northern coast. Spreading the transects out more equidistantly would have been more ideal in getting greater coverage along the coast. 19 However, this was limited by logistical constraints such as budgets and boat access. Transect location was also selected based on the climate stations set up by NOAA.
Furthermore, obtaining temperature data for all transects at a high enough resolution was a challenge as there were problems with SST at the interface of land and sea. Consequently, temperature was dropped as a biophysical parameter for the model. Chapter 3 data was based on the NOAA climate station design, hence drawing relationships between the cryptofaunal communities and benthic composition was limited by the lack of replication of benthic phototransects at each station. The sheer lack of marine invertebrate sequence data in public genetic repositories limited the resolution of the biodiversity assessment as well. The coral health surveys in Chapter 4 were limited to four sites and non-permanent transects for the resurvey. Although the in situ temperature data was limited to three sites, consistent, interesting patterns in seasonal temperatures were found between the sites. Even with these limitations, the surveys still delivered significant insights into the structure, function, and state of coral reefs along the north coast of Timor-Leste.
20
Broad-scale overview of thesis components Overall Aims To investigate, describe, and understand the composition, biodiversity, and health of reef slope communities along the north coast of Timor-Leste.
Chapter 2 Chapter 3 Chapter 4 How does benthic composition and How does coral reef associated marine What is the status of coral health functional diversity of coral biodiversity vary with benthic along the north coast? What were communities vary across the north composition? the impacts on coral health after the coast and what are potential drivers? 2016-2017 global bleaching event? Data Data - Brachyuran crab diversity Data - Benthic composition - Coral disease and compromised - - Morphospecies groups Coral morphological functional health surveys in 2015, 2017 - DNA barcoding, metabarcoding diversity - Delta 15N macroalgal surveys, - Environmental data - Benthic phototransects seawater nutrients in 2015 - Human population density - In situ temperature loggers Data Processing Data Processing - - DNA barcoding Remotely sensed sea surface - Automated mage Analysis - Metabarcoding bioinformatics temperature - Environmental data acquisition - Image analysis of phototransects and processing Data Processing - Coral disease/health prevalence Analysis Analysis - - Stable isotope analysis - Inter/intra-site variability Rarefaction curves - - Sea surface temperature data - Drivers of composition–distance- nMDS plots, environmental fitting based redundancy analysis - Benthic composition correlations Analysis - Drivers of total coral cover–linear with biodiversity data - PCO plots between surveys - Community composition mixed-effects model Outputs - Site differences Outputs - Cryptofaunal biodiversity of - Drivers of coral disease - Benthic composition variability Timor-Leste Outputs across the north coast - Relationships between - Current health status of reefs - Drivers of benthic composition to biodiversity and benthic - Susceptibility of Timorese reefs inform management composition to bleaching events 21 Description of Datasets
Table 1-1 Column Headings: Data—data stream; Method—method of data collection; Collection—parties involved in data collection; Processing—parties involved in data processing; Description—further description of data and which chapter data is utilized in. Acronyms: XL Catlin—XL Catlin Seaview Survey; CK—Catherine Kim; NOAA—United States National Oceanic and Atmospheric Administration; SI—Smithsonian Institute
Data Method Collection Processing Description Benthic Twenty-six kilometer-scale transects were Composition, Coral Kilometer-scale XL Catlin, collected in 2014 in Timor-Leste by the XL XL Catlin Morphological phototransects CK Catlin Seaview Survey and analyzed in Functional Diversity Chapter 2 by CK. The NOAA 2013 phototransects collected 2013: 30 m phototransects along the north coast were utilized for Benthic at 139 sites comparison for Chapters 2 and 3. Composition, Coral 2014: 15 m phototransects NOAA NOAA, CK CK completed the benthic image analysis of Morphological
collected at NOAA climate the NOAA climate station phototransects Functional Diversity stations collected in 2014 which were used in Chapters 2 and 3. Coral disease and Belt transects at four sites and two depths in BENTHIC Coral Health compromised health CK CK 2015 and 2017 for Chapter 4.
surveys The > 2 mm size fraction of invertebrates > 2 mm size were sorted and counted to morphospecies ARMS NOAA NOAA Morphospecies upon the re-collection of the ARMS units used in Chapter 3. The brachyuran crabs were preserved upon NOAA, DIVERSITY Crab Genetics DNA barcoding SI, CK collection and CK subsampled and CK
completed the DNA barcoding for Chapter 3.
22
Metabarcoding of ARMS samples and Invertebrate DNA metabarcoding NOAA NOAA, SI bioinformatics was completed by NOAA and Metabarcoding SI. Data were analyzed by CK for Chapter 3. Temperature data were collected by NOAA Subsurface Subsurface temperature NOAA, NOAA, CK (2012–2014) used in Chapter 2 and by CK Temperature recorders CK (2015–2017) for Chapter 4. NOAA’s Coral Reef Watch
Sea Surface Data retrieved, processed, and analyzed by regional virtual station in NOAA CK Temperature CK for Chapter 4. Timor-Leste Measured distance to the nearest river mouth to each subtransect collected by the Riverine input GIS CK CK XL Catlin Seaview Survey for the model in Chapter 2. Measured relative exposure at the start,
ENVIRONMENTAL Wave Exposure GIS-based model CK CK middle, and endpoints of each kilometer-
scale transect for the model in Chapter 2. Timor- Assigned population density of the nearest
Human Population 2015 Timor-Leste Census Leste CK district to each subtransect for the model in Census Chapter 2. Seawater nutrients and macroalgal samples Macroalgal δ15N and HUMAN Nutrients CK CK collected for stable isotope analysis for seawater nutrient analysis
Chapter 4.
23 Study site
Timor-Leste is located between 8–9°S latitude and 126°E longitude northwest of Australia at the southern edge of the CT. Timor-Leste comprises the eastern half of the easternmost island of the Nusatenggara group bordered to the west by the Indonesian part of Timor island. This area covers 18,900 sq km with 700 km of coastline including Pulau Ataúro and Jaco (henceforth Ataúro and Jaco Island) and the western, isolated province of Oecusse. The 2015 census recorded a population of 1,167,242 people (RDTL, 2015). The country is divided into 13 districts, 11 of which are on the coasts (Erdmann and Mohan, 2013; Figure 1-1). The CT ecoregion encompassing Timor-Leste, the North Arafura Sea islands, is one of two ecoregions declared data deficient in the delineation of the CT (Veron et al., 2009).
Figure 1-1 Timor-Leste and its 13 districts. The Ombai and Wetar Straits lie to the north of the island and the Timor Sea to the south and is influenced by the Indonesian ThroughFlow.
Geology and oceanography of Timor island Timor island, located on the north-western edge of the Australian continental tectonic plate, resulted from the deformation between the collision of the northern margin of the continental plate and the Banda Arc (i.e., the line of volcanic islands extending from Java to Wetar;
24
Karig et al., 1987; Sandlund et al., 2001). The island drifted northward during the Miocene and Pliocene, coming to its current location in relatively recent geologic time (Audley- Charles, 2004; Hall, 2001). The island has rapidly uplifted during the Pliocene-Quaternary (i.e., the last five million years, Mega-annum [Ma]). The rate of vertical uplift is spatially variable, but broadly speaking the same pattern of relief is visible across the island and adjoining ocean basins (Karig et al., 1987). Coral reefs have been uplifted at the southern edge of Ataúro Island and along the north coast from Manatuto to Lautem (i.e., east of Dili) at an average rate of 0.5 m/1,000 yr in recent geologic history. Comparatively, the rate of uplift is an order of magnitude less in Dili (Chappell and Veeh, 1978). The bedrock is mainly sedimentary calcareous rocks that differentiate Timor from the surrounding volcanically derived Indonesian islands (Sandlund et al., 2001).
The island is mountainous, the highest peak at 2,964 m, with very steep terrain. There are some sizeable (20–30 km wide) coastal plains along the southern coast. The northern coasts house steep mountains butting up to the sea and littoral terraces of coral origin, some uplifted to 300–700 m above sea level. Fossil reefs have been found at 2,000 m elevation. Overall, about 44% of the island has slopes greater than 40% (Sandlund et al., 2001). This steep topography continues into the ocean as the island drops off into a 3 km trench just 20 km from the north coast (Alongi et al., 2012a; Boggs et al., 2012; Keep et al., 2009). Over the past few million years, the island and central area of the CT have been consistently surrounded by deep waters over at least eight Pleistocene glaciations involving a decrease of ~ 130 m sea level (Erdmann and Mohan, 2013; Siddall et al., 2003; Veron et al., 2009). This repeated exposure of reefs and steep terrain potentially created diverse shallow habitats, always adjacent to deep oceans (> 150 m), driving reticulate evolution and resulting in the global center of marine biodiversity (Veron et al., 2009).
Oceanographically, Timor-Leste is bound by the Banda Sea and semi-enclosed Wetar Strait to the north and the Timor Sea to the south (Figure 1-1). Timor-Leste lies within the ITF— an important, complex oceanographic corridor connecting the Pacific and Indian Oceans through the Indonesian Seas. It is regulated by ocean-scale wind stress (Godfrey, 1989), El Niño Southern Oscillation (ENSO; Meyers, 1996), the Indian Ocean Dipole (Potemra and Schneider, 2007), and regional monsoonal wind patterns over Southeast Asia (Susanto et al., 2007). The ITF plays an important role in the global climate system (Macdonald, 1993). Timor island is surrounded by two of the three export passages into the Indian Ocean: the Ombai Strait to the north and the Timor passage to the south. Both are approximately 1.5 25 km in depth respectively exporting 4.9 Sverdrup (Sv–106 m3/s) and 7.5 Sv to the Indian Ocean annually (Gordon et al., 2010). There is a general north-south throughflow in the Less Sunda Islands and some, mostly subsurface flow, in the opposite direction. Local currents are strong and variable throughout the region and generally tidally-induced. Localized upwelling is also common either influenced by the tides or the ITF (DeVantier et al., 2008).
Climate The climatology of the north coast is dominated by the northern monomodal rainfall pattern, encompassing a single wet season from December to May. The southern coast experiences a longer, southern bimodal rainfall pattern (Nov–Apr, May–Jul; Keefer, 2000). Although the country is not large, the north and south coast experience significant differences in climate and topography (Ayling et al., 2009; Erdmann and Mohan, 2013). The West Pacific Monsoon is characterized by westerly winds during the wet season and south-easterly trade winds during the dry season (DNMG et al., 2015). Rainfall is the least along the north coast lowlands (< 1,000 mm/yr) and reaches a maximum in the high-altitude areas (> 2,500 mm/yr; Keefer, 2000). Rain is typical in torrential downpours where maximum daily rainfall has been recorded at 275 mm in Dili, roughly 30% of annual expected rainfall. This has serious implications for surface runoff, erosion, and flooding, especially in degraded watersheds (Alongi et al., 2012a; Sandlund et al., 2001). As a tropical country, there is little variation in monthly mean temperatures throughout the year in Timor-Leste. The north coast is characterized by a mean temperature of above 24˚C with generally no more than 3˚C between the coolest and warmest months in each area (Keefer, 2000).
There is a significant change in the timing and volume of rain with ENSO events. Some areas experience up to 50% less than average rainfall (e.g., Ainaro and Lautem) and some greater rainfall (e.g., Baucau and Oecussi). Overall, the wet season is delayed two to three months, with rainfall during January and March decreased as much as 75% of typical years. The year after an ENSO can also have higher than average rainfall, increasing the potential for flooding (BMRC, 2003). Changes in timing and amount of rainfall can have significant implications for crop planting and food security (Barnett et al., 2007), resulting in increased fishing pressure in the coastal environment.
26
Coastal ecosystems along the north coast Boggs et al. (2012) mapped coastal habitats along the north coast of Timor-Leste using Landsat imagery and classified mangrove forests, coral dominated forereef and escarpment, coral dominated reef flat (average depth 1.6 m), deep water, and seagrasses. Deepwater covered the largest area (62,708 ha) followed by coral dominated forereef and escarpment (1,558 ha), then predominantly bare reef flat (1,266 ha; Boggs et al., 2012). Comparatively, NOAA mapped most of the Timorese coast five years later using WorldView- 2 satellite imagery and estimated 6,030 ha hard substrate (likely to be coral reef habitat), 4,190 ha soft substrate, 1,790 ha seagrass, and 2,900 ha mangrove. Significantly, the majority of benthic habitat area was classified as unknown as classification quality is highly dependent on image quality and good visibility. Additionally, the accuracy of benthic habitat classifications was unable to be assessed because of a lack of ground-truthing data (PIFSC, 2017). The coastal physiography is characterized by steep coastal gradient with cliffs and rocky headlands interspersed with pocket beaches and narrow fringing reefs. There is an absence of significant coastal plains, limited development of estuaries, and coastal habitats are constrained to a linear, narrow extent approximately 3 km in width with many areas less than 1 km wide (Boggs et al., 2012).
Coral reefs are found in a variety of environments along the north coast. The 2012 RAP assessed 20 northern sites across a range of reef environments. Depths surveyed ranged from low tide to > 60 m. Most coral growth was determined to be shallower than 30 m, and reefs surveyed included sheltered and exposed areas. Coral communities were primarily found in areas of hard reefal or non-reefal substrate with a mean of 78% (± 18% standard deviation [SD]) cover with low amounts of sand (14 ± 16%). Influxes of freshwater runoff during the monsoonal rains from seasonal rivers and potential subterranean groundwater may limit reef growth in some areas (Erdmann and Mohan, 2013). Boggs et al., (2012) estimate a 40% loss in mangroves between 2000–2008 and an 80% decrease since records began in 1940. This indicates a high need for conservation, preservation, and rehabilitation of coastal marine habitats along the Timorese north coast.
Conservation efforts and customary law in Timor-Leste Timor-Leste has committed to conserving its marine resources by establishing NKSNP at the eastern end of the country in 2007 (Figure 1-2). The park was established as an International Union for the Conservation of Nature Category V Protected Area which
27 maintains the local people and their subsistence livelihoods, which is dependent on natural resources within the park. Therefore, the park implements multiple-use zoning (e.g., no- takes zones, buffer zones, temporal closures, species-specific take limits) to ensure the protection of natural resources and while incorporating the needs of its inhabitants. This community-based zoning and management of the park cover 22,360 ha of marine environments of the total 55,600 ha.
Tara bandu, or the customary law around the protection of natural resources, has also become a significant means of marine conservation in-country over the past few years. Tara bandu is a key cultural asset transcending all aspects of society, and is a major conflict resolution tool relating to resource use and ownership (Miyazawa, 2013). As a result, tara bandu is recognized as a community-based approach to sequester carbon, protect organic carbon stocks in the form of natural resources (i.e., forests, mangroves, etc.,) and secure livelihoods (Cardinoza, 2005). It is also internationally recognized as a best practice for establishing policies for the use of indigenous Traditional Knowledge and practices for adaptation to climate change (UNFCCC, 2014). Benefits include community support and ease of amendment; however, there is no concrete monitoring of resources and is likely best suited to rural areas (Cardinoza, 2005; Chandra et al., 2016). Naturally, tara bandu is one of many tools that should be implemented toward the goal of sustainable management of Timorese natural resources.
Figure 1-2 Reef flat off Jaco Island in Nino Konis Santana National Park on the eastern end of Timor-Leste in 2014. Photo: C. Kim 28
Survey and temperature logger locations As a small coastal country within the Indo-Pacific, it is possible to sample from the whole north coast of Timor-Leste on a longitudinal gradient while remaining at the same latitude. This natural setting allows for the direct comparison of the coral reef communities along this east to west gradient. Additionally, 23.4% of the country’s population is located in the capital district of Dili, located in the western region with low populations elsewhere (NSD Timor- Leste, 2015). Both the NOAA and XLCSS surveys extend across most of the north coast and overlap between sites. NOAA commenced USAID funded scientific cruises in conjunction with Conservation International (CI) Timor-Leste from 2012-2014. The XLCSS expedition to Timor-Leste took place in July of 2014 conducting 26 kilometer-scale phototransects along the north coast (Figure 1-3). One of the driving factors with the XL Catlin transect site selection was the location of the existing NOAA climate stations.
Figure 1-3 Location of the main datasets used in the thesis: (1) the 26 XL Catlin Seaview Survey kilometer-scale phototransects collected in July 2014; (2) eight NOAA climate stations where data from 25 ARMS, 13 temperature loggers, and phototransects were collected between 2012–2014; and (3) coral health sites at four locations around Dili where coral health and disease belt transects were conducted in November 2015 and July 2017, nutrient assays in 2015, and temperature loggers deployed between survey points. The red line indicates the direction of the Indonesian Throughflow.
29 NOAA established eight climate stations (Figure 1-3) on the north coast. These stations monitored a variety of physical and biological data and were the site of deployed ARMS. In situ, hourly, temperature data were collected by the NOAA at the eight stations (SBE 39 Temperature Recorder, Sea-Bird Electronics, Inc.) across the northern coast from October 2012 and September 2014 (PIFSC, 2017; Figure 1-3). Additional, satellite remotely sensed SST data from the Coral Reef Watch Timor-Leste (CRWTL) regional virtual climate station in Timor-Leste were incorporated into the analysis. Temperature data was also collected at the four coral survey sites overlapping with three of the NOAA climate stations. HOBO temperature loggers (Onset Computer Corporation, Bourne, MA USA) were deployed logging every 30 minutes from November 2015 through July 2017 at 5 and 10 m depth each.
Collaborators
CI Timor-Leste is the primary environmental non-governmental organization within the country. Since arriving in Timor-Leste in 2010, CI has been working with communities and governments toward sustainable models of natural resource management. In 2012, CI was a major contributor toward the RAP and has been instrumental in the NOAA and XLCSS research efforts in-country. Collaboration and local assistance from CI Timor-Leste was essential for the success of the fieldwork undertaken in Chapter 4.
The proposed work requires collaboration and datasets from NOAA’s Pacific Islands Fisheries Science Center, specifically the Coral Reef Ecosystem Program. USAID funded the NOAA Timor-Leste expeditions in partnership with CI Timor-Leste from 2012 to 2014. These are the most comprehensive marine surveying efforts within the country to date encompassing biological, physical, chemical, and climatological work. The NOAA cryptofauna ARMS data, benthic surveys, and subsurface temperature measurements are an integral component of the proposed questions. These data are accessible online:
https://www.coris.noaa.gov/activities/projects/timor-leste/.
The SI National Museum of Natural History partnered with NOAA in developing the ARMS methodology, which was used for the Census of Coral Reefs component for the ten year international Census of Marine Life initiative. The DNA barcoding of the brachyuran crabs from the Timor-Leste ARMS was completed at the SI Laboratories of Analytical Biology high- throughput facility in Washington, D.C., while the metabarcoding laboratory work and bioinformatics were completed by NOAA and SI collaborators. 30
The distribution, functional diversity, and compositional drivers of coral reef ecosystems along the north coast of Timor-Leste
Photo: Underwater Earth
31 Abstract
Coral reefs are structured by natural and anthropogenic processes. The north coast of Timor-Leste is subjected to a variety of physical, land-based, and anthropogenic influences, shaping the distribution of its natural features including coral reefs. Here, kilometer-scale surveys of the coral reefs along outer reef slopes (at 10 m) were used to estimate the potential roles of natural and human impacts on benthic composition. A distance-based redundancy analysis was conducted on the benthic composition with relative wave exposure, distance to rivers, and human population density as covariates. Live coral cover was tested using a linear mixed-effect model with these variables and the ratio of branching to massive corals standardized by coral cover as a proxy for habitat complexity. Wave exposure, distance to rivers, and human communities were significant drivers of overall benthic composition, but only accounted for 9.3% of the variability. Turf algal communities on substrate were dominant, with cover ranging from 26.2 (± 3.2% SE) to 64.8 (± 2.6%) per transect. Comparatively, hard coral cover ranged from 5.4 (± 0.6%) to 33.0 (± 0.9%). There was a significant positive correlation between coral cover, the interactive effect of wave exposure, and the ratio of branching to massive corals (r2 = 76.3%). Relative wave exposure had the largest effect when the ratio of branching to massive corals was also greater. Timor- Leste’s north coast is protected from wave exposure—being exposed to relatively few large storms and severe wave events—which is likely to promote the persistence of hard corals and favor more structurally-complex reefs (i.e., reefs with a greater ratio of branching to massive corals). Despite these expectations, these reefs were still largely dominated by massive corals as opposed to the more sensitive branching corals. Human population density and distance to river mouths were significant drivers of benthic composition, but not in the case of the total coral cover model. On a global scale, Timor-Leste’s population is low and the effects of localized anthropogenic impacts often do not manifest themselves until very high population levels have been reached. Assessing both conventional and kilometer- scale transects, the mean coral cover was comparable between methods—15.0% (± 0.4%) from the kilometer-scale transects, 15.3 (± 0.8%) measured by the 30 m NOAA transects in 2013, and 21.8 (± 4.6%) average from the 15 m NOAA transects collected in 2014. The conventional-scale transects did not capture the same degree of variability. The limitations across methods are important to consider whenever drawing comparisons; however, conventional transects may be more dependent on transect placement and, therefore, less likely to capture the full degree of heterogeneity of reefs. 32
Introduction
Coral reef ecosystems are shaped by biophysical factors (e.g., wave exposure, sea surface temperature) which often combine with human-related impacts (e.g., fishing, pollution, ocean warming, and acidification; Rogers and Laffoley, 2013). Understanding the different roles of these factors requires examination at a range of scales, especially in cases where variation in these drivers is typically larger between regions than neighboring sites within a region (Guidetti and Sala, 2007). Implementing novel techniques for collecting information over larger scales and decreasing the processing time associated with conventional image analysis methods, will assist in the identification of drivers of change across these important ecosystems. The identification of specific drivers that are applicable within different regions will help underpin evidence-based management of coral reefs in the future (Leslie and McLeod, 2007).
Biological baselines and environmental change Biological baselines are critically important in terms of understanding how natural drivers and human activities play roles at local and global scales. Yet, many areas across the globe have little or no baseline for their coastal biological resources due to limited financial resources, capacity, or opportunity. Timor-Leste, for example, has suffered from recent armed conflict limiting the extent to which its coral reefs have been studied and exploited (ADB, 2014). In one scenario, impacts over recent decades may have been less given that industrial food production (i.e., large-scale commercial, fishing, and agricultural activity) has been lower than other sites in Southeast Asia due to the conflict (Wever, 2008). On the other hand, integrated local threats to reefs such as watershed-based pollution, marine-based pollution, and overfishing continued, degrading resources despite the unrest (ADB, 2014; Alongi et al., 2012a; JICA, 2010; Wever, 2008). Although commercial fisheries, where landings are sold at markets, were developed during the Indonesian occupation, few reached an industrial scale during this period and were dismantled post-independence. However, overfishing of reef fish through small-scale subsistence means was possible within the limited extent of the continental shelf and associated coral reef and seabed habitats (ADB, 2004; Boggs et al., 2012; da Costa et al., 2002). This was supported by surveys revealing low abundances of prized large-bodied fishes such as snappers and groupers (Allen and Erdmann, 2013; Dutra and Taboada, 2006). With the destruction of most of the commercial fishing infrastructure during the post-referendum violence in 1999
33 (Cook, 2000), there was an even greater reliance on small-scale fishing by coastal communities (Barbosa and Booth, 2009).
Surveying reefs on a scale that can describe widespread, non-point source impacts with sufficient ecological resolution is challenging. The kilometer-scale monitoring of the coral reefs of Timor-Leste, therefore, provides a link between conventional and satellite remote sensing of coral reefs from low-orbiting satellites. Conventional methods, defined as diver- propelled surveys on the scale of tens to several hundreds of meters, can provide fine resolution such as species-level detail but is time-consuming and costly (Bryant et al., 2017; González-Rivero et al., 2020, 2016, 2014). Remote sensing has also increased understanding of coral reef ecology in defining large-scale patterns, but is ultimately limited by its precision that ranges from a few meters to multiple kilometers (Mumby et al., 2004). Increasing the spatial scale of coral reef science, incorporated with other large-scale methods on a kilometer-scale such as towed surveys, has the potential to improve our understanding of these ecosystems to an extent and resolution more applicable to the management of these systems at geographic scales (Bryant et al., 2017; González-Rivero et al., 2020, 2016, 2014). As such, a propelled underwater vehicle was used to expand data collection to kilometer-scale transects 1.5–2 km in length.
Potential environmental drivers of reef slope coral communities Environmental, biophysical parameters such as water temperature, wave exposure, sedimentation, and salinity affect the composition, morphology, and ecology of coral reefs (Stoddart, 1969). In recent decades, environmental parameters such as temperature and their relationship to climate change have been a critical area of study. Significantly, sea surface temperature (SST) has increased by 0.57˚C from 1880 to 2015 (Hughes et al., 2017a). This is of particular concern to coral reefs, as it has resulted in three pan-tropical bleaching events in the past three decades with significant coral mortality (Heron et al., 2016a; Hughes et al., 2017b, 2017a). As such, rising ocean temperatures and associated mass bleaching events from thermal stress are currently the greatest environmental threat to reefs. However, the interactive effects of multiple stressors such as water quality, ocean acidification, and fishing should not be ignored (Harborne et al., 2017; Hughes et al., 2017a). How these multiple stressors converge and affect coral reefs in Timor-Leste is relatively unknown. Here, a baseline of coral reef abundance and composition was established across the north coast of Timor-Leste.
34
While temperature, salinity, and sedimentation play important roles on coral physiology (Goreau et al., 2000; Hoegh-Guldberg, 1999; Loya et al., 2001; Sheridan et al., 2014), physical drivers such as wave energy directly affect the geomorphology and composition of coral reefs (Chollett and Mumby, 2012; Denny, 1994; Graus and Macintyre, 1989; Sheppard, 1982). Climate change is likely to modify global-scale patterns of atmospheric pressure and induce changes in global wave heights. This change is latitudinally-dependent with the equatorial region likely to experience a decrease in average wave height (Mori et al., 2010). Moreover, increasing intensity of tropical cyclones with climate change has been associated with greater damage to coral reefs (Cheal et al., 2017). However, Timor-Leste’s location at 8–9˚S latitude is not associated with the development of tropical cyclones (Pielke et al., 2019). Both potential environmental and physical drivers are discussed in more detail in the sections below.
2.2.2.1 Temperature
Water temperature is an important abiotic factor that shapes hard coral growth and the development of coral communities and reefs. Temperature influences growth rates, photosynthesis, and respiration of reef-building corals and their algal symbionts (Coles and Coles 1977; Jokiel and Coles 1977; Glynn 1996). Hard corals have an optimal temperature range which is related to local conditions. Deviating from these optimal conditions, in either direction can lead to reduced growth, loss of algal symbionts, disease and mortality among affected corals (Coles and Coles 1977; Jokiel and Coles 1977; Hoegh-Guldberg and Smith 1989; Saxby et al. 2003; Hoegh-Guldberg and Fine 2004). Different species have different tolerances and thermal ranges and tropical organisms, including corals, generally have a smaller range of thermal tolerance (Marshall and Baird, 2000). Regionally, corals are subjected to a range of temperature regimes and within sites, individual corals exhibit different thresholds for bleaching (Hughes et al., 2003). Notably though, for most of the recent El Niño-driven underwater heatwave, there was high coral mortality irrespective of varying bleaching sensitivities amongst coral taxa (Hughes et al., 2017b). Temperature is an important environmental factor to consider as global warming events are becoming one of the largest threats to corals threatening the framework-building capacity of coral reefs globally (Andersson and Gledhill, 2013; Hughes et al., 2018, 2003).
35 2.2.2.2 Wave exposure
Wave exposure is a significant driver of coral communities growing along the outer exposed reef slope habitats (Chollett and Mumby, 2012; Dollar, 1982; Done, 1982; Graus and Macintyre, 1989). Mixing of inshore and oceanic water is associated with the replenishment of food and nutrients, sediment removal, moderation of temperature, salinity, and dissolved oxygen, all of which can be beneficial to corals (Jokiel 1978). There is a range of optimal water movement for scleractinian, or hard corals, and different coral species occupy different niches within the range of tolerable physical stress (Storlazzi et al., 2005, 2002). Although there are considerable variability and plasticity within broad functional morphological groups, generally speaking, massive colonies can cope with greater wave action than more fragile forms such as foliose, plating, and branching colonies. These delicate morphologies tend to prosper in locations with less hydrodynamic stress such as at depth or embayments (Chappell, 1980; Sheppard, 1982; Storlazzi et al., 2005). There is variation within broad morphological categories; for example, robust branching corals, including some pocilloporids, thrive where waves break, while other fragile branching varieties have limited capacity to inhabit these turbulent environments (Storlazzi et al., 2002).
2.2.2.3 Riverine inputs
Terrestrial runoff from land that is being cleared of forests for development, logging, and agriculture is a growing problem globally. Runoff increases impacts on nearshore environments, including elevated nutrient inputs, turbidity (decreased light), and sedimentation (smothering) on coral reefs (Fabricius, 2005). Furthermore, the terrain of Timor-Leste is vulnerable to erosion when exposed to monsoonal rains (Alongi et al., 2012a; JICA, 2010). High-levels of sedimentation on reefs have been linked to decreased hard coral cover, and sublethal effects include altering coral reef community composition toward tolerant species, decreased coral recruitment, slower coral growth, and altered coral metabolism (Fabricius, 2005; Golbuu et al., 2011; McClanahan and Obura, 1997; Rogers, 1990). The riverine outputs in Timor-Leste are highly seasonal due to the Indo-Austral monsoon cycle where the northwest monsoon winds from December to March bring rain to most of the country.
The impacts of terrestrial runoff in Timor-Leste are growing aligned with the 24% loss of forest cover from 1972 to 1999 as a result of illegal logging, firewood harvesting, wildfires, and cattle grazing (Alongi et al., 2012a; JICA, 2010; Sandlund et al., 2001). The importance 36 of managing the negative downstream effects to nearshore environments of land-use practices such as deforestation has been long known. In areas with highly vulnerable forests, the impacts on reefs were predicted to be worse than those associated with climate change (Bartley et al., 2014; Maina et al., 2013; McCulloch et al., 2003; Mcmanus, 1988).
Structural complexity Hard corals are often referred to as ecosystems engineers providing structure and habitat for other organisms making structural complexity an important metric of reef condition (Alvarez-Filip et al., 2011, 2009; Darling et al., 2017; Graham and Nash, 2013; Kovalenko et al., 2012; Wild et al., 2011). It is hypothesized that spatial and temporal heterogeneity in structure—providing a diversity of microhabitat types—is a major component in driving the positive relationship between structural complexity and species diversity (Crowder and Cooper, 1982; Kolasa et al., 2012; MacArthur and MacArthur, 1961). Additionally, greater structural diversity has been associated with supporting greater biodiversity (Kovalenko et al., 2012; Price et al., 2011). Consequently, assessing and tracking structural complexity on reefs through time in the same sites and regions can offer insights into overall ecosystem health. There are a variety of methods to estimate habitat complexity on reefs, from the chain and link method (Risk, 1972) to light detection and ranging (LiDAR) technology (Wedding et al., 2008). Given the importance of branching corals in structural complexity (Graham and Nash, 2013), the ratio of branching to massive corals as a proxy was developed where an increase in the ratio of branching to massive corals would be a shift to higher structural complexity.
Localized anthropogenic impacts Anthropogenic factors can also influence the composition and distribution of coral reef habitats. These human-induced impacts can be direct, such as those associated with fishing activities and coastal construction, or indirect, like eutrophication (Mora et al. 2011). The population of Timor-Leste is not large – only 1,183,643 total as estimated in the 2015 census. The majority of residents (244,584, 23.4%) live within Dili, the capital municipality (NSD Timor-Leste, 2015). Rural towns are divided between mountainous and coastal districts and rely on subsistence agriculture and fishing (Alongi et al., 2012a; Alonso Población, 2013; Chandra et al., 2016; JICA, 2010; RDTL, 2011; Sandlund et al., 2001). Even with the relatively low population, the associated human impacts have the potential for
37 significant effects given the current level of infrastructure and proposed development over the next decade (RDTL, 2011).
Subsistence-level livelihoods can impact marine ecosystems with fishing, the most damaging activity. The fishing pressure within Timor-Leste is mostly small-scale, artisanal fishers utilizing low-technology, small boat inshore fishing practices and subsistence reef gleaning (Alonso Población, 2013; Barbosa and Booth, 2009; Boggs et al., 2012; Erdmann and Mohan, 2013; Kingsbury et al., 2011). During the Indonesian occupation, however, uncontrolled fishing using improvised explosive devices (blast fishing) was prolific (McWilliam, 2002). Fishing using explosives and the removal of large-bodied fish can lead to top-down trophic cascades that may result in herbivore reduction (Guillemot et al., 2014; Jackson, 2001). Notably, illegal foreign fishing practices are still a threat and damage from blast fishing was apparent during the RAP surveys in 2012 (ADB, 2014; Erdmann and Mohan, 2013).
Subsistence agriculture and livestock practices are generally small-scale, predominantly located in the mountainous districts, and primarily without the use of chemical fertilizers. The Timor-Leste Strategic Development Plan 2011-2030 outlines a substantial increase in the country’s agricultural output—which is needed to meet food security goals—as a significant land-use impact that may develop in the future (RDTL, 2011). As such, the focus of anthropogenic impacts will be at the suco, or subdistrict level, of population density and potential effects on coral reef benthic composition. The limited area of reefs on the northern coastal reefs makes these ecosystems more vulnerable to coastal impacts (Boggs et al., 2012).
Coral reefs of Timor-Leste The north coast of Timor-Leste is characterized by a very narrow coastal region that extends from mountainous regions to steep sloping nearshore coral reefs that cover approximately 2,000 ha. The reef flat encompasses about 485 ha (Boggs et al., 2012). Previous assessments of hard coral communities are rare, although those undertaken report coral cover of 18.4–45.7% in northeast Ataúro Island (Ayling et al., 2009; Wong and Chou, 2004). The RAP semi-quantitative biodiversity surveys in 2012 estimated there were 400 species of hard coral which is on par with other CT locations. Hard coral cover was estimated at 28% ranging from 5–70% over 20 timed swims, with four distinct coral communities described: digitate Acropora-branching, Pectiniid–laminar or thin plates, Fungiid–free-living, 38
Symphyllia/Diploastrea–massive (Turak and Devantier, 2013). The aim of these surveys however was to report species for biodiversity counts, with the coral cover estimated at the end of the dive. The reliability of these measures for estimating coral abundance is therefore questionable.
The Coral Reef Ecosystem Program (CREP) in NOAA’s Pacific Islands Fisheries Science Center also collected benthic images along 30 m transects in conjunction with fish surveys at 150 sites (139 hard bottomed) in Timor-Leste in 2013. Coral cover was averaged by district, comparable to regions in this analysis, with 16 sites in Oecusse, 22 on Ataúro Island, 14 in Dili, 13 in Manatuto, 13 in Baucau, and 19 in Lautem. The district Lautem was separated into Com and Jaco Island for comparison purposes with 13 and 6, 30 m phototransects respectively (Figure 2-1). These NOAA phototransects were collected in June 2013 and the data showed hard coral cover with an average of 15.3 ± 0.8% (standard error [SE]) and a range from 0.0–42.3% (PIFSC, 2017). NOAA returned in 2014 and collected 30 m phototransects at eight climate stations encompassing seven districts: Oecusse, Bobonaro, Liquica, Dili (including Ataúro Island), Manatuto, Baucau, and Lautem (including Com and Jaco Island; Figure 1-2; Figure 2-1). Hard coral cover ranged from 6.4– 39.7% between the climate stations with an overall average of 21.8 ± 4.6%.
39
Figure 2-1 Locations of National Oceanic and Atmospheric Administration (NOAA) 2013, NOAA 2014, and XL Catlin Seaview Survey benthic phototransects collected in Timor-Leste. All data publicly available–see PIFSC 2017 and Rodriquez-Ramirez et al. (2020). (Bottom Inset) Close-up of Ataúro Island with the three datasets.
The variability of coral reefs in Timor-Leste is poorly understood and described. Ecological data on coral reef baselines at large scales has recently become available via rapid, high definition photo transects at kilometer-scale, combined with machine-learning algorithms enabling fast and accurate analysis of large datasets (Bryant et al., 2017; González-Rivero et al., 2014, 2016, 2020; Rodriquez-Ramirez et al., 2020; I. D. Williams et al., 2019). This technology was used to explore the large-scale patterns that are associated with the coral reefs along a large extent of the north coast of Timor-Leste. This is especially useful in areas 40 such as the CT where coral reefs are diverse and abundant, and large-scale monitoring is essential for the management of these vast ecosystems.
Aims and objectives
The current study aimed to explore the distribution and abundance of coral reef communities lining the outer reef slope at 10 m along the northern Timor-Leste coastline. Novel benthic data collection and analysis techniques were used to explore how potential drivers of benthic structure vary at large scales. Based on previous surveys, reefs along the north coast were expected to be dominated by massive coral morphologies, have less coral with increasing wave exposure and human population density, and have a positive relationship with structural complexity. Three key questions concerning the coral reefs of Timor-Leste are addressed here:
1) How do the benthic composition and functional diversity of reef-building coral communities vary along the north coast of Timor-Leste?
2) How does the abundance and structure of coral reefs at 10 m along the north coast in Timor-Leste vary with the key environmental parameters of wave exposure and the distance to river mouths?
3) What human impacts explain the variability of benthic composition and structure of coral reefs in Timor-Leste?
Materials and methods
Study site The northern coast of Timor-Leste stretches approximately 250 km and is characterized by a rocky and steep shoreline with a very narrow continental shelf subsequently housing narrow fringing reefs (Boggs et al., 2012). Islands to the north are protected from waves with clearer deeper waters dropping off sharply to a three km marine trench about 20 km offshore (Alongi et al., 2012a; Figure 2-3). Timor-Leste is infrequently affected by major tropical storms, averaging less than once per season, or eleven since 1969 (BOM, 2019; DNMG et al., 2015). All but one storm track progressed to the south of the country. Storms pose even less of a threat to north coast reefs (BOM, 2019).
41
Figure 2-2 Timor-Leste is a small island nation. (Main map) The 26 geolocated kilometer-scale phototransects that were collected by the XL Catlin Seaview Survey in 2014. Color of transect markers indicate population density of adjacent suco, or subdistrict, from the 2015 census. Between two to five transects were collected in every district. (Bottom Inset) Close-up of Jaco Island transects with red dots indicating start, middle, and end GPS points of phototransects that were used for wave exposure points. Blue dots are geolocated subtransects with overlap between the bottom two phototransects.
There are over a hundred rivers in the country, few of which are perennial with water flowing year-round (Sandlund et al., 2001). The largest river, the Lóis, flows to the north coast. The Laclo river, near the district capital of Manatuto, houses one of the largest watersheds in- country, spanning five districts and over 1,386 km2 (Alongi et al., 2012a; JICA, 2010). Discharge from Laclo varies from 9.1 m3/s in September to 69.8 m3/s in March based on
42 records between 1952–1974 (JICA, 2010). In comparison, the Comoro catchment area on the edge of Dili has a mean monthly streamflow of 2.99 m3/s ranging from 0.5 m3/s July to November and 12.3 m3/s in March.
Environmental parameters 2.4.2.1 Temperature
Satellite-derived sea surface temperature (SST) is available for Timor-Leste although it has a spatial resolution typically on the scale of kilometers (Mumby et al., 2004; Strong et al., 2011). While higher resolution satellites are now available, such as Sentinel-3, the very steep nature of Timorese reefs puts the area of interest very close to the coast where ocean products are the least accurate or have no data. Here, in situ temperature data from eleven SBE 39 Temperature Recorder (Sea-Bird Electronics, Inc.) deployed by the NOAA from September 2012–September 2014 logging every hour was assessed. Eight NOAA climate stations were set up on the north coast monitoring a variety of physical and biological data including the deployment of ARMS over the same period (Figure 2-3; PIFSC, 2017).
One logger was deployed in Com at 4.6 m depth while Beacou, South Ataúro, and Baucau each had a logger deployed between 12–15 m. Two loggers each were deployed at Dili, North Ataúro, and Manatuto between 5–14 m depth (Figure 2-3). No temperature loggers were deployed in Oecusse and no kilometer-scale transects were collected in Beacou. As coral mortality following coral bleaching from elevated sea temperatures has become one of the most important threats to reefs globally (Hoegh-Guldberg, 1999; Hughes et al., 2018) the seven-day rolling average of daily (24 h) maximum temperature was calculated by district (zoo R package; Zeileis et al., 2019). To assess whether temperatures were structuring Timorese reefs at a longer timescale, the maximum monthly mean (MMM) was calculated, using all logger data from 4–10 m depth (including Beacou) to compare with NOAA’s satellite-derived Coral Reef Watch MMM for the Timor-Leste Regional Virtual Station (CRWTL). The CRW Regional Virtual Station MMM was the maximum value of the twelve average Monthly Mean climatology representing the average SST for each month calculated over the period of 1985-2012 (Skirving et al., 2020).
43
Figure 2-3 Locations of the NOAA temperature loggers deployed across the north coast of Timor-Leste. Eleven loggers total were deployed at each of eight sites from 4–15 m depth from October 2012 through October 2014 logging every hour. Depths of temperature loggers are as follows: Dili–6.1 m and 14 m, South Ataúro–13.6 m, North Ataúro–6.1 and 13.6 m, Manatuto–4.9 m and 14.6 m, Baucau–12.8 m, Com–4.6 m, Jaco Island–14.6 m. The logger at Beacou was only incorporated into the maximum monthly mean across the two years for the whole north coast.
2.4.2.2 Wave exposure
Cartographically-based wave exposure models have been successfully used to predict the presence of dominant coral community types in the Caribbean (Chollett and Mumby, 2012). A GIS-based relative wave exposure model (GREMO), was used to calculate fetch length extending radially every 22.5˚ at given points and incorporates wind climate data (Figure 2-4; Pepper 2009). A directional discount was applied based on average wind speed and percentage of windblown per radial direction. The wind rose data for Dili was acquired from meteoblue climate data based on 30 years of hourly weather model simulations at 30 km resolution (meteoblue). The output was then scaled for a unitless relative wave exposure value. Relative wave exposure was calculated at the start, middle, and end GPS points of every transect. Each subtransect was assigned a wave exposure value based on the closest calculated point from GREMO using the R RANN package (Arya et al., 2019).
44
Figure 2-4 Calculated radial fetch lines every 22.5˚ in a GIS-based relative wave exposure model (GREMO) from the start, mid, and endpoints of each transect collected along the north coast of Timor-Leste by the XL Catlin Seaview Survey in 2014. Directional discount was applied based on the average wind speed and percentage of windblown per radial direction.
2.4.2.3 Riverine inputs
Coral cover has been previously correlated to distance to river mouths (distance to river; Golbuu et al., 2011). Distance in km to nearest river mouth (a proxy for riverine inputs and henceforth distance to river) was consequently calculated for each subtransect in ArcGIS.
2.4.2.4 Human population
Each subtransect was assigned a human population density value from the nearest suco from the 2015 census in ArcGIS (Figure 2-1; NSD Timor-Leste, 2015). The lowest population density adjacent to any subtransects was at Jaco Island, with 13.6 persons per km2. The suco housing the capital, Dili had the highest population density with 449.8 persons per km2 (Table 2-3).
45 Table 2-1 Population density from of sucos, or subdistricts, adjacent to the benthic phototransects taken from the Timor-Leste 2015 census.
Municipality/ Suco Number of Suco Total Area in Population District Subtransects Population km2 Density per km2 Oecusse Nipane 188 1,124 52.86 21.3 Oecusse Costa 69 14,350 78.04 183.9 N and S Ataúro Beloi 120 1,678 54.60 30.7 Island North Ataúro Biqueli 42 2,076 29.67 70.0 Island South Ataúro Macadade 46 1,632 38.36 42.5 Island Dili Meti Aut 88 2,074 4.61 449.8 Manatuto Uma 103 3,433 98.30 34.9 Caduak Manatuto Sau 11 3,890 13.77 282.6 Baucau Bahu 10 8,156 28.78 283.4 Baucau Buruma 27 3,245 18.45 175.9 Baucau Caibada 103 1,984 10.10 196.5 Com Com 53 2,353 59.89 39.3 Com Mehara 35 2,262 107.82 21.0 Jaco Island Tutuala 151 1,252 92.10 13.6
Benthic composition 2.4.3.1 Data collection
To determine the benthic composition at kilometer-scales, we used methods developed by the XL Catlin Seaview Survey (XLCSS), a project using a standardized protocol to establish global, kilometer-scale baselines of coral reefs. The data are freely available on an online repository, the XL Catlin Global Reef Record and the annotated photoquadrats are open source (www.globalreefrecord.org, González-Rivero et al., 2014; Rodriquez-Ramirez et al., 2020).
Benthic community structure was measured in 26 phototransects, each averaging ~ 2 km in length. High-definition photos were collected along the north coast of Timor-Leste in July of 2014 (Figure 2-1). The approach uses a specialized camera system, mounted on a diver propulsion vehicle, consisting of three synchronized Canon 5D Mark-II cameras with fish- eye lenses oriented 120˚ apart. This allowed for the capture of 360˚ underwater imagery for outreach purposes on the GoogleOcean platform, an additional objective of the project. The
46 downward-facing photo was used for subsequent benthic analysis. Pictures were taken every 3 s throughout a 45 min dive with a GPS unit towed on the surface at a set distance behind the diver. All photos were georeferenced with GPS coordinates based on time synchronization of the GPS unit and camera. The transects were performed at 10 m depth (± 2 m) for shallow water reef surveys with the downward-facing camera perpendicular to the substrate. Altitude, or height off of the benthos, was recorded by the altimeter and the corresponding sensor facing the benthos, with the desired altitude range between 1–2 m above the reef (González-Rivero et al., 2014).
An average of 798 georeferenced benthic photos was collected per phototransect for a total dataset of 20,751 images. The benthic, wide-angle photos were flattened, color-corrected, and cropped into one to six 1 m2 photoquadrats representing 1–6 m2 of the reef, depending on the altitude of the camera. The number of quadrats per flattened image was determined by the photo area calculated using altitude off the reef (González-Rivero et al., 2016, 2014). Given the use of a wide-angle lens and varying topographic complexity of reefs, a 0.5 m change in altitude off the reef could result in a significant increase in the photo area. However, most benthic photos (> 99% or 20,577 out of 20,751 images) produced one or two photoquadrats.
2.4.3.2 Data analysis
Benthic composition and structure were derived from the photoquadrats following the automated approach using machine learning as described in González-Rivero et al. (2020). Initially, expert annotators conducted standard point count image analysis on photoquadrats to build a training library of color and texture descriptors coinciding with the label set of five major functional groups (hard coral, macroalgae, other invertebrates, soft substrate, hard substrate, and other; Table 2-1) using 551 images. For the training, 100 randomized point counts per photoquadrat were conducted in CoralNet, the online repository for image analysis (Beijbom et al., 2012; Kohler and Gill, 2006). The five major functional groups (Table 2-1) were subset into ecologically relevant functional groups, then further distinguished into broad taxonomic-morphological labels. For example, within the branching hard coral morphological group there were four branching Acropora specific labels (bottlebrush, arborescent, digitate, and tabulate), branching Pocillopora spp., branching Stylophora spp., branching Porites spp., branching Seriotopora spp., and branching other (González-Rivero et al., 2016). This allowed for the hard coral major functional group to be
47 specified into broad morphological groups: branching, massive, plating, and free-living. The branching morphological group included tabulate coral forms and non-scleractinian corals, namely Millepora spp., and the massive morphological group included encrusting morphologies. See Table S2-1 for the full labelset. The ratio of branching to massive corals was calculated for each subtransect as a proxy for structural complexity by dividing the percent cover of branching corals by massive corals and standardizing by the total coral cover of the subtransect. The percent of each morphology was scaled with the following formula before calculation to avoid undefined ratios from zero values: y’ = [y(N-1) + ½] / N, where N is the number of samples (Smithson and Verkuilen, 2006).
Table 2-2 The five major benthic functional groups for benthic composition used in the automated image analysis of phototransects collected in Timor-Leste.
BENTHIC FUNCTIONAL GROUPS Functional Group Description Hard corals Scleractinian corals Algae Turn on hard substrate, cyanobacteria, calcareous algae, macroalgae Other Soft coral, sponges, echinoderms, other sessile and Invertebrates mobile invertebrates, etc. (non-coral) Hard substrates Limestone and volcanic substrate Soft substrates Sand, rubble, sediment Other Trash, transect tape, fish, unclear, etc.
The CAFFE deep-learning neural network framework for image analysis (Jia et al., 2014) was used to automate the analysis of the full Timor-Leste dataset (20,751 x 1 m2 quadrats). CAFFE calculates the classification probability of a point using the expertly trained image set descriptors (Beijbom et al., 2015, 2012; González-Rivero et al., 2016; Jia et al., 2014). Accuracy was tested by comparing machine and manual annotations of the same independent image dataset with 50 points per photoquadrat in CoralNet (González-Rivero et al., 2016, 2014). Human inter-annotator accuracy has been quantified at ~ 70% which is the target for machine accuracy (Beijbom et al. 2012). Once accuracy was checked, CAFFE then automatically annotated the remaining > 97% of photoquadrats with 50 randomized points per photoquadrat.
Phototransects were divided into subtransects (Figure 2-1; Table 2-2) to limit the impact of small-scale variability (Levin, 1992; Wiens, 1989). Photoquadrats were aggregated using 48 hierarchical clustering based on distance, with 100 m maximum distance, using the GPS coordinates of the images. This aimed to prevent the clustering of photoquadrats from different areas of the reef especially on non-linear transects (Vercelloni et al., 2020b). The distance-based groupings resulted in varying numbers of photos ranging from one to 54 photos per subtransect. Subtransects with greater than three photos, allowing for calculation of the mean of coral cover per subtransect were used for further analysis.
Table 2-3 Number of kilometer-scale transects per district collected by the XL Catlin Seaview Survey in Timor-Leste in 2014. Each transect was hierarchically grouped into subtransects, with a maximum of 100 m length, to create replication within each kilometer-scale transect.
District # of Phototransects # of Subtransects Oecusse 5 257 South Ataúro 3 109 North Ataúro 3 132 Dili 2 88 Manatuto 4 114 Baucau 3 140 Com 3 88 Jaco Island 3 151
Statistical analysis All analyses were conducted in R version 3.6.3 (The R Core Team, 2020) and PRIMER7 (Clarke and Gorley, 2015). The aov function in the R base package was used for ANOVAs unless otherwise stated.
2.4.4.1 Environmental parameters
To test for differences between sites along the north coast, ANOVAs were conducted on environmental parameters that were visually inspected for normality and heteroscedasticity (hist, qqplot, qqnorm in R). The weekly rolling average of daily maximum temperature from the 11 deployed temperature loggers was tested, with the district as a factor with seven levels (South Ataúro, North Ataúro, Dili, Manatuto, Baucau, Com, and Jaco Island) as there was no NOAA climate station with temperature logger data at Oecusse. To test whether wave exposure and river distance varied between transects, two, one-way ANOVAs were conducted on the three relative wave exposure points per transect (square root transformed) and distance to river, with transect as the factor. Tukey’s Honestly Significant Differences test (TukeyHSD; TukeyHSD in R) was performed to see pairwise district differences for
49 temperature between districts and transect differences for wave exposure and distance to river.
2.4.4.2 Benthic composition
To test for differences in benthic composition between transects, a permutational analysis of variance (PERMANOVA in PRIMER7; Anderson et al., 2008; Anderson, 2001) with 9,999 permutations was used to test for the effect of transect as a random factor on the Bray- Curtis resemblance matrix of fourth-root transformed benthic functional groups: hard coral broken up into the four major morphological groups—massive, branching, thin/foliose/plating or TFP, and free-living—in addition to macroalgae, other invertebrates, soft substrate, turf on hard substrate, and other. A pairwise PERMANVOA and homogeneity of dispersion test assessing within-transect variance (PERMDISP in PRIMER7) were also performed. Non-metric multidimensional scaling (nMDS in PRIMER7) plots were used to visualize benthic composition on the transect centroid distance matrix and dispersion plotting all subtransects. A similarity profile test (SIMPROF in PRIMER7; Clarke and Gorley, 2015) was also conducted on the transect centroid distance matrix, and groups were overlaid on the corresponding nMDS.
Subsequent ANOVAs were conducted with individual benthic parameters, total hard coral, and coral morphologies (e.g., massive, branching, TFP, and free-living), algae, other invertebrates, turf on hard substrate, and soft substrate. All parameters were visually checked for normality (hist, qqplot, qqnorm in R) and transformed (all square root transformed, soft substrate was log-transformed) except for turf on hard substrate. TukeyHSD post hoc test was conducted following each ANOVA for pair-wise comparisons (TukeyHSD in R).
To explore the effect of the biophysical variables on benthic composition, a distance-based redundancy analysis (dbRDA, dbrda in the vegan R package; McArdle and Anderson, 2001; Oksanen et al., 2019) was performed on the Bray-Curtis resemblance matrix of the fourth root transformed major benthic components. Significance was tested with a Monte Carlo permutation test running 999 permutations (anova in vegan) and visualized with an ordination plot.
50
2.4.4.3 Drivers of hard coral and coral morphologies
To elucidate potential drivers of hard coral cover, a linear mixed-effect model in R package nlme (Pinheiro et al., 2020) was used. Covariates included relative wave exposure, distance from rivers, the ratio of branching to massive corals, and human population density, as fixed effects. Interactions were tested for the first three covariates. All variables were continuous, and parameters were visually checked for normality and heteroscedasticity (hist, qqplot, and qqnorm in R). Relative wave exposure and human population density were log-transformed. The ratio of branching to massive corals was scaled with the following equation to avoid log transforming zeros: y’ = [y(N-1) + ½] / N, where N is the number of samples (Smithson and Verkuilen, 2006). The variance inflation factors were calculated for all predictor variables (environmental parameters, population density, and the ratio of branching to massive corals) to check for multicollinearity (car R package; Fox and Weisberg, 2019; Zuur et al., 2009).
Predictor variables were also mean-centered to facilitate model convergence (Morrongiello and Thresher 2015) and transect was used as the random effect. Total hard coral cover was modeled with all covariates fitted by restricted maximum likelihood estimation (Pinheiro and Bates 2000). Models were selected using the Akaike Information Criterion (AIC; Akaike, 1973) and Bayesian Information Criterion (BIC; Burnham and Anderson, 2004). Model residuals were visually inspected to confirm goodness of fit. Partial effects plots were created using the ggplot2 R package (Hadley Wickham, 2016). Influential points in the model were calculated using Cook’s distance (car R package) and the final model was run again removing the points where the Cook’s distance was four times greater than the mean.
Results
The kilometer-scale transects showed a high degree of heterogeneity of north coast Timorese reefs. All environmental parameters and benthic categories varied significantly by transect and significantly influenced benthic composition (Figure 2-5, Figure 2-6, Table 2-4, Table 2-5). Turf algae on hard substrate made up the largest proportion of benthic composition overall ranging from 26.2 (± 3.2%; mean ± SE) at Oecusse 4 to 64.3 (± 2.5%) at North Ataúro 1 (Figure 2-5). There was an average of 15.0% (± 0.4) live coral cover across all phototransects with the lowest of 5.4 (± 0.6%) cover at Baucau 3 and highest cover of 33.0 (± 0.9%) at Jaco Island 2. Coral cover was the most influenced by environmental parameters with a significant, positive interaction between wave exposure and structural
51 complexity (i.e., the ratio of branching to massive corals). The north coast is comparatively protected and generally not affected by large swells and storms to the point where greater wave exposure at the extremities of the coast at Oecusse and Jaco Island promoted more coral cover. Structural complexity was highest at the transects in Ataúro and Jaco Islands, where coral cover was high with a large proportion of branching corals. Distance to river and human population density were not significant in the coral cover model, but overall watershed health in-country is poor from decades of logging and subsistence-level impacts such as fishing are a constant threat to reefs. More refined metrics may be necessary to capture these effects. The complexities of these relationships, in addition to the larger oceanographic context of the region, are important areas of future research.
Variability of environmental parameters along the Timorese coast The range in averaged weekly maximum temperature for dates preceding the survey was small, 28.7–29.01˚C, but statistically different between districts (ANOVA F(6,4891) = 6.795, p < 0.0001). Com (29.0 ± 0.0˚C; mean ± SE) and Dili (28.9 ± 0.0˚C) had the highest average weekly maximum temperature (p < 0.05; Table 2-4). Baucau (28.7 ± 0.1˚C) had the lowest average weekly maximum temperature and was not significantly different from South Ataúro (28.8 ± 0.0˚C), Manatuto (28.8 ± 0.0˚C), and Jaco Island (28.8 ±0.0˚C). Com also had the largest range between weekly averages at 5.0˚C (26.6–31.6˚C) and Manatuto had the lowest range of 3.9˚C (26.8–30.7˚C). The difference between the mean and range of weekly maximum temperatures was only 0.3 ˚C and 1.0˚C respectively between the seven districts. As there was no temperature data available from Oecusse, which encompassed five out of the 26 transects, temperature was excluded from subsequent analysis as a potential driver of benthic composition and live coral cover.
The CRWTL MMM is 29.5˚C (NOAA Coral Reef Watch, 2018). The MMM calculated from the two years of in situ temperature logger data of all deployed loggers from 4–15 m was 29.9˚C (± 0.01), which is approximately half a degree warmer than the MMM. Accumulation of bleaching stress degree heating weeks begins at MMM + 1˚C (Strong et al., 2011). The monthly means from in situ temperature data did not reach two years before the survey.
52
Table 2-4 Temperature average, standard error, maximum, minimum, and range of ten temperature loggers deployed from 5–10 m depth at eight NOAA climate stations from October 2012 through October 2014.
Climate Mean of Std Maximum Minimum Range Station 7-day Error averages South Ataúro 28.80 0.04 31.14 26.57 4.57 North Ataúro 28.88 0.04 31.43 26.69 4.74 Dili 28.94 0.04 30.91 26.47 4.43 Manatuto 28.75 0.04 30.66 26.78 3.88 Baucau 28.69 0.04 30.91 26.47 4.43 Com 29.02 0.05 31.61 26.57 5.04 Jaco Island 28.79 0.05 31.01 26.35 4.65
Wave exposure was statistically significant between transects (ANOVA F(25,52) = 10.150, p < 0.0001; Figure 2-5). Oecusse 5 (11.8 ± 0.3) had the highest relative wave exposure, significantly higher than all but four transects: Oecusse 3 (11.5 ± 0.1), Oecusse 4 (11.5 ± 0.0), Jaco Island 1 (7.4 ± 5.4), and Jaco Island 2 (7.1 ± 5.5; Figure 2-6; p < 0.05). Only reefs in the eastern and westernmost areas of the country in Jaco Island and Oecusse respectively were exposed to fully developed seas (Figure 2-4).
53 Figure 2-5 Relative wave exposure calculated in a GIS-based relative wave exposure model (GREMO) in ArcGIS at the start, middle, and endpoints of each transect averaged and plotted per transect. The x-axis is transects grouped by district ordered from west to east on from left to right. Transects in the same district are the same color. Error bars represent standard error.
With the numerous rivers in Timor-Leste, the distance of a transect to a river was always less than 20 km. Distance to the nearest river was significantly different by transect (one- way ANOVA F(25,1053) = 9026, p < 0.0001; Figure 2-7) where Com 1 (20.0 ± 0.1 km) was significantly further away from rivers than all other transects. Oecusse 4 (0.45 ± 0.05 km) was significantly closer to all other rivers than all other transects except South Ataúro 1 (0.5 ± 0.1 km; p < 0.05; Figure 2-7).
54 Figure 2-6 Distance [km] of each subtransect across the north coast of Timor-Lest to nearest river mouth calculated in ArcGIS averaged by district. The x-axis is transect grouped by district running from west to east on the north coast of Timor-Leste from left to right. Transects in the same district are the same color and error bars represent standard error.
Kilometer-scale benthic composition across the north coast of Timor-Leste Benthic composition varied significantly between transects (PERMANOVA pseudo- F(25,1078) = 20.426, p(perm) < 0.001) and all transects were significantly different (p(perm) < 0.05) except for the following: Oecusse 2–Dili 1, Oecusse 1–Oecusse 4, Manatuto 1– Manatuto 2, Com 3–Manatuto 1, and Baucau 1–Oecusse 5 (Figure 2-7). An overlay of SIMPROF groups shows six distinct groups and four transects (Com 1, Baucau 3, Oecusse 1, and Oecusse 4) which were distinct from all other transects (Figure 2-7). Although transects from the same districts did group together (North Ataúro 2 and 3, and Manatuto 2 and 4) and some transects within the same district were not significantly different (Oecusse and Manatuto), geography does not appear to be the sole driver of benthic composition similarity. Within transect variability, or dispersion, there was also significant differencs between transects (PERMDISP F(25,1053) = 53.039, p(perm) < 0.001) which could be a consequence of the distance covered by kilometer-scale of the transects. Oecusse 1 (19.0 ± 1.2) and Oecusse 4 (32.2 ± 1.6) were significantly different from each other and all other 55 transects as seen by the large spread of the subtransects within each transect (p(perm) < 0.05, Figure 2-8). These two transects had a large proportion of soft substrate and TFP corals and Oecusse’s separation from the contiguous north coast could indicate that other large-scale features such as geology influence the reefs in the district.
Figure 2-7 A nMDS plot of the Bray-Curtis resemblance matrix of fourth root transformed coral reef benthic composition from kilometer-scale phototransects in Timor-Leste–2D stress = 0.07. Bubbles represent benthic composition at centroids (the center of all subtransects calculated from the resemblance matrix) of each transect to aid visualization. Gray dashed lines represent groupings from similarity profile testing (SIMPROF). Letters and numbers on bubbles represent the region and transect number. For example, S1 is South Ataúro transect 1. Letters are as follows: O–Oecusse, SA–South Ataúro, NA–North Ataúro, D–Dili, M–Manatuto, B–Baucau, C–Com, J–Jaco Island. The pie chart represents benthic composition averaged by transect. TFP–thin/foliose/plating coral morphologies
56 Figure 2-8 A nMDS plot of the Bray-Curtis distance matrix of fourth root transformed coral reef benthic composition from kilometer-scale phototransects in Timor-Leste– 2D stress = 0.07. All subtransects are plotted to visualize dispersion or within transect variance. Letters represent the region as follows: B–Baucau, C–Com, D–Dili, J–Jaco Island, M–Manatuto, NA–North Ataúro, SA–South Ataúro, O–Oecusse. Shape and label numbers represent transect number.
Individual benthic components were also significantly different by transect (Table 2-5). Lone transects without SIMPROF groups seem to have a low or high cover of a benthic component. For example, transects Oecusse 1 and Oecusse 4 were the only transects that contained subtransects with no hard coral cover, corresponding to subtransects three and 15 respectively. This could have also contributed to the high dispersion of these transects (Figure 2-8). Baucau 3, however, had the lowest coral cover (5.4 ± 0.6%) which was statistically similar to 12 other transects including Oecusse 1 (7.1 ± 1.0%) and Oecusse 4 (12.0 ± 1.6%; p < 0.05). Transect Com 1 had the highest proportion of macroalgae which differentiates it from the remaining transects (Figure 2-7). High macroalgal cover often suggests potential nutrient pollution or depauperate herbivore communities depending on the particular species of algae present.
57 Table 2-5 Significant ANOVA results for testing differences between transects collected in Timor-Leste in 2014 on the following benthic parameters. Massive corals included encrusting morphologies and the branching group included tabulate morphologies. All but turf on substrate benthic categories were square root transformed and soft substrate was log-transformed, scaled from 0. df – degrees of freedom.
Benthic parameter df Residuals F-value p-value Interaction Total coral cover 25 1053 32.29 <0.0001 Transect Massive coral 25 1053 46.23 <0.0001 Transect Branching coral 25 1053 34.89 <0.0001 Transect TFP coral 25 1053 19.72 <0.0001 Transect Free-living coral 25 1053 22.74 <0.0001 Transect Other Invertebrates 25 1053 21.62 <0.0001 Transect Algae 25 1053 19.25 <0.0001 Transect Turf on Substrate 25 1053 21.17 <0.0001 Transect Soft Substrate 25 1053 31.81 <0.0001 Transect
Coral cover was highest at the extremities of the country, the western and easternmost districts (Oecusse and Jaco Island), and Ataúro Island. Jaco Island 2 (33.0 ± 2.5%) had the highest coral cover, significantly greater than all transects except Oecusse 5 (26.3 ± 1.7%), South Ataúro 1 (11.5 ± 0.8%) and 3 (19.2 ± 1.5%), and Manatuto 3 (16.2 ± 1.2%) and 4 (21.9 ±0.9%; p < 0.05; Figure 2-9). These regions are potentially more conducive to developing fringing reefs. Jaco and Ataúro Islands were also more exposed to open seas while the middle of the northern coast, with few rivers, may be more favorable to the development of hard corals. The region between Dili and Com (Dili, Baucau, and Manatuto) was also comparatively sheltered (Figure 2-5) and encompasses the most densely populated districts on the north coast (Table 2-3). These factors could have contributed to the comparatively low coral cover (Figure 2-9). Massive corals were the most abundant coral morphology with the highest percentage at South Ataúro 2 (27.3 ± 1.4%), significantly higher than all other transects (p < 0.05). Branching corals were highest at Jaco Island 2 (19.2 ± 1.8%) which was significantly higher than all but two transects (Com 1–12.4% ± 1.4, and North Ataúro 3–11.7% ± 0.7; p < 0.05). The cover of TFP and free-living morphological groups was much lower. The maximum TFP cover was found at Oecusse 5 (3.5 ± 0.4%) and significantly more than all but four transects. Free-living coral cover was greatest at North Ataúro 2 (2.3 ± 0.4%) and significantly higher than all but Com 1 (1.1 ± 0.1; Figure 2-9).
58
Figure 2-9 Hard coral morphological groups (branching, massive, other or free-living, and thin/foliose/plating) from image analysis of 2014 XL Catlin Seaview Survey photos averaged by transect. Error bars represent standard error. Regions on the x-axis go from west to east across the north coast of Timor-Leste. The number of transects per district varies between two to five.
Overall, turf on hard substrate was the dominant benthic category across all transects, averaging 45.5% (± 0.6)–three times higher than the average coral cover of 15.0% (± 0.4). Oecusse 4 and Com 1 had the transect minimum and maximum cover of turf on hard substrate, respectively (26.3% ± 3.2 and 64.8% ± 2.6). Soft substrate was the second most abundant benthic group averaging 17.8% (± 0.7). Oecusse 4 also had the highest composition of soft substrate (45.5 ± 6.4%) which was significantly higher than 14 transects (p < 0.05). It appears Ataúro Island in the channel was less conducive to the development of benthic soft substrates as all transects on the Island had low cover ranging from North Ataúro 3 with 3.2% (± 0.5) to South Ataúro with 7.4% (± 2.1). Com 1 (2.4% ± 1.0) was the only transect with a significantly lower soft substrate (p < 0.05; Figure 2-7). The geomorphology of the western side of Ataúro Island, reef flats that drop off to vertical walls, would not be conducive to the build-up of soft substrates and there could be similar steep features on the Com 1 transect.
Other invertebrates, mostly comprised of sponges, were also more abundant than hard corals with an overall average of 16.7% (± 0.3). There was variation in the proportion of 59 other invertebrates at transects ranging from 7.2 (± 0.8) at Baucau 1 to 32.8 (± 1.7) at Baucau 3. The constant water movement with the Indonesian Throughflow could be favorable for sponges. Macroalgae, on the other hand, had low cover, with a maximum at Com 1 with 3.6 (± 0.3%), significantly higher than all transects except North Ataúro 1 (2.5 ± 0.2%; p < 0.05). Three of the four transects with the lowest macroalgae were in Oecusse (transects 1, 2, and 4). This could be related to the high cover of soft substrate and comparatively large wave action in the district, and therefore less suitable for macroalgal growth (Figure 2-7).
The environmental variables relative wave exposure, distance to river, and human population density explained 9.3% of the total variation of the benthic community in the dbRDA. This indicates that there are likely other factors that influence coral reef communities in Timor-Leste. However, the overall model was significant (Monte Carlo permutation test anova, pseudo-F(3,1075) = 37.977, p < 0.001). The first two dbRDA coordinate axes were statistically significant with the first, dbRDA1, explaining 6.1% of total variation (pseudo- F(1,1075) = 71.9405, p = 0.005; Figure 2-10). Axis dbRDA1 is most strongly associated with distance from rivers and benthic composition of transects Com 1, North Ataúro 1, 2, and 3, and Jaco Island 2 greatly influenced by being farther away from rivers. Branching coral, free- living morphologies, and macroalgae were positively associated with increasing distance from divers while soft substrate had an inverse relationship. The dbRDA2 axis represents 3.5% total variance (pseudo-F(1,1075) = 41.1766, p = 0.005) in the benthos with increasing relative wave exposure associated with increasing massive and TFP coral morphologies in transects 3, 4, and 5 in Oecusse, and South Ataúro 2. This axis also aligns with increasing population density in the opposite direction and transects in Baucau (2 and 3) and Manatuto (1, 2, and 4) closely aligned. These transects appears to be associated with greater amounts of soft substrate and turf algae on hard substrate present in benthic composition (Figure 2-7). Hard coral morphologies had the highest scores in the dbRDA which indicated they were the most influenced by these parameters (Figure 2-10). The conditional effects of each environmental parameter were also significant with wave exposure accounting for 3.1% (Monte Carlo permutation test, F(1,1076) = 42.533, p < 0.001), distance to river 5.4% (F(1,1076) = 70.111, p < 0.001), and population density for 0.4% (F(1,1076) = 4.302, p < 0.001) of community structure variance.
60
Figure 2-10 A distance-based redundancy analysis (dbRDA) ordination plot illustrating the relationship between three predictor variables (relative wave exposure, distance to river, and human population density) and major benthic components of the reef along the north coast of Timor-Leste. Massive, Branching, TFP (thin/foliose/plating), and Free-living represent coral morphological groups and the following benthic groups are shown: Algae– macroalgae, Soft substrate, and Turf algae on hard substrate. Numbers represent transect averages of dbRDA scores colored by district to aid visualization.
Predictors of live coral cover along the north coast of Timor-Leste The best fit hard coral model included relative wave exposure, distance to river, and the ratio of branching to massive corals. The full model explained 73.28% (r2 = 0.7328) of the variation in coral cover along the north coast of Timor-Leste (Table 2-6; Table S2-1; Table S2-2). Sixty-two influential points were identified, and the model was rerun without these points, which improved r2 to 76.30% as well as model AIC and BIC (Table 2-6). Estimates for both models are reported and the model removing influential points is discussed further. All covariates were mean-centered, and therefore the model intercepts represent the hard coral cover at mean values of all covariates. Coral cover was 12.8% on reefs with a relative wave exposure of 4.2, 8.1 km away from rivers, and 99 people per km2. The intercept also represents a ratio of branching to massive corals at 0.147. If using the average total coral cover (15.0% or 0.150), a ratio of 0.147 would represent roughly equal proportions of 61 branching and massive corals (i.e., 0.147 divided by 0.150 average coral cover is 0.98 of a branching coral for every 1 massive coral). The range of the ratio of branching to massive corals was 0.0-7.2 (with influential points removed) indicating some subtransects had up to seven times more branching than massive morphologies. A few very large ratios skewed the mean as 79% of the values were less than the mean of 0.147 indicating that most subtransects had more massive than branching coral morphologies. Few transects at the extremities of the country (Jaco 2 and 3, Com 1, South Ataúro 3) were responsible for the large ratio values (Figure 2-11). This could be associated with greater distance to rivers as branching corals are more sensitive to sedimentation and/or increased relative wave exposure in these districts (Figure 2-10).
Table 2-6 Model coefficients for a linear mixed-effects model of coral cover. The response variable was hard coral cover with fixed effects wave exposure, river distance, and the ratio of branching to massive coral morphologies with transect as the random effect. All covariates were mean-centered. Coral cover was square root transformed and E’ is the back-transformed estimates. Wave exposure, the ratio of branching to massive corals, and population density were also log-transformed.
Full Model Influential Points Removed Coefficient Estimates E' SE p-value Estimates E' SE p-value Intercept 0.356 *** 0.127 0.014 <0.001 0.358 *** 0.128 0.014 <0.001
Wave Exposure 0.151 *** 0.257 0.019 <0.001 0.142 *** 0.250 0.02 <0.001 River -0.002 0.125 0.002 0.416 -0.002 0.127 0.002 0.369 Ratio 0.111 *** 0.218 0.005 <0.001 0.112 *** 0.221 0.005 <0.001 Population -0.034 0.104 0.018 0.056 -0.022 0.113 0.017 0.189 Wave x River 0.001 0.127 0.005 0.9 0.008 0.134 0.005 0.118 Wave x Ratio 0.083 *** 0.193 0.016 <0.001 0.078 *** 0.190 0.016 <0.001 River x Ratio -0.001 0.126 0.001 0.288 -0.001 0.127 0.001 0.432 Wave x River x Ratio -0.004 0.124 0.003 0.216 -0.002 0.127 0.003 0.473 ICC 0.41 0.48 Observations 1079 1021 Marginal/ Conditional r2 0.511 / 0.713 0.510 / 0.746 AIC/BIC -2168.922/-2114.192 -2277.182/-2223.065 log-Likelihood 1095.461 1149.591 * p<0.05 ** p<0.01 *** p<0.001
62
Figure 2-11 (a) Ratios of branching to massive corals, with influential points removed, standardized by the total coral cover per subtransect for reefs located along the north coast of Timor-Leste. Branching and massive morphologies at the subtransect level were scaled to avoid undefined ratios (dividing by zero). (b) Log ratio of branching to massive corals. Ratios were scaled before transforming to avoid undefined ratios.
The interaction of relative wave exposure and the ratio of branching to massive corals was significant in the model (Table 2-6; Figure 2-12). There was an overall positive correlation between wave exposure and total coral cover. This effect was greatest when reefs were more structurally complex (a higher ratio of branching to massive corals). From the model estimates, a log unit increase in the interaction between wave exposure and the ratio of branching to massive corals results in a 6.2% increase in coral cover (the difference between wave x ratio estimate, 0.190, and intercept, 0.128; Table 2-6).
63
Figure 2-12 The partial effects plot of the significant, two-way interaction of wave exposure and the ratio of branching to massive morphologies from the linear mixed-effects model on hard coral cover with transect as a random effect. Twenty-six kilometer-scale phototransects were collected along the north coast of Timor-Leste in 2014. Covariates were adjusted from mean-centered and both parameters were back-transformed from a log transformation. The y-axis is square root back-transformed coral cover and colored bands represent 95% confidence intervals. Points represent raw data used in the model.
Discussion
The analysis of kilometer-scale phototransects revealed substantial heterogeneity of coral reefs growing on the outer reef slope, along Timor-Leste’s north coast. Turf on hard substrate was the dominant benthic category, similar to other coral regions using the same kilometer-scale method. Total coral cover was low at an average of 15.0% (± 11.4 SD), which was lower than Indo-Pacific averages derived from meta-analyses (Bruno and Selig, 2007; Vercelloni et al., 2020a). Benthic composition across the north coast was significantly influenced by biophysical parameters and human population density. Although noteworty, the model indicates that there are likely other factors that influence the benthic composition. The total coral cover model with relative wave exposure, distance to rivers, the ratio of branching to massive corals, and human population density explained 76.3% of the variance
64 in the data. The study recorded a significant positive interaction between wave exposure and the ratio of branching to massive corals which contributes to further understanding of overall drivers of the composition of coral reefs in the infrequently studied Timor-Leste region. Other factors shaping benthic composition in the country’s unique oceanographic setting within the Indonesian ThroughFlow (ITF) warrant further investigation.
Comparison of conventional and kilometer-scale methods Timor-Leste has benefited from a suite of international aid-funded coral reef research in the last decade using a variety of methods. The location of kilometer-scale transects was selected to be in the same areas as the established NOAA climate stations (Figure 2-12). Thus, there was an overlap in the sampling between the regions surveyed by kilometer- scale transects and phototransects collected by Cora Reef Ecosystem Program (CREP) in 2013 and 2014. This provides a unique opportunity to compare the kilometer-scale methods presented here with conventional methods utilized by NOAA. The three datasets span two orders of magnitude in the number of images analyzed and, therefore, the area of reef assessed (Table 2-7). The NOAA datasets were also manually annotated and the kilometer- scale transects utilized machine learning to classify the benthic images. Automated image analysis has efficiency estimations 200 times greater than manual methods, which also equates to cost savings. The manual analysis was estimated to cost US $4,536 versus US $3,193 for the automated annotation of the kilometer-scale transects for five times the number of photoquadrats (see Supplemental 2.1). The accuracy is also comparable at a broad functional group level (see Supplemental 2.2; Beijbom et al., 2015; Bryant et al., 2017; González-Rivero et al., 2020; I. D. Williams et al., 2019).
65 Table 2-7 Comparison of methods of coral reef benthic composition datasets in Timor-Leste shown in Figure 2-13.
Dataset n # Images Method NOAA 2013 139 4,192 Benthic phototransects (30 m) in conjunction with fish surveys taken across the north coast of Timor-Leste. NOAA 2014 7 232 Characterization of a 10 by 5 m grid with ~30 images collected at the eight NOAA climate stations along the north coast of Timor-Leste. Seven stations included here. XL Catlin 2014 1079 20,750 1,079 subtransects from 26 kilometer-scale phototransects collected along the north coast of Timor-Leste by the XL Catlin Seaview Survey in 2014. Wong&Chou 2004 4 - Reef Check surveys conducted at four sites on Northeast Ataúro Island at shallow (5.8 - 7.1 m) and deep (12 - 14 m) reefs. Bruno&Selig 2007 390 - Meta-analysis of coral reef studies from 1968 - 2004. Only Timor-Leste data included are the Wong&Chou 2004 data. Vercelloni et al., 49 112,379 49 reefs grouped from 144 kilometer-scale 2020a transects collected by the XL Catlin Seaview Survey in 2014 in the Indo-Pacific region. Includes the data from Timor-Leste in this analysis.
Overall, the three different surveys agreed on the broad functional group trends across the north coast. Turf on hard substrate was a dominant component and the cover of hard corals, soft corals, and macroalgae varied between districts. There was agreement that hard corals were generally most prevalent on reefs in Ataúro Island, Com, and Jaco Island districts of the country. There were, however, large differences in the amount of coral cover classified between conventional and kilometer-scale transects. The NOAA 2014 coral cover estimates differed by over 10% from one or both of the other datasets in all districts but Baucau. The districts with high coral cover were consistent between the 2013 NOAA surveys and kilometer-scale analysis (Figure 2-13). In the 2013 NOAA survey, Ataúro Island had the highest coral cover (20.5 ± 2.0% SE), followed by Com (20.1 ± 2.7%), and Jaco Island (19.8 ± 3.7%; PIFSC, 2017). The kilometer-scale district averages of coral cover reported here were also highest at Ataúro Island (south 22.7 ± 1.3%; north 18.2 ± 0.7%) and Jaco Island (19.0 ± 1.3%). Conversely, the NOAA 2014 transects quantified 37.3% (± 3.4) and 9.7% (± 1.8) at North and South Ataúro respectively, more than 10% higher and almost 10% lower 66 in these two regions (Figure 2-13). The differences between years are more likely from differences in methods versus changes over a year as the NOAA 2014 and XL Catlin Seaview Survey (XLCSS) data, collected only a few months apart, had large differences. Conventional transects lacking replication did not accurately capture heterogeneity of benthic composition at a regional scale and are more likely to be affected by biased transect placement as seen between the inconsistency between the NOAA 2014 phototransects and the NOAA 2013 and kilometer-scale datasets. Shorter transects biased toward coral dominated areas may overinflate coral cover percentages and give skewed perspectives when attempting to compare across studies or regions. The kilometer-scale transects remove this bias of transect placement that can influence conventional scale transects. However, the kilometer-scale transects can also include non-reef environments such as sand dominated areas as seen in the Oecusse transects, which also could contribute to the low coral cover average.
Conventional surveys at the eight established NOAA climate stations would have more value if monitored through time. The climate stations were surveyed three years in a row from 2012–2014. Unfortunately, the 2012 dataset was not analyzed by NOAA. The three NOAA climate stations surveyed in both years indicated that coral cover was increasing (Figure S2-1). Differences at some stations were large (> 10%; Figure 2-13); however, permanent transects were not set up. Com and Jaco Island are remote locations and had higher average coral cover and a larger increase between 2013 and 2014 than Baucau. This could indicate of greater human impacts in Baucau. The NOAA fish surveys, however, indicated that Baucau had comparable fish biomass to Lautem (Com and Jaco Island district), suggesting that fishing was not a significant impact. See Supplemental 2.2 for a detailed comparison between climate stations.
67
Figure 2-13 Comparison of the district/regional mean coral cover of three different datasets collected across the north coast of Timor-Leste and Indo-Pacific meta-analyses. Phototransects were taken by NOAA in June 2013 and Sept–Oct 2014 and by the XL Catlin Seaview Survey (XLCSS) in Jul–Aug 2014. The NOAA 2013 surveys at Ataúro Island were averaged collectively because the surveys did not follow the same geographic segregation that the NOAA 2014 and XLCSS followed. For the NOAA 2014 data, the standard error (SE) represents that of a single transect collected at each of the NOAA climate stations. The XLCSS collected 26 transects approximately 1.5–2 km in length across the north coast which were clustered into subtransects. The four surveys by Wong & Chou were done as part of Reef Check surveys (www.reefcheck.com) on northeast Ataúro Island at shallow (5.8–7.1 m) and deep (12–14 m) sites in 2004. The Indo-Pacific region values for comparison were drawn from Bruno & Selig (2007) and Vercelloni et al. (2020a). SE bars are shown except for Vercelloni et al. (2020a) which are standard deviation, and Bruno & Selig (2007) which are 3rd quantile.
The NOAA 2013 fish and benthic surveys and kilometer-scale transects in 2014 were never intentioned as long-term monitoring sites and are thus more useful collecting more surveys across a large geographic scale. In this analysis, space-for-time substitution, the fundamental assumption that “drivers of spatial gradients of species composition also drive temporal changes in diversity” (Blois et al., 2013), was utilized. Space-for-time has mostly been tested in terrestrial ecosystems. Here, the lack of time series data in assessing drivers was supplemented with a larger spatial scale of data. The NOAA climate stations represent
68 conventional monitoring of a small extent of reefs through time. Ideally, monitoring would encompass large spatial extents of reefs through time which few programs, such as the Australian Institute of Marine Science’s long-term monitoring program and CREP, accomplish. But, this is limited to nations with the resources implementation ability. Comparatively, the capacity to conduct large monitoring programs is limited in Southeast Asia and the CT. Although the kilometer-scale transects collected more spatial coverage of coral reefs, they were less spaced out along the coast than the NOAA 2013 surveys (Figure 2-1). Depending on the overarching goal for instance – for instance, spatial planning or predictive modeling – surveying a greater geographic extent, as done by NOAA in 2013, could be more useful than more intensive surveying of few locations.
Broad trends in benthic composition Contrary to expectations, turf on hard substrate was the dominant component of Timorese reefs along the north coast. Coral dominated communities have generally been described as healthy reefs with phase shifts of these communities to other states (e.g., macroalgal- dominated) touted as used as evidence of disturbance (Done, 1992a; Hughes, 1994). Several meta-analyses have argued against this bimodal portrayal of coral reef community structures (Bruno et al., 2003; Zychaluk et al., 2012). The results of this analysis support a broader view of healthy coral communities, with turf on hard substrate being a dominant functional group along with hard corals and soft substrate (Figure 2-7). Of course, this is in the context of baselines that have potentially shifted, as coral cover has decreased globally in the Caribbean and Indo-Pacific over the last several decades (Bruno and Selig, 2007; De’ath et al., 2012; Gardner et al., 2003; Schutte et al., 2010). An analysis of coral reefs in the Hawaiian archipelago reports three major coral reef regimes: calcifying, turf, and macroalgal/sand regimes (Jouffray et al. 2015). These largely correspond to the dominant benthic functional groups found on Timorese reefs, although they are not as distinct. This may be attributed to greater diversity in the CT as compared to those in Hawai‘i.
There is a paucity of coral survey data in the CT compared to well-studied Indo-Pacific reefs such as the Great Barrier Reef (GBR) and Hawai‘i. Comparisons with conventional Indonesian reef surveys indicate considerably less hard coral in Timor-Leste. A small study in the Spermonde archipelago had an average of 46.4% (± 8.2) coral cover at the deep site (~ 10 m, n = 3) and 43.6% (± 23.2) at the shallow site (0–3 m, n = 2; Muller et al., 2012). The Spermonde archipelago has a similar oceanic and climatic regime as Timor-Leste but
69 differs geologically, with a 30 to 50 km carbonate shelf housing ~ 120 reef islands compared to the narrow shelf (< 3 km) of Timor-Leste and its steep fringing reefs (Boggs et al., 2012; Kench and Mann, 2017). Coral disease surveys on fringing reefs in Wakatobi National Marine Park in southeast Sulawesi, Indonesia recorded 8.8–74.7% coral cover. There was a significant decrease between 2005 and 2007, with three out of five sites decreasing by ~ 20%. These site averages included reef flat and crest environments in addition to reef slope transects and the maximum reef extent was ~ 2 km from shore (Haapkylä et al., 2009b). The inclusion of multiple reef environments could account for the higher coral cover values although reef slope environments often have the greatest coral cover (Williams et al., 2011a). These studies were conducted several years before the data presented here. With a recorded 20% drop in coral cover between two years and surveys conducted over ten years ago, the current coral cover of these sites may be comparable to the 15% average presented here. Semi-quantitative surveys from the Rapid Marine Assessment (RAP) in Timor-Leste and Indo-West Pacific coral reef regions indicate that coral cover in Timor-Leste was comparable to the 11 areas surveyed with the same methods. Average Timor-Leste coral cover was estimated at 28% across the 20 stations surveyed, well within the range of 20–41% across regions surveyed within the CT. Timor-Leste coral species were most similar to Bali and Komodo in Indonesia, and species abundances indicate that coral fauna surrounding Timor island are more influenced by the ITF than the Indian Ocean (Turak and Devantier, 2013).
Across all the datasets, most of the benthic category ranges were similar. Turf algae on hard substrate was consistently a dominant functional group across surveys (PIFSC, 2017; Figure 2-7). In 2013, the NOAA surveys found district averages of turf on substrate ranged from 35.4 (± 4.8%) to 54.5 (± 4.3%; PIFSC, 2017) comparable to the 36.3 (± 0.1%) to 57.6 (± 0.2%) range of kilometer-scale transects. In contrast, the 2014 NOAA surveys quantified a much larger range of 9.6 (± 0.0%) to 61.5 (± 0.1%) at the NOAA climate stations (PIFSC, 2017). The other invertebrates/soft coral categories also had similar ranges across datasets. Dili and Lautem (Com) had the highest and lowest values respectively for the NOAA 2013 data (Dili: 24.0 ± 3.5%, Com: 6.0, ± 1.3%, PIFSC, 2017) and kilometer-scale transects (Dili: 21.1 ± 0.1%, Com: 11.7 ± 0.1%). The 2014 NOAA survey Baucau had the highest soft coral cover (41.3 ± 0.5%) and Jaco Island the lowest soft coral cover (0.4% ± 0.4%; PIFSC, 2017).
Similar trends in macroalgal cover were found between surveys with more algae classified through benthic image analysis by NOAA. In the NOAA 2013 image analysis, Lautem had 70 the maximum macroalgal cover 9.2 ± 3.4% (PIFSC, 2017), versus 1.6 ± 0.1% averaged for the Lautem transects (Com and Jaco Island) in this analysis. In 2013, two sites in Lautem had greater than 30% macroalgal cover and were responsible for this high district average. One of the sites was the NOAA climate station at Jaco Island, which in both years, had greater than 40% macroalgal cover with a high proportion of Halimeda spp. (Figure 2-7; PIFSC, 2017; Figure S2-2). The other high macroalgae (> 30%) site in Com was mostly comprised of encrusting macroalgal forms (e.g., Lobophora spp., Peysonnelia spp., etc.) growing among branching corals with few cyanobacteria and Halimeda spp. classifications (PIFSC 2017, Figure S2-3). The macroalgal community does not appear to be indicative of macroalgal blooms associated with increased nutrients and sedimentation. Lautem has comparatively few rivers compared to other districts and is also sparsely populated (Figure 2-6, Table 2-1). Macroalgae are also highly seasonal and the surveys were conducted in different months which could contribute to differences. Disparities in algae between the NOAA and kilometer-scale datasets could also be attributed to the manual and automated image analysis methods employed. Algae classifications have the highest error out of the major functional groups for automated image analysis (Beijbom et al., 2015; Bryant et al., 2017; González-Rivero et al., 2016, 2020; I. D. Williams et al., 2019) which is further discussed in Supplemental 2.2.
When considering coral estimates based on the 2 km transects, the coral cover across the north coast of Timor-Leste was consistent with values at the regional Indo-Pacific reefs from a meta-analysis and global analysis of the XLCSS kilometer-scale transects. A global analysis of reef assembly patterns using the same kilometer-scale methods on forereef slopes at the same depth (including these data) revealed that epilithic algal matrix (EAM akin to turf on hard substrates category), was the dominant component on reefs, ranging from 51.4% (± 11.2% SD) to 65.6% (± 13.4% SD; Vercelloni et al., 2020a). Turf on hard substrate was a more variable and significant benthic component in Timor-Leste, ranging from 26.2 (± 3.22) to 64.3 (± 2.5%) per transect. This variability is likely because some regions in Timor-Leste had a large cover of soft substrates such as sand (2.4 ± 1.0%-45.5 ± 6.4% transect range). Timor-Leste had the highest average of soft substrate in the Southeast Asia region of the global analysis at 17.2 (± 3.3%). The lowest was in Tubbataha Natural Marine Park in the Philippines, atoll formations in the Sulu Sea, with 2.1 (± 1.4%; Vercelloni et al., 2020a). Varying amounts of soft sediment within the reef framework, across different locations, could be account for by differences in geology and rates of erosion.
71 Hard corals were also a significant component of Timorese reef slope habitats (Figure 2-7). The cover of hard, reef-building corals had a range of 5.4 (± 0.6%)–33.0 (± 0.9%) averaged by transect, similar to the 28% hard coral cover estimated from the 2012 RAP semi- quantitative timed swims (Turak and Devantier, 2013). The overall kilometer-scale coral average of 15.0 (± 0.4%) was comparable to the yearly averages of the NOAA datasets, 15.3 (± 0.8%) and 21.8 (± 4.6%) in 2013 and 2014, respectively, and the regional Southeast Asia value of 18.8 (± 10.4% SD) from a global analysis (Vercelloni et al. 2020a). The conventional-scale phototransects with a coral cover range of 0.0–42.3% (2013) and 6.3– 39.7% (2014), however, did not document the same degree of variability as the kilometer- scale transects with 0.0–64.2% range by subtransect, 2.8–53.6% range across Southeast Asia (Vercelloni et al. 2020a), and the 5–70% range from the RAP survey (Turak and Devantier, 2013). Timor-Leste also had comparable proportions of the main coral morphologies compared to the Southeast Asia region. Cover of massive and branching corals was remarkably consistent with 8.3 (± 01.8%) and 4.9 (± 1.6%) in Timor-Leste for each morphology respectively, and 8.0 (± 0.7%) and 5.0 (± 0.5%) for the Southeast Asia averages. There was less cover of TFP corals (1.4 ± 0.4%) in Timor-Leste and more free- living corals (0.4 ± 0.2%) than the Southeast Asia averages (5.5 ± 1.8% and 0.2 ± 0.1%; Vercelloni et al., 2020a).
An Indo-Pacific meta-analysis analyzed 6,001 surveys and 651 monitoring sites using a variety of methods (e.g., Reef Check, line intercept transects, manta tows), including the GBR long-term monitoring program from 1968–2004. The Indo-Pacific was split into ten subregions: East Indonesia & Papua New Guinea (PNG), West Indonesia, GBR, the Hawaiian Islands, Mainland Asia, the Philippines, Southwestern Pacific, South Pacific, Taiwan and Japan, and Western Pacific. The GBR and the Philippines encompassed over half of the survey data with 213 and 136 sites, respectively. Timor-Leste was included in the East Indonesia and PNG subregion and only four Reef Check survey sites on the northeast of Ataúro Island from 2004 represented the country (Bruno and Selig, 2007; Wong and Chou, 2004). Despite the overrepresentation of data from a few subregions, the subregional coral cover means were not significantly different in 2003, with a region-wide mean of 22.1% (95% CI: 20.7, 23.4, n = 390). The Timor-Leste data was not included in this average as it was collected in 2004. The authors infer that coral cover had declined from 42.5% (95% CI: 39.3, 45.6, n = 154) for the reference period of 1980–1982 to 22.1% in 2003. A linear regression on the subregional means indicates 0.37% coral was lost per year from 1968–
72
2004. As there was little data from Timor-Leste included in these Indo-Pacific analyses, it is difficult to assess whether these rates of reef change over time apply to Timorese reefs as well. Given, however, the relatively low abundance of coral (an average of 15.0%), cover could easily have decreased from higher values in previous decades. Alternatively, there appear to be localized areas, namely Ataúro and Jaco Island, tharboring relatively high coral cover (> 30%). Another potential explanation is that much of the physical and environmental parameters, such as the steep geomorphology of reefs and oceanography of the north coast, are not conducive to high coral cover reefs.
Drivers of benthic composition and coral cover in Timor-Leste It was anticipated that the environmental and human population density parameters tested would significantly shape benthic composition and coral cover. While this was the case, relative wave exposure, distance to river, and human population explained only 9.3% of benthic composition variance compared to 76.3% for the total coral cover model. The parameters were chosen because of their relative importance for coral growth. Of course, these parameters also affect other major functional groups. Furthermore, some transects had portions dominated by loose substrate which may be due to other forces not considered such as geologic composition. Oecusse and Bacau transects had high proportions of loose substrate, and these regions are comprised of geologically young volcanic pillow basalts, a potential contributing factor (Lee, 2016). Additionally, it is likely that oceanographic processes such as currents, internal waves, and upwelling play an important role in structuring forereef benthic composition given the influence of the ITF, but were outside of the scope of this study.
2.6.3.1 Wave exposure – the protected north coast
Wave exposure was a significant driver of benthic composition and hard coral cover. There was an unexpected positive relationship with coral cover. The relative wave exposure model GREMO considers average annual wind speed, wind direction, and fetch which would minimize the effects of large storms (Pepper, 2009). The methodology focusing on wind- driven waves was suitable in Timor-Leste, as the annual maximum wave height in-country is rarely above 0.5 m and there is a low incidence of tropical cyclones given its location close to the equator (DNMG et al., 2015). The unitless, relative wave exposure values calculated in GREMO were not directly comparable to other sites outside of this analysis. However, using the Dili area maximum wave height of 0.5 m as a benchmark for the 3.9 (± 0.1) average
73 relative wave exposure calculated for the Dili transects, the maximum relative wave exposure from GREMO of 11.9 (± 0.3) was extrapolated to ~ 1.5 m wave height. Notably, a few locations on the north coast, such as the exposed eastern side of Jaco Island, could experience greater wave heights. As most of the north coast has comparable or lower relative wave exposure than Dili (Figure 2-5), it is a reasonable upper bound of wave energy. The general hypothesis follows that more hydrodynamic stress actually favors structurally robust corals (massive), over more delicate morphologies (branching, foliose corals, etc.) until high wave exposure impedes all coral growth and development (Goreau, 1959; Graus et al., 1977; Sheppard, 1982; Storlazzi et al., 2005). Maximum wave stress along the north coast of Timor-Leste, however, appears to be within the optimal range of wave stress where increasing wave energy promotes the growth of corals favoring branching colonies (Wallace, 1999). The inflection point where wave energy becomes destructive depends on the specific coral species and morphology (González-Rivero et al., 2014; Storlazzi et al., 2005).
A maximum wave height of 1.5 m is low in comparison to regions in the Pacific that experience regular swells and storms. The wave exposure regime of the Hawaiian islands is well-characterized with a 1–8 m annual range of significant wave heights (Moberly and Chamberlain, 1964). Subsequently, waves in Hawai‘i can reach destructive levels that can damage corals and restrict species distribution patterns (Dollar, 1982; Jokiel et al., 2004; Storlazzi et al., 2005). Surveys of the main Hawaiian island found that reefs exposed to the winter North Pacific Swell, with significant wave height from ~ 3–8 m, had less coral cover, species richness, and coral diversity (Jokiel et al., 2004). A doubling of wave energy (i.e., 15 to 30 kW/m) in the Northern Line Islands due south of Hawai‘i, likely with similar wave heights, had a strong negative correlation with coral cover, decreasing by 20–60%. Specifically, Acropora spp. corals were most affected, decreasing from 30% to almost zero. This, however, was specific to one atoll and the effect was not found on a neighboring atoll. This was attributed to differences in coral community composition (massive Porites versus Acropora dominated), higher levels of fine sediment, and exclusion of finer-scale physical oceanographic forcings such as internal tides and lagoonal outflow (Williams et al., 2013). Thus, both large-scale and local wave environments and hydrographic regimes are important in structuring reefs. Given the relatively low wave exposure regime along the north coast (limited exposure to open seas, estimated 1.5 m maximum wave height, low incidence of cyclones), wave exposure calculated for the north coast appears to be below the threshold of waves causing significant physical damage to corals (Dollar, 1982; Done, 1992b;
74
González-Rivero et al., 2014; Storlazzi et al., 2005, 2002; Williams et al., 2013). Increased wave exposure promotes the formation of coral reefs in Timor-Leste, likely through increased water movement influencing temperature regulation and access to nutrients (Hearn et al., 2001; West and Salm, 2003). The more likely cause of physical damage on Timorese reefs is human impacts such as boat anchors, fish traps, and gleaning.
2.6.3.2 Riverine inputs structuring Timorese coral reefs
The distance to river parameter was a significant driver of benthic composition, but not in the total coral cover model as anticipated (Table 2-7). The cover of branching corals, free- living corals, macroalgae, and turf on hard substrate was associated with increasing distance from rivers while soft substrate increased closer to rivers (Figure 2-8). Rainfall is highly seasonal due to the West Pacific Monsoon. It averages 100 mm per month during the wet season and less than 30 mm during the dry season (DNMG et al., 2015). Torrential rains likely result in pulse sedimentation events into the coastal environment exacerbated by a history of slash and burn agriculture, logging, and steep terrain (Alongi et al., 2013; Sandlund et al., 2001). However, the seasonal nature of rain events may limit the effect of river distance and associated sedimentation from structuring nearshore coral reef communities. The north coast of Timor-Lest is not characterized by large bays that can hold sediments on reefs, resulting in decreased coral cover (Cleary et al., 2006; Golbuu et al., 2011). The currents and upwelling associated with the ITF likely flush the coastline, washing sediments away from reefs.
In terms of total live coral cover, Dili, Manatuto, and Baucau had the least amount of live coral, all with < 10% average across all transects per district. These districts also house significant watersheds. Manatuto is in the Laclo watershed, the second largest in the country encompassing 1,297 km2. The Comoro watershed in Dili is 248 km2 and Seical near Baucau is 489 km2 (Costin and Powell, 2006). Dili subtransects were on average close to the Comoro River (3.8 ± 0.1 km) and, even though subtransects in Manatuto and Bacau were farther from rivers (7.8 ± 0.3 and 10.4 ± 0.1 km, respectively), the sedimentation from associated watersheds could be still be influencing reefs kilometers away during the rainy season. Alongi et al., (2012a) estimate that the sediment load in the Laclo watershed in Manatuto is eight to 14 times the natural total yield. Comparatively, districts where coral cover was highest, Ataúro Island and Jaco Islands, were also in small watersheds (140.7 and 11.1 km2 respectively; Costin and Powell, 2006) with no major rivers. Further work
75 connecting watershed health, seasonality of rainfall, and the potential effect of currents on sedimentation on reefs in Timor-Leste is imperative.
Massive corals are the dominant hard coral morphology across the north coast (Figure 2-4), which could be a sign of sedimentation impact as they are generally considered more stress- tolerant (Golbuu et al., 2008). Alternatively, reefs dominated by large massive corals could be a sign of competitive exclusion (Williams et al., 2011a); however, without colony size data it is difficult to corroborate this theory. Our data indicating that high coral reefs are localized on Jaco and Ataúro Island is supported by the RAP semi-quantitative surveys (Turak and Devantier, 2013). Reefs surveyed east of the Com NOAA climate station consisted of diverse, high coral cover communities. Although only eight surveys between Dili and Com were collected, the remaining mainland sites were characterized by massive corals, lower coral cover, higher soft coral, and higher turf algae. Further investigation of watershed health and sedimentation along the north coast could potentially explain this disparity between the Ataúro and Jaco Island regions and the remaining coastline with a more refined measurement of sedimentation. The effects of sedimentation on reefs should continue to be monitored especially with continued land-use change, development, and monsoonal rainfall in Southeast Asia expected to increase with climate change (Cruz et al., 2007).
2.6.3.3 The role of human population density
The validity of applying anthropogenic proxies such as local human population densities is the subject of considerable debate (Aronson and Precht, 2006; Bruno and Valdivia, 2016; Grigg and Dollar, 2005). Human population density as a proxy for anthropogenic impacts assumes the number of people correlates with intensity of activity (Sanderson et al. 2002). A global meta-analysis on coral cover and human population found no correlation between human impacts on coral reefs and the number residents nearby (Bruno and Valdivia, 2016). Conversely, regional studies have found significant impacts on coral reefs that correlate with human population density (Brown et al., 2017; Mora, 2008; Wedding et al., 2018).
The human population density was a significant driver in benthic composition, but not in the coral cover model which went against expectations (Figure 2-9; Table 2-6). Greater human population density was associated with less coral, especially massive and TFP morphologies, a finding consistent with other studies identifying negative correlations with coral cover (Brown et al., 2017; Mora, 2008). However, the maximum population density 76 included in the analysis was 450 persons/km2 in Dili which is low in terms of urbanization globally. For example, Jakarta has approximately 23 million in 2,700 km2, equal to a population density of about 8,500 persons/km2 (van der Meij et al. 2010). Some analyses indicate that human impacts do not have large effects until very high population densities are reached (Wedding et al., 2018). The context in which populations interact with coral reefs is important, because reefs play a considerable role in subsistence livelihoods in developing countries (Tilley et al., 2020). The lack of infrastructure in Timor-Leste may not be conducive to industrial fishing at this point in time; however, this is likely to change as the country develops. Currently, historical blast fishing, a reliance on subsistence fishing, and gleaning, contribute to reef impacts, despite a relatively low population density (Andréfouët et al., 2013; Teh et al., 2013; Tilley et al., 2020). This issue is explored further in Chapter 4.
Structural complexity Here, the ratio of branching to massive corals was used as a proxy for structural complexity. As predicted, there was a positive effect of structural complexity that interacted with relative wave exposure (Figure 2-11). Our data support the finding that high coral cover is generally associated with greater structural complexity in the Indo-Pacific and that branching corals are especially important contributors to this complexity (Graham and Nash, 2013). In the face of a changing climate, the increasing frequency of mass bleaching events and ocean acidification, the structural complexity of reefs is a vital measurement to track (Hoegh- Guldberg, 1999; Wild et al., 2011). Healthy coral communities underpin diverse structural habitats and the recent mass bleaching event has already resulted in negative accretion rates of reefs (Alvarez-Filip et al., 2009; Perry and Morgan, 2017). Although the ratio of branching to massive corals is not a direct corollary to reef measurements of rugosity, many studies use subjective visual estimations of the structural complexity of reefs (Williams and Polunin 2000). This ratio metric of habitat complexity is easily attainable through conventional image analysis and can be applied to existing large-scale datasets to monitor trends in structural complexity.
Conclusion
In this chapter, the variability of biophysical parameters and localized anthropogenic impacts was explored for coral reefs along Timor-Leste’s north coast. The future of coral reefs relies on sustainably managing localized impacts in the face of global climate change (Kennedy et al., 2013). These large-scale surveys have established modern kilometer-scale baselines 77 of benthic composition across the north coast, and have explored relationships between potential drivers of benthic composition and coral cover for outer reef slope communities at 10 m. These reefs have a high degree of heterogeneity across kilometer-scales. The benthic composition and the amount of hard coral cover were comparable to previous small-scale surveys (Erdmann and Mohan, 2013; PIFSC, 2017; Wong and Chou, 2004), corroborating the accuracy of the novel kilometer-scale benthic image collection with automated image analysis (Beijbom et al., 2015; Bryant et al., 2017; González-Rivero et al., 2014, 2016, 2020; I. D. Williams et al., 2019). However, finer-scale classifications of certain groups such as algae and non-coral invertebrates still need to be improved.
The average coral cover of 15% was low, potentially representing a historical decline (Bruno and Selig, 2007). Of course, there are of course areas where hard coral cover was much higher, namely Ataúro and Jaco Islands. It is troubling that the average coral cover across a large extent of the country was low, even without the presence of industrialized activities such as fishing and agriculture. Although livelihoods post-independence in Timor-Leste have been largely subsistence-level, the almost 25 years of Indonesian occupation saw higher resource extraction in both terrestrial and marine environments. While impacts to reefs have likely decreased from pre-independence times, the continued subsistence-level impacts have not allowed reefs to recover from historical environmental degradation during Indonesian and Portuguese occupation. Additionally, coastal marine resources may have been even more important during periods of violence or food insecurity during the last half- century (Barbosa and Booth, 2009).
The oceanographic context of Timor-Leste may act as a protective factor against changing ocean temperatures caused by climate change (Boggs et al., 2012; Erdmann and Mohan, 2013). The water movement of currents and upwelling associated with the location in the ITF potentially limits the maximum water temperature reached on reefs and flushes nearshore reefs. This is further explored in Chapter 4. The relative protection from shielding islands to the north and low incidence of storms maintains wave exposure regimes within the optimal range for coral growth.
Land-clearing is already having negative impacts on coastal ecosystems in Timor-Leste. Concerningly, these impacts will be further exacerbated in the future without watershed rehabilitation and increased coastal development (Alongi et al., 2013, 2012a, 2012b; JICA, 2010). Subsequently, improving land management in coastal areas and river catchments 78 should be a key environmental health maintenance priority for coastal ecosystems such as coral reefs and must be a major concern for coral reef management in this region (Bartley et al., 2014; Burke et al., 2012; Mcmanus, 1988). Land to sea (or Ridge to Reef) management is challenging enough for well-developed governments such as Australia to implement, and the need for such policies is even more critical in Timor-Leste. Failing to consider and manage downstream effects of increased land-clearing and development on nearshore marine environments would be detrimental for coral reefs (Alongi et al., 2013). Especially as in-country land-clearing practices for agriculture, grazing lands, and firewood are likely to increase (Alongi et al., 2012a). Within an immediate timeframe, human deforestation has been predicted to be a larger threat than climate change-induced changes regarding sedimentation rates in Madagascar (Maina et al., 2013). This could also be true in Timor-Leste, given the the planned development of infrastructure including major works on the coastal roads, agriculture, and economies outlined by the government strategic plan (Cruz et al., 2007; RDTL, 2011).
Reefs along Timor-Leste’s north coast had a high degree of habitat complexity. Flattening of reefs is considered a sign of coral reef degradation associated with less diverse assemblages of fishes and invertebrates (Alvarez-Filip et al., 2009; Darling et al., 2017; Graham and Nash, 2013). Continuing to assess reef structural diversity is necessary to monitor these reefs for signs of decline.
Human population density was linked to benthic composition, but not total coral cover. In countries like Timor-Leste where subsistence livelihoods are critical to survival, filling in the knowledge gap of these subsistence-level interactions is essential to understanding impacts on coral reefs. Understanding the socio-ecological ties to coral reefs is even more important considering the young population demographics and high population growth rate of the country (G. J. Williams et al., 2019). The growing population, coupled with social issues such as food security and unemployment, will result in increasing pressures on coral reefs and other natural resources.
The work presented here can assist in finding solutions in managing reefs across large scales while maintaining livelihoods for a population that relies on these resources. Fortunately, Jaco Island is already protected in the NKSNP, and community marine protected areas (MPAs) through tara bandu, or customary law, have been established on Ataúro Island. These protections would still benefit from a national overarching plan and 79 managing reef resources in other more populated regions of the country through protections, seasonal closures, and so on, are important to develop. As development in Timor-Leste will be a key feature of the economy in upcoming decades, effectively managing the impacts through environmental impact assessments, as well as enforcement of regulations, is essential. Furthermore, tourism has also been targeted as a key industry for development in which marine tourism will be an essential part, as demonstrated by the launch of the Marine Tourism Association in 2019. This group could provide guidelines for sustainable eco-tourism with practices minimizing impacts on coastal waters.
Importantly, developing the current Timorese generation’s capacity to study and monitor reefs should be a priority, in addition to the creation of an MPA network. Currently, the monitoring of marine resources is largely done by NGOs, international aid groups, and foreign researchers. While the need for monitoring will likely continue to be filled by outside groups in the near future, the hope is that a new generation of Timorese researchers will be ready to lead the continued assessment of marine ecosystems. Timor-Leste houses unique coral reef resources that face a myriad of impacts. Further research to understand their unique ecology and socio-ecology is necessary to make informed decisions on how to best preserve these resources for future generations.
80
Cryptic marine invertebrate diversity of Timor-Leste and benthic composition
Photo top and bottom: PIFSC, 2017
Middle: CREP, 2018
81 Abstract
Cryptofauna, defined as the “inner life of coral reefs,” live within the small holes and crevices of the reef matrix and are essential to reef processes such as nutrient cycling, bioerosion, and trophodynamics. Despite their ecological importance, cryptofauna have been predominantly ignored in coral reef assessments, partly due to two challenges: (1) taxonomically identifying this vast and diverse community, and (2) extracting cryptofauna from the reef matrix. However, the advent of high throughput DNA sequencing, combined with the development of standardized sampling units called Autonomous Reef Monitoring Structures (ARMS), has facilitated cryptofauna assessments across habitats. Here, the diversity of cryptofauna in Timor-Leste from four size fractions of cryptofauna, (> 2 mm, 500 μm–2 mm, 106–500 μm, and sessile) collected from ARMS deployed across the north coast of Timor-Leste was examined. The > 2 mm size fraction specimens were sorted into morphospecies groups and counted. The brachyuran crabs from this fraction were DNA barcoded and the remaining size fractions were processed via DNA metabarcoding techniques. Benthic composition groups were compared with brachyuran crab diversity and correlated with corresponding metabarcoded sequences. A locally estimated scatterplot smoother (LOESS) regression was plotted with overall metabarcoded diversity and hard coral and rubble cover. The > 2 mm cryptofaunal community was significantly different between sampling stations with arthropods, echinoderms, and molluscs being the most common phyla. Thirty-seven percent of the brachyuran crabs were new barcodes and overall crab diversity was significantly correlated with the amount of massive coral. DNA barcoding revealed 6,740 Operational Taxonomic Units (OTUs), 19.2% of which were unclassified. Arthropods and annelids were the most abundant taxa overall except for the sessile fraction where poriferans and molluscs were the most abundant. Hard and soft coral metabarcoded sequences correlated with the corresponding benthic cover of the same groups. There were negative and positive relationships between overall diversity and percent live coral cover and rubble, respectively. The analyses here supports the Timor- Leste cryptofaunal communities as one of the most biodiverse coral reef regions globally. Additionally, the results corroborate rubble as a driver of these hidden organisms as previously described. Overall, there are still many challenges to fully quantifying cryptofaunal diversity—namely a lack of sequences in public genetic repositories and sampling. The biodiversity and role of these organisms on reefs requires further investigation, especially in the context of a changing world. 82
Introduction
The biodiversity of cryptofauna inhabiting the substructure of reefs (Choi and Ginsburg, 1983; Meesters et al., 1991) have been relatively unexplored. A few conspicuous groups such as fishes and corals have received considerable attention due to their presence in shallow water reef systems. These few well-studied taxa have often been used as proxies for biodiversity assessments of reef ecosystems. The use of surrogates, or few taxa representing overall biological diversity (Ward et al., 1999), may not accurately capture patterns across all organisms (Beger et al., 2007; Bellwood and Hughes, 2001; Mellin et al., 2011). It is important to accurately assess the diversity of less understood groups such as cryptofauna given that many play important roles in functioning reefs.
Ecosystems such as coral reefs are highly biodiverse and are integral in maintaining ecological resilience. High levels of biodiversity provide potential ecological redundancy where multiple species may contribute to specific functional roles, such as herbivory, nutrient cycling, calcification, and bioerosion (Naeem, 1998). The ecological function of one species that is lost from an ecological setting can be replaced by other species (performing a similar role), thereby continuing to maintain the ecological function in the ecosystem. This insurance effect is especially important on long timeframes in fluctuating environments (Lawton and Brown, 2012; Loreau, 2000; Walker, 1992; Yachi and Loreau, 1999). Greater species diversity allows for a larger pool of functional traits, similar to genetic diversity, that may be selected with unpredictable environmental change enabling continued ecosystem function (Norberg et al., 2001; Yachi and Loreau, 1999). Ecosystems that are sensitive to environmental change, such as coral reefs, may benefit from increased resiliency provided by the functional redundancy attributed to high levels of biodiversity. However, functional redundancy can be challenging to measure (Loreau, 2000).
Ecological roles of cryptofauna on coral reefs Cryptofauna, also known as ceolobites, includes a wide array of organisms that live within small holes and crevices of the reef matrix (Choi and Ginsburg, 1983; Meesters et al., 1991). The voids within reefs include a considerable amount of habitat, taking up an estimated 50– 70% space of the total reef volume including the reef matrix of living corals on the surface, underlying skeletal structure, and other calcareous biogenic materials (Choi and Ginsburg, 1983). The invisible structure beneath also encompasses 60–75% of the total surface area (Buss & Jackson, 1979; Jackson et al., 1971; Logan et al., 1984). Thus, the numbers and 83 biomass of cryptofauna usually equal or exceed that of the surface dwellers (Choi and Ginsburg, 1983; Enochs, 2012; Fautin et al., 2010; Grassle et al., 1973; Knowlton et al., 2010; Small et al., 1998). As such, cryptofauna are an important component of reef processes such as nutrient cycling, bioerosion, and trophodynamics and different invertebrate groups such as corals, crustaceans, soft corals, and sponges, play vital roles in reef function (Achlatis et al., 2019; Rasheed et al., 2002; Richter and Wunsch, 1999; Szmant-Froelich, 1983).
Arthropods, echinoderms, and molluscs are the dominant taxa across most cryptofaunal studies (Bouchet, 2006). While investigating cryptofauna generally, this study focused particularly on crustaceans from the mobile, > 2 mm size fraction collected with ARMS. Crustaceans in other studies have received considerable attention, with well-developed DNA sequencing techniques (e.g., barcoding; Bouchet, 2006; Plaisance et al., 2011b). Brachyuran crabs, within crustacea, are the most common decapods in numerous ARMS studies and are important players on reefs with diverse ecological roles (Plaisance et al., 2011a, 2011b). Two of the 30 families found on coral reefs, Trapeziid and Tetraliid crabs, are well-known obligate commensals with hard corals (Castro, 1976; Patton, 1966; Paulay et al., 2003; Stewart et al., 2006). These obligate corallivores feed on coral mucus and tissue while providing beneficial services to the host, including decreased mortality rate, faster growth, less sediment load, less bleaching, and protection from crown-of-thorns seastars (COTS; Knudsen, 1967; Pratchett, 2001; Rotjan and Lewis, 2008; Stewart et al., 2006; Stimson, 1990). Coral commensals rely on cues from their host for settlement and coral colonies ~ 1 year post-settlement have been observed hosting juvenile crabs (Stewart et al., 2013). ARMS deployments, on the order of years, would be enough time for corals to settle on the plates and, thus, attract coral commensals such as coral crabs. Other symbiotic relationships involving invertebrates have demonstrated nutritive benefits to the host (Mokady et al., 1998; Spotte, 1996). Invertebrates, such as crustaceans, are important in nutrient cycling incorporating particulate organic matter into the reef food web by consuming fecal matter from planktivorous fishes (Rothans and Miller, 1991). Macro and micro- predators such as stomatopods and isopods can significantly alter the distribution of their prey (Jones and Grutter, 2008; Reaka, 1987) and are, in turn, sources of food for nocturnal fishes (Reichelt, 1982).
Conventional benthic composition techniques often quantify the cover of non-hard coral invertebrate groups such as soft corals and sponges, which can be both conspicuous and 84 cryptic (Bayer, 1957). Both groups have high diversity in Indo-Pacific reefs (Bayer, 1957; van Ofwegen, 2000), in addition to being effective competitors for space with hard corals (Bell and Smith, 2004; Dai, 1990; Jackson and Winston, 1982). Additionally, sponges can play very important roles as bioeroders (Goreau and Hartman, 1963; Pomponi, 1980; Schönberg et al., 2017; Zundelevich et al., 2007), filtering large quantities of water (Pile et al., 1997), and are basal to the food web converting dissolved organic matter to particulate organic matter (Achlatis et al., 2019; de Goeij et al., 2013). Soft corals have been classified as pioneering colonizers of cleared substrate and, once established, are very stable with a higher disturbance tolerance compared to branching and tabulate hard corals (Done, 1992b; Fabricius, 1995; Ninio and Meekan, 2002).
Habitat structure, benthic composition, and species biodiversity The habitat heterogeneity hypothesis states that greater species diversity is fostered by more niches and methods of exploiting resources (Tews et al., 2004). This relationship between habitat and species diversity has been demonstrated in both terrestrial and marine ecosystems and is reliant on scale (micro, macro, etc.) and taxa groups studied (Fabricius et al., 2014; Ralph, 1985; Sullivan and Sullivan, 2001; Tews et al., 2004). However, the majority (61%) of habitat and species diversity studies focus on vertebrates (Tews et al., 2004), which only account for 3% of all animal species (May, 1988). Thus, most global species diversity is largely ignored. Corals are the structural engineers of reefs, driving the habitat complexity that other reef organisms rely on (Graham and Nash, 2013; Kovalenko et al., 2012; Wild et al., 2011). There is debate as to the relationship between the structural complexity of reef habitat and the diversity among resident cnidarians, fish, and cryptofauna. A higher proportion of live coral cover has been linked to more diverse communities through a visual survey of conspicuous macroinvertebrates (Idjadi and Edmunds, 2006). Conversely, the opposite effect has also been demonstrated with reef cryptofauna communities assessed across a degradation framework of varying live coral cover and intact reef framework (Enochs, 2012; Enochs and Manzello, 2012a).
3.2.2.1 Coral reef degradation framework
For coral reefs, habitat heterogeneity has been previously defined as a degradation framework based on contiguous zones of coral cover and framework composition and integrity (i.e., intactness). The four zones were defined as follows: low degradation framework (LDF) required greater than 80% live coral and intact frameworks; medium
85 degradation framework (MDF) was defined as 20–80% coral cover and eroded large framework fragments; high degradation framework (HDF) had < 20% coral cover and highly eroded frameworks with rubble; and lastly, unconsolidated coral rubble surrounding the reef framework (Enochs, 2012; Enochs and Manzello, 2012a). Overall, dead coral substrates housed a greater number of Operational Taxonomic Units (OTUs, akin to species) of motile cryptofaunal compared to live coral colonies. Species richness was found to increase with decreasing coral cover from LDF (107.1 Chao1 estimated richness) to rubble frameworks (276.0; Enochs and Manzello, 2012a). It should be noted that the MDF range of coral cover is very large and may be too high as even the LDF condition in the study had an average of 76.67% coral cover (± 9.21%, 95% CI), which was still within the 20–80% MDF range (Enochs, 2012). Coral reefs with > 80% coral cover are a rare occurrence, for example, the maximum subtransect coral cover from the kilometer-scale phototransect data presented in Chapter 2 was 64%.
Dead coral frameworks that house greater species diversity than live coral can be explained through the heterogeneity hypothesis and greater niche habitat availability (Enochs and Manzello, 2012a; Idjadi and Edmunds, 2006; Tews et al., 2004). These substrates can be colonized by sessile taxa whereas living coral tissue would inhibit settling of other organisms. Colonization by algae and encrusting sessile fauna would enable the occupation of herbivores and grazing carnivores. Additionally, sediments typically swept off by live coral tissue can accumulate on dead surfaces providing food for deposit feeders (Preston and Doherty, 1994). On reefs, it appears that dead coral with diverse food sources and sheter promotes species diversity of cryptofauna (Kohn and Leviten, 1976; Tews et al., 2004).
3.2.2.2 Cryptofaunal species diversity, abundance, and biomass relationships to habitat
The observation that the most eroded degradation framework may foster the highest species diversity of cryptofauna is somewhat counterintuitive. Reef framework erosion is an important driver of cryptofaunal diversity as it increases structural complexity quickly providing diverse shelters (McCloskey, 1970; Moran and Reaka, 1988; Moran and Reaka- Kudla, 1991). Rubble provides refugia from epibenthic and nektonic predators, while potentially harboring vulnerable epibenthic juveniles. This facilitates the development of unique, unspecialized communities controlled by within-framework predation limiting the dominance of a single species (Bakus, 1966; Moran and Reaka, 1988; Reaka, 1985). Additionally, the mobility of rubble zones limits the recruitment and growth of space
86 competitive fauna, further supporting diversity (Jackson, 1985). Of course, at the extreme of framework erosion, coral rubble is broken into sand and silt reducing habitable space or protection from surface predators, therefore decreasing community diversity (Bailey-Brock et al., 2007).
Interestingly, species abundance and biomass showed the opposite relationship, where live corals harbored greater abundance and biomass of cryptofauna per unit volume than dead coral substrates (Enochs, 2012). For dead coral substrates specifically, MDF encompassed the highest biomass and abundance of cryptofauna per unit volume. Additionally, live versus dead coral habitats supported different community compositions. For example, Pocillopora damicornis colonies were dominated by arthropods (83.0%), while species diversity within dead coral substrates was more evenly spread between echinoderms (36.0%), arthropods (32.9%), and molluscs (25.0%; Enochs, 2012). All corals, however, do not contribute equally and large size-class P. damicornis colonies (the maximum diameter of 33 cm) were found to house disproportionately high abundances and biomass of cryptofauna. This applied to spherical colonies, and the effect is lost with corals that become more planar with increasing size (Enochs, 2012). Additionally, the structure and abundance relationship vary between taxa. A visual assessment of a natural CO2 seep found crustaceans and echinoderms had reduced numbers in less complex quadrats, while bivalves and polychaetes were more prevalent with less structure. Other groups had the highest abundance at intermediate levels of habitat complexity (Fabricius et al., 2014).
Living corals are undoubtedly important for supporting reef communities. Many corals build distinct spatial structures providing resources, shelter, or other services that other taxa groups, mainly fish, use (e.g., Kerry & Bellwood, 2015; Wilson et al., 2019). This, however, does not appear to be the case for cryptofaunal communities as described above. Corals seem to support unique, specialized cryptofaunal communities as they are relatively inhospitable environments with an array of defense mechanisms (e.g., mesenterial filaments, sweeper tentacles, and allelopathic tentacles; Lang and Chornesky, 1990), cnidocytes (e.g., specialized stinging cells), and the production of large volumes of mucus (Kirsteuer, 1969). Corals also provide a suite of foods sources from metabolic products (Knudsen, 1967; Rotjan and Lewis, 2008; Stimson, 1990) and protection from predators as physical shelter (Bakus, 1966; Edwards and Emberton, 1980; Vytopil and Willis, 2001), These factors may support greater abundance and biomass of a less diverse community in live corals. Many cryptic coral symbionts are crustaceans specialized for coral environments 87 with highly evolved mandibles and walking legs. The chitinous exoskeletons of these organisms could also protect against cnidocyte stings and facilitate the dominance of arthropods in live corals (Bruce, 1976; Enochs, 2012).
Although the apex of coral reef cryptofaunal communities is found in rubble habitats, maintenance of live coral is essential given their role as reef framework builders. Coral calcification must equal or exceed erosion to preserve reef frameworks (Enochs and Manzello, 2012b). The loss of live coral will ultimately result in the loss of cryptofaunal communities, albeit at a delayed timescale, when rubble erodes to sand and silt (Lang and Chornesky, 1990). Furthermore, much of the work studying the relationship between reef heterogeneity and cryptofaunal diversity was conducted in the Eastern Pacific which has relatively depauperate diversity compared to the CT (Enochs and Manzello, 2012b). Here the relationship between cryptofaunal species richness and reef benthic composition was assessed.
Novel methods of systematically quantifying coral reef cryptofauna Until recently, most work done on cryptofauna has been from visual surveys including manually sorting through substrates (Bouchet et al., 2009; Enochs, 2012; Fabricius et al., 2014; Idjadi and Edmunds, 2006; Plaisance et al., 2009). Cryptofauna are, by definition, difficult to detect and study, and sampling on coral reefs typically involves destructive methods which are labor and time intensive (Enochs and Manzello, 2012b, 2012a; Plaisance et al., 2011b, 2009). Additionally, the conventional taxonomy of cryptofauna is time-consuming, requiring a high level of taxonomic expertise across a suite of phyla (Knowlton, 2000, 1993). These challenges are being addressed with the use of artificial structures and the increasing affordability of genetic methods, including DNA barcoding and high throughput sequencing (Hebert et al., 2003a). This combined approach can detect and catalog many more species than visual assessments (Bourlat et al., 2013; Leray and Knowlton, 2015; Plaisance et al., 2009).
3.2.3.1 Effect of artificial substrates on cryptofaunal communities
Artificial substrates have been used for settlement and recruitment experiments of invertebrates for decades (Adey and Vassar, 1975). More recently, biodiversity studies have deployed settlement plates. The standardization of surveying methods takes some of the above factors into account allowing for the quantification and identification of marine cryptofauna at an unprecedented level. In this regard, several studies have tested the 88 efficacy of different materials used for settlement plates targeting epibenthic organisms including terracotta, concrete, metal, ceramic tile, tires, and polyvinyl chloride (PVC), all with varying results (Adey and Vassar, 1975; Burt et al., 2009; Fitzhardinge and Bailey-Brock, 1989; Harriott and Fisk, 1987; Kennedy et al., 2017; Mallela et al., 2017; Salinas-de-León et al., 2011). A study between ceramic tiles and PVC poles found that crustose coralline algae (CCA) recruited best to the PVC poles although the recruited communities were not ground-truthed to adjacent communities (Mallela et al., 2017). Recruitment of CCA on PVC tiles was found to be the most representative of adjacent adult communities (Kennedy et al., 2017) while other factors, such as site differences, have been deemed more influential than substrate material for coral settlement (Minton et al., 2007; Salinas-de-León et al., 2011). Of course, many other parameters like plate orientation (Birkeland et al., 1981; Carleton and Sammarco, 1987; Kennedy et al., 2017; Salinas-de-León et al., 2011), plate size (Field et al., 2007), surface complexity (Carleton and Sammarco, 1987; Diaz-Pulido and McCook, 2004; Whalan et al., 2015), reef geomorphology (Kennedy et al., 2017; Tomascik, 1991), attachment method (Mundy, 2000), and experimental period of deployment (Field et al., 2007) affect settlement and recruitment.
Standardization also provides an avenue for monitoring for future change (Bourlat et al., 2013). ARMS were created with these goals in mind (Leray and Knowlton, 2015), and systemic marine cryptobiota sampling has begun to be quantified across a variety of marine habitats, including coral reefs (Al-Rshaidat et al., 2016; Hurley et al., 2016; Leray and Knowlton, 2015; Plaisance et al., 2011b, 2009; Ransome et al., 2017; Servis et al., 2020). In addition to artificial substrate material, temporal and spatial variability of both pre- and post-settlement stages influences population heterogeneity in sessile invertebrates (Connell, 1985; Fraschetti et al., 2002; Stoner, 1990; Thorson, 1966). Pre-settlement events (primary recruitment limitation), involve water column processes that affect planktonic larval mortality and larval supply (Fraschetti et al., 2002), while post-settlement (secondary recruitment limitation) affects early post-larval supply, or realized recruitment through benthic settler mortality (Hunt and Scheibling, 1997). Several physical and ecological processes affect the decoupling of settlement to the adult community including predation, competition for food and space, life-histories, herbivory, water movement, light availability, and so on (reviewed in Pineda et al., 2010). Both pre- and post-settlement events on artificial substrates would be influenced by the immediate surrounding environment in terms of availability of resources such as food and nutrients. Additionally, different taxa reach mature
89 communities on settling plates at different rates, and environments (forereef, lagoon, etc.) within groups can influence growth rates (Adey and Vassar, 1975). There is debate on the relative importance of pre- and post-settlement limitations on adult communities as these processes depend greatly on taxa and context (Menge, 2000, 1991). ARMS are intended to capture cryptofaunal communities representative of the larger reef area and are, therefore, deployed on the scale of years (two years in Timor-Leste). Thus, on longer timescales, ARMS communities are more likely to be dependent on post-settlement limitations such as predation, which is highly influential in maintaining tropical species diversity (Freestone et al., 2011).
3.2.3.2 Genetic approaches of quantifying cryptofaunal diversity
In conjunction with standardized sampling methods, genetic analyses greatly increase the capacity for assessing cryptofaunal diversity. DNA barcoding utilizes a short segment of DNA, or a barcode, for species identification (Hebert et al., 2003a; Tautz et al., 2003). The Cytochrome c Oxidase Subunit I (COI) region on the mitochondrial gene, a highly conserved region, is suitable for sequencing and identifying species across different taxa (Brown, 1985; Hebert et al., 2003b). The technique utilizes the robust universal primers HCO2198 and LCO1490 which can distinguish almost all animal phyla (Folmer et al., 1994). These primers have been updated (jgHCO2198 and jgLCO1490) to decrease degeneracy rates (Geller et al., 2013). COI barcoding has shown reliable accuracy for marine invertebrate groups such as gastropods and crustaceans (Meyer and Paulay, 2005). The most recent advances in assessing cryptofaunal diversity has involved high throughput sequencing. While DNA barcoding provides species-level data at the individual level, metabarcoding captures free- living organisms in the entire community (Leray and Knowlton, 2015). DNA barcoding is not without its critics, who describing it as a shortcut approach funneling away much-needed funds for taxonomy (Will et al., 2005). However, identification of species via traditional morphological methods is time-intensive and requires expert training. Best practices point to using a combination of genetic and taxonomic approaches for integrated taxonomy (Dayrat, 2005; Will et al., 2005).
Cryptofaunal communities typically have a high degree of uniqueness and endemism, posing another challenge for traditional taxonomy. The sampling and taxonomic identification difficulties that lead to the underestimation of cryptic biodiversity would likely also fail to fully quantify endemic species as dense geographic sampling and large sample
90 sizes per site would be necessary (Hortal et al., 2006; Plaisance et al., 2009). For example, 44% of cryptic crustacean operational taxonomic units (OTUs) collected from dead Pocilloporid colonies were singletons, or species only sampled once, out of 403 successful sequences. Also, 33% of OTUs were unique to one out of six sampled islands (Plaisance et al., 2009). A similar study with 525 unique crustacea OTUs collected from dead coral heads and ARMS artificial sampling devices across reefs in the Indo-Pacific to Panama indicates a similar percentage of singletons and even higher locality uniqueness at 81% (Plaisance et al., 2011a). The use of genetic techniques allows for a greater resolution to patterns of diversity across local to global scales, but are reliant on existing genetic databases.
3.2.3.3 Autonomous Reef Monitoring Structures
ARMS are used to capture four fractions of differing cryptofauna communities: a > 2 mm fraction, which is typically barcoded at an individual level, the 500 μm–2 mm and 106–500 μm fractions which target meiofauna, and the sessile fraction, or all organisms attached to the main surfaces of ARMS plates (bulk scrapings). Organism size influences trends in biodiversity with communities of smaller organisms harboring more diversity (Leray and Knowlton, 2015). Sorting ARMS into different size classes allows for the testing of size as a factor in cryptofaunal diversity. Leray and Knowlton (2015) analyzed six ARMS deployed on oyster and subtropical coral reefs and found the > 2 mm to be much less diverse than smaller size fractions partly due to the nature of barcoding versus metabarcoding. The smallest size fraction (106–500 μm) had 1.54–1.96 times greater rarefied diversity than the other two metabarcoded sizes. DNA metabarcoding was found to be reliable for OTU presence- absence and useful for OTU relative abundance where accuracy increases at coarser resolutions such as phylum (Leray and Knowlton, 2015).
Arthropods and annelids were consistently abundant in all fractions across previous ARMS studies; however, poriferans, bryozoans, cnidaria, and chordates typically account for a larger proportion of reads in the sessile fraction (Leray and Knowlton, 2015; Pearman et al., 2018; Ransome et al., 2017). Plaisance et al. (2011b) compared cryptofaunal communities from 14 Pocilloporid dead corals and nine ARMS units deployed simultaneously at Heron Island Research Station in the southern Great Barrier Reef (GBR). The Bray-Curtis similarity indices (BCI) of pairwise comparisons between methods (BCI = 0.177) were similar to the pairwise comparisons between dead coral heads only (BCI = 0.191). Overall, the BCIs of pooled dead corals and ARMS (BCI = 0.41) and pooled ARMS (BCI = 0.53) were within the
91 range of the reported BCI mean (0.359–0.667) for within site similarity of corals across the Indo-Pacific (Dornelas et al., 2006). Additionally, the BCIs of pooled dead corals and ARMS at Heron Island were much higher than any between site BCIs (0.002–0.240) between other coral reef regions (i.e., Ningaloo, Moorea, Line Islands, French Frigate Shoals) compared in the study (Plaisance et al., 2011a).
Intra- and inter-site diversity similarity has also been investigated at various scales. The ARMS units deployed within two reef types, a temperate oyster reef, and coral reef, were more similar to each other than across habitat types. At regional scales, it seems that local habitat and environmental factors influence differences in marine diversity. Plaisance et al., (2011b) assessed cryptic crustacean diversity of ARMS across different reef habitats in an atoll in the Northwest Hawaiian Islands. ARMS were placed in forereef, backreef, and lagoon patch reef habitats, at four sites total (i.e., one backreef and lagoon site, and two forereef sites). The two forereef sites were more alike, despite having the largest geographic distance between these sites. The four sites on the same atoll were more similar to each other in comparison to similar surveys conducted on reefs across the Indo-Pacific. However, the two forereef habitats across the two sites (atoll and Indo-Pacific) displayed greater community similarity than comparing different reef zones across the two sites (Plaisance et al., 2011b). These findings support the theory that broad habitat types of the local and/or adjacent reef structure has a strong influence on cryptofaunal community composition (Plaisance et al., 2011a).
Aims and objectives
In this chapter, the diversity of coral reef cryptofauna present in the outer reef slope habitats of Timor-Leste’s north coast was explored. Notably, the present study is among the first to investigate the cryptic biodiversity of coral reef habitats in Timor-Leste. It was expected that greater heterogeneity of reef habitats would lead to a greater diversity of cryptofauna in the ARMS, but that species richness would be highest at sites with significant dead substrate available such as dead coral rubble.
The study focused on several questions concerning the diversity of coral reef cryptofauna of Timor-Leste. They were:
92 1) How do benthic composition and cryptofaunal biodiversity, as measured by DNA barcoding of brachyuran crabs and high throughput DNA metabarcoding from ARMS units, vary across established National Oceanic and Atmospheric Administration (NOAA) climate stations along the north coast of Timor-Leste?
2) What are the potential drivers of the benthic composition on brachyuran crab diversity collected from the > 2 mm fraction from ARMS?
3) Does cryptofaunal biodiversity correlate with local community structures as measured by NOAA phototransects at the climate stations taken in 2014? How well do the ARMS capture the composition of the surrounding benthos based on phototransects?
Methods
Autonomous reef monitoring structures (ARMS) ARMS were developed to systematically survey marine cryptofauna as a part of the Census of Marine Life. The Census was a ten-year initiative that used artificial units of constant size and volume across a variety of marine habitats including coral reefs (Knowlton et al., 2010). ARMS were installed and processed in Timor-Leste using the standardized approach that has been used to deploy over 800 units worldwide (PIFSC, 2017). Units are comprised of nine 22.5 x 22.5 cm PVC plates constructed into a cube structure with alternating enclosed and semi-enclosed layers (Leray and Knowlton, 2015; PIFSC, 2017; Figure 3-1).
93 Figure 3-1 Newly deployed Autonomous Reef Monitoring Structures (ARMS) at National Oceanic and Atmospheric Administration Station 1 Coral Gardens (Timor-Leste) in July 2014. Three weighted units were deployed at each station except Station 8 Jaco Island where four were deployed. Square plates of ARMS are 22.5 x 22.5 cm. Photo: CREP, 2018.
Thirty-two ARMS were deployed at ten climate monitoring stations in Timor-Leste on a NOAA expedition from October 15–25th, 2012 at 12–15 m depth in replicate sets of three (four were deployed at two stations). ARMS were positioned within a 10 x 5 m area of reef in conjunction with other NOAA monitoring equipment including temperature loggers and calcification accretion devices. Two years later (September 17th–October 19th, 2014), 25 ARMS were recovered from eight stations. At this time, 15 m phototransects were also collected at each climate station (discussed in Chapter 2). Two stations were unable to be accessed for ARMS recapture (PIFSC, 2017).
94 Figure 3-2 Eight NOAA climate stations were established across the north coast of Timor- Leste in 2012. Twenty-five ARMS were deployed and recovered with three per station (except for Station 8 where four were deployed). Brachyuran crabs from the mobile, > 2 mm size fraction from all stations, minus Station 7, were DNA barcoded. DNA metabarcoding was conducted on homogenized samples of the remaining size fractions (500 μm–2mm, 106 μm–500 μm, and sessile) per ARM.
Upon retrieval, ARMS were encapsulated within a 106 μm nitex-lined crate, brought to the surface, and transported to a land-based processing location where they were disassembled plate by plate. Water from the processing container was filtered through three sets of sterilized sieves, 2 mm, 500 μm, and 106 μm. The > 2 mm size fraction was sorted and counted to morphospecies and the brachyuran crabs were preserved in 25% dimethyl sulfoxide (DMSO) for DNA barcoding (Leray and Knowlton, 2015; PIFSC, 2017, Figure 3-3). Due to logistical, time, and monetary constraints, only brachyuran crabs, the most abundant of the decapods from previous ARMS studies, were DNA barcoded (Plaisance et al. 2009, 2011b).
95 Figure 3-3 Preserved brachyuran crabs for DNA barcoding collected via Autonomous Reef Monitoring Structures deployed at eight climate stations across the north coast of Timor- Leste in 2014. Photo from PIFSC (2017) report.
The 500 μm–2 mm and 106–500 μm size fractions (henceforth referred to as 500 μm and 106 μm fractions) were decanted as in Leray and Knowlton (2015) to separate the sediments from the biological materials for metabarcoding. Sessile biomass was scraped from each ARMS plate (Figure 3-4), homogenized, filtered through a 40 μm net, subsampled, and preserved in 25% DMSO for metabarcoding.
96 Figure 3-4 Photographs of Autonomous Reef Monitoring Structures plates from a single unit recovered from climate Station 2 Beloi, Timor-Leste in 2014. There are nine plates per ARMS unit and the topside and underside of each plate are photographed (except for the bottom plate) for 17 total plate images. Plates with cross hatches represent semi-closed layers. Plates were scraped and homogenized as the sessile size fraction before sampling for DNA metabarcoding. Surface area is ~ 0.09 m2 per ARMS unit. Photo from PIFSC (2017) report.
DNA barcoding Crab legs were subsampled and placed into 96 well Costar plates (Corning) for total genomic DNA extraction using the AutoGenPrep 965 Automated DNA Isolation System (AutoGen, Inc, Holliston, MA) at the Smithsonian Institution’s (SI) Laboratories of Analytical Biology. DNA was eluted to a final volume of 120 μl with 20 μl as the working stock. Ten μl polymerase chain reaction (PCR) amplifications were performed with 1 μl of DNA extract, 0.3 μl of 0.5mM jgLCO1490 forward and jgHCO2198 reverse primers, 2.5 mM MgCl2, 1.75 μl 10x buffer, 0.25 μl of 10mg/μl bovine serum albumin, 0.5 μl of 0.5mM dNTPs, and 0.1 μl Taq polymerase. The PCR cycle conditions consisted of 3 mins at 95˚C followed by 40 cycles of 30 s at 95˚C, 30 s at 42˚C, 45 s at 72˚C, and 5 mins at 72˚C. PCR products were run on a 2% agarose gel stained with GelRed (Biotium) purified with Exo-SAP-IT (Affymetrix), and cycle-sequenced using ABI BigDye terminator V3.1 (Applied Biosystems). Sequencing reactions were cleaned using Sephadex G-50 (GE Healthcare) and run on an ABI 3730 genetic analyzer (Applied Biosystems) in both directions. The sequence data are 97 clustered into Operational Taxonomic Units (OTUs) via bioinformatics and a 5% dissimilarity threshold for species discrimination (Plaisance et al. 2011b).
DNA metabarcoding DNA metabarcoding laboratory work was completed by collaborators at NOAA and the SI National Museum of Natural History. Out of a total of 75 samples from 25 ARMS (three size fractions per ARMS), 66 were metabarcoded with the following protocol. DNA was extracted using MO-Bio PowerMax Soil extraction kits from 10 g of homogenized sessile scrapings and decanted 500-μm and 100-μm meiofauna fractions. Extractions were quantified using the Biotum AccuClear Ultra High Sensitivity Quantification Kit (BAUHSQ) and examined on agarose gels. A 313 base pair fragment of COI was amplified using the reverse primer, jgHCO2198 (Geller et al., 2013), and the degenerate forward primer, mlCOIintF (Leray et al., 2013), in conjunction with a PCR touchdown protocol performed in triplicate with 16 initial cycles: denaturation for 10 seconds at 95°C, annealing for 30 seconds at 62°C (–1°C per cycle), and extension for 60 seconds at 72°C, followed by 25 cycles at 46°C annealing temperature (Leray et al., 2013; Leray and Knowlton, 2015). PCRs were inspected on agarose gels and triplicate PCR products were pooled, cleaned using Agencourt AMPure beads, and quantified using BAUHSQ. PCR products were ligated to dual-end Illumina adapters using the Kappa Systems Hyper-Prep sample kit. Resulting sample libraries were validated by visualization on an Agilent 2100 BioAnalyzer, quantified using qPCR, pooled, and sequenced on an Illumina MiSeq platform (Appendix 1.8.2).
Forward and reverse reads were merged using PEAR (Zhang et al., 2014) and demultiplexed using FASTX Barcode Splitter (http://hannonlab.cshl.edu/fastx_toolkit). Primers were removed using a multistep process. The -g linked adapter parameter in Cutadapt version 2.3 (Martin, 2011) was applied on the merged fastq files, specifying the output to be trimmed only sequences. The original fastq files were reverse complemented using USEARCH (Edgar, 2010) and run through the same Cutadapt specifications. Lastly, the two Cutadapt outputs were concatenated into one trimmed fastq file per sample. Sequences below or above the 295-340 bp length were removed with Cutadapt. Low-quality sequences were filtered and discarded using UPARSE fastq_filter with maxee = 1 and qmax at 60 (Edgar and Flyvbjerg, 2015). Sequences were dereplicated (min. unique size = 2) and clustered with simultaneous chimera removal using USEARCH (cluster_otus 97% identity).
98
The pre-processed dereplicated reads of all samples (including singletons) were matched against the respective OTUs with a minimum match of 97% using usearch_global and strand plus within USEARCH. Singleton OTUs were removed in the resulting cluster output and only OTUs with a read abundance above 0.01% in at least one sample were considered in the downstream analysis to reduce the number of false positives due to PCR and sequencing errors (Bista et al., 2017; Bokulich et al., 2013; Elbrecht et al., 2017). Resulting OTUs were annotated based on a local BLASTn against a curated reference database containing 16,679 COI sequences specific to coral reef fauna from the Moorea Biocode Inventory (Meyer C. Moorea Biocode Project FASTA data Merritt: Collection: Moorea Biocode Collection ark:/13030/m5478zfg. California Digital Library Version 1: 2016–05–14) and additionally assigned using the R package, Informatic sequence classification trees (INSECT), that takes a probabilistic approach (hidden Markov model) to assignment against a classification tree built from 396,413 sequences extracted from the MIDORI database and GenBank (Wilkinson et al., 2018). To control for the effects of library size estimates (numbers of sequences; Gotelli and Colwell, 2001; Weiss et al., 2017) sample fractions (sessile, 106 – 500 µm, and 500 µm-2mm size fractions) were subsampled to an even depth of 69,750 sequences. Phyla with low sequence abundances (Chaetognatha, Chordata, Echiura, Entoprocta, Gastrotricha, Hemichordata, Kinorhyncha, Nematoda, Nemertea, Platyhelminthes, Sipuncula, Xenacoelmorpha) and plantae (Chlorophyta, Rhodophyta, Ochrophyta) were pooled and cnidaria was split into scleractinia and non-reef building cnidaria (non-scleractinian OTUs) for subsequent statistical analyses.
Benthic composition at NOAA stations A single phototransect, ranging from 26 to 30 photos, was taken at every climate station during the 2014 NOAA expedition (PIFSC, 2017). Sampling at each station was conducted in a 5 x 10 m area of the reef. This dataset was used as a metric of comparison of benthic composition in Chapters 2 and 4 and for habitat comparison to cryptic biodiversity measured here (Chapter 3). Using a digital camera mounted on a pole, approximately 30 images per transect were taken at 1 m intervals along either side of a 15 m transect covering 0.04 m2 reef area per image. Randomized point benthic image analysis was conducted on ten points per image in Coral Point Count using the NOAA two-tiered benthic classification scheme (Kohler and Gill, 2006). Tier 1 consists of the major benthic functional groups (i.e., hard coral, soft coral, CCA, turf algae, macroalgae and seagrass, sessile invertebrate, mobile fauna, sediment, and unclassified). Tier 2 breaks down these functional groups further into 99 eight morphological categories for hard coral (i.e., branching, columnar, encrusting, foliose, free-living, massive, tabular, and non-scleractinian), CCA and turf algae were classified as growing on rubble or hard substrate, and sessile invertebrates were split into taxonomic groups such as sponges and giant clams (PIFSC, 2017).
Statistical analyses Statistical analyses were performed using R version 3.6.3 (The R Core Team, 2020) and PRIMER7 (Anderson et al., 2008; Clarke and Gorley, 2015). To determine station level differences in benthic composition and coral morphological composition, a permutational analysis of variance (PERMANOVA, 9,999 permutations in PRIMER7) was used on Bray- Curtis similarity of fourth root transformed benthic composition (eight groups: coral, soft coral, sponge, algae, sand, rubble, hard substrate, and other) and square root transformed coral morphology (branching including columnar and tabulate morphologies, massive, encrusting, foliose, and free-living) from the image analysis of phototransects. Non- parametric Kruskal-Wallis tests (kruskal.test in R) were used to test for differences in the percent distribution of the cover of hard coral, soft coral, rubble, and coral morphologies between NOAA climate stations. Dunn’s test with Bonferroni corrections was used for pairwise comparisons (dunnTest in FSA R package; Ogle et al., 2020). Sponge cover was zero-inflated and not tested for station level differences.
To test for differences of community composition for the > 2mm size fraction and crab abundance, a one-way PERMANOVA in PRIMER7 was performed on the Bray-Curtis similarity matrix on the fourth root transformed > 2 mm morphospecies counts grouped by phyla (Annelida, Arthropoda, Chordata, Cnidaria, Echinodermata, Mollusca, Nemertea, Platyhelminthes, Sipuncula) and crabs with the station as the factor. One-way analysis of variance (ANOVA) tests were performed on the most abundant > 2 mm phyla (arthropods, echinoderms, and molluscs) for station level differences. An nMDS plot was constructed (metaMDS in the vegan R package; Oksanen et al., 2019) on Bray-Curtis dissimilarity matrix of crab composition with significant factors from an environmental ordination (envifit in vegan) of benthic composition parameters plotted. Bray-Curtis dissimilarity was converted into similarity (i.e., similarity = 1–dissimilarity) to compare with BCI values in the literature.
Rarefaction curves were constructed using the rarefied OTU matrix (rarecurve in vegan). Subsequent community analysis at the phyla level was done in PRIMER7 using the classified sequence subset from the subsampled, standardized OTU matrix. To test whether 100 community composition varied by station and size fraction, a PERMANOVA was conducted on fourth root transformed, Bray-Curtis similarity matrix of the phyla classifications with station and size as random and fixed factors respectively. The similarity between communities was visualized via nMDS and phyla contributing most to the similarity between stations was determined using similarity percentages (SIMPER) analysis. One-way ANOVAs (aov in R) to elucidate station level differences were performed on the abundances of sequences for the following taxa: scleractinians, alcyonaceans, and poriferans.
To test how representative the metabarcoded samples were of the surrounding benthic composition, Spearman’s correlation tests (cor.test in R) were performed on classified sequences for hard coral, soft coral, and sponges from the sessile fraction and corresponding benthic category per site. Sequence data per ARM and image analysis data from all 232 quadrats were matched by the station. Additionally, correlations between coral morphological sequences and benthic classification were also conducted for each site. Tier 2 image classifications (from the NOAA classification system) were summed for consistent morphological groups. Branching, columnar, and tabular morphological forms were summed to a single branching category and encrusting was included with massive corals for correlations between ARMS coral sequences and coral cover from image analysis. Spearmen correlations were calculated between hard coral tier 2 morphologies (branching, foliose/plating, free-living, massive/encrusting) and coral sequences. Annotated hard coral sequences to family level classifications were assigned a morphology as described in Table 3-1 and summed by ARMS unit. OTU richness was calculated per station and plotted with average coral cover and rubble per station from benthic image analysis with locally estimated scatterplot smoother (LOESS) to test whether there was a significant relationship with cryptic diversity and the two benthic components.
Table 3-1 Morphological assignments of coral families from classified operational taxonomic units.
Morphological Coral Family Classification Massive/Encrusting Poritidae, Psammocoridae, Merulinidae, Agariciidae Branching Polcilloporidae, Dendophyllidae
101 Results
This study aimed to assess cryptofauna and its relationship to benthic composition across eight different coral reef slope locations in Timor-Leste using four different size classes. It was anticipated that Timorese reefs would harbor considerable cryptofaunal diversity considering corroborated with 78 brachyuran crab and 6,750 metabarcoded OTUs. Cryptofaunal communities were significantly different between sites; however, relationships to benthic components were less clear. Overall, there was considerable diversity across size classes, potential subspecies and endemism in the > 2 mm DNA barcoding, and a lack of matches for metabarcoded OTUs in the genetic public repositories such as GenBank.
The > 2 mm size fraction and DNA barcoding of brachyuran crabs ARMS deployed at the eight NOAA climate stations in Timor-Leste collected 2,535 individual organisms in the > 2 mm, mobile fraction. Community composition of the > 2 mm fraction was significantly different based on station (one-way PERMANOVA F(7,24) = 2.1481, p(perm) < 0.01) where Station 8 was significantly different from all other stations except for Station 3 (p < 0.05, Figure 3-5). Arthropods (one-way ANOVA F(7,17) = 14.87, p < 0.0001) and molluscs (one-way ANOVA F(7,17) = 4.291, p < 0.01) were statistically different by station, but not echinoderms. Arthropods were significantly more abundant at Station 3 (189.0 ± 13.4) while Station 8 had the most molluscs (83.0 ± 4.4; pairwise p < 0.05, Figure S3-1).
102 Figure 3-5 Visualization of the > 2 mm size class of 25 Autonomous Reef Monitoring Structures deployed along the north coast of Timor-Leste at eight climate stations from 2012–2014. A non-metric multidimensional scaling on a Bray-Curtis similarity matrix of the distance between station centroids was completed on the fourth root transformed community matrix grouped by phyla–2D stress = 0.03. Pie-charts indicate the abundances of individual organisms of the most abundant phyla. Dark blue–Annelida, red–Arthropoda, green–Chordata, light blue–Mollusca, and pink–Echinodermata.
3.5.1.1 Brachyuran crab diversity
A total of 269 out of 278 brachyuran crabs encompassing 16 families (Figure 3-6) were successfully barcoded from 22 ARMS in Timor-Leste. Crab samples from Station 7 Com were misplaced and unable to be analyzed. Thirty-seven percent (28 out of 75) of the OTUs were unique to Timor-Leste, likely a subspecies (Knowlton, 1993); most were rare with 45% of species only sampled once, and 25% of species sampled two to four times. Crabs varied significantly by station (one-way ANOVA F(7,17) = 11.12, p < 0.0001) with Stations 3 and 8 supporting the highest abundance of crabs (40.3 ± 3.8, p < 0.05). The most common species was Chloridiella laevissima found at all but Station 4 with 18 individuals in total.
103 Figure 3-6 Abundance of brachyuran crabs by NOAA climate station averaged by Autonomous Reef Monitoring Structures unit deployed in Timor-Leste from 2012–2014. Units have a surface area of ~ 0.09 m2. Crab groups are as follows: Coral crabs–Tetraliidae and Trapeziidae; Other–Acidopsidae, Carpiliidae, Dynomenidae, Leucosiidae, Palicidae, Parthenopidae, Percnidae, Pilumnidae, Xanthidae; Decorator–Epialtidae, Inachidae, Majidae, Pisidae; Swimming–Portunidae. Error bars represent standard error.
The rarefaction curves for crab OTUs do not reach asymptotes indicating that more sampling effort would increase the number of species found (Figure 3-7). Crab communities were significantly different between stations (p(perm) < 0.01) with Stations 1 and 8 driving this difference (Figure 3-8).
104 Figure 3-7 Rarefaction curve of individual brachyuran crabs sequenced versus the number of species per station collected via 22 Autonomous Reef Monitoring Structures (ARMS) in Timor-Leste from 2012–2014. ARMS were summed by station with three units per station except Station 8 where four were deployed. Numbered labels indicate individual climate station, see Figure 3-2.
The BCI ranged from 0.1013-0.3810 for pairwise comparisons between stations (i.e., 0 = no shared species and 1 = the same community composition) indicating that crab communities are highly variable across stations (Table 3-2; Figure 3-6). The nMDS plot of crab community composition shows that ARMS deployed at the same station do cluster together to some degree; however, five out of the seven stations have at least one ARMS that does not group within the station.
105 Table 3-2 Brachyuran crab community Bray-Curtis similarity index (BCI) as collected by 22 Autonomous Reef Monitoring Structures deployed at eight climate stations from 2012–2014. Station 7 is omitted. The BCI values ranging from 0-1 (0 meaning no commonalities between communities and 1 being identical) are under the gray diagonal. Table above the diagonal represents number of shared species between stations. The station is climate station number as indicated in Figure 3-2.
Station 1 2 3 4 5 6 8 1 Coral Gardens 6 6 2 8 4 5 2 Beloi 0.2667 7 2 10 4 6 3 Beacou 0.2737 0.1778 3 13 7 12 4 Dili Rock 0.1176 0.1379 0.1013 4 3 4 5 Manatuto 0.2647 0.3810 0.2832 0.1923 7 11 6 Baucau 0.2857 0.1569 0.2574 0.2000 0.2973 7 8 Jaco Island 0.1250 0.1538 0.2553 0.1500 0.3509 0.2157
The only benthic parameter that was significant with brachyuran crab communities was percent massive coral cover (r2 = 0.3790, p = 0.0190) and soft coral cover was almost significant (r2 = 0.2844, p = 0.0570; Figure 3-8).
106
Figure 3-8 Non-metric multidimensional scaling of Bray-Curtis dissimilarity matrix of crab community composition of Autonomous Reef Monitoring Stations (ARMS) deployed in Timor-Leste from 2012–2014. 2D stress = 0.20. Colors represent different stations and arrows indicate significant environmental parameters: massive coral (r2 = 0.3790, p = 0.0190) and almost significant soft coral cover (r2 = 0.2844, p = 0.0570). Labels are climate station number (1-6,8) and ARMS unit (A-C, one D at station 8).
DNA metabarcoding of 500 μm–2 mm, 106–500 μm, and sessile size fractions After subsampling, there were 6,750 OTUs out of 9,135,166 filtered sequences (for raw sequences per sample see Table S3-1). Half or 3,373 of the total OTUs were singletons or only appeared once and 1,293 OTUs (19.2%) were unclassified. Classifications of OTUs included 19 animal phyla and three major groups of algae. Rarefaction curves grouped by sample (ARMS unit) and fractions did not reach asymptotes, indicating more sampling would find greater numbers of unique species (Figure 3-9).
107 Figure 3-9 Individual-based rarefaction curves on the rarefied operational taxonomic unit (OTU) matrix based on (a) the number of sequenced fractions representing individual samples at each station and (b) pooled fractions across ARMS units at each station. The red curve represents the total of all OTUs combined at each station. Error bars represent standard deviation.
There were station and fraction differences of community composition with no interactions (two-way PERMANOVA, Table 3-3, Figure 3-10, Figure 3-11). Distinctions between size fractions were preserved across stations. Annelids contributed 14.52–17.69% across all stations except Stations 3 and 6 where arthropods (17.7%) and non-reef building cnidarians (14.7%) contributed the most to the community similarity within stations. Poriferans were
108 dominant in the sessile size fraction and contributed 17.9% to similarity within the sessile fraction. They were consistently abundant across stations averaging 20,742 ± 1,347 sequences (Figure 3-10; one-way ANOVA: F(7,14) = 0.617, p = 0.734). Scleractinian (one- way ANOVA: F(7,14) = 10.830, p = 0.0001), and alcyonacea (soft coral) sequences (one- way ANOVA: F(7,14) = 3.961, p = 0.0137) were significantly different by station (p < 0.05). Although low in abundance overall, Station 7 (1,350 ± 310) had significantly more scleractinian coral sequences than all others while Station 6 (11,464 ± 4,992) had significantly more soft coral sequences except for Station 8 (2,275 ± 2,227).
Table 3-3 PERMANOVA table of results testing main effects of station and size fraction, random and fixed effects respectively, on the fourth root transformed Bray-Curtis similarity matrix of classified operational taxonomic units sequenced from Autonomous Reef Monitoring Structure samples deployed in Timor-Leste. df–degrees of freedom
Factor df Sums of Mean Pseudo-F P(perm) Unique squares sums of permutations squares Station 7 1,919.4 274.2 2.8317 0.0001*** 9,899 Size 2 7,066.8 3,533.4 35.7070 0.0001*** 9,937 Station x 14 1,386.0 99.0 1.0224 0.4460 9,872 Size Residuals 41 3,970 96.8 Total 64 14,858
109
Figure 3-10 Classified (a) sequences and (b) operational taxonomic units (OTUs) grouped by phyla, for samples collected from Autonomous Reef Monitoring Structures deployed in Timor-Leste from 2012–2014. Sequences/OTUs percentages averaged by fraction (sessile, 106–500 μm, and 500 μm–2 mm) across all ARMS units at each station. Colors indicate phyla: Annelida, Arthropoda, Bryozoa, Echinodermata, Mollusca, NonReefCnidaria–non- scleractinian cnidaria, Other–pooled invertebrate phyla with low abundance (Chaetognatha, Chordata, Echiura, Entoprocta, Gastrorticha, Hemichordata, Kinorhyncha, Nematoda, Nemertea, Platyhelminthes, Sipuncula, Xenacoelmorpha), Plantae (Chlorophyta, Rhodophyta, Ochrophyta), Porifera, and Scleractinia–reef-building hard corals.
110
The community composition, visualized with an nMDS (2D stress = 0.14), shows the sessile fractions are grouped with overlap between the 106 μm and 500 μm mobile fractions. All fractions were significantly different from each other (pairwise PERMANOVA, degrees of freedom [df] = 2, pseudo-F = 35.7070, p(perm) = 0.0001) with stronger differences between the sessile and 106 μm and 500 μm fractions (Table 3-4).
Table 3-4 Pairwise PERMANOVA comparison of size fraction on the fourth root transformed Bray-Curtis dissimilarity matrix of classified operational taxonomic units sequenced from Autonomous Reef Monitoring Structures deployed in Timor-Leste from 2012–2014.
Pairwise Groups T P(perm) Unique permutations 106 μm, 500 μm 2.0108 0.0125* 9,959 106 μm, Sessile 7.2427 0.0002*** 9,937 500um, Sessile 6.9554 0.0002*** 9,934
Figure 3-11 Non-metric multidimensional scaling on fourth root transformed Bray-Curtis similarity matrix of rarefied operational taxonomic units classified to phyla across all Autonomous Reef Monitoring Structures at all NOAA climate stations in Timor-Leste–2D stress = 0.14. The shape and color of points refer to the size fraction (106-500 µm, 500 µm- 2mm, and sessile) and labels represent the station number.
111
ARMS communities were also significantly different by station (pairwise PERMANOVA, df = 7, pseudo-F = 2.8317, p(perm) = 0.0001). Station 3 had the highest proportion of sponges, Stations 2, 4, and 8 were dominated by annelids, and Stations 1, 5, and 7 were dominated by arthropods (Figure 3-12, 2D stress = 0.03). Geographically, Stations 1 and 2 on Ataúro Island have the greatest proportion of hard corals and are close together in the nMDS; however, outside those two stations, the stations do not follow a geographic pattern.
Figure 3-12 A non-metric multidimensional scaling of the station centroids of a fourth root transformed Bray-Curtis similarity matrix on the metabarcoded sequences summed by Autonomous Reef Monitoring Structure–2D stress = 0.03. Pie-charts indicate the abundances (number of sequences) of the major taxa groups.
Benthic composition at the NOAA climate stations The benthic composition varied significantly by stations (one-way PERMANOVA; F(7,224) = 20.845, p = 0.0001, Figure 3-13) as did the individual benthic parameters tested. Hard coral (Kruskal-Wallis χ2 = 80.175, p < 0.0001) was the highest at Stations 7 (39.7 ± 3.5%; mean ± SE), 2 (37.3 ± 3.4%), 8 (31.9 ± 5.9%), and 4 (29.3 ± 3.1%) which were not significantly different. Stations 5 (6.3 ± 1.5%), 1 (9.7 ± 1.8%) and 3 (11.5 ± 2.9%) had the lowest coral cover by a significant margin (p < 0.05). Station 6 had the greatest proportion of soft coral (41.3 ± 0.1%, Kruskal-Wallis χ2 = 92.082, p < 0.0001) compared to all other stations with minimal soft coral present at Station 8 (0.4 ± 0.0%). Rubble (Kruskal-Wallis χ2
112
= 109.43, p < 0.0001) was greatest at Stations 5 (46.3 ± 5.8%) and 3 (39.6 ± 5.5%) with the least amount of rubble at Station 2 (0.7 ± 0.7%; Figure 3-13).
Figure 3-13 Tier 1 benthic classifications from National Oceanic and Atmospheric Administration image analysis at climate stations in Timor-Leste. Approximately 30 images were taken 1 m apart along a 15 m transect at each station in 2014. Rubble and hard substrate include turf on rubble/hard substrate and CCA on rubble/substrate.
Coral morphological categories were significantly different between stations (one-way PERMANOVA F(7,169) = 10.069, r2 = 0.18749, p = 0.0001) but were not significantly different between Stations 1 and 3, 1 and 5, and 5 and 6 (p(perm) < 0.05). Massive (Kruskal- Wallis χ2 = 69.794, p < 0.0001), encrusting (Kruskal-Wallis χ2 = 52.317, p < 0.0001), branching (Kruskal-Wallis χ2 = 77.181, p < 0.0001), and foliose corals (Kruskal-Wallis χ2 = 78.316, p < 0.0001) varied significantly by station. Station 7 was dominated by massive and encrusting corals with the highest cover in both groups (15.3 ± 2.5% and 15.0 ± 2.1% respectively) significantly more than all other stations (p < 0.05). Branching corals were highest at Station 2 (24.6 ± 3.6%) which was greater than all sites except Station 4 (14.7 ± 2.6%). There is a notable absence of branching corals at Station 8, but this station had significantly more foliose corals (24.4 ± 6.4%) compared to other locations surveyed.
113
Figure 3-14 Results from tier 2 of National Oceanic and Atmospheric Administration benthic classification scheme of phototransects collected at climate stations in Timor-Leste in 2014. Tier 1 hard coral classification was divided into morphological categories which were subsequently summed into five major categories: branching (including columnar and tabulate forms), foliose, free-living, encrusting, and massive.
3.5.3.1 Cryptofaunal relationships to benthic composition
In the largest size fraction, Stations 3 and 8 had the highest abundance of organisms dominated by arthropods and the greatest species richness with 121 and 127 taxa groups, respectively (Figure 3-15). Station 3 also had among the highest proportion of rubble and hard substrates (Figure 3-13) and Station 5 that had the highest cover of rubble with the third highest species richness (taxa groups) of arthropods with 74. However, Station 8 also had the highest abundance of the two next most abundant > 2 mm phyla (molluscs and echinoderms; Figure 3-15) and had a high abundance of corals and macroalgae indicating there are more nuaced relationships between the benthos and certain taxa (Figure 3-14). Stations 3 and 5 had the highest number of OTUs (Figure 3-9) and greatest proportion of rubble. Diversity across all size fractions (>2 mm and OTUs) provide further evidence of a positive relationship between rubble and cryptofuanla species richness. In assessing crab diversity and the benthos, only percent massive coral cover was significant (r2 = 0.3790, p
114
= 0.0190) with soft coral cover almost significant (r2 = 0.2844, p = 0.0554, Figure 3-8). ARMS from Stations 2 and 1 were most associated with this relationship although neither station had a particularly high cover of massive corals (Figure 3-14).
Figure 3-15 Mean abundance of the three most common phyla collected in the > 2 mm size fraction of Autonomous Reef Monitoring Structures (ARMS) deployed along the north coast of Timor-Leste from 2012—2014. Abundances were averaged per station with three ARMS, except for Station 8 where four ARMS were deployed. Station represents climate station number as represented on Figure 2 with stations moving generally from west to east moving right across the x-axis. Error bars represent standard error.
3.5.3.2 Correlations between benthic cover and sessile cryptofaunal sequences
The OTUs classified as hard corals and soft corals showed positive correlations with corresponding benthic cover as measured by the NOAA benthic phototransects collected at each station. Sponges showed no correlation between the two datasets. However, r2 values were low (hard coral–r2 = 0.0421, p < 0.0001; soft coral–r2 = 0.0528, p < 0.0001; sponges– r2 = 0.0047, p = 0.069) indicating weak relationships (Figure 3-16).
115
Figure 3-16 Spearman’s correlations between sequence diversity of (a) hard coral, (b) soft coral, and (c) sponge metabarcoded from Autonomous Reef Monitoring Structures in Timor- Leste and percent cover of corresponding benthic parameters derived from NOAA image analysis of 232 photoquadrats from all eight stations in 2014. The red line represents a linear regression with gray 95% confidence interval.
116
When the hard coral sequences were further divided into branching and massive coral morphological groups, massive corals were the most common across all stations except for Station 8 (Figure 3-17). The cover of massive corals on adjacent transects had significant correlations with hard coral sequences from the ARMS although the variance captured was low (r2 = 0.1173; Figure 3-18).
Figure 3-17 Subsampled operational taxonomic units of hard corals classified into broad morphological categories (branching, foliose, free-living, and massive/encrusting) based on family classification averaged by station. The standard error of summed morphologies per site is displayed. Each station in Timor-Leste had three Autonomous Reef Monitoring Structures except for Station 8 which had four. Family classifications into morphological groups were as follows: Branching–Meandrinidae, Merulinidae, Pocilloporidae; Massive– Agariciidae, Coscinaraeidae, Euphyllidae, Poritidae (Table 3-1).
117
Figure 3-18 Spearman correlations plots of hard coral (a) branching and (b) massive morphologies of sequences categorized into coral morphological groups from 25 ARMS in Timor-Leste and corresponding coral cover morphologies from benthic image analysis of 232 photoquadrats at the same stations. The red line represents a linear regression with a gray 95% confidence interval.
3.5.3.3 Overall species richness and benthic composition
The OTU richness ranged from 684 (± 4)–888 (± 29) and was not significantly different between stations (one-way ANOVA; F(7,10) = 0.755, p = 0.636). Correlations between overall OTU richness and average coral and rubble cover per station revealed that richness
118 decreased with increasing coral cover while there was a positive association with rubble (Figure 3-19). OTU richness was highest at low (~ 10%) coral cover with richness increasing around 30% coral cover. There was a gap in the rubble cover between 20-40%, but despite this, the relationship with richness remained positive. Even with the limited data, the correlations support pervious findings that rubble substrates are more conducive for greater cryptofaunal diversity.
Figure 3-19 Operational taxonomic unit richness from Autonomous Reef Monitoring Structures with units containing sequences from all fractions in Timor-Leste plotted with (a) coral cover and (b) rubble cover from image analysis of 232 photoquadrats averaged by station. The red line is a locally estimated scatterplot smoother (LOESS) with gray representing a 95% confidence interval. 119
Discussion
This chapter explored the diversity of coral reef cryptofauna across the north coast of Timor- Leste. In doing so, biodiversity was assessed as measured by DNA barcoding of brachyuran crabs and DNA metabarcoding of three size fractions from 25 ARMS units deployed in- country. Cryptofauna diversity was further explored relative to coral reef benthic composition of the sampling sites. The relatively intensive survey (~ 18.93 m2 surface area of ARMS plates and ~ 0.125 m3 total volume of ARMS units) revealed high diversity in Timor-Leste, greater than other studies in the Indo-Pacific. Even so, the rarefaction curves indicated that full diversity was not quantified.
Patterns associated with > 2 mm cryptofauna The two significantly different > 2 mm phyla by station, arthropods and molluscs, followed similar patterns in abundance, indicating that (1) not all taxa respond the same and (2) factors other than the presence of ARMS influenced recruitment to the units. The three most abundant phyla (arthropods, echinoderms, and molluscs) in the > 2mm fraction were the same as found in Enochs (2012) in the Eastern Pacific. The proportion of the > 2 mm major phyla from the ARMS (61.3% arthropods, 30.4% molluscs, 6.9% echinoderms) was less evenly distributed as was previously found in dead corals (32.9%, 25.0%, 36.0% respectively; Enochs, 2012). The presence of these cryptofaunal groups was more characteristic of dead coral than live coral indicating the artificial nature of the ARMS are more comparable to dead coral sampling. Artificial or dead substrates are potentially more welcoming to a great number of cryptobiota as they do not have the defense mechanisms found on living corals. Polychaetes were the most abundant phyla quantified out of dead coral fragments in previous studies (Moreno-Forero et al., 1998) and were comparatively low in the ARMS. This would indicate that the plastic-based plates of ARMS could be less conducive for certain organisms such as polychaetes. Artificial sampling devices tend to collect the lower total number of species and rare species. However, overall diversity as measured by Bray Curtis similarity index (BCI) was comparable between cryptofaunal communities collected from dead corals and ARMS simultaneously sampled at Heron Island (Plaisance et al. 2011b). All sampling methods have inherent biases toward different types of taxa, although clear trends in particular groups are associated with ARMS when it comes to sampling reef cryptofauna.
120
3.6.1.1 Patterns of brachyuran crab diversity
As part of the epicenter of marine biodiversity, it was expected that the diversity of brachyuran crabs would be greater than in other Indo-Pacific locations. The brachyuran crab diversity between stations was highly variable with BCI ranging from 0.101-0.381. Comparatively, a survey of crustacean diversity on an atoll in the Northwest Hawaiian Islands was more similar with a BCI range of 0.244–0.401 (Plaisance et al., 2011b), but also a larger range than previously observed for CT communities (0.20–0.26 for Indonesia, Papua New Guinea, and the Solomon Islands; Dornelas et al., 2006). The 75 brachyuran crab OTUs sequenced here was greater than previous ARMS studies. At Heron Island in the southern GBR, 14 OTUS were captured from nine ARMS units and 49 OTUs total combining ARMS and dead corals (n = 23). Similarly, the central Pacific study which combined coral heads and ARMS (n = 28) resulted in 38 crab OTUs (Plaisance et al., 2011b). It is important to note that ARMS in these studies were deployed for one year. The combination of ARMS plus dead coral heads, which could be in the environment longer than two years (the deployment period in Timor-Leste), would average out this time discrepancy. The abundance of crabs in Timor-Leste (n = 278) was comparable to the 275 found on 22 dead coral heads across six central Pacific islands (Kingman Atoll, Palmyra Atoll, Tabuaeran, Kiritimati, Northern Line Islands, and Moorea). OTUs for brachyurans were not given, but there were 108 OTUs for 536 individual decapods and a rough estimate would indicate half of those OTUs would be brachyurans (Plaisance et al., 2009). Indeed based on these comparisons, Timor-Leste’s high biodiversity of brachyuran crabs supports the well-known biogeographic patterns of global marine biodiversity (Veron et al., 2009).
Diversity of Timorese cryptofauna revealed by DNA metabarcoding Metabarcoding revealed a high diversity of species with very few abundant and many (50.0%) singleton OTUs as expected. The likely explanations of this pattern include a paucity of COI references for most marine invertebrate groups, COI misidentifications in databases, and limitations with the COI region for phylogenetic assessments (Leray and Knowlton, 2015; Ransome et al., 2017). The diversity captured in Timor-Leste fits well within the patterns of OTU richness of ARMS deployed globally following the biogeographic gradient of marine diversity (Table 3-5; Leray and Knowlton, 2015; Veron et al., 2009).
Comparing average OTU richness between size fractions supports previous findings of the greater diversity of smaller organisms (Azovsky, 2002; Leray and Knowlton, 2015). The
121 sessile fraction was dominated by sessile taxa as expected—mostly sponges, but also plantae, molluscs, non-reef cnidarians (mostly soft corals), arthropods, and annelids the latter two which can be either sessile or motile (Figure 3-10). Both motile fractions were dominated by arthropods and annelids. These general patterns were similar to other ARMS studies (Leray and Knowlton, 2015; Pearman et al., 2018; Ransome et al., 2017). Here, there were fewer bryozoans in the sessile fraction which could be due to different sequencing technologies used (Ransome et al., 2017).
Table 3-5 Timor-Leste OTU richness compared to Autonomous Reef Monitoring Structures cryptofaunal biodiversity studies in Florida, Mo’orea, and the Red Sea. The ratio is the total OTUs divided by total OTUS in Timor-Leste.
Site Total #ARMS OTUs Ratio Reference OTUs units per ARM Florida, USA 1,391 3 - 4.9 Leray and Knowlton, 2015 Mo’orea, 3,372 3 - 2.0 Ransome et al., 2017 French Polynesia Red Sea 5,420 34 660 ± 152 1.2 Pearman et al., 2018 Timor-Leste 6,750 25 761 ± 27 present study
Size Fraction Average OTU richness Ratio Sessile 279 ± 14 1.1 106–500 µm 319 ± 27 1.0 500 µm–2 mm 187 ± 11 1.7
Our understanding of cryptofaunal diversity is at an early stage, given the small number of COI sequences that had matches, even at a coarse taxonomic resolution, in public sequence repositories. The issue is further exacerbated by a large majority of coral reef science focused in developed countries, where monitoring and sampling are most accessible, while most coral reefs and related biodiversity is housed in the CT (Plaisance et al., 2011a; Veron et al., 2009). For example, public genetic libraries are skewed toward sampling from developed regions in the Atlantic, Mediterranean, and Pacific oceans. Many CT communities and nations are at an early stage of building genetic libraries yet face the destruction of these resources as coastal populations and activities increase (Burke et al., 2012). Closing the gap between assessing biodiversity in developed and developing countries and identifying the sheer number of unclassified OTUs requires much more work.
122
Benthic composition of reefs across climate stations The benthic composition was expected to vary significantly between climate stations. Live coral cover ranged from 6.3–39.7%. These values were comparable to the previous NOAA phototransects collected at 139 sites across the north coast of Timor-Leste (range 0.0– 42.3%) and the XLCSS kilometer-scale phototransects (range 5.4–33.0%) as discussed in Chapter 2. These levels of coral cover place Timorese reefs within the high (0–20%) to medium (20–80% coral cover) degradation framework (Enochs, 2012; Enochs and Manzello, 2012a). However, the studies defining these frameworks were conducted on an eastern Pacific reef with depauperate diversity characterized by two main genera of corals; thus, the low, medium, and high degradation framework designations outlined may not apply to reefs with much greater diversity as in the Indo-Pacific. Stations 2, 4, 7, and 8 had greater than 20% coral cover while the remaining three stations had less (Figure 3-13). While the benthic composition data presented here is limited, Chapter 2 indicated that these values are comparable to more extensive conventional and kilometer-scale phototransects collected (Figure 2-13). These values are also comparable to Indo-Pacific regional analyses of coral cover, ranging from 22–24%, although there were few data points from Timor-Leste (Bruno and Selig, 2007; Graham and Nash, 2013). The coral cover required for the low degradation framework is well outside of the range expected in Timor-Leste and potentially the CT.
3.6.3.1 Relationships between benthic composition and > 2 mm fraction
This study was limited to the percent cover of benthic components to assess heterogeneity of habitat. Exploring these commonly collected monitoring metrics as indicators of habitat complexity is useful. However, in broadly interpreting the habitat heterogeneity hypothesis. For example, a reef featuring several benthic components would have greater available niches than one dominated by one parameter such as coral cover. It was established in Chapter 2 that there was high variability in benthic composition across reefs along the north coast of Timor-Leste. Stations 1, 3, and 5 had a high cover of turf algae which is consistent with that seen in the kilometer-scale study (Figure 2-7) and the NOAA phototransects in 2013 (PIFSC, 2017). These three stations also have the highest OTU richness (Station 3— 647 ± 50; Station 5—645 ± 87; and Station 1—632 ± 88), with Stations 3 and 5 having the highest cover of coral rubble (> 35%) with low coral cover (< 15%). This finding supports previous research on the importance of rubble as a promoter of cryptofaunal diversity (Enochs and Manzello, 2012a). Additionally, turf on hard substrate could also be important 123 habitat for facilitating cryptofaunal communities through supporting boring cryptofauna (bacteria, fungi, cyanophytic algae, chlorophytes, rhodophytes, and sponges) that chemically dissolve coral skeleton, allow for the entrance of other fauna, and subsequently increased diversity (Hutchings, 1986; May et al., 1982). The density of perforating organisms, however, has been inversely related to morphologic characteristics of dead corals such as thickness (Moreno-Forero et al., 1998) which would indicate a non-linear progression. Initially, colonization of boring organisms would be slow but would create additional voids for subsequent fauna on the newly dead substrate.
For cryptofaunal relationships with coral cover, it was expected that coral commensals, such as coral crabs, would be more abundant at sites with greater coral cover. Coral crabs were successfully captured using ARMS, but there were no obvious patterns as to the abundance of morphospecies groups and hard coral cover. Coral crab abundance was low overall (range 1–3 per station) Stations 3 and 8 had the most arthropods which dominated the > 2 mm composition, with Station 8 having high coral cover (31.85%) while Station 3 was predominately turf algae (61.54%, Figure 3-5). Additionally, the hard coral morphological composition between the two stations was different. Station 3 was more evenly divided between five morphologies whereas Station 8 was dominated by foliose and encrusting corals (Figure 3-6). Coral crabs are typically present in branching corals (Abele and Patton, 1976) and finer metrics of coral habitat such as branching coral genera data, etc. are likely more suitable for assessing the abundance of commensal crabs.
The effect of coral mortality on associated cryptofaunal communities varies between studies. While live corals support greater abundance and biomass of cryptobiota (Caley et al., 2001; Enochs, 2012; Enochs and Hockensmith, 2008), overall diversity is less than with dead corals (Coles, 1980; Enochs and Manzello, 2012a). The changes in the cryptofaunal community following coral mortality are time-dependent. Immediately post-mortality (~ two months) the diversity of associated fauna declines as food and protection from the host is no longer available (Caley et al., 2001; Coker et al., 2009). This changes over time (greater than a few months to years) as the erosion of the substrate increases niche habitats allowing for more speciose communities (Enochs, 2010). Monitoring the changes in live to dead coral cover of the benthos, could potentially also elucidate shifts in associated cryptofaunal communities.
124
3.6.3.2 Correlations between benthic cover and sessile metabarcoded sequences
Out of the invertebrate benthic cover parameters tested, hard and soft corals were significantly correlated with the corresponding ARMS OTUs, but not sponges, a taxon with many cryptic species (Figure 3-16). Thus, benthic image analysis captures non-cryptic benthic cover well, but not the proportion of cryptic taxa. Based on sequence abundance, sponges comprised a large proportion of the sessile community found in the ARMS (Figure 3-10), which was not represented with the image analysis of photoquadrats at the same sampling stations (Figure 3-13). However, the ARMS at Station 6 were overgrown with Xenia spp. upon retrieval (Figure 3-10) which was reflected in the disproportionate amount of non- reef building cnidarian sequences classified in the motile fractions for that station (41.33% ± 4.86; Figure 3-20). This was likely a localized event but was captured by the ARMS with the highest abundance of soft coral sequences at that station (Figure 3-10).
Basal metazoans, such as cnidarians and poriferans, show less COI species divergence than other phyla due to low rates of mitochondrial divergence (France and Hoover, 2002; Hebert et al., 2003b; Huang et al., 2008; Shearer et al., 2002). However, conducting the analyses at a phyla level would address this sensitivity. The ability to predict the abundance of cryptofaunal taxa based on standardized benthic image analysis depends on the taxa in question. Taxa with cryptic life stages which then grow to be conspicuous, for example, soft coral could potentially be predicted by conventional phototransects. For taxa that remain cryptic like many sponges, this would not be feasible. However, much more work would be required to refine these relationships for more conspicuous taxa.
There was a significant relationship between coral sequences and the benthic cover of massive corals, but the variance (r2) captured was very low and not ecologically relevant (Figure 3-19). The other remaining functional groups had very few sequences classified (< 200), making conclusions from these correlations difficult to form. Additionally, coral morphology can vary greatly within families (Miller, 1994; Veron, 2000) and each classified family was assigned to one morphological functional group. Despite these limitations, maximizing the use of standardized monitoring data such as phototransects is imperative especially in data limited regions. Although finer resolution of these relationships is desirable, the general trends provided from using these methods are still valuable.
125
Figure 3-20 Autonomous Reef Monitoring Structure (ARMS) unit at Station 6 in Timor-Leste. Upon retrieval in 2014, unit was covered with Xenia spp. octocoral. The red cable tie is on the bottom corner of the ARMS unit.
3.6.3.3 Benthic composition relationships with overall metabarcoded diversity
With these novel genetic tools, it was possible to test relationships of cryptofaunal biodiversity with the benthic structural complexity as derived from image analysis. Previous work in the eastern Pacific indicated dead coral and MDFs (defined as 20–80% coral cover) supporting the most biodiverse cryptofaunal communities (Enochs and Manzello, 2012b, 2012a). This is consistent with the intermediate disturbance hypothesis, where nonequilibrium states maintained by disturbances drive more diverse communities until diversity then declines as disturbances continue to increase beyond a certain point (Connell 1978). The validity of this hypothesis has been refuted in more recent years (Chesson and Huntly, 1997; Roxburgh et al., 2004; Shea et al., 2004), and the same reef frameworks in the east Pacific, depauperate communities with high coral cover, may not apply to highly variable reefs in the Indo-Pacific. The data here suggest that percent coral cover for the LDF
126 framework should be lower, perhaps around 40-50%, although this could also be a result of shifting baselines of coral cover through time (Knowlton and Jackson, 2008)
When comparing OTU richness with percent live coral and rubble, two opposing relationships were found. Coral cover was negatively associated with OTU richness while percent rubble revealed a positive relationship. Although no statistical analyses were done, these investigations support previous work indicating that rubble is an key driver of cryptofaunal diversity on coral reefs (Enochs and Manzello, 2012a). Although differences were not significantly different, Stations 3 and 5 that had the highest proportion of rubble (> 30%) also had the highest station averages of OTU species richness (841 ± 1 and 888 ± 29 respectively; Figure 3-19). Stations 2 and 7 had the highest coral cover (> 35%), low rubble (< 10%; Figure 3-13), and moderate levels of richness (562 ± 54 and 587 ± 17 respectively). High coral cover can serve as a barrier to the recruitment of cryptofauna and invertebrate coral symbionts are often specialized to inhabit live coral. Live corals were found to have specific, low-diversity cryptofaunal communities in high abundance (Enochs, 2012; Enochs and Manzello, 2012a). Live coral, however, is essential in providing a dead coral substrate that fosters a greater diversity of habitat niches (Enochs and Manzello, 2012a).
Although having a variety of benthic components is important for cryptofaunal diversity, not all parameters contribute equally as indicated by opposite relationships between corals and rubble for overall diversity. The limited replication and high variability in benthic coral cover and ARMS diversity demonstrate a need for more data. Timor-Leste’s cryptofaunal diversity was as high as was expected. However, a more in-depth assessment of the diversity of the benthos and measurements of other structural components, including reef framework thickness and void space in the CT are areas of future research.
Conclusion
There was significant variation of cryptofaunal communities across all ARMS size fractions, within stations, and between stations. Overall, habitat within the locations sampled seemed to play a bigger role in structuring cryptofaunal communities–more so than geography at a district level across the coast. Of course, geography at this scale can influence habitat. This was demonstrated in Chapter 2 where two out of five kilometer-scale transects in Oecusse had a high proportion of sand within them. However, there were no ARMS deployed in Oecusse and thus the cryptofaunal communities of this district were not assessed. Unique
127 geological features notwithstanding, it would be expected that reefs closer each other would harbor similar cryptofaunal communities through greater connectivity. While this was observed to some degree, there was also significant variation in total metabarcoded species richness (100s of OTUs; Figure 3-19) between ARMS units meters apart at the same site. It is likely that the microhabitat of ARMS placement (rubble patch, next to coral, etc.) influences the captured cryptofaunal communities in addition to the micro-scale physical parameters such as flushing of the environment (Choi and Ginsburg, 1983; Enochs et al., 2011; Gischler and Ginsburg, 1996).
The research described in this chapter represents a first attempt to elucidate relationships of cryptofaunal diversity with benthic parameters. Representation of the cryptic sessile communities in metabarcodes versus image analysis varied between taxa. Unfortunately, there was a lack of replication of the benthic composition data at sites. Better assessment of the benthos surrounding cryptofaunal sampling units could further refine these relationships. This could eventually allow for the estimation of cryptofaunal diversity, from more well-known relationships, between the benthic community structure and environmental and anthropogenic pressures.
The baselines of reef biodiversity in its entirety are only beginning to be understood, yet coral reefs are changing at an unprecedented rate (Hoegh-Guldberg and Bruno 2010). Cryptofaunal communities maintain high levels of diversity in dead coral and rubble substrates (Enochs and Manzello, 2012a). In this regard, cryptofaunal communities may be more resilient to changing environments. However, greater substrate variability that supports the greatest diversity in cryptic communities is driven by living coral (Enochs and Manzello, 2012a). The process of reef accretion and erosion is a dynamic and coral cover of 10% is required to maintain net positive reef accretion (Perry et al., 2013). Thus, it seems that the ideal coral reef habitat for cryptofaunal communities would encompass at least 10% coral to maintain reef accretion with a large proportion of dead coral substrates and rubble.
Coral reef cryptofaunal communities are potential beneficiaries of degrading coral reefs as they thrive in habitats dominated by dead coral substrate. However, without the key habitat architects producing more substrate, cryptofaunal diversity will decline only at a delayed timeframe compared to corals (Enochs and Manzello, 2012b). Proper management of coral reefs, such as mitigating sediment run-off, not only sustains the conspicuous diversity of corals and fishes, but also the cryptofauna that harbors the most diversity on reefs. 128
A baseline for Timor-Leste: Community composition, disease, and mass coral bleaching
Photo: C. Kim
129
Statement of author contributions:
Kim CJS, Roelfsema C, Dove S, Hoegh-Guldberg O. (in review) The condition of coral reefs in Timor-Leste before and after the 2016-2017 marine heatwave. Oceans-1001163. doi:10.1101/2020.11.03.364323
This work was submitted to the Oceans Special Issue “The Future of Coral Reefs: Research Submitted to the International Coral Reef Symposium 2020, Bremen Germany” on October 30th, 2020. It is currently in review.
C.K., C.R., and S.D. conceptualized and designed the project. C.K. carried out the field work and research investigation with resources provided by O.H-G. C.K. analyzed the data and wrote the manuscript with the assistance of S.D. and O.H-G. for interpretation and revisions.
130
Abstract
Coral reefs are under a myriad of local and global stressors that affect the condition (health) of reefs. Coral disease is exacerbated by warming ocean temperatures as well as variation in weather patterns such as the El Niño Southern Oscillation, which are driving global bleaching events with increasing frequency and intensity at global scales. The severity of bleaching, however, is not geographically uniform. Timor-Leste is a developing country in the Coral Triangle that lacks the capacity to monitor its coral reef resources. Here, we deployed 24, 15 x 2 m coral health belt and line intercept transects at 5 and 10 m in Nov 2015 and July 2017 at four sites in Timor-Leste. Temperature logger data was collected between surveys at three sites and, in 2015, macroalgal and seawater samples were collected for δ15N and nutrient analysis, respectively. The prevalence of disease was low in both years with White Syndrome the most common at the Rural-N (0.9 ± 0.1%) in 2015 and growth anomalies at Urban-W (0.6 ± 0.3%) in 2017. Change in coral cover between surveys was attributed to reef heterogeneity and there were significant differences in coral cover between site, depth, and year (4.5 ± 1.5%-58.2 ± 1.7%). Coral community structure varied significantly by site and depth as did the variability of community structure within and between sites. There was a significant site and depth interaction for seawater nutrients, where combined nitrate and nitrite, and phosphate at Rural-N 10 m had the highest concentrations (1.04 ± 0.06 µM and 0.15 ± 0.01 µM respectively) and δ15N site averages ranged from 4.03 ± 0.01‰ for Halimeda spp. to 4.57 ± 0.09‰ for Chlorodesmis spp. In situ temperature was compared to remotely sensed Coral Reef Watch (CRW) experimental virtual station sea surface temperature (SST). CRW is an important and widely used tool; however, SST was significantly warmer (> 1˚C) than in situ temperature during the austral summer, accruing 5.79 degree heating weeks. In situ temperature effectively showed no accumulation. The divergence between SST and temperature loggers also aligned with the monsoon season (Dec-May) in-country. Both temperature and nutrient data point to potential upwelling which is consistent with the country’s location within the Indonesian Throughflow. The comparison of logger and SST indicate that bleaching stress in Timor- Leste is potentially mitigated by seasonal and oceanographic dynamics as there was no significant coral mortality associated with the recent global bleaching event. At the same time, there was a large range in live coral cover and coral community structure as these reefs are impacted by a variety of non-climate related stressors including fishing and gleaning. Local seasonal and oceanographic processes are important to study in reef 131 environments as SST bleaching products are unable to account for nearshore processes. Timor-Leste is a potential climate refugium and the effect of a changing climate should continue to be monitored. The immediate conservation science lies in assessing local impacts and sustainable socio-ecological interactions on coral reefs.
Introduction
Timor-Leste is a developing country on the southern edge of the Coral Triangle (CT). It has limited infrastructure following decades of war and isolation and is one of the poorest nations on the planet. The CT is the global center of marine biodiversity (in numbers of species) and houses of 29% of the world’s coral reefs (Burke et al., 2011; Veron et al., 2009). Much of this diversity, however, is under threat due to a range of local and global stresses (Burke et al., 2011; Harvell et al., 2007; Hoegh-Guldberg et al., 2007; Jackson, 2001). Globally, climate change-induced coral bleaching via ocean warming and coral disease are among the main threats facing coral reefs. But, they are understudied in the CT compared to other reef regions. For example, a Web of Science search (Oct 1st, 2020) using the terms “coral disease” AND “Caribbean” returned 148 hits and “coral disease” AND “Indo-Pacific” resulted in 55 hits, ten of which were general Indo-Pacific studies, eight were studies in the Great Barrier Reef (GBR), five in Hawai‘i, and five in the Indian Ocean, leaving 27 spread across the CT and remaining Pacific Islands. Furthermore, many coral diseases have been linked to increasing ocean temperatures, nutrient pollution, sedimentation, and fishing (Bruno et al., 2007; Pollock et al., 2014; Raymundo et al., 2009; Yoshioka et al., 2016). Global mass coral bleaching events, driven by anomalous increases in SST maintained over time, have been occurring with increasing frequency (Heron et al., 2016b); however, like disease, there is a paucity of data concerning the incidence and severity of bleaching in the CT. Additionally, Southeast Asian reefs, encompassing the CT, are disproportionately threatened at a local level compared to other regions of the world (Burke et al., 2012). Timor- Leste has been viewed as a pristine reef region because of its relative isolation, but the reefs in-country have only recently begun to be assessed. This work aims to quantify coral health and determine potential global and local impacts in this newly independent country.
Local threats to the coral reef of Timor-Leste In addition to the threat posed by global climate change, there is a range of local impacts on Timorese reefs, with 92% of reefs at high or very high risk due to fishing pressure, watershed-based pollution, coastal development, and pollution from marine activities 132
(shipping, oil and gas extraction; Burke et al., 2012). While the extent of destructive fishing practices has been decreasing since the Indonesian occupation from 1974 to 1999 (Macaulay, 2003), there is still an estimated 5,000 fishers that focus their fishing effort, without dynamite, on the narrow, productive shelf that supports coral reefs (Barbosa and Booth, 2009; Kingsbury et al., 2011). Fishing markets are limited to a very localized distribution given that the infrastructure for markets (e.g., ice-making, efficient and timely transport networks) is undeveloped (McWilliam, 2002). Additionally, gleaning, or harvesting invertebrates from intertidal flats for consumption, known locally as meti, is commonly practiced by women and children and has its own, often significant, impacts (Andréfouët et al., 2013; McWilliam, 2002; Tilley et al., 2020). Similarly, agricultural practices are generally limited to small-scale, subsistence farming without the use of non-organic fertilizers and pesticides, although the development of such practices is outlined to improve food security (RDTL, 2011).
Environmental pollution from waste (e.g., household food scraps, paper, plastic, cardboard, bottles), construction, agriculture, motor vehicles, and tourism is largely concentrated in urban areas close to the capital of Dili (Sandlund et al., 2001). Watershed-based pollution is widespread, with deforested landscapes leading to large volumes of unsettled sediment and pollution flowing downstream and into coastal waters. An estimated 24% of forests in- country have been lost from 1972 to 1999 due mostly to slash and burn agriculture and logging, during the Indonesian occupation (Alongi et al., 2012a; JICA, 2010; Sandlund et al., 2001). In the future, coastal development will be an increasing threat with significant development planned over the next decades (Alongi et al., 2012a; Burke et al., 2012; JICA, 2010; Sandlund et al., 2001).
Disease in the context of coral reef health While rapid ocean warming has increased the frequency and intensity of mass coral bleaching and mortality (Hughes et al., 2018), other outcomes of stress have also increased, including the prevalence of coral disease. The term coral disease is defined here as any impairment of coral condition resulting in physiological dysfunction (Harvell et al., 2007; Maynard et al., 2015a; Wobeser, 1981). Disease can further be classified into infectious disease caused by microbial agents such as a bacterium, fungus, virus, or protist that is transmissible between organisms, or abiotic diseases such as those caused by environmental agents such as temperature stress or impacts like physical damage from
133 storms, anchors, or SCUBA divers (Raymundo et al., 2008). Here, the classification in Beeden et al., (2008) was used to differentiate between diseases caused by infectious agents from compromised health that results from negative impacts associated with environmental stress on the physiology of a coral colony. Coral disease has been a major contributor to the decline of corals in other regions such as the Caribbean (Aronson and Precht, 2001), and also severely threatens reefs in the Indo-Pacific (Harvell et al., 2007; Myers and Raymundo, 2009; Weil et al., 2012; Willis et al., 2004). By contrast, there have been relatively fewer studies of coral disease in the CT (Table 4-1; Sutherland et al., 2004). In this study, diseases were defined as syndromes caused by pathogens and recorded abiotic diseases such as coral bleaching (i.e., significant loss or reduction in algal symbionts) under the broad category of compromised health.
Disease and other signs of compromised physiology (as classified in Beeden et al., 2008) are one of many indicators coral reef condition (loosely defined as coral health). Understanding the signs of declining coral condition has the potential to alert reef managers to potential problems such as changing local threats. Therefore, it is important to document lesions, morphologic abnormalities, predation, physical breakages (storms, anchors), and aggressive interactions which may result in tears or breaks in the tissue, partial mortality, and stress to the coral host. Disease can be endemic and highly visible (Aronson and Precht, 2001), or present in low frequency in any given population (Willis et al., 2004). Tracking disease and other signs of compromised health through time can also be paired with other datasets (lack of herbivore biomass, hurricane incidence, COTS outbreaks, environmental parameters, etc.), and is related to key physiological parameters such as growth rates, fecundity, and community composition of reefs (Raymundo et al., 2008). These types of measurements are absent at most sites in Timor-Leste, hence highlighting the importance of the present study as a crucial baseline on the conditions of important marine resources.
134
Table 4-1 An overview of coral disease prevalence within the Coral Triangle. Disease abbreviations are as follows: AtrN–Atramentous necrosis, BrB–Brown band disease, BBD– Black band disease, GAs–Growth anomalies, N–Necrosis, SATL–Subacute tissue loss, SEB–Skeletal eroding band, PR–Pigmentation Response, PUWS–Porites Ulcerative White Spot, UWS–Ulcerative white spot, WP–White Plague, WS–White syndrome, YBD–Yellow band disease.
Reference Location Diseases Prevalence Lamb et al. 2018 Included: Bali, Sulawesi, BBD, BrB, Range: 0.1 ± 0.09% West Papua, Indonesia GAs, SEB, WS – 43.9 ± 5.1% Lamb et al. 2017 Spermonde Archipelago, BBD, BrB GAs, Total disease: 0.2 Indonesia SEB, WS ±0.1%, 5.9 ± 1.1% Johan et al. 2015 Kepulauan Seribu Marine BBD, WS <1% of each National Park, Indonesia Sabdono et al., Panjang Island, UWS, WP, WP: 17.76 ± 8.6% 2014 Indonesia YBD UWS: 6.59 ± 0.08% YBD: 2.88 ± 0.05% Muller et al. 2012 Spermonde Archipelago, AtrN, BrB, GAs Shallow: 8.2 ± 9.9% Indonesia SEB, WS Deep: 9.6 ± 2.7% Haapkylä et al. 2009 Wakatobi National Park, GAs, WS 2007: 0.6% Haapkylä et al. 2007 Indonesia 2005: 0.3% Raymundo et al. Tubbataha Natural BBD, BrB, Total disease: 2018 Marine Park, Philippines GAs, SEB, 2.4 ± 0.1% – UWS, WS 13.5 ± 5.4% Raymundo et al. Cebu, Philippines GA, PR, SATL, Fished: 2.26 ± 2011 WS 2.27% MPA: 7.13 ± 3.41% Raymundo et al. Central Philippines BBD, BrB, Range: 0.25–7.9% 2009 GAs, SEB, UWS, WS Kaczmarsky et al. Philippines Porites GAs, PUWS: 0–53.7% 2006 PUWS GAs: 0–39.1% Raymundo et al. Central Visayas, GA, N, PR, Total disease mean: 2005 Lingayen Gulf, PUWS, WS 8.3 ± 1.2% Philippines Raymundo et al. Sumilon Island, PUWS Range: 0 – 72.6 2003 Philippines ±16.5% Miller et al. 2015 Sabah, Borneo, Malaysia AtrN, BBD, 0 – 0.44 colonies BrB, per m2 PR, WS
Water quality and Timorese coral reefs Pollution arising from disturbed coastal regions and watersheds poses a serious threat to coral reefs. This type of pollution includes a wide range of compounds such as agrichemicals (pesticides), inorganic nutrients (nitrate, ammonia, and phosphate), soils and sediments, 135 and fossil fuel residues that flow from disturbed landscapes. Many of these compounds negatively affect coral physiology by reducing calcification rates, fecundity, fertilization success, and larval development (Fabricius, 2005). This can degrade reef communities, reducing coral cover, community composition diversity, and structural complexity (Aronson et al., 2004; Cleary et al., 2006). High levels of marine pollution can increase the prevalence and severity of disease and susceptibility to bleaching (Baker et al., 2007; Vega Thurber et al., 2014; Voss and Richardson, 2006; Wagner et al., 2010; Wooldridge and Done, 2009). Dissolved inorganic nitrogen (DIN = ammonium + nitrate + nitrite) measurements on reefs are generally < 1.5 µM (with individual species ammonium, nitrate, and nitrite < 1 µM) with lower phosphate concentrations (< 0.3 µM; Table S2-1; Amato et al., 2016; Dinsdale et al., 2008; Osawa et al., 2010; Smith, 2001; Vega Thurber et al., 2014). A greater prevalence of disease has been associated with elevated concentrations of DIN from anthropogenic sources (fertilizer, sewage pollution, etc.) and phosphate ranging from 3.6–5.6 µM and 0.3– 0.4 µM, respectively (Aeby et al., 2011; Amato et al., 2016; Bruno et al., 2003; Dinsdale et al., 2008; Kaczmarsky and Richardson, 2010; Table S4-1). Elevated nutrients are often synonymous with large human populations. However, there is some debate surrounding the link between human population and coral health (see Bruno and Valdivia, 2016). Additionally, upwelling can elevate nutrients, and combined nitrate and nitrite concentrations in a northeast Pacific plume ranged from 2–25 µM (Hill and Wheeler, 2002).
The isotopic signature of nutrients such as nitrogen can often act as a tracer for different sources of coastal pollution, with different forms having different impacts (e.g., sewage can increase pathogen concentrations) and solutions (Costanzo et al., 2001; Dailer et al., 2012; Lapointe et al., 2005, 2004; Lin et al., 2007; Moynihan et al., 2012; Redding et al., 2013; Savage and Elmgren, 2004; Sutherland et al., 2010; Umezawa et al., 2002; Yoshioka et al., 2016). Stable isotope analyses of nitrogen stored in macroalgae can provide a nutrient signal integrated over time versus water sampling, which is highly variable over space and time. Additionally, nutrients are quickly assimilated by organisms (Fry et al., 2003). Generally, δ15N signatures in algae associated with urban wastewater are > 10‰ (Dailer et al., 2010; Gartner et al., 2002; Heaton, 1986; Tucker et al., 1999); however, values as low as 4.5‰ have been argued as impacted by anthropogenic sources of nutrients (Lapointe et al., 2004). Depleted δ15N values (1–3.5‰) can be sourced from either synthetic fertilizers (Dailer et al., 2010; Heaton, 1986) or pristine mangroves (Lamb et al., 2012). Natural and synthetic fertilizers display a large range from -4–+4‰ of δ15N values. Nitrogen fixation
136 typically has a negative δ15N signature between -2–0‰ (Montoya et al., 2002). Upwelling can have variable δ15N values with enrichment in the 10–12‰ range off the Peruvian coast (Firstater et al., 2010). Depleted values as low as 4.8‰ have been recorded, but values are typically in the 5–6‰ range from work in the Florida reef tract, South China Sea, and eastern Indian and Pacific sectors of the Southern Ocean (Huang et al., 2013; Lamb et al., 2012; Lapointe et al., 2005; Leichter et al., 2007; Li et al., 2015; Sigman et al., 2000). Given the lack of inorganic fertilizer use and waste infrastructure in Timor-Leste, nearshore waters were expected to have δ15N signatures higher than upwelling (5–6‰) which is indicative of sewage pollution (> 10‰). Both fertilizer use and waste infrastructure are expected to be developed as described in the national strategic development plan (RDTL, 2011). Furthermore, tourism is also a targeted industry for development. Tourism potentially increases nutrients introduced into the natural environment via human waste production and greater use of fertilizers for hotel landscaping. As such, regular water quality monitoring through time will be important for these and other reasons.
Global Impacts–ocean warming and mass coral bleaching and mortality The mass global bleaching event in 2016–2017 was the longest and most intense in history (Eakin et al., 2017; Hughes et al., 2018). This El Niño-associated thermal event had global, but patchy impacts on coral reefs. For example, unprecedented mortality following intense thermal stress was observed on the northern GBR, with only minor bleaching being reported on the southern GBR where thermal stress was minimal (Hughes et al., 2017b). Quantification of potential thermal impacts was however limited to highly developed regions with the financial and scientific capacity to quickly monitor the situation as it unfolded. Few reports exist of the impacts of the same event in the CT. Arguably, the CT has the most to lose from the degradation of reefs (Burke et al., 2012). NOAA’s Coral Reef Watch virtual station in Timor-Leste (CRWTL) reported anomalous warming between the two survey periods of November 2015 and July 2017. Between the end of January and the end of May in 2016, and again for January and February 2017, the water temperature of the regions attained degree heating weeks (DHWs) above 4˚C-weeks, but less than 8˚C-weeks (NOAA Coral Reef Watch, 2018). A DHW range of 4 to 8˚C-weeks has been associated with 30– 40% bleaching (Hughes et al., 2017b; Strong et al., 2011), suggesting that corals may have bleached twice within the 20-month sampling interval. Surviving corals, however, would have had four to five months to recover before resurveying in July 2017. Typically, mortality is not expected below 8˚C-weeks DHWs (Liu et al., 2006), although this is variable between 137 species (Baird and Marshall, 2002; Marshall and Baird, 2000). Corals that have been hit with a recent thermal event that is sufficiently warm to lead to temporary bleaching in some corals may nonetheless be vulnerable to disease or other signs of compromised health (Harvell et al., 2007, 2002, 1999; McClanahan et al., 2009; Weil et al., 2009). Additionally, corals may endure sublethal effects for months after the event as they attempt to rebuild energy reserves (Hoegh-Guldberg et al., 2007; van der Zande et al., 2020). For example, surveys tracking tabulate Acroporids on the GBR during the 2017 bleaching event found that 48% of surviving colonies at the period of maximum DHW accumulation (8.3˚C-weeks) displayed signs of WS. Additionally, colonies with WS displayed seven times as much tissue-loss as than only-bleached colonies (Brodnicke et al., 2019).
Aims and objectives
The aims of the present study were three-fold. The first was to investigate the presence or absence of coral disease at these sites along the northern coast of Timor-Leste, and to explore the concentration and source of nutrients (from their isotopic signature) in coral reefs in this region. This was accomplished using established techniques for detecting and recording coral disease while using seawater nutrient and nitrogen stable isotope analyses of macroalgae to help identify the source of the potential nutrient pollution. Secondly, benthic composition was assessed between the four sites. The third aim was to explore the prevalence and severity of coral disease and other signs of compromised health before and after the 2016–2017 global mass bleaching event with repeated coral health surveys and in situ and remotely sensed SST data. If bleaching occurred, it was unlikely to be present when the second survey was conducted months after the peak of the event. The effect of severe bleaching would be observable with a significant reduction in live coral cover and remnant effects of milder bleaching may persist through observations of compromised health and disease.
Given that 6.8% of the urban population of Timor-Leste lacks access to basic toilet facilities (NSD & UNFPA, 2011), nutrient levels were hypothesized to be higher on reefs closer to the capital. Higher nitrogen stable isotope values (> 10‰) were expected to be paired with a higher prevalence of disease and signs of potential compromised coral health near the city of Dili as compared to that of more remote locations. However, elevated δ15N could also be from other sources such as deep water nutrients from upwelling. Additionally, the anthropogenic effect was expected to be stronger at 5 m depth compared to 10 m, as the 138 signal should be stronger closer to the shore, especially in zones with limited wave action and embayments with longer water retention periods (Couch et al., 2014). The Timor-Leste Strategic Development plan outlines the significant infrastructure and economic goals that could have both positive and negative outcomes for downstream reef health. For example, improving access to waste infrastructure across the country would likely have benefits to human and environmental health alike. On the other hand, increased use of inorganic fertilizers and pesticides for agricultural productivity could have a positive or negative impact depending on management response (Bruno et al., 2003; Guzmán and Holst, 1993).
Consequently, this chapter focused on answering three key questions:
1) What is the prevalence of coral disease and compromised health for coral communities at four sites around the Dili capital area? What factors influence the prevalence of disease along the north coast of Timor-Leste (i.e., human population, inorganic nutrients)?
2) What is the benthic composition and coral cover across the four sites surveyed? Is there a difference between the rural and urban sites assessed?
3) Did Timor-Leste reefs experience significant heat stress over the 2016–2017 global heating event; and, did the coral communities incur significant mortality or other signs of compromised health or disease as a consequence of this event?
Methods
Study Site This study was undertaken along the coast of Dili, Timor-Leste to quantify coral health to complement a growing body of coral reef science in the area. Previous indications of reef health were largely anecdotally derived from surveys with other objectives. Dili, the capital (8˚33’S and 125˚34’E), houses a quarter of the country’s population with 252,884 recorded in the 2015 Census (RDTL, 2015). The seasonal Comoro River runs through Dili, with output ranging from less than 0.5 m3/s from July to November to 12.3 m3/s in March during the northwest monsoon season from December to May (DNMG et al., 2015; JICA, 2010). The present study was conducted in two, three-week field trips that occurred in November of 2015 and July of 2017 during the dry season. The dry season offers safer surveying conditions (sites were not accessible during the monsoon season) but would also limit 139 terrestrial run-off inputs such as nutrients and is the season of lower ocean temperatures. While future studies should expand the results here by examining the dynamics of coastal systems during the wet season, they were not investigated in this chapter.
Surveys were conducted at four sites. Two sites flanked Dili and were representative of reefs under urban influences (Urban-W with 5,017.9 people/km2 in the Dom Alexio subdistrict; Urban-E with 779.5 people/km2 in the Cristo Rei subdistrict; Figure 4-1) and two sites were representative of reefs under rural influences. The rural sites were approximately 40 km north on Ataúro Island (Rural-N with 66.2 people/km2 in the Ataúro subdistrict) and 40 km east of the capital near Manatuto (Rural-E with 37.4 people/km2 in the Laclo subdistrict; (RDTL, 2015), both with small villages (Figure 4-1). Access to Rural-N was limited to motorized boat transportation (water taxis and a local ferry) and there are few motorized vehicles on the island. A single-lane road connects Dili to the Rural-E. Sites were chosen for logistics and to complement NOAA climate station data collection surveyed between 15– 27th of November 2015 and 15–29th of July 2017.
Figure 4-1 Survey sites in Timor-Leste around the capital of Dili. Rural-N in the island in the channel, Rural-E 40 km east of Dili, and Urban-W and Urban-E flanking Dili. The highly seasonal Comoro river can be seen just east of Urban-W. The four sites were sampled at two points in November 2015 and June 2017. 140
Rural-N and Urban-W were chosen because they were existing NOAA climate stations—2 and 4, respectively (Figure 3-2). Rural-E was located near NOAA climate station 5 but was not at the exact site due to inaccurate GPS points. Two kilometer-scale transects analyzed in Chapter 2 were collected near Urban-E (Dili district in Chapter 2) and three were collected on the east side of Ataúro Island (North Ataúro district in Chapter 2), the location of Rural- N. Three kilometer-scale transects were collected in Manatuto, the site of Rural-E (Figure 1-3; Figure 2-2).
Coral community composition and coral health surveys To assess benthic cover and coral health, we deployed 15 m line intercept transects (English et al., 1997) and 15 m x 2 m belt transects (Raymundo et al., 2008). At each of the four sites, three transects were laid at 5 (reef flat) and 10 m (reef slope) depths for a total of 24 transects total across all sites. The first transect at each site was chosen haphazardly with the subsequent transects being 5 m away at the appropriate depth contour. For the line intercept transect, the benthos under the 15 m tape was categorized into a major benthic category (hard coral, soft coral, substrate/sand, macroalgae, turf algae, cyanobacteria, and CCA). On the coral health belt transects, every coral colony within the belt transect area was identified to genus and assessed visually for coral disease and signs of potential compromised coral health consisting of overgrowth by macroalgae, turf and cyanobacteria, encrusting invertebrates (sponges, tunicates, flatworm infestation), burrowing invertebrates (worms, barnacles), signs of predation (fish and Drupella spp. snails), signs of bleaching (partial or total loss of algal symbionts appearing white), signs of coral response (pigmentation, mucus), and physical damage (sedimentation, breakage). This was done as per protocols developed by the Global Environment Facility and World Bank Coral Disease Working Group (Figure 4-2; Figure 4-3; Raymundo et al., 2008). Any uncertain diagnoses were photographed and used in later consultation. The prevalence of disease and compromised health was calculated as the number of corals affected by disease/compromised category divided by the total number of corals in the transect. The same transect start GPS coordinates at the surface were used for the second survey, in July 2017, with the same direction considering currents, etc.
141
Figure 4-2 Examples of disease documented during the surveys undertaken in Timor-Leste between November 15-27th, 2015. (a) WS–White Syndrome band of distinct tissue loss on tabulate Acroporids with white skeleton abutting live tissue with exposed skeleton gradually colonized by turf algae; (b) GA–growth anomaly on an Acroporid; (c) TRE–Trematodiasis bright pink nodules (unconfirmed for the presence of trematode) with burrowing worms also present. Red boxes indicate a close-up. See Table S4-3 for more information.
142
Figure 4-3 Examples of compromised health documented during the surveys undertaken in Timor-Leste between November 15-27th, 2015. (a) bleaching–loss of symbionts from coral tissue; (b) burrowing barnacles and worms; (c) flatworm infestation likely Waminoa spp.; (d) predation by Drupella spp. snails; (e) unexplained tissue loss; (f) CCA overgrowth; (g) cyanobacteria overgrowth; (h) Colonial tunicate overgrowth; (i) pigmentation; j) filamentous turf algae overgrowth. Red boxes indicate a close-up. See Table S4-3 for more information. 143
Measurement of nutrient concentrations and stable isotope ratios Seawater samples were collected for measuring the concentration of inorganic nutrients as an indicator of nutrient pollution. Macroalgal samples were collected for stable isotope analysis to explore the origin of inorganic nitrogen. Three replicate 100 ml seawater samples were collected on each transect 0.5 m above the benthos and kept on ice until filtering through a 0.22 μm pore membrane filter and stored frozen. Seawater samples were analyzed within four months for ammonium, nitrite, nitrate, and phosphate using flow injection analysis at the Advanced Water Management Center (The University of Queensland). Nitrite had mostly zero values and was combined with nitrate for analyses. Three replicates of Halimeda spp. and Chlorodesmis spp. macroalgae (approximately 5 g dry weight) were collected when found on each transect, rinsed, and air-dried for transport. In the laboratory, the macroalgal samples were re-dried at 60˚C for a minimum of 24 h before homogenization using a mortar and pestle and analysis at the Cornell University Stable Isotope Laboratory (Finnigan MAT Delta Plus isotope ratio mass spectrometer) for δ15N analysis.
In situ and satellite temperature data HOBO pendant temperature loggers (Onset Computer Corporation, Bourne, MA USA) were deployed at every site and depth in November 2015 recording temperature every 30 mins. All were retrieved in June 2017 except those from Rural-E. Remotely sensed satellite SST data from the NOAA’s CRWTL virtual regional station was downloaded from August 2015 through August 2017. This product uses 5 km2 resolution to predict bleaching stress across an entire jurisdiction such as Timor-Leste versus values at every 5 km pixel (NOAA Coral Reef Watch, 2018).
Statistical analyses All analyses were conducted in R version 3.6.3 (The R Core Team, 2020) and PRIMER7 (Anderson et al., 2008; Clarke and Gorley, 2015). Repeated measures permutational multivariate analysis of variance (PERMANOVA) with 9,999 permutations were conducted to test for significant effects between sites (Rural-N, Rural-E, Urban-W, Urban-E), depths (5 m, 10 m), and years (2015, 2017) on a Bray-Curtis similarity matrix of transformed (square root) benthic cover categories, transformed (square root) prevalence of disease and compromised health, and zero-adjusted, transformed (square root) Bray-Curtis similarity matrix of the number of colonies per coral genus (i.e., the count of coral genera per transect;
144
Anderson et al., 2008; Clarke and Gorley, 2015). The same transects were attempted to be surveyed based on GPS points, although transects were not marked in situ. All multivariate tests were also tested for homogeneity of dispersion akin to the homogeneity of variance in univariate tests (PRIMER7, Anderson et al., 2008). Repeated measures analysis of variance (ANOVA in the car, emmeans, nlme R packages; Fox and Weisberg, 2019; Lenth, 2020; Pinheiro et al., 2020) was used to test hard coral, categories of disease and compromised health (on the bleaching category transformed), the number of coral genera, and Shannon diversity index on coral genera for significant effects between sites, depths, and years. All data were square root transformed. Principal coordinate analysis (PCO) was run on the same transformed resemblance matrix of coral genera to visualize coral community structure. A repeated measures ANOVA was also conducted on the log-transformed number of Acroporids per transect between site, depth, and year. Normality for ANOVAs was assessed using histograms, qqplot, qqnorm, and leveneTest functions in R.
Nutrient data were only collected in 2015 and a two-way ANOVA with factors, site, and depth + was done on the seawater nutrient data including DIN (transformations: log–NH4 and DIN, - - square root–NO3 + NO2 .). Three, two-way ANOVAs (Anova in car R package) were used to test for significant differences in δ15N, N%, and the ratio of carbon to nitrogen (C:N) for each of the two genera of algae, Halimeda spp. and Chlorodesmis spp., with the same factors. Only three samples of Chlorodesmis spp. were collected on a singular transect at Rural-E and were removed from the analysis. Variables were visually inspected for normality (qqplot and qqnorm) and tested for homogeneity of variance (leveneTest). Percent nitrogen was log-transformed for Halimeda spp. Post hoc tests were conducted using multcomp and emmeans packages for Halimeda spp. and Chlorodesmis spp. (Hothorn et al., 2008). The effect of 2015 nutrients on the same resemblance matrix of the prevalence of disease and compromised health of the same year was analyzed using a PERMANOVA (9,999 + - permutations in PRIMER7) with site and depth as factors and covariates NH4 , NO3 , and 3- PO4 from the collected seawater data.
The monthly temperature average was calculated from the 24 hr daily maximum temperature obtained from in situ temperature logger data and remotely sensed CRWTL data. Temperature logger data were pooled by site to test for differences in the monthly means of in situ temperature between site using a one-way repeated measures ANOVA. Differences in the monthly temperature means between austral season (summer Jan–Mar, fall Apr–Jun, winter Jul–Sept, spring Oct–Dec) and method (in situ loggers, remotely sensed 145
SST from CRWTL) were also tested with a two-way repeated ANOVA. Both analyses employed random intercept models with residual autocorrelation structures to account for temporal autocorrelation of individual temperature loggers and CRWTL measurements (nlme R package). Post hoc tests were conducted using the emmeans R package. To assess levels of thermal stress, remotely sensed DHWs were retrieved from CRWTL online (NOAA Coral Reef Watch, 2018).
Results
There was a low prevalence of disease at the four sites surveyed in Timor-Leste which were similar at rural and urban sites. Contrary to our hypothesis, White Syndrome (WS) was the most prominent at Rural-N with a prevalence of 0.9 ± 0.2% in 2015, while Growth Anomalies (GAs) was the most prevalent at Urban-W in 2017 with 0.6 ± 0.3%. Benthic community composition and coral cover varied significantly between site, depth, and year, and there was a trend of rural sites having more live coral; however, a greater number of sites are needed to draw this conclusion as there was not enough replication to statistically test the rural versus urban distinction. The changes in benthic composition between surveys were attributed to heterogeneity versus coral mortality following the 2016–2017 global bleaching event. This was supported by the in situ temperature data collected between surveys which never surpassed MMM + 1˚C to accumulate DHWs as the CRWTL SST product did over the same time. The underlying coral community structure was significantly different at the four sites and significant differences in variability between and within sites contributed to this effect which can be a sign of impact. Lastly, seawater nutrients and δ15N were not significantly elevated at urban sites or consistently greater at 5 m depth versus 10 m. The prevalence of disease was significantly associated with phosphate concentrations as the highest combined nitrate and nitrite and phosphate were documented at Rural-N at 10 m, the site of highest disease prevalence.
Prevalence of coral disease and indicators of compromised health Overall, the majority of hard corals at sites surveyed appeared healthy as assessed on belt transects. The sites averaged 65.7 ± 1.70% in both years with low (< 1%) prevalence of diseases. There were no clear distinctions between rural and urban sites. In 2015, there was 0.9 ± 0.2% prevalence of WS at Rural-N. Thirteen cases were found on Acropora spp. corals accounting for 61.9% of total disease and one case on a Montipora spp. Rural-N also had the highest prevalence of GAs the same year with 0.6 ± 0.2%. There was also one case 146 of unconfirmed Trematodiasis, which requires microscopic confirmation of the larval trematode. Disease prevalence was lower in 2017 with the highest prevalence of WS at Rural-N again (0.5 ± 0.1%) and Urban-W with the most GAs (0.6 ± 0.3%). All cases of WS were documented on Acroporids in 2017 while GAs were less host-specific found on nine genera across both years. The prevalence of compromised health was much higher than diseases with an average of 37.4 ± 3.9% across sites and years (Figure 4-4; Table S4-4).
Figure 4-4 Prevalence of disease and indicator of compromised coral health from 15 x 2 m belt transect surveys at four sites in Timor-Leste from November 15-27th, 2015 and June 15-29th, 2017. WS–White Syndrome, AllAlgae–combined macroalgae, turf, and cyanobacteria overgrowth, BL–Bleaching, BUR–Burrowing invertebrates (Vermetid worms, barnacles, etc.), CCA–Crustose coralline algae overgrowth, OTH–combined pigmentation, predation, invertebrate infestation/overgrowth, TL–Unexplained tissue loss.
Prevalence of disease and compromised health categories varied significantly by year and site (repeated measures PERMANOVA, pseudo-F(3,47) = 3.7611; p = 0.0042) and site and depth interactions (pseudo-F(3,47) = 4.4228; p = 0.0094). Rural-N had the lowest prevalence of disease and compromised health compared to all sites in 2015 (22.43 ± 0.92%) and 2017 (33.84 ± 4.25%; p(perm) < 0.05). However, Rural-N was the only site where the prevalence of compromised health and disease increased between survey years (Figure 4-4). Despite this, Rural-N was characterized by the highest percentage of healthy corals significantly higher than all other sites in 2015, with 78.0 ± 0.9% but not significantly lower in 2017 (61.7 ± 4.7%; three-way ANOVA, χ2(3) = 12.5135, p = 0.006; p < 0.05). This 147 site also had the lowest prevalence of algal overgrowth on corals in 2015 (5.3 ± 1.2%) significantly lower than Urban-W in the same year (20.3 ± 1.8%; χ2(3) = 58.42713, p < 0.001) and the lowest amount of bleaching in both years (6.0 ± 0.9% in 2015, 0.8 ± 0.2%; Figure 4-4).
Coral cover and community composition at four sites A total of 9,521 corals of 51 genera were counted over 1,440 m2 of the surveyed in 2015 and 2017. The benthic composition as measured by LIT surveys was significantly different between the four sites. The rural sites had higher coral cover than the urban sites and the overall patterns of coral cover were consistent across survey years (Figure 4-5). Coral diversity also varied significantly; however, lower or higher genera diversity did not fall along rural versus urban distinctions. Urban-W at 10 m had strikingly low coral diversity while Rural-N was the only site dominated by tabulate and branching Acroporids and consistently high (> 40%) coral cover over survey years.
Benthic composition varied spatially with a significant site and depth interaction (repeated measures PERMANOVA, pseudo-F(3,47) = 4.5117, p(perm) = 0.0041) and temporally by year (pseudo-F(1,47) = 34.0270, p(perm) = 0.0002). At 5 m depth, Rural-E had comparable benthic composition to both urban sites but was only similar to Urban-W at 10 m (p(perm) > 0.05). Urban-W was the only site that varied significantly between depths (p(perm) < 0.05). Coral cover was significantly different with a three-way interaction (repeated measures ANOVA χ2(3) = 13.6947, p = 0.0034). Urban-W at 5 m had the lowest coral cover in both years (mean ± SE; 4.8 ± 1.8% in 2015 and 4.5 ± 1.5% in 2017) and Rural-N 5 m (58.2 ± 1.7%) and Rural-N 10 m (56.9 ± 3.3%) had the highest live coral cover respectively in 2015 and 2017 (p < 0.05; Figure 4-5). Overall, hard coral cover was higher at rural sites (37.3 ± 5.3%) than urban sites (12.9 ± 3.8%).
148
Figure 4-5 Benthic composition cover from 15 m line intercept transects by site and depth for 2015 and 2017 survey periods in Timor-Leste (north coast). Major categories include Hard Coral, CCA–crustose coralline algae, Invert–mobile invertebrates, Macroalgae, Soft Coral, Substrate/Sand, Turf Algae.
Although 51 genera were found across the four sites, a few genera dominated the reef, namely Porites (17.4% and 13.0% in 2015, 2017), Fungia (13.7% and 19.0% in 2015, 2017), and Montipora (12.9% and 13.4% in 2015, 2017). The maximum generic richness of 33 ± 2 was present at Rural-N with the minimum at Urban-W at 10 m cataloguing 18 ± 2 genera. The Shannon diversity index showed site and depth differences (three-way repeated measures ANOVA χ2(3) = 24.3377, p < 0.0001) with Urban-W 10 m (1.7 ± 0.2) driving this interaction (Figure 4-6). The coral diversity was similar across rural and urban sites except for Urban-W at 10 m.
149
Figure 4-6 Shannon Diversity Index calculated per site and depth on genera present per belt transect in Timor-Leste. Top bars are at 5 m depth with 10 m below. Sites on the x-axis are split into the 2015 and 2017 survey periods.
Coral diversity, as measured by the generic abundance on belt transects, also differed significantly by a site and depth interaction which was driven by site-level distinctions versus rural and urban boundaries (repeated measures PERMANOVA pseudo-F(3,47) = 3.3011, p(perm) = 0.0018). Diversity at Urban-E was significantly different from all other sites (p(perm) < 0.05) at 5 m and all sites were significantly different at 10 m (p(perm) < 0.05). Rural-E and Urban-W were significantly different within sites between depths (p(perm) < 0.05). Sites were generally distributed along axis two of the PCO with Rural-N most positively associated with tabulate Acroporids, Galaxea, Goniopora, Montipora, and Stylophora genera, while shallower transects were aligned along axis one with more Pocillopora, Platygyra, and massive Porites corals (Figure 4-7). Dispersion, or variability within the coral genera, was also significantly different for the site and depth interaction (F = 10.638, p(perm) = 0.0001) indicating that variability within sites and depths contributed to significant differences in generic abundance across sites. Specifically, the dispersion was significantly lower at Rural-N compared to Urban-E and Urban-W at 10 m and greater at Urban-W 10 m compared to the same site at 5 m, Urban-E 10 m, and Rural-E 10 m (p(perm) < 0.05). Site dispersion, or spread of site markers, increased moving down PCO axis two and generally less at 5 m (open symbols) than at 10 m (solid symbols; Figure 4-7).
150
Figure 4-7 Principal Coordinate Analysis biplot of coral genera diversity from belt transects. Shapes indicate site with empty and solid markers indicating 5 and 10 m depths, respectively. Color indicates survey year: blue–2015 and green–2017. Abbreviations are coral abundance as follows: ACRtab–Acropora tabulate, FUN–Fungiids, GAL–Galaxea, MONTI–Montipora, GONIO–Goniopora, PLA–Platygyra, POC–Pocillopora, PORmass– Porites massive, and STY–Stylophora.
There was also a significant site and year interaction for coral community structure (repeated measures PERMANOVA pseudo-F(3,47) = 2.1432, p(perm) = 0.0071). Coral diversity between Rural-N and all other sites and Urban-W and Rural-E were significantly different in both years (p(perm) < 0.05) and differences in dispersion were also significant where Rural- N and Urban-W had the least and most variability respectively. Dispersion at these sites was significantly different from all other sites for both in years in the case of Rural-N and 2015 for Urban-W (Figure 4-7).
Community composition varied significantly between sites, which invariably having different dominant genera. While rural sites had more coral cover overall, coral community composition was distinct between sites at differing levels between depths. Urban sites did have low coral cover (< 10%) at either 5 or 10 m consistently between years (Figure 4-5). 151
Additionally, Urban-W had the lowest coral diversity at 10 m (Figure 4-6). Rural-N stood out with the highest coral cover, the greatest number of genera, and the largest proportion of tabulate and branching Acroporids. At both depths, Rural-N had significantly more tabulate (repeated measures ANOVA χ2(3) = 88.7746, p < 0.0001) and branching Acroporid colonies (repeated measures ANOVA χ2(3) = 38.3591, p < 0.0001) than all other sites with 21.1 ± 0.7 and 11.0 ± 0.4 colonies per transect respectively (p < 0.05). All other sites averaged less than five Acroporid colonies per transect for both morphologies. Although not definitive, there is some evidence that suggests that the rural sites are in better shape in terms of coral cover than the urban sites. However, the marked presence of Acroporids at Rural-N seems to indicate that this site is distinctive from the remaining sites versus clear rural and urban distinctions.
Nutrients and stable isotopes To provide insight into the origin of land-based pollution, seawater nutrient levels and N stable isotopes of macroalgae were simultaneously assessed. Nutrients were not elevated at the urban sites although there were significant site and depth interactions (two-way ANOVA F(3,63) = 3.208, Pillai = 0.398, p = 0.0012). Nitrate, nitrite, and phosphate were - - responsible for these interactions (two-way ANOVA NO3 + NO2 : F(3,63) = 10.899, p < 3 0.001; PO4 -: F(3,63) = 4.560, p = 0.006). Rural-N 10 m had significantly higher combined - 3 nitrate and nitrite (NO3 + NO2: 1.05 ± 0.07 μM) and phosphate (PO4 - 0.15 ± 0.01 μM; Table S4-5), than all other sites at 10 m, but comparable levels of both at 5 m (Figure 4-8). The combined nitrate and nitrite were higher than concentrations of nitrate sampled on reefs in west Hawai‘i (0.38 ± 0.11 μM) and Majuro Atoll in the Marshall Islands (0.45 ± 0.45 μM; Osawa et al., 2010; Smith et al., 2001a). Phosphate levels in Timor-Leste were similar to the values from Kingman and Kiribati atolls (range 0.1 ± 0.003 μM to 0.3 ± 0.024 μM; Dinsdale et al., 2008) and Hawaiian reef slopes (0.24 ± 0.07 μM; Smith et al., 2001a).
152
+ - - 3- Figure 4-8 Seawater nutrient concentrations (top to bottom DIN, NH4 , NO3 + NO2 , PO4 ) sampled in triplicate on each transect at four sites (Urban-W, Urban-E, Rural-N, Rural-E), two depths (5 m and 10 m), and three transects per depth in Timor-Leste in 2015. Bold line is the median, box ends are the first and third quartile, lines are 95% confidence interval of the median, and points are Tukey’s outliers.
Although NH4+ was not significantly different between surveyed sites except Rural-E 5 m, the range of 1.32 ± 0.17 μM to 2.69 ± 0.78 μM was more than the previously recorded values between 0.3–2.2 μM on reefs (Moynihan et al., 2012; Osawa et al., 2010; Smith et al., 2001). Ammonium values reported here are closer to groundwater measurements from Majuro Atoll and reefs downstream ~ 5 km of 22 sewage outfalls in Zanzibar that are also popular day- snorkeling trips (Moynihan et al., 2012; Osawa et al., 2010), but not elevated to levels associated with sewage pollution (> 10 µm; Amato et al., 2016b). DIN was marginally significant with a site and depth interaction (two-way ANOVA F(3,63) = 2.777, p = 0.0484), but pairwise test showed no significant comparisons (p < 0.05). Mean DIN (2.49 ± 0.20 μM) was between concentrations recorded in pristine (1.3 ± 0.08 μM) and populated (3.6 ± 0.1
153
μM) atolls in the Northern Line Islands and more than concentrations in the Florida Keys (1.15 ± 0.05 μM; Dinsdale et al., 2008; Vega Thurber et al., 2014).
Stable isotope values were remarkably consistent across sites, again, with no elevated levels at the urban sites compared to the rural sites. Delta 15N stable isotopes, tissue N %, and C:N all had significant site differences for both algae species, except for C:N in Chlorodesmis spp. where the ratio was higher at 5 m than 10 m depth (5 m: 10.80 ± 0.313, 10 m: 9.45 ± 0.448). Urban-E had significantly lower δ15N for both algae species (Table 4-2). The overall δ15N range between 4–5‰ of sampled here was not indicative of sewage pollution (typically > 10‰; Heaton, 1986) although sewage-derived values as low as 3‰ have been reported (Costanzo et al., 2001). The values are higher than synthetic or biologically-fixed nitrogen (~ 0‰; Heaton, 1986) and are in the range of upwelling values (4.8–6‰). Urban-E also had lower N% for Halimeda spp., but higher N% for Chlorodesmis spp. The N% (2.36 ± 0.10%–3.31 ± 0.05%) and C:N (9.71 ± 0.06%–11.78 ± 0.50%) values of Chlorodesmis spp. were comparable to values from an Ulva spp. bioassay (N%: 2.0 ± 0.7%–3.0 ± 0.6%; C:N: 9.9 ± 1.7%–15.4 ± 6.8%) where the algae were deployed in cages on reefs located in high-nitrogen sites associated with onsite sewage disposal systems, municipal wastewater injection facility, and large-scale agriculture in Maui (Amato et al., 2016). The N% was much lower for Halimeda spp. and the C:N ratio much greater as expected given that it is a calcifying alga. The ratio of C:N followed an inverse relationship to N% with lower N% values producing higher C:N ratios (Table 4-2).
154
Table 4-2 Delta 15N stable isotope, N%, and C:N ratio ANOVA results of two genera of algae sampled in replicates at the four sites (Urban-W, Urban-E, Rural-N, Rural-E), two depths (5 m and 10 m), and three transects per depth in Timor-Leste in 2015. Bolded values are significant results with mean, standard error, and post hoc groupings presented per site. No Chlorodesmis spp. was sampled at Rural-N or Rural-E at 10 m and the three samples collected from a single transect at Rural-E 5 m were removed for the ANOVAs.
Halimeda df F-value p-value Rural-E Rural-N Urban-E Urban-W spp. δ15N Site 3 3.8199 0.0121* 4.26‰ 4.31‰ 4.03‰ 4.26‰ Depth 1 0.5442 0.4624 0.01 0.01 0.01 0.01 Site x 3 1.3801 0.2531 ab b a b Depth N % Site 3 5.779 0.0011* 0.62% 0.82% 0.48% 0.76% log10 Depth 1 0.2103 0.6475 0.01 0.02 0.00 0.01 Site x 3 1.06 0.3698 ab b a b Depth C:N Site 3 3.3989 0.0207* 28.24 24.77 31.56 25.02 Depth 1 0.0687 0.7938 0.4 0.4 0.23 0.31 Site x 3 1.2926 0.2811 ab a b a Depth Chlorodesmis spp. δ15N Site 1 10.0028 0.0064* 4.57‰ - 4.11‰ 4.47‰ Depth 1 0.1747 0.6819 0.09 0.02 0.04 Site x 1 2.4127 0.1412 a b Depth N % Site 1 6.0924 0.0261* 2.63% - 3.31% 2.36% Depth 1 0.3489 0.5635 0.1 0.05 0.1 Site x 1 0.3849 0.5443 b a Depth C:N Site 1 4.2869 0.0561 11.78 - 9.71 10.95 Depth 1 6.0953 0.0261* 0.5 0.06 0.23 Site x 1 1.6411 0.2196 a a Depth
Testing the drivers of prevalence of disease and compromised health with seawater nutrient concentrations in 2015, they differed significantly between site (p(perm) = 0.0001), depth 3- (p(perm) = 0.0016), and PO4 (p(perm) = 0.0162, Table 4-3). Dispersion based on distances to centroids grouped by site and depth was significantly different (one-way ANOVA F(7,16) = 5.931, p < 0.01) and was greatest at Rural-N and significantly larger than all other sites at
155
10 m and Urban-E and Rural-E at 5 m (p < 0.05). Phosphate was significantly higher at Rural-N 10 m compared to other sites at the same depth (Figure 4-8) and Rural-N had the highest prevalence of WS (Figure 4-4).
Table 4-3 PERMANOVA analyses testing the effect of site, depth, and seawater nutrient + - 3- concentrations (NH4 , NO3 , and PO4 ) on the of the prevalence of coral disease and signs of compromised coral health from surveys in Timor-Leste in 2015. Bolded values are significant results with significance at p = 0.05.
Source of Degrees Sums of Means of Pseudo-F r2 p(perm) Variation of Squares Squares Freedom Site 3 0.52615 0.175383 7.4077 0.42076 0.0001* Depth 1 0.12196 0.121964 5.1514 0.09753 0.0016* + NH4 1 0.04585 0.045849 1.9365 0.03667 0.1002 - NO3 1 0.01789 0.017892 0.7557 0.01431 0.5945 3- PO4 1 0.07698 0.076982 3.2515 0.06156 0.0162* Site x Depth 3 0.15385 0.051284 2.1661 0.12303 0.0220
Temperature and the prevalence of bleaching The average temperature difference between the 5 and 10 m temperature loggers across the three sites was 0.3˚C (± 0.0˚C) and thus the loggers were pooled by site for further testing of site differences. The monthly averages of the temperature recorded by the in situ loggers were not significantly different by site (one-way ANOVA: χ2(2) = 0.176, p = 0.9159; Figure 4-9).
Comparison of the monthly mean of all in situ temperature loggers and CRWTL SST was significantly different by a season and method interaction (two-way ANOVA: χ2(3) = 8.205, p = 0.0420). Although the CRWTL satellite-derived SST was not significantly higher than the in situ logger temperatures across corresponding seasons from pairwise tests (p > 0.05), the elevated CRTWL temperatures during the austral summer (Jan–Mar, CRWTL = 30.7 ± 0.2˚C and in situ = 29.1 ± 0.1˚C) and austral spring (Oct-Dec, CRWTL = 30.5 ˚C and in situ = 29.7 ˚C) were ecologically significant, 1.6˚C and 0. 8˚C warmer, respectively. During both the summer and spring, CRWTL SST surpassed the MMM + 1˚C bleaching threshold, while the in situ temperature remained below this level of DHW accumulation (Figure 4-10).
156
Figure 4-9 Mean temperature by month for remotely sensed sea surface temperature (CRWTL–Coral Reef Watch Timor-Leste) and in situ temperature loggers (Rural-N, Urban- E, Urban-W at 5 and 10 m depth) between sampling periods in 2015 and 2017. The dashed blue line is the maximum monthly mean (MMM) from CRWTL data and the solid blue line is MMM + 1˚C. Error bars represent 2 standard error units.
During the 639 days between surveys, major heat stress events occurred. CRWTL indicated there was 190 days (30.2%) of bleaching warning (0˚C-weeks < DHW < 4˚C-weeks) and 161 days (25.2%) at bleaching alert 1 (4˚C-weeks <= DHW < 8˚C-weeks). The accumulation of DHWs was limited to November 29th, 2015–July 12th of 2016 (224 days) and November 13th, 2016 through March 16th, 2017 (119 days) which corresponds to the months where the CRWTL monthly averaged temperatures were greater than the climatological maximum monthly mean (MMM; Figure 4-9). The accumulation of DHWs between 2015–2016 was almost 8 months, nearly twice as long as the DHW accumulation from 2016–2017. The in situ temperature data, however, never reached the MMM + 1˚C threshold for bleaching, and based on these data there would be no accumulation of DHWs.
157
Figure 4-10 Plot of the significant season × method interaction between the Coral Reef Watch Timor-Leste virtual station (CRWTL) remotely sensed sea surface temperature and in situ temperature logger data collected between Nov 2015 and Jul 2017. The dashed blue line is the maximum monthly mean (MMM, 29.5˚C) from the CRWTL data and the solid blue line is MMM + 1˚C (30.5 ˚C), the bleaching threshold for the accumulation of degree heating weeks. Error bars represent 95% confidence intervals.
There was a three-way interaction between site, depth, and year on bleaching prevalence (three-way repeated-measures ANOVA, χ2(3) = 18.671, p = 0.0300; p < 0.05; Figure 4-4). All sites at the same depth showed a decrease in the prevalence of coral bleaching between surveys, which is expected as the second survey was conducted at the onset of austral winter. However, only Rural-E at 10 m (13.4 ± 0.7% and 2.8 ± .3% in 2015 and 2017, respectively) and Urban-W at 5 m (17.4 ± .6% and 1.8 ± 1.3%) had significant decreases in the prevalence of bleaching. Rural-N in 2017 (1.1 ± .5%) had significantly less bleaching than all other sites other than Urban-E (3.3 ± 1.3%; p < 0.05).
Discussion
This chapter focused on producing a baseline for the condition of four outer reef slope communities that were either near or relatively distant to the capital of Dili, in Timor-Leste. A higher prevalence of coral disease and other signs of compromised health was expected at urban sites where elevated nutrients and sewage pollution were at potentially high levels. Our study observed major differences between sites in terms of community composition, 158 disease prevalence, and potential exposure to local threats, but disease prevalence was low overall along with nutrient (inorganic) levels which were consistent across sites. Furthermore, there was evidence of human subsistence activities influencing the health of a reef at one of the urban sites. Second, Timorese reefs were subjected to bleaching during the 2016–2017 global bleaching with the accumulation of > 4˚C-weeks. This, however, was not supported by the in situ temperature logger data. Whilst recording similar temperatures in cooler months to that observed by satellite average for the region, the loggers recorded significantly lower temperatures over the summer months, never reaching the MMM + 1˚C threshold. Mortality associated with the event was low by comparison to regions that experienced > 8˚C-weeks during the bleaching event and high coral mortality.
Health condition of Timorese reefs Prevalence of disease and compromised coral health was expected to be greatest at urban sites with larger nutrient input and greater δ15N values at the shallow 5 m surveys. Contrary to expectations, disease was highest at Rural-N at 5 m with levels of WS at ~ 1% in both survey years, and combined nitrate and nitrite and phosphate levels were highest at Rural- N at 10 m. The low levels of disease detected in the current study agree with previous surveys (Ayling et al., 2009; Erdmann and Mohan, 2013), although no previous studies were specifically quantifying disease and compromised health.
WS was the main pathology consistently observed during surveys. WS is a collective term for conditions resulting in white bands of tissue or skeleton in the Indo-Pacific. The WS documented at Rural-N was likely an infectious disease (Willis et al., 2004). Direct transmission of WS spreading between two Acroporid corals was observed in the field and the disease follows the classic density-dependent host-pathogen relationship where there was a positive association between host abundance and disease prevalence (Anderson and May, 1979; Lafferty and Holt, 2003; Poteet, 2006). This phenomenon has been demonstrated in coral disease ecology. Here, all but one case of WS were on Acroporids, and 13 out of 17 cases in 2015. All 10 cases in 2017 were documented at Rural-N which had the highest density of Acroporids (Aeby et al., 2010a; Bruno et al., 2007; Haapkylä et al., 2009b; Myers and Raymundo, 2009). In the Indo-Pacific, WS is known to target Acroporids (Bruno et al., 2007; Maynard et al., 2015b; Willis et al., 2004). The few cases at other sites could have been from other causes such as unidentified predation. However, there was sufficient reason to believe that WS at Rural-N was caused by an infectious
159 pathogen. There was likely coral mortality caused by the WS, inferred from the proportion of dead coral on some colonies (Figure 4-2a) as is typical with WS progression on Acroporids (Aeby, 2005; Roff et al., 2006). The low prevalence of WS was likely not responsible for the decrease in coral cover at Rural-N 5 m as the coral cover increased at 10 m at the same site and there was no significant difference in WS prevalence between depths (Figure 4-4). The pathogen causing WS at Rural-N is unknown but was likely Vibrio spp. bacteria that have been previously associated with diseases of multiple marine organisms including corals and humans (Amaro and Biosca, 1996; Cervino et al., 2004; Linkous and Oliver, 1999; Milton et al., 1992). These bacteria have also been implicated as a causative agent of WS (Sussman et al., 2008; Ushijima et al., 2014, 2012). The low prevalence of WS found in Timor-Leste is likely typical background levels of disease versus an outbreak, although the prevalence of WS should continue to be monitored.
Overall, the low prevalence of coral disease in Timor-Leste was consistent with surveys in other areas of the CT (Haapkylä et al., 2007; Johan et al., 2015; Muller et al., 2012). Other CT studies, such as reefs in Kepulauan Seribu Marine National Park of Indonesia, had less than 1% prevalence of WS (Johan et al., 2015). Disease prevalence was also low, 1.5 ± 1.0%, and 4.6 ± 6.0% including WS at deep and shallow sites respectively in the Spermonde Archipelago and Wakatobi, Sulawesi (Muller and van Woesik, 2012). Previous surveys in Wakatobi National Park detected a lower prevalence of coral disease at 0.6% in 2005 (Haapkylä et al., 2007) and 0.33% in 2007 (Haapkylä et al., 2009b). The low prevalence of coral disease in the CT supports the disease-diversity hypothesis which predicts that higher host species diversity should result in decreased severity of a specialist pathogen through increasing interspecific competition (Aeby et al., 2010b; Elton, 1958; Plank, 1963). Although coral species were not recorded on the belt transects, four morphologies of Acroporids— branching, bushy, tabulate, and corymbose—were recorded having WS, likely encompassing different species. The majority (> 50%) of cases were on tabulate Acroporids which indicates that different morphologies (and species) have different susceptibility to WS as documented on other Pacific reefs (Aeby et al., 2010b). Of course, there are examples of higher disease prevalence in the CT such as Porites ulcerative white spot (PUWS) which is mainly in the Philippines with a range of 0.0–72.6%. The high prevalence of PUWS was negatively correlated with the human population (Kaczmarsky and Richardson, 2010; Kaczmarsky, 2006; Raymundo et al., 2003).
160
WS is a dynamic disease and can occur in outbreaks that can devastate Acroporid populations (Hobbs et al., 2015). A 150-fold increase in WS was observed over five years on the GBR and field studies in the Indian Ocean found an average of 36% decline with a maximum of 96% decline of tabulate Acroporids six months after the outbreak. Mean percent cover remained low in the following five years which is consistent with other long-term monitoring (Hobbs et al., 2015; Willis et al., 2004). The selective pressure of WS on tabulate Acropora via mortality can contribute significantly to the overall coral community structure (Hobbs et al., 2015). The causes of WS outbreaks have been linked to sediment plumes from dredging and terrestrial runoff and elevated ocean temperature (Pollock et al., 2014; Sheridan et al., 2014). A significant interaction between weeks of heating and coral cover with the prevalence of WS was identified in the GBR. Subsequent modeling also found that colder than average winter temperatures decreased the likelihood of a WS outbreak during a hotter than average summer (Bruno et al., 2007; Heron et al., 2010). This is especially relevant given the recent global bleaching event and expected increase in the prevalence and severity of marine diseases given continued ocean warming (Altizer et al., 2013). A significant relationship between WS and coral bleaching co-infection was found on the GBR during the 2016–2017 global bleaching event. Acropora colonies that exhibited both WS and bleaching had seven times more tissue loss than solely bleached colonies (Brodnicke et al., 2019). Cooler temperatures could be a protective factor against outbreaks of WS in Timor- Leste given the cooler subsurface temperatures on reefs compared to SST during the monsoon season which coincides with the yearly ocean temperature maximum. Increased sedimentation from catchments, however, is a continued threat, as watershed health in Timor-Leste is poor. there is a need for future work assessing impacts and sources of sedimentation on reefs and coral health.
The prevalence of indicators of compromised health was much greater than the prevalence of disease at surveyed sites (Figure 4-1). Rural-N at 5 m had the highest prevalence of non- coral invertebrate overgrowth (Figure 4-4), which could be explained by greater coral cover eliciting more coral-invertebrate interactions as the cover of invertebrates was comparable between all sites. The infestation of flatworms was found at all sites except Urban-W 10 m with similar prevalence as surveys in Indonesia (Haapkylä et al., 2009b), with some severe cases (Figure 4-3c). Flatworms consume coral mucus, reduce heterotrophic feeding, and decrease photosynthesis in high densities (Barneah et al., 2007; Naumann et al., 2010; Wijgerde et al., 2012), although their role in coral reef environments is not well understood.
161
There is also a notable absence of turf overgrowth at Rural-N while the remaining sites have high levels which could be indicative of depauperate herbivore communities or elevated nutrients at these locations (McClanahan, 1997; Vermeij et al., 2010). Different competitive interactions, such as burrowing barnacles, CCA overgrowth, and turf overgrowth, were also more commonly found on genera with specific genera Platygyra, Montastrea, and massive Porites, all massive species. The highest cyanobacteria overgrowth was found at Rural-W at 10 m which was most associated with branching Montiporids and Poritids.
Coral community composition and human impacts The underlying coral community composition was different across the four surveyed sites that influenced the prevalence of coral disease. Rural-N had the highest coral cover and diversity dominated by Acroporids which is comparable to the biodiversity assessment of the same site in the 2012 Rapid Marine Assessment (Erdmann and Mohan, 2013). While damage to reefs may correlate with the local population density of humans (Brown et al., 2017; Bruno and Valdivia, 2016; Smith et al., 2016; Wedding et al., 2018) rural sites in the present study did not have a lower prevalence of coral disease. Anecdotal observations suggested that site-specific factors, such as ease of access to the reef, could be associated with reduced reef health in terms of reduced coral cover, coral diversity, and other signs of compromised health.
In the present study, significant differences were identified between the state of coral reefs between Rural-N and the three mainland sites (Urban-W, Urban-E, and Rural-E). Localized impacts along the northern coast of Timor-Leste include watershed-based pollution and fishing and gleaning (Alongi et al., 2012a; Edyvane et al., 2012; McWilliam, 2002; Mills et al., 2017). Geography, season, and factors such as land-use, accumulated wave exposure, and storm exposure are likely to be important but were not studied here (Chollett and Mumby, 2012; Roff et al., 2015; Wolff et al., 2016). However, Rural-N may be subjected to less sedimentation than the other three sites as Ataúro Island does not have any major rivers as on Timor island. Dili (encompassing Urban-E and Urban-W) and Rural-E in Manatuto are both near major rivers (the Comoro and Laclo rivers respectively). It was apparent in Chapter 2 that the wave exposure of the relatively protected north coast affects the functional morphology of reefs and overall coral cover. The two regions at the western and easternmost ends of the country had the greatest relative exposure (Figure 2-5) and the wave exposure of the four sites in this study was comparable to each other. Temperature
162 likely has a negligible influence on community composition as the temperature logger data was consistent between the three sites where loggers were successfully retrieved, Rural-N, Rural-E, and Urban-W (Figure 4-9). Additionally, the mean in situ temperature data across seven districts along the north coast from Chapter 2 was within 1˚C of each other (Table 2-4). This leads us to localized human impacts as a key source of impact on coral reefs.
Fishing is likely to play a key role in Timor-Leste. Observations of extensive rubble slopes at the site Urban-W may be due to blast fishing although the damage does not appear to be recent (Erdmann and Mohan, 2013). The estimated number of seaworthy craft has also decreased from 2,027 unmotorized dugout canoes and 160 motorized vessels during late Indonesian occupation (Kantor Statistik, 1997) to 800 canoes following the onset of violence in 1999 (Sanyu Consultants Inc., 2001). Gleaning is largely overlooked when assessing fisheries although most (> 80%) of households in coastal communities participate in gleaning activities in Timor-Leste (Tilley et al., 2020) and a conservative estimate identifies 25% of all coral reef fishers in Southeast Asia as gleaners (Teh et al. 2013). Fishing versus gleaning is highly gender-stratified and women glean more regularly than men fish (~ 20 vs ~ 15 days/month; (McWilliam, 2002; Tilley et al., 2020). Additionally, women had a ~ 99% success rate when gleaning in Timor-Leste highlighting its importance in maintaining food security during low crop periods and bad weather (da Costa et al., 2013; Tilley et al., 2020). Increased gleaning could also be a sign of diminishing fishing returns requiring more members of the family to partake in securing food (Cesar et al., 2003) or economic crises (Gillett, 2009). Gleaning may have played an even greater role in food security during recent times of violence and instability resulting in degraded coral reef flats, particularly in densely populated areas. Human gleaning and the associated trampling of intertidal reefs have been demonstrated to have deleterious effects on coral cover on reef flats although depths greater than 5 m are generally out of reach (Andréfouët et al., 2013; McWilliam, 2002; Woodland and Hooper, 1977).
Urban-W site had more fishing activity than the other sites from observations while conducting fieldwork and also had the greatest signs of blast fishing. The subdistrict of Dom Alexio encompassing this site had the highest population density out of the four sites with 5,017.9 people/km2 compared to 779.5 people/km2 at Urban-E and 79.3 people/km2 nationally. The low coral cover at 5 m and the minimal diversity at 10 m could be attributed to the high subsistence and recreational usage at this site. Women were gleaning for invertebrates on the low tide, small children were playing in the surf and on the reef flat, and 163 men were net-fishing from small boats (personal observation; Figure 4-11). Observations of activity at the rural sites were limited to one or two individuals fishing. The Urban-E site had recreational infrastructures such as parking and picnic tables and the activity appeared to be recreational versus extractive although a large fish trap was observed at ~ 12 m. Additionally, Urban-E is surrounded by a mountainous ridge that limits access largely to vehicle transport from one road. While distance to river is a sensible explanation between community-level differences between Rural-N and Rural-E (Chapter 2), relative ease of access in a densely populated area differentiated Urban-W. Urban-W is within walking distance to a comparatively urban area with fishing boats lining the beach while Urban-E is tucked in at the edge of Dili Bay surrounded by steep hills and more affluent neighborhoods.
Figure 4-11 Gleaning and recreational activity at Urban-W site in Timor-Leste during 2015 surveys. Urban-W is in the Dom Alexio subdistrict of Dili which had a population density of 5017.9 people per km2 in the 2015 Timor-Leste census and had more activity compared to other sites surveyed. Women can be seen in the foreground gleaning and children playing on the reef flat further offshore.
164
Water quality and coral communities in Timor-Leste Combined nitrate and nitrite and phosphate averages were highest at Rural-N at 10 m, which was contrary to expectations. These increased concentrations at the deeper surveys at Rural-N could be indicative of upwelling (Firstater et al., 2010; Leichter et al., 2003). Leichter et al. (2003) found nitrates ranged from 0.5–2.0 μM when warmer water (26–28˚C) was influenced by significant influxes of cold pulses on Florida reefs. The 1.04 ± 0.19 μM nitrate found at Rural-N 10 m was the highest sampled and falls within this range. Slightly elevated nutrients off Ataúro Island in the channel at 10 m may suggest upwelling of deeper, nutrient- rich water (Boggs et al., 2012; Erdmann and Mohan, 2013). Other sources of nutrients at depth to consider is submarine groundwater discharge (Risk et al., 2009). Rural-N was the only barrier reef surveyed and not likely not affected by such inputs. Cyanobacteria overgrowth was only found at high levels at Urban-W 10 m in 2015 (6.1% prevalence) which can be indicative of elevated nutrients or other disturbances (e.g., ship strikes, etc.; Burgett, 2012; Littler et al., 2006; Thacker et al., 2001). Overall, seawater nutrients and stable isotope values were not significantly higher at this site during our sampling. Additionally, the prevalence decreased to 0.0% in 2017 which indicated the overgrowth was likely caused by an ephemeral bloom of cyanobacteria.
Phosphate was associated with the highest prevalence of disease and compromised health (Table 4-3) and the highest values of each parameter were both at Rural-N which suggests that increased phosphate is associated with a higher prevalence of disease (Figure 4-8; Figure 4-4). High levels of inorganic nutrients are a major driver of reef degradation (Fabricius, 2005); however, the levels of inorganic nutrients and disease found in this study were not elevated to values associated with pollution. Phosphate was comparable to values from Kepulauan Seribu, Indonesia, ranging from uninhabited to developed islands, with a 3- - maximum of 0.09 μM PO4 and 0.05 μM NO3 across surveyed sites (Johan et al., 2015). The phosphate threshold for algal blooms for Floridan reefs has been established at 0.1 µm and the values presented here were mostly below this threshold (Lapointe, 1997).
Nutrients measured in the Laclo river in 2006 (Table 4-4) indicated low levels of pollution from sewage and nutrients from fertilizer (Alongi et al., 2012a). The report concluded that nutrient levels in Laclo were comparable to other tropical rivers (Eyre, 1994; Mitchell et al., 1997; Robertson et al., 1998) and tidal seawater samples were similar to unpolluted mangrove sampling in Australia (Alongi et al., 1992). Ammonium decreased from ~ 4 μM
165 approximately 10 km upstream from the mouth of the Laclo, to ~ 2 μM at the mouth, and then to < 1 μM about 20 km west of the mouth in Metinaro. Combined nitrate and nitrite dropped from > 8 μM to almost zero from the river sampling to Metinaro. There was a similar pattern with phosphate concentrations of 0.5 μM in the Laclo which dropped to 0.05 μM in Metinaro (Table 4-4). In the present study, the site Rural-E at Manatuto was approximately half-way between the mouth of the Laclo River and Metinaro. As such, seawater nutrient samples at Rural-N 5 m were in-between values sampled at the Laclo River and Metinaro. These data were collected ten years apart, in different locations (i.e., riverine and coastal sampling versus at 5 m depth on the reef) and thus, it is difficult to draw conclusions based on these limited measurements. Additionally, Rural-E is about 12 km west of the mouth of the Laclo and how much riverine outputs affect the downstream coast is not known. However, both surveys were taken during the dry season, and based on the evidence available it does not appear that nutrients on the reef at Rural-E are greatly elevated by anthropogenic inputs.
Table 4-4 Nutrients from water samples collected in the Laclo River and Metinaro in 2006 as reported in Alongi et al. (2012a). Values from Rural-E (5 m depth) in 2015 approximately half-way between the Laclo and Metinaro in Timor-Leste are also shown (marked with a *).
+ - - 3- Site NH4 μM NO2 + NO3 μM PO4 μM Laclo River Upstream 4.04 ± 0.93 8.22 ± 0.38 0.49 ± 0.09 Laclo River Mouth 1.98 ± 1.03 8.65 ± 1.32 0.54 ± 0.01 *Rural-E at 5 m (12 km from mouth) *1.20 ± 0.28 *0.90 ± 0.16 *0.11 ± 0.01 Metinaro (20 km from mouth) 0.80 ± 0.09 0.05 ± 0.00 0.05 ± 0.01
The stable isotope data were consistent across sites and depths sampled (range 2.5–5.5‰ excluding outliers) and fall within pristine oceanic values ranging from 2–3‰ (Costanzo et al., 2001; Titlyanov et al., 2011) and upwelling values ranging from 5–6‰ (Huang et al., 2013; Lamb et al., 2012; Lapointe et al., 2005; Leichter et al., 2007; Sigman et al., 2000). Sewage-affected waters have generally higher δ15N values from 8–22‰ (Costanzo et al., 2005; Dailer et al., 2012; Heaton, 1986; Lin et al., 2007; Thornber et al., 2004) and our data was not indicative of high levels of δ15N enrichment. Although the mean was significantly higher for the Chlorodesmis spp. at Urban-W versus Urban-E, the sampling of that alga was sparse compared to the Halimeda spp. Additionally, there was no Chlorodesmis spp. found at Rural-N for sampling. Calcareous algae are good integrators of nitrogen over weeks to months versus days with fleshy macroalgae (Gartner et al., 2002). Similar values were
166 recorded for both algae collected across sites and depths which indicates that the influx of nitrogen has been stable across several months. This is likely due to sampling at the end of the dry season which lasts from March to November with little terrestrial runoff hence no significant concentrations of nutrients and sediments. There were a few outliers of much higher (12.17‰, 15.12‰) and lower (-6.79‰) δ15N values with the Halimeda spp. sampling which could be indicative of localized inputs on a scale of tens of meters of nutrients such as fish waste or groundwater discharge.
Previous studies reveal that macroalgal δ15N signatures decrease with depth on range from 5 to 35 m because of land-based pollution concentrated at the surface (Lapointe et al., 2005; Lin et al., 2007; Smith et al., 2005; Umezawa et al., 2002). The influence of upwelling is less clear as both δ15N depletion and enrichment have been reported with upwelling (Firstater et al., 2010; Fourqurean et al., 1997; Huang et al., 2013; Lapointe et al., 2005). For example, in a Peruvian upwelling center δ15N in Ulva spp. was found to be more enriching from 10– 12‰ δ15N than an adjacent sewage outfall with at 5–9‰ (Firstater et al., 2010). Lapointe et al. (2005) found δ15N values from macroalgae to decrease with depth in southeast Florida reefs. Shallow reefs (< 5 m) averaged 8.58 ± 0.87‰, which was significantly higher than mid-depth (25–30 m) reefs 6.70 ± 0.89‰, and deep reefs (40–43 m) were the lowest with 6.30 ± 1.26‰. They concluded sewage-pollution affected shallow reefs and the deep reef δ15N correlated with lower water temperatures which were indicative of upwelling. Sigman et al., (2000) recorded δ15N values of 4.8‰ in association with upwelled nitrate in the North Atlantic Ocean. A recent study in the Maldives found a consistently narrow range of 5.2– 5.8‰ δ15N for three coral species and 4.3–5.7‰ for their symbionts with samples collected at 10 and 30 m depth (Radice et al., 2019). These values were attributed to the seasonal dynamics of upwelling in the archipelago.
In summary, assigning direct links between the condition of coral reefs and the source of nutrients is difficult. In the present study, the mean δ15N values of 4.3‰ and 4.2‰ for Chlorodesmis spp. and Halimeda spp. respectively are higher than those reported from the open ocean (2–3‰). Given that the algae were collected at the end of the six-month dry season (and tissue turnover occurs within several months in most algal species), it is unlikely our sampling captured the effects of terrestrial and river run-off and potential sewage pollution. Studies in the Florida Keys showed significant seasonal differences in sampling macroalgae for stable isotope analysis. Delta 15N was twice as high in the wet season than the dry season and elevated values were closely tied with rain events (Lapointe et al., 2004). 167
Further research into seasonal differences in seawater nutrients would further elucidate whether values obtained in this study are representative of background oceanic nutrients, upwelling, or land-based anthropogenic influences. Sampling along the coastline and a gradient of increasing distance from shore would reveal more insight about land-based sources of nutrients. The seawater nutrient and stable isotope data are likely indicative of oceanic influence with potential upwelling in the absence of aquaculture industries and the heavy use of inorganic fertilizers and pesticides in Timor-Leste coupled with sampling conducted after months of no rain. The higher seawater ammonium, nitrate, nitrite, and phosphate nutrients at Rural-N 10 m depth could indicate short upwelling events that are too ephemeral to be assimilated by calcareous macroalgae over weeks to months. Rural- N’s location in the Timor Strait with the large volumes of water movement through the channel (Gordon, 2005) could be more conducive to localized upwelling than the mainland sites.
Elevated Temperature and the Prevalence of Bleaching The surveys in the present study were conducted right before the austral summer during the 2015 ENSO event which triggered mass bleaching worldwide including North Australia, the Hawaiian Islands, the Maldives, South Pacific Islands, Southeast Asia, East Africa, the Red Sea, and the Caribbean (Hughes et al., 2018). The CRWTL virtual monitoring station indicated that the temperature began rising above the MMM in November 2015. However, care must be taken given that the satellite data only measures the temperature of the first 10–20 μm of the ocean surface (Liu et al., 2013) and can be ineffective in coastal waters due to the 5 km2 or greater pixel size mostly encompassing open ocean versus coastal waters. Additionally, Timorese reefs are very steep and close to the coast where satellite data are unreliable due to the potential interference of land temperatures and artifacts.
In situ temperature varied between the four sites and months. Most interestingly, there was a divergence between the in situ and CRWTL temperature data during the austral summer months. The surveys in 2015 were conducted in November at the onset of austral summer and the yearly ocean temperature maximum. In 2017, the surveys were conducted in July approaching the yearly ocean temperature minimum. Timor-Leste appeared to have experienced lower levels of bleaching compared to other regions of the world such as the Northern GBR (NGBR), one of the most severely affected by bleaching. The CRWTL accumulated DHWs for 55% of the days between survey periods compared to 49% of days
168 over the same time in the NGBR. However, the magnitude of DHWs in the NGBR reached 13.59˚C-weeks, more than double the 5.79˚C-weeks maximum in Timor-Leste. Low-levels of bleaching have been associated with the 2-3˚C-weeks, > 4 ˚C-weeks with 30-40% corals bleached, and an average of 70-90% of corals bleached with > 8 ˚C-weeks based on the relationship between in situ bleaching surveys and DHWs on the GBR (Hughes et al., 2017b; Strong et al., 2011). The bleaching severity of the NGBR was greater than 60% bleached for all surveyed reefs in 2016 and, although there is no data on the extent or severity of bleaching on reefs in Timor-Leste, DHW data would project mass coral bleaching in Timor- Leste of around 30–40%.
Local dive operators reported mass coral bleaching at Jaco Island, the easternmost point of the country at the end of March. By the end of May, 90% of Goniopora spp. on Ataúro Island were bleaching (Figure 4-12a), massive Porites spp. from 5–18 m deep (Figure 4-12b) and staghorn Acroporids in the shallows at Jaco Island. Bleaching reportedly began in the shallows and progressively affected corals at deeper sites. The timing of the bleaching also matches the in situ temperature logger data where the mean monthly temperatures exceeded the MMM in March 2016 versus the CRWTL SST which had been above the MMM since November 2015 (Figure 4-9). The in situ temperatures never exceeded the MMM + 1˚C which is the threshold for mass bleaching. The range of the temperature loggers during December 2015 was from 27˚C to almost 31˚C so reefs did experience elevated temperatures, but not for prolonged periods. March and April of 2016 were when the in situ mean temperature began to creep over the MMM and close the gap with the CRWTL data. The loggers approached MMM plus 1˚C in May of 2016 five months after the CRWTL temperatures had been above the bleaching threshold (Figure 4-9). The in situ data is limited to the Dili, Ataúro Island, and Manatuto areas which may not be representative of temperature regimes in the Jaco Island region. Even so, anecdotal evidence of the most bleaching in May 2016 in both Ataúro Island and Jaco Island follows the temperature timeline of the logger data and points to CRWTL erroneously predicting bleaching too early in Timor-Leste.
169
a
b
Figure 4-12 Photos by Tony Crean, a local dive operator, emailed on May 31, 2016. (a) Bleached Goniopora spp. at Adara on the west coast of Ataúro Island, primarily steep wall environments. An estimated 90% of Goniopora spp. was bleached with no other hard corals affected on Ataúro Island reefs. Multiple other genera of coral were visibly unbleached. Depth of photo unknown. (b) Image of Jaco Island, the easternmost point of Timor-Leste with bleached massive Porites spp. corals. Similar percent of corals bleached from 5–18 m.
170
Based on the comparison of in situ temperature logger data in Timor-Leste and the CRWTL satellite-derived SST the CRWTL virtual climate station overestimates the bleaching stress in-country. During the austral summer, CRWTL SST was more than 1.5˚C warmer than the in situ data at 10 and even 5 m depth in both 2016 and 2017 (Figure 4-9; Figure 4-10). This could be due to seasonal changes in the oceanography that increased water movement, upwelling or internal waves, along the north coast, coinciding with the northwest monsoon in the austral summer. The significant divergence between the in situ and CRWTL temperatures during the austral summer point to upwelling influencing temperature on shallow reefs yet does not reach the surface affecting remotely sensed SST data. The northwest monsoon season from December to March is associated with a weak reversal in the flow of the ITF (Sprintall et al., 2014). Whether this is associated with coastal upwelling remains unclear; however, there is clear seasonal variability of the ITF. Additionally, the temperature range in December 2016 was 27–31˚C, nearly 4˚C, in all six loggers compared to 29–31˚C in December 2017. ENSO could be strengthening upwelling along the north coast of Timor-Leste bringing up cooler water to shallow reefs while the SSTs are elevated above the bleaching threshold. The effect of upwelling could be a protective factor for Timorese reefs as in situ temperature did not reach the elevated CRWTL SST temperatures until May of 2016. However, the divergence in temperatures between CRWTL and logger temperature data appears to be seasonal as the temperatures converged around April/May of 2016 when seasonal upwelling may subside (Figure 4-9). This coincides with the reports of mass bleaching in-country. It could also be that the exposure to cooler upwelled waters regularly acclimatizes the corals to cooler waters and then makes them more sensitive to elevated temperatures once upwelling stops.
Conclusion
The present study set out to understand the nature of both local and global threats to the relatively understudied coral reefs of Timor-Leste. Baseline information on these systems is limited despite the current and future importance of marine resources to Timor-Leste. Coral reefs of the north coast of Timor-Leste are characterized by high amounts of coral cover which as much as 58.2 ± 6.4%. There were also low levels of disease present at the four sites surveyed. The concern is that sites close to the urban areas of the capital city, Dili, are showing signs of significant degradation with < 5% hard coral cover at 5 m at one of the two urban sites. Also, there is the problem of heat stress from climate change driving additional
171 pressure on coral reef systems. Like coral reefs everywhere, a failure to act to rapidly and reduce emissions of greenhouse gases from burning fossil fuels and land-use change will see further and more rapid degradation of the coral reef resources of Timor-Leste (IPCC 2018).
The human population density of sites did not correlate with the condition of coral reefs. The only consistent presence of coral disease was found only at a rural site with low human population density. Other signs of compromised coral health such as algal overgrowth, burrowing barnacles, etc. were much more widespread across all sites. The underlying differences in coral community structure were key to the prevalence of WS on tabulate Acroporids which may have been shaped by human impacts such as subsistence livelihoods and degradation of watersheds (Chapter 2). There were two lines of evidence at local scales—temperature, and nutrients both seawater concentrations and algal δ15N signatures—that indicated upwelling was a significant feature influencing the shallow reefs of the north coast of Timor-Leste. In order for upwelling to serve as a beneficial refuge to coral reefs two criteria must be met: (1) the threat and upwelling coincide and (2) the temporal overlap produces a significant decrease in thermal stress in upwelling areas (Chollett et al., 2010). The upwelling phenomenon in Timor-Leste meets these requirements and appears to lessen the impact and length of bleaching events. More research across larger spatial and temporal scales is necessary to further understand this important process. While it is fortunate the mass bleaching event did not cause large scale coral mortality in Timor-Leste, the sublethal effects pose another threat to already highly impacted reefs.
Upwelled waters are hypercapnic (CO2-rich; Feely et al., 2008) which is another point of concern. NOAA’s monitoring noted that Timor-Leste had the lowest calcification accretion rates out of all Pacific sites sampled (PIFSC, 2017) which could be the result of upwelling. This lower accretion rate could affect Timorese reef’s ability to cope with sea-level rise and recover from disturbance such as physical damage and bleaching. There is, however, evidence that calcifying organisms can withstand these seasonal increases in acidity potentially relying on increased heterotrophic feeding (Leichter and Genovese, 2006; Rixen et al., 2015). Further research on the oceanography of the region and interactions between environmental parameters (light, temperature, CO2, salinity, etc.,) are critical to understanding and effectively managing the country’s marine resources.
172
Over 300 million people rely on coastal resources for economic livelihoods and cultural practices in the CT (ADB, 2014). Sustainable management of these resources ties into larger socio-economic issues in Timor-Leste such as food security. Fishing is an important means of protein and nutrient vital for food security nationally and is dominated by artisanal, low-efficiency methods (ADB, 2014; Alonso Población, 2013; RDTL, 2011). The fishing industry in-country is largely contained to subsistence practices because of a lack of reliable road, electric, and refrigeration infrastructure. As these systems are developed, and the capabilities and supply chains to transport fish increase, they must be monitored. Additionally, meti, or gleaning, is an important component of subsistence fishing. In urban areas, the differences between ease of access between Urban-W and Urban-E seemed to play an important role in the types of human impacts present (recreation versus extractive). Localized impacts such as fishing (including blast fishing before independence) and gleaning were observed over the last several decades and likely affected coral cover and diversity in both urban and rural areas.
To address these large and small-scale impacts, coral reef management needs targeted local and national actions. Ultimately, the health of coral reefs is tied to the unique social and economic development needs of the country. At a local level, the resurgence of tara bandu with increased interest in community marine protected areas (MPAs) is promising for the future of Timorese reefs. Tara bandu is customary law that administers prohibition designations in communities banning practices such as tree cutting (McWilliam, 2003; Miyazawa, 2013; Yoder, 2005). These designations are well-adhered to within communities but appear to be more effective in rural areas (Miyazawa, 2013). For MPAs, certain reefs are established as no-take, ecotourism zones where visitors pay a small fee for snorkeling and diving. The reef housing the Rural-N site in this study was designated as an MPA six weeks before the resurvey in July 2017. A small fee was paid, and fishermen were observed bypassing the reef by canoe to fish on the adjacent reef to the north. Currently, the formation of MPAs are centered on Ataúro Island, but the success and income generated for the community are seeing this practice expand to other communities. The level of community engagement in designating marine reserves provides a positive outlook for coral reef management.
Assuming the international community takes strong action under the Paris Climate Agreement, it will be very important for countries such as Timor-Leste to establish effective management of their coral reef resources. This lies at the heart of international strategies 173 such as the 50 Reefs project (Beyer et al., 2018; Hoegh-Guldberg et al., 2018). At a country- scale, developing a national network of marine protected areas, rebuilding healthy watersheds, and understanding vulnerability to climate change is key to the sustainable management of coral reefs. Ideally, a systematic approach would be taken to establish no- take and mixed-used zones across all Timorese waters incorporating MPAs and the national marine park. Establishing a national plan before the development of tourism and industrial industries would be beneficial in ensuring a certain level of sustainable management of coastal resources. Lastly, monitoring the heat stress and bleaching of Timorese reefs must continue and further research of the oceanography along the coasts (especially possible benefits from upwelling) will advance the understanding of the country’s vulnerability to climate change.
174
General discussion
Photo: C. Heatherington
175
Thesis significance
Despite their importance to humans, our understanding of the coral reefs in the CT is limited, with many gaps in the ecology, environmental variability, local anthropogenic impacts, and effects of global climate change (Bertzky and Stoll-Kleemann, 2009; Burke et al., 2012; Clifton, 2009). Timor-Leste is one of the poorest countries in the world, with an under-funded science and resource management infrastructure despite interest from international science and aid communities (Erdmann and Mohan, 2013; PIFSC, 2017). This thesis provides further information on the state of coral reefs on Timor-Leste’s north coast. Specifically, it explored the distribution and abundance of reef-building corals, associated biodiversity (brachyuran crabs and cryptofauna in particular), and prevalence of disease from human threats such as pollution, human activities, and ocean warming. The research included the application of novel techniques increasing the scale of conventional coral reef ecology methods on the last frontier of the CT. Conventional benthic image collection and analysis were scaled up two to three orders of magnitude while maintaining comparable ecological resolution. This fills an important gap between small- and large-scale analyses (Bryant et al., 2017; González-Rivero et al., 2014). The CT is known as the marine biodiversity hotspot (Veron et al., 2009) and with high throughput sequencing, an astounding amount of the biodiversity in coral reef cryptofaunal communities was revealed (Leray and Knowlton, 2015). Lastly, countries in the CT lack the capacity to react quickly to events such as the global bleaching pandemic in 2016 and 2017. Although small in scale, the benthic data collected before and after this event plus in situ temperature data is an important contribution as the severity and extent of bleaching in the CT during this time is largely unknown. Understanding the baselines of coral disease is important as it is likely to increase with changing oceans (Harvell et al., 2019, 2007). Working internationally comes with a host of unique challenges, and this thesis came together by utilizing existing contacts at NOAA and SI and by forging new collaborations in-country with CI Timor-Leste and the Ministry of Agriculture and Fisheries.
Ecological implications of thesis results
Drivers of coral reef composition along the north coast The first objective of Chapter 2 was to assess benthic composition at a kilometer-scale. Timor-Leste’s north coast is characterized by steep reef slopes with low average coral cover
176
(15.0%) that is also highly variable (Figure 2-9, Ayling et al., 2009; Boggs et al., 2012; Erdmann and Mohan, 2013; PIFSC, 2017; Wong and Chou, 2004). Overall, algal turf was the dominant benthic functional group with a coverage of 26.2 (± 3.2%) to 64.8 (± 2.6%). Hard coral cover also varied between kilometer-scale transects, with values ranging from 5.4 (± 0.6%) to 21.9 (± 0.9%). The average coral cover in Timor-Leste of 15.0% is less than Indo-Pacific region-wide estimates of coral cover (22.1% and 24.8%) in two meta-analyses (Bruno et al., 2007; Graham and Nash, 2013). Bruno and Selig (2007) also argue the current average represents a shifting baseline with a decrease from 42.5% since the 1980s. The presence of macroalgae was low, but high cover has been found in localized surveys previously (PIFSC, 2017; Turak and Devantier, 2013).
Teasing apart biophysical and localized anthropogenic impacts is challenging and proxies for anthropogenic impacts such as local human population density have been the subject of much debate over the years (Aronson and Precht, 2006; Bruno and Valdivia, 2016; Grigg and Dollar, 2005; Wedding et al., 2018). Subsequently, the second objective investigated relative wave exposure, distance to river, and human population density as potential drivers of benthic composition. These parameters were significant in driving benthic composition, but only explained 9.3% of the total variance suggesting that other parameters not examined here are more influential. A linear mixed-effects model was built for coral cover, with the same potential drivers plus the ratio of branching to massive corals, as a proxy for structural complexity. The analysis detected a two-way interaction between wave exposure and the ratio of branching to massive corals (Table 2-6). Increasing wave exposure was shown to have a positive effect on the total coral cover (Figure 2-12; and, this effect was greatest when reefs were more structurally complex (i.e., a higher ratio of branching to massive corals). The positive effect of wave exposure is possibly a consequence of the protection against wave stress provided by islands to the north of Timor-Leste. Additionally, the low incidence of large storms in Timor-Leste limits extreme wave events that could damage corals. The distance to the nearest river was found to structure benthic composition but not overall coral cover. Given the very steep terrain of the country, logging practices, and the degraded watersheds have led to the deterioration of water quality (Alongi et al., 2013, 2012a; JICA, 2010; Sandlund et al., 2001).
Riverine inputs are generally associated with a range of impacts from sedimentation, nutrient pollution, and the inundation of freshwater from major storms and floods. Timor- Leste is a unique case where sedimentation on coastal ecosystems has been identified as 177 the main effect, as little nutrient and industrial pollution have been identified on the south coast (Alongi et al., 2013, 2012a). It is likely these findings apply to the north coast as well as coastlines dominated by small coastal communities except Dili. However, the increasing population and emphasis on the development of Timor-Leste in the next few decades are likely to increase sedimentation and material loads (Alongi et al., 2013). Several reports have indicated the terrestrial effects of degraded watershed health such as landslides and flooding (Alongi et al., 2012a; JICA, 2010; Sandlund et al., 2001) and evidence that the downstream sedimentation on rivers is affecting the structure of coral reefs was presented. Erdmann and Mohan (2013) recommended watershed restoration as an important feature of conserving coral reefs from their biodiversity surveys. Given the large-scale analysis presented in this thesis, we provide evidence that watershed restoration and management should be a priority for the benefit of both terrestrial and marine ecosystems.
Human population density was not significant in the model (Table 2-6). Using a more appropriately scaled metric might yield more conclusive results. Timor-Leste is an interesting system as many of anthropogenic impacts are generally limited to the community level. For example, an estimated 80% of fishing is localized with the remaining component coming from illegal, unreported, and unregulated vessels (Kingsbury et al., 2011). With the absence of developed infrastructure, commercial fishing, and industrialized agriculture, the associated impacts on nearshore environments such as fishing for exportation and nutrient run-off from fertilizers are currently minimal (Alongi et al., 2012a).
Finding appropriately scaled environmental parameters to run as covariates for benthic hard coral parameters was challenging as Timor-Leste lacks environmental monitoring programs. Furthermore, the resolution of easily accessible remotely sensed global datasets such as SST is generally too coarse for local-scale management. Additionally, the accuracy of remotely sensed data is questionable in nearshore environments and many of the reefs on the north coast are less than one km from shore because of the very steep continental shelf (Alongi et al., 2013; Boggs et al., 2012). Incorporation of in situ measurements was attempted, but the spatial coverage was lacking and, in the end, were not significant in the models. Reflecting on the greater oceanographic context of the CT, the threats of rising temperatures may not limit coral growth and composition in some areas where the influxes of cooler waters and coastal upwelling counters rising sea temperatures (Gordon, 2005). The significance of factors that were approximations using spatial analysis, relative wave
178 exposure and distance to river, was the most ecologically interpretable as they matched the scale of coral variability.
Cryptofaunal diversity of Timorese coral reefs Coral reefs are described as high-biodiversity ecosystems largely based on conspicuous taxa such as hard corals and fishes. The capacity to assess the full extent of coral reef cryptofaunal communities has been improved with the advent of genetic techniques. The objectives of Chapter 3 focused on assessing the cryptofaunal diversity of Timorese reefs from ARMS units through the DNA barcoding of brachyuran crabs, analyzing of metabarcoded sequences of three size fractions, and relating diversity to benthic composition.
First, the DNA barcoding of 269 brachyuran crabs revealed 75 OTUs, 28 of which were unique to Timor-Leste. Most species were rare—45% of sequences were singletons and 25% were only sampled two to four times. The local abundance of massive corals correlated with the diversity of the crab communities in the collection devices (ARMS; Figure 3-8). For the metabarcoded sequences, 5,475 out of 6,750 OTUs were classified, or assigned a taxonomic classification through database match or probabilistic approach). As with most metasequencing analyses, one of the current, major limitations is the lack of identified reference sequences in public repositories (Leray and Knowlton, 2015). Classifications are also skewed toward well-studied areas near developed countries (Plaisance et al., 2011a). However, differentiation between the sessile community composition and the motile metabarcoded fractions was observed (Figure 3-11).
The data collected in this project allowed us to quantify cryptic diversity on reefs and to explore the relationship between patterns of cryptic biodiversity and the benthos (see objective 3). While the capacity to identify all OTUs in metabarcodes was a challenge, hard coral and soft coral non-cryptic benthic categories from phototransects matched the abundance of respective sequences found in ARMS sequences (Figure 3-15a, Figure 3-15b). The correlation was not significant between sponge cover and sponge sequences (Figure 3-15c). Conventional phototransects give a good representation of conspicuous invertebrates, but not of phyla with cryptic forms like sponges. The correlations were not as robust breaking up coral cover and sequences into morphological groups (Figure 3-17).
179
Here, ARMS in stations that were geographically closer were not necessarily alike to each other in terms of cryptofauna composition (Figure 3-5, Figure 3-12). Complex habitat structure together with a combination of live coral cover and carbonate substrate showed the greatest diversity when manually assessing cryptofauna (Enochs and Manzello, 2012a). There was not a clear positive relationship between cryptofaunal diversity and coral cover, but there was a positive association with rubble. The MDF paradigm with 20–80% coral may not be an applicable model to Indo-Pacific reefs, as they are more heterogeneous. An upper boundary of 40-50% coral cover may be more reasonable as few places in Timor-Leste have > 80% coral cover. The heterogeneity of reefs may be the key to high cryptofaunal diversity providing a diverse range of microhabitats and niches. In Chapter 2 it was also demonstrated how relative wave exposure, distance to river, and human population density shape coral composition across large scales. Further work could extend these relationships with biophysical and anthropogenic parameters to benthic composition and cryptofaunal community composition.
Coral health and bleaching in Timor-Leste The final focus of this thesis (Chapter 4) was the state coral reefs and the health of hard corals with respect to its proximity of the capital city Dili, as well as potential coral mortality associated with the recent 2016-2017 global mass bleaching event. Overall, the surveys showed that the prevalence of coral disease for coastal reef systems in Timor-Leste was low (Figure 4-4). Phosphate was significantly and positively associated with the prevalence of disease and compromised coral health, but likely because the highest levels of phosphate and WS were both found at Rural-N. The values from seawater nutrient and δ15N macroalgal values were indicative of oceanic signals, including upwelling at the Rural-N site at 10 m versus terrestrially based inputs. While more work is required, the absence of elevated nutrients is likely to be an effect of sampling during the dry season as opposed to the existence of low nutrient levels in general. This issue needs to be further addressed if we are to understand potential pollution threats above and beyond the natural spatial and temporal variability of coastal nutrients and sediment.
The composition of coral genera differed between the four survey sites. WS was found at low levels at the Rural-N site on Ataúro Island and was the only site dominated by tabulate Acroporids. There was a notable absence of Acroporids from the mainland sites. This may be a consequence of the lack of a developed river system on Ataúro Island and fewer human
180 impacts. Within the mainland sites (Rural-E, Urban-E, and Urban-W), a gradient of localized impacts influenced coral cover. Even though fishing and gleaning are at a subsistence level, the surveys indicate that these practices can have substantial impacts on local reefs. The Urban-W site, with < 5% coral cover at 5 m, was a popular gleaning and fishing area, and appeared to have a history of dynamite fishing. Observationally, Urban-W and Urban-E were both popular recreational beaches, but the accessibility of Urban-W makes it more vulnerable to impacts from fishing and gleaning.
The severity and extent to which the CT was affected by the 2016–2017 global bleaching event were largely undocumented. An additional objective of this chapter was to assess coral health before and after the bleaching event in Timor-Leste. Although the SST of CRWTL did exceed the bleaching threshold (MMM + 1˚C), these data were 1.5˚C higher than the in situ temperature loggers during the austral summer (Jan–Mar; Figure 4-9). This provides a second line of evidence that local oceanography, such as upwelling and internal waves, influences shallow reefs, even at 5 m, and could be a significant protective factor against prolonged warming for Timorese reefs. However, when this effect ceases in April/May, reefs are subjected to elevated temperatures which are supported by bleaching reports from local diving operators. Corals that have been acclimatized to influxes of cooler water may be more sensitive to temperature increases. ENSO has been associated with weaker ITF transport (Gordon, 2005), which could be strengthening localized upwelling along the north coast of Timor-Leste by bringing up cooler water while there are surface temperature anomalies. This is evidenced by the larger, cooler in situ temperature range in December 2016 (27–31˚C) compared to December 2017 (29–31˚C). Mass coral bleaching from warming oceans is a threat to Timorese coral reefs. However, oceanic factors mitigate the duration and extent of elevated temperatures during the warmest part of the year. While this is fortunate as many regions globally (NGBR, the Maldives, etc.) experienced mass mortality associated with the bleaching event (Hughes et al., 2017b), the sublethal effects of bleaching are an additional impact to already highly-threatened reefs. Timor-Leste, however, had the lowest calcification accretion rates out of all Pacific reefs monitored by NOAA (PIFSC, 2017), which would potentially limit or slow the recovery of Timorese reefs compared to other locations. Bleaching in-country should be monitored and at this point in the development, sustainable management nationally and locally is imperative.
181
Management implications and recommendations
The establishment of NKSNP (Nino Konis Santana National Park) remains the largest conservation area in Timor-Leste to date (Weeks et al., 2014). While the national park includes more than 10% of the country’s coral reefs in a designated MPA, there is an on- going need for management and enforcement (White et al., 2014). At a national level, Timor- Leste has since become a Party to the Convention on Biological Diversity and prepared a National Ecological Gap Assessment (Grantham, 2010) and a National Biodiversity Strategy and Action Plan (NBSAP, 2011). However, there is continued weak institutional support coupled with inadequate national laws and regulations (NBSAP, 2011). The research undertaken during this thesis argues for the consideration of coral reef management to involve multiple scales of analysis, including community composition, biodiversity, and health. Fortunately, workable solutions for localized impacts such as sedimentation exist with benefits on short timescales (years; Borrelli et al., 2017). Comparatively, climate change requires intergovernmental agreement in the oncoming decades (IPCC, 2018).
Land degradation and subsistence livelihoods have already impacted coral reefs in Timor- Leste. Across the north coast, the proximity to a river has a significant effect on the structure of coral reefs which calls for improving watershed health. This is also tied to social-economic factors, as communities, especially in rural areas, are reliant on subsistence-based livelihoods off the land and sea (Tilley et al., 2020). Slash and burn agriculture and logging were practiced during the Indonesian occupation (Macaulay, 2003), and the need for fuel in rural mountainous regions and clearing for agriculture continues to drive deforestation leading to downstream sedimentation on reefs (Alongi et al., 2013, 2012a). Within Dili, accessibility of reefs for fishing and gleaning seems to play a role in the degree to which reefs are impacted. Management for coral reefs must follow a similar approach of actions at several scales, including top-down and bottom-up approaches.
At a national level, Timor-Leste is beginning to benefit from an increasing number and variety of datasets regarding its coastal environments and ecosystems. In addition to increasingly outlining key processes that affect Timor-Leste coral reefs and other coastal ecosystems, these data are helping support logical decisions as to how the Timorese may best manage these valuable assets. A key challenge in conservation globally is the identification of where and when to spend limited funds effectively (Margules and Pressey, 2000). Marine spatial planning requires broad coverage of spatial data to make decisions in 182 the zoning of marine areas. These data are scarce across coral reef ecosystems globally, especially in the CT region. Current spatial datasets are limited to the presence or absence of reefs, necessitating the assumption in planning that all reefs are equal in their condition, and therefore equal in their contribution towards conservation goals equal (Burke et al., 2011; Evans, 2015). Area-based spatial prioritizations overestimate the conservation benefit, and incorporating reef quality (i.e., coral cover) results in significant differences, improving habitat quality scores overall (Vercammen et al., 2019). The kilometer-scale in Chapter 2 along with large-scale datasets collected by NOAA would allow the incorporation of variability into prioritization analyses and these data in Timor-Leste have been used to demonstrate this (Nolan et al. in review).
It would also be remiss not to include mention of the importance of Traditional Knowledge in the on-going protection and management of the marine resources of Timor-Leste. In this regard, there has been a recent emphasis of tara bandu, or Timorese traditional law (McWilliam, 2003; Yoder, 2005), which has been applied to MPAs as tools for maintaining coral reefs and dependent species. These areas can function as both protected areas from fishing and/or ecotourism zones where tourists pay small fees for recreational use that provides income for the community. The Rural-N site in Chapter 3 became one such protected area weeks before the second survey of the present thesis study in 2017. In this case, a small fee was paid to the village to conduct the surveys in Chapter 4 in the new MPA. At the time of writing, there are now 12 locally managed marine areas on Ataúro Island. Analysis of co-management of small-scale fisheries codified through tara bandu indicated that it was an effective means of engaging communities in resource management to meet multiple objectives of national governments and local communities alike (Tilley et al., 2019). The Timorese marine environment has so much to offer for tourism, from coral reefs to migrating blue whales (Grantham, 2010). Developing a reputation and demand for ecotourism can have an important role in sustainable development overall and benefit local coastal communities directly. However, the current COVID-19 travel restrictions are likely to have lasting impacts on the development of these industries.
Recommendations This thesis recommends the following management and monitoring interventions. Firstly, regular monitoring of coral reefs is needed. Over the few years surveys were conducted in Timor-Leste for this work, changes concerning reefs, both good and bad, have been
183 observed. Construction near coastal environments increased with the upgrading of the National Road Dili to Baucau and the Tibar Bay Port Project. On the positive end of the spectrum, the last decade has seen a suite of coral reef data collected from the combined efforts of NOAA, XLCSS, surveys conducted, and the establishment of Blue Ventures in- country. Notably, the reliance on foreign research is of concern and the need for institutional commitment to monitoring is imperative. The continued developments demonstrate the need for consistent monitoring of coral reefs, and the development of Timorese marine scientists is essential toward the practicality and realization of this goal. Additionally, with the application of tara bandu for MPA establishment, measuring changes or recovery via ecological metrics (i.e., benthic composition, fish biomass, etc.) through time is essential to determine the conservation efficacy of this practice. Currently, the only reefs that are consistently surveyed are those of Ataúro Island as the base for Blue Venture’s Reef Check surveys, and there is a need to monitor other regions as well.
A second recommendation highlights the need for oceanographic research. The work in Chapter 4 demonstrated the relevance and importance of measuring basic oceanic parameters such as temperature. Temperature data at three sites uncovered potentially the most significant finding of the thesis–Timor-Leste is a potential climate refugium in terms of ocean warming. This finding corroborates Timor-Leste as a reef region less vulnerable to climate impacts demonstrating maximal conservation return on investment from a global analysis (Beyer et al., 2018). As such, further understanding of the complex oceanography of the ITF is critical to the proper management of coastal marine ecosystems.
Lastly, Timor-Leste as a climate refugium has important implications for conservation priorities. The cumulative effects of local anthropogenic impacts such as overfishing, sedimentation, and climate impacts are comparatively less than other reef regions that have been severely affected by the recent mass bleaching events. Thus, managing these localized impacts is of utmost importance. Expanding community-based, tara bandu marine conservation efforts is key to managing these anthropogenic impacts. However, the success of this process is dependent on proper consultation and development with the community and, again, the measurement of appropriate ecological metrics must underpin any determinations these MPAs improves reef condition. Furthermore, land-sea management has huge conservation potential in-country. Management and reversal of poor watershed health could have significant downstream effects for marine coastal ecosystems which should be a research priority. 184
Future directions
The global community must act swiftly to mitigate the effect of climate change to limit global temperature increase to 1.5˚C (IPCC, 2018). Coral reefs are among the most potentially affected ecosystems with warming in the pipeline. In addition to urgent international action to curb global carbon emissions, managing localized anthropogenic impacts on coral reefs are equally important (Kennedy et al., 2013). The balance between the utilization of natural resources for communities who rely on them, and the need to maintain sustainability is challenging, requiring large-scale transdisciplinary, collaborative solutions. It has been argued that, in the Anthropocene, coupled assessments of biophysical and socio-ecological systems are imperative to accurately predict and manage natural resources including coral reefs (G. J. Williams et al., 2019). Timor-Leste is an excellent case study as to the importance of this novel approach to coral reef ecology.
In conclusion, Timor-Leste and its unique coral reef ecosystems are at a crossroads. Currently, there is effectively no industrialized fishing, agriculture, or economies, limiting most of the impacts to subsistence level effects. This is likely to change in the future (Alongi et al., 2013, 2012a; RDTL, 2011; Sandlund et al., 2001), and hence the infrastructure and policies for maintaining the coastal resources of Timor-Leste must be implemented as a priority. The evidence and understanding compiled by this thesis research present an optimistic view that there is still time to act, with incentives and grassroots support for sustainable management of coral reefs potentially helping prevent a future narrative of decline and degradation. Timor-Leste houses incredible coral reefs and biodiversity. Utilizing small and large-scale datasets, land to sea management, and considering cultural factors will deliver solutions that will enable coral reefs to support communities that rely on them. Of course, while Timor-Leste is comparatively less impacted by climate change-driven ocean warming, this potentially only provides the country more time as global mass bleaching events are predicted to increase in frequency and severity. Although the coral reefs of Timor-Leste are not likely to be imminently lost due to coral bleaching, addressing the climate change crisis through global commitments to lower greenhouse emissions is as imperative for Timor-Leste as it is for the rest of the world.
185
Supplemental materials
Table S2-1 Full labelset used for automated image analysis.
Label Functional Group Description BRA_TAB_Ac Hard coral Acropora corymbose/tabular/plate BRA_DIG_Ac Hard coral Acropora digitate BRA_ARB_Ac Hard coral Acropora branching BRA_BLC Hard coral Branching bleached BRA_BOT_Ac Hard coral Acropora bottlebrush BRA_FIN_Se Hard coral Fine branching non-Acroporids Seriatopora BRA_OTH Hard coral Other branching genus: Anacropora/Echinopora/Montipora/Tubastrea BRA_RND_St Hard coral Branching Stylophora BRA_SMO_Po Hard coral Branching Porites BRA_VER_Po Hard coral Pocillopora species MASE_BLC Hard coral Massive/Submassive/Encrusting (MASE) bleached MASE_LRG_I Hard coral MASE: Isopora MASE_LRG_O Hard coral MASE: Large rounded polyps MASE_MEA_L Hard coral MASE: Lobophyllia MASE_MEA_O Hard coral MASE: Meandering other MASE_SMO_P Hard coral MASE: Porites MASE_SML_O Hard coral MASE: Small or invisible polyps TFP_BLC Hard coral Thin/Foliose/Plating bleached TFP_SMO_Po Hard coral TFP: Porites TFP_RDG_Al Hard coral TFP: Visible relief structures TFP_RND_Al Hard coral TFP: Visible round corallites NN_HEL Non-hermatypic Non-hermatypic: Heliopora NON_FREE Non-hermatypic Non-hermatypic: Free-living (Fungia etc) NON_MIL Non-hermatypic Non-hermatypic: Millepora SINV_SFC_A Soft Coral Common large Alcyoniide SINV_SFC_E Soft Coral Sea fans/plumes/branching whips SINV_SFC_O Soft Coral Other soft-corals no common Alcyoniidae etc. SINV_SPO_E Sponge Branching/rope forms SINV_SPO_M Sponge Massive or encrusting sponges SINV_SPO_V Sponge Hollow sponge forms/cups/barrels/tubes etc. SINV_TUN Other Invertebrates Individual tunicates SINV_OTH Other Invertebrates Other sessile invertebrates Bryozoa clams SINV_HEX_O Other Invertebrates Other sessile invertebrates soft hexacorrallia SINV_HYD Other Invertebrates Hydroids feathery types MINV_CRI Other Invertebrates Crinoids MINV_OTH Other Invertebrates Sea cucumbers/sea urchins/sea stars/lobster
186
ALGAE_OTH Algae Other algae CAL_CCA_DC Algae Calcifying calcareous crustose algae: DHC CAL_CCA_RB Algae Calcifying Calcareous Crustose Algae: Rubble CYANO_DHC Algae Cyanobacteria smothering dead coral CYANO_RB Algae Cyanobacteria smothering rubble CYANO_Sub Algae Cyanobacteria on rock or other substrates EAM_DHC Algae EAM: Dead hard coral EAM_RB Algae Epilithic algal matrix smothering rubble MACR_Cal_H Algae Calcifying algae: Halimeda MACR_Cal_O Algae Foliose algae: Other MACR_Cal_P Algae Calcifying algae: Padina MACR_Fil_A Algae Filamentous Macroalgae MACR_Fol_B Algae Foliose Strap/Branched algae MACR_Fol_F Algae Foliose fan-shaped algae MACR_Fol_P Algae Foliose Feathery algae MACR_GLOB Algae Algae: Large Visible Globules MACR_LTH_O Algae Leathery Macrophyte: Other LSUB_RUB Sediment Loose Substrate: Rubble LSUB_SAND Sediment Loose Substrate: Sand LSUB_SEDI Sediment Loose Substrate: Sediment FISH Other Fish Unc Other Unclear Unk Other Unknown
187
Table S2-1 ANOVA of models for model selection. Coral is square root transformed and relative wave exposure, the ratio of branching to massive corals, and human population density were all log-transformed. All covariates were also mean-centered.
Model 1 Coral ~ wave 2 Coral ~ wave x river 3 Coral ~ wave x river x ratio 4 Coral ~ wave x river x ratio + population
Model df AIC BIC logLik Test L.Ratio p-value 1 4 -1651.337 -1631.402 829.6685 2 6 -1654.518 -1624.615 833.2590 1 vs 2 7.1809 0.0276 3 10 -2244.026 -2194.188 1132.0131 2 vs 3 597.5082 <.0001 4 11 -2245.838 -2191.017 1133.9192 3 vs 4 3.8123 0.0509
188
Table S2-2 Model selection coefficients with square root transformed coral cover as the response variable with the addition of covariates relative wave exposure, distance to river, the ratio of branching to massive corals, and human population density. All covariates were mean-centered and wave exposure, and the ratio of branching to massive corals was log- transformed.
1 2 3 4 Coefficient p-value p-value p-value p-value Intercept <0.001 <0.001 <0.001 <0.001
Wave Exposure <0.001 <0.001 <0.001 <0.001
River 0.141 0.471 0.371
Wave x River 0.028 0.794 0.877
Ratio <0.001 <0.001
Wave x Ratio <0.001 <0.001
River x Ratio 0.371 0.278
Wave x River x Ratio 0.384 0.217
Population 0.050 Observations 1079 1079 1079 1079 ICC 0.38 0.35 0.41 0.39 Marginal R2 / 0.245 / 0.535 0.229 / 0.500 0.502 / 0.705 0.522 / 0.708 Conditional R2 AIC -1651.337 -1654.518 -2244.026 -2245.838 BIC -1631.402 -1624.615 -2194.188 -2191.017 log-Likelihood 829.669 833.259 1132.013 1133.919 * p<0.05 ** p<0.01 *** p<0.001
189
Figure S2-1 Comparison of benthic composition at three NOAA climate stations surveys in Timor-Leste in 2013 and 2014: Baucau, Com, and Jaco Island. Phototransects were 30 m in both years and analyzed according to the NOAA benthic image analysis protocol. See supplemental Figure S2-2 and Figure S2-3, and Figure S2-4 for representative benthic images.
190
Figure S2-2 Representative photos of NOAA surveys at Jaco Island (a) LAU-63 in 2013 at latitude and longitude coordinates -8.410837, 127.3122, and (b) LAU-05 in 2014 at -8.4108, 127.3122. Coral reefs were dominated by thin/foliose/plating coral colonies and Halimeda sp. algae were common. File names are LAU-63_2013_A_19.jpg and LAU- 05_2014_A_19.jpg, respectively, and found at http://accession.nodc.noaa.gov/0166378.
191
Figure S2-3 Representative photos of NOAA surveys at Com (a) LAU-26 in 2013 at latitude and longitude coordinates -8.346345, 127.1610, and (b) LAU-01 in 2014 at -8.34638, 127.1610. This site was dominated by massive corals, free-living corals, and soft coral. File names are LAU-26_2013_A_23.jpg and LAU-01_2014_A_13.jpg respectively and can be found at http://accession.nodc.noaa.gov/0166378.
192
Figure S2-4 Representative photos of NOAA surveys at Baucau (a) BAU-09 in 2013 at latitude and longitude coordinates -8.419592, 126.4271, and (b) BAU-04 in 2014 at -8.4196, 126.4271. The site was dominated by soft coral. The file names are BAU-09_2013_A_22.jpg and BAU-04_2014_A_15.jpg respectively and can be found at http://accession.nodc.noaa.gov/0166378. 193
Efficacy and accuracy of conventional and kilometer-scale methods
Five times as many benthic quadrats were collected by the kilometer-scale surveys than conventional phototransects (Table 2-7). This relationship holds when standardizing by the number of survey days. The 26 kilometer-scale surveys were collected over nine days amounting to > 2,000 images per day compared to ~ 200 images per day from the NOAA 2013 surveys over 21 days. The NOAA 2013 field surveys had multiple goals including fish surveys and collection of water samples which is likely to decrease the efficacy of phototransect collection (PIFSC, 2017). In assessing the efficiency of conventional and automated image analysis, González-Rivero et al., (2020) have estimated that automated image analysis is 200 fold more efficient than manual annotations. They estimate the coast of the manual annotation is US $5.41 per image (at Australian level wages) and US $0.07 per image with automated analysis. Using these costs and taking into account the different number of points per image for the two protocols (i.e., five NOAA quadrats is equal to one kilometer-scale quadrat for the number of randomized points), the estimated costs for the NOAA 2013 and kilometer-scale image analysis is US $4,536 and US $1,453 respectively. Conservatively adding the coast of training experts for a new region for the automated analysis (US $1,740) brings the total to US $3,193, still more than US $1,000 less than the cost annotating the NOAA 2013 dataset.
The accuracy between manual and automated image analysis is similar at a broad functional group level and comparable to human inter-annotator variability (Beijbom et al., 2015; Bryant et al., 2017; González-Rivero et al., 2020, 2016; I. D. Williams et al., 2019). Beijbom et al. (2015) tested intra-annotator accuracy by comparing archived annotations and re- annotation of the same points by the same annotator one to six years later. Mean average errors (MAE) of benthic cover estimates between the original reference annotations and the semi-automated (manual versus automated error) method was closer to the MAE between the reference data and re-annotation of the same annotator (intra-annotator error) than the MAE between the reference and new annotators (inter-annotator error). The hard coral functional group had the lowest MAE of all broad functional groups with 1.3 ± 0.6%, 1.3 ± 0.4%, and 2.2 ± 0.4% error for intra-annotator, semi-automated, and inter-annotators respectively. The 1.3% MAE of coral cover corresponded to a relative error of ~ 4–6% at the 22–31% coral cover of the image sets tested. The turf algae functional group had the highest 194
MAEs at 5.0 ± 1.6%, 5.6 ± 1.7%, and 11.9 ± 2.0% for intra-annotator, semi-automated, and inter-annotators respectively (Beijbom et al., 2015).
Estimates of at the functional group level were highly correlated between kilometer-scale and conventional methods of photoquadrat collection. Bryant et al., (2017) tested the accuracy of the kilometer-scale benthic image collection coupled with semi-automated image annotation with conventional photoquadrat methods on a scale of tens of meters in the Maldives. The two methods were conducted concurrently on the same transect during the same dive. There was significant agreement between conventional methods, analogous to the NOAA methods, and kilometer-scale methods at a functional group level where all six functional groups were significantly correlated. The least correlated functional group was the other category (Pearson’s r = 0.641, p = 0.0101), then macroalgae (Spearman’s r = 0.6739, p = 0.0073), and the most correlated was hard coral (Pearson’s r = 0.9526, p < 0.0001). González-Rivero et al. (2020) compared established sites of monitoring programs in Bermuda, GBR, Main Hawaiian Islands, and the Pacific Islands that were within a two-km radius of the kilometer-scale transects collected in the region. There was an overall agreement in coral cover estimates across regions (linear mixed-effect regression pairwise comparisons = 0.691) with an average error of 2.9 ± 0.68% comparable to inter-annotator error.
Accuracy of automated image analysis at a functional group level has only improved with fully automated image analysis. Correlations between manual and automated image analysis in the main Hawaiian Islands and American Samoa were > 97% (Pearson’s r) for coral cover and the mean difference between automated and manual estimates was 0.6 ± 3.4% and 1.0 ± 2.7% per region (I. D. Williams et al., 2019). A global analysis (e.g., the Central Pacific Ocean, Western Atlantic Ocean, Central Indian Ocean, Southeast Asia, and Eastern Australia) of the automated method used here indicated that error in abundance estimations varied more by classes (functional groups) than the study region. The Atlantic and Pacific Ocean regions had the highest agreement between automated and manual methods for hard corals with absolute error ranging from 1–2%. Hard coral error ranged between 3–5% for the remaining regions and the error for other invertebrates, soft coral, and other major benthic classes was consistently below 2% (González-Rivero et al., 2020). The algal functional group has consistently higher variance in error in all studies presented (Beijbom et al., 2015; González-Rivero et al., 2020, 2016; I. D. Williams et al., 2019). The error for algae in the global analysis was 3–5% and the highest error for any label was the 195 epilithic algal matrix (EAM) ranging from 5–7% (González-Rivero et al., 2020). EAM and other turf algae classifications are typically defined as diverse assemblages of algal groups (e.g., macroalgae, cyanobacteria) and is inherently highly variable. This high variability of the classification leads to high variability within and among human analysts which leads to larger variance in turf algae estimates resulting in inconsistent training of the automated classifier (Beijbom et al., 2015; I. D. Williams et al., 2019). The fixed window size of the image identification penalizes smaller, patchy, and/or less well-defined organisms (González-Rivero et al., 2020). These complexities concerning the algal functional group could be rectified in the future with multi-scale or regional machine networks to account for taxa-specific sensitivity (González-Rivero et al., 2020; Zoph et al., 2018). Additionally, light spectral signature (e.g., fluorescence, reflectance) may be additional parameters that could be utilized to define phototrophic or pigmented organisms (Beijbom et al., 2015; Chennu et al., 2017; González-Rivero et al., 2020)
Comparison of benthic composition across survey years- accuracy and algae
Here, the kilometer-scale transects and conventional transects collected by NOAA in 2013 and 2014 were compared. All three datasets were collected within 13 months of each other which eliminates changes in reef benthic composition over time as a major contributor to any differences. Despite methodological and surveying differences, the kilometer-scale and NOAA 2013 datasets revealed similar patterns of benthic composition in major coral reef functional groups along the north coast of Timor-Leste; however, the NOAA 2014 phototransects were more variable across the major benthic components likely due to lack of replication at the district level.
The NOAA surveys between the two years overlapped at three of the climate stations previously established by NOAA: Baucau, Com, and Jaco Island. This allowed for some direct comparisons (Figure 2-13). These surveys were taken at the same GPS points; however, they were not marked as permanent transects in situ. Thus, surveys were likely taken at different locations on the same reef which could be responsible for some of the variability between years. The dominant benthic categories were not consistent across the three sites but were consistent between survey years at the same site. Baucau was dominated by other invertebrates which were mostly soft corals (43.7 ± 16.3% SE, 45.7 ±
196
14.3% in 2013 and 2014 respectively), while Com was dominated by turf on hard substrate in 2013 (54.3 ± 14.6%) and then coral in 2014 (39.7 ± 11.1%). Lastly, Jaco Island was dominated by macroalgae, mostly Halimeda sp., both years (62.3 ± 11.2%, 41.9 ± 15.5% in 2013 and 2014 respectively). The coral cover did increase at all three sites between years, the least at Baucau (13.3 ± 2.6% SE, 17.0 ± 14.9%) and more at Com (24.0 ± 0.4%, 40.0 ± 0.4%) and Jaco Island (20.0 ± 0.2%,31.9 ± 0.6%; Figure S2-1). Along with non-permanent transects, another source of variation could be the small number of points per image in the NOAA image analysis protocol. For example, the ten points per image could less accurately quantify Halimeda sp. interspersed between corals common at Jaco Island versus reefs dominated by massive corals (Figure S2-1).
The kilometer-scale analysis also reported a relatively high proportion of macroalgae for Com and Jaco Island. The range of macroalgal benthic group for these subtransects was 0.0–10.9% and seven subtransects from Com and Jaco Island had macroalgal cover greater than 5%. Similarly, to the NOAA surveys, Jaco Island macroalgae was dominated by Halimeda sp. ranging from 0.0–10.1% by subtransect. The Halimeda sp. cover was patchy with only 15 out of 151 subtransects in Jaco Island with a cover > 1%. Similarly, there were only four out of 19 NOAA 2013 surveys in Lautem that had > 1% Halimeda sp. cover. All three datasets indicate that the high macroalgae on Jaco Island is mostly Halimeda sp. and not likely a sign of impact from nutrients or sedimentation as Jaco Island is very remote with no permanent settlements or rivers. Macroalgae abundance can also be highly seasonal. The three datasets were collected in roughly the same time of year, but different months July through October, which could be attributed to some of the differences in Halimeda sp. cover.
Macroalgae in Com, on the other hand, mostly classified as CCA (1.15 ± 0.10%), cyanobacteria (0.2 ± 0.0%), and filamentous algae (0.7 ± 0.1%) in this analysis. Encrusting macroalgae was much more prevalent in the NOAA 2013 surveys than the kilometer-scale transects. These algae can be a naturally dominant component of coral reefs such as in American Samoa where NOAA surveys of both populated islands and unpopulated atolls found an average of 37.9 (± 1.2%) encrusting algal cover in 2010 (Heenan and Williams, 2013). Encrusting alga encompasses a wide range of colors from red to brown and pink or red species and could be mistaken as CCA. Interestingly though, when comparing CCA in the kilometer-scale versus NOAA datasets, the NOAA 2013 surveys averaged more, 7.13 ± 1.37% across 19 phototransects in Lautem versus a maximum of 5.4% for kilometer-scale 197 subtransects in Com (in Lautem). A lower amount of CCA classifications from the automated image analysis does not point to the misidentification of encrusting macroalgae as CCA.
Out of 19 macroalgal labels (including CCA and cyanobacteria) used in the automated image analysis, only seven returned annotations: the two CCA, the two cyanobacteria, Halimeda sp., filamentous algae, and branched foliose algae labels. These missing macroalgal types could be absent from Timorese reefs, or more likely, the accuracy of the finer macroalgal classifications is lower than other functional groups such as hard corals (Beijbom et al., 2015; Bryant et al., 2017; González-Rivero et al., 2020, 2016; I. D. Williams et al., 2019). As macroalgae generally have a low cover on reefs and can be cryptic, it can be difficult to amass enough randomized points during the image analysis training process for automation. Accuracy of the broad macroalgal functional group between automated and conventional image analysis is on par with other functional groups such as hard coral; however, accuracy for finer classifications for macroalgae was not tested (Bryant et al., 2017). The comparative analysis of conventional versus kilometer-scale transects was also conducted in the Maldives where coral reef diversity of corals and algae is much less than the CT and reduced complexity can decrease the error between manual and machine annotations (González-Rivero et al., 2020). Algal functional groups are consistently higher in error with studies testing manual versus semi- and fully- automated image analysis (Beijbom et al., 2015; Bryant et al., 2017; González-Rivero et al., 2020, 2016; I. D. Williams et al., 2019). It is likely that common, conspicuous alga such as Halimeda sp. have comparable accuracy as compared to the overall macroalgae functional group.
Less conspicuous groups that share characteristics such as color or morphology have high inter- and intra- operator error which makes accurate training of the machine through manual annotations for complex groups difficult (Beijbom et al., 2015; González-Rivero et al., 2020, 2016). Bryant et al. (2017) did not find a significant correlation between massive encrusting sponges between the two methods and determined that this was due to a difference in image resolution. The semi-automated image analysis, which had the lower image resolution of the two methods, had consistently higher amounts of dead hard coral and lower amounts of other invertebrates including sponges. In Chapter 2, the photoquadrats from the kilometer- scale analysis were higher resolution (1031 x 1031 versus 401 x 601 pixels) and the average sponge classifications for the Com and Jaco Island kilometer-scale transects were 2.57 ± 0.17% and 4.47 ± 0.21% respectively. The Lautem average from the NOAA 2013 surveys was less with 1.03 ± 0.34%. Higher resolution images result in a higher cover of some 198 smaller sessile invertebrates such as sponges. For image analysis undertaken for Timor- Leste, visual similarity between some encrusting macroalgae and sessile invertebrates could be a potential source of confusion for manual and automated image analysis alike.
The consistency of the macroalgae functional group varied depending on the specific algal group in question. Halimeda sp. patches consistently occurred on Jaco Island reefs in all datasets, while the high occurrence of encrusting macroalgae at a Com site in 2013 was only observed in one datasets. The high encrusting macroalgae site in Com had close to average biomass of herbivorous fishes with 5.93 g/m2 compared to the mean of 8.13 g/m2 across all 150 fish survey sites NOAA conducted in 2013. This is relatively low compared to other Pacific reefs surveyed by NOAA, but the norm in the Timorese seascape where 87 sites had lower herbivore biomass compared to the high encrusting macroalgal site in Com. A lack of herbivorous fishes at this site does not appear to be the cause of the high encrusting macroalgae cover. However, different functional groups of herbivores feeding modes have a significant influence on different benthic components including macroalgae and encrusting macroalgae. Specifically, high encrusting macroalgae was associated with relatively high biomass of grazers/detritivores feeding on algal turf and brushing the epilithic algal matrix for detritus (Heenan and Williams, 2013). There could be relatively high biomass of grazers and detritivores associated with this high encrusting macroalgae area. Point sources of pollution have also been associated with high cover of algae although nutrients versus herbivory as the dominant factor structuring algal communities are debated (Lapointe et al., 2005; Roff et al., 2015). Com and Jaco Island are distant from river sources of nutrients and sedimentation which does not make this a likely cause. The cause of the singular high encrusting macroalgae site in Com is unknown, but available evidence indicates it was not caused by either removal of herbivores or riverine inputs.
199
Supplemental materials
Table S3-1 Raw sequences per sample from Autonomous Reef Monitoring Structures by the US National Oceanic and Atmospheric Administration deployed in Timor-Leste from 2012–2014. Number of Sequences per Size Fraction Station ARMS unit 106 µm 500 µm Sessile 1 Coral Gardens A 194,088 128,256 277,175 1 Coral Gardens B 210,063 199,596 299,628 1 Coral Gardens C 169,428 200,455 291,123 2 Beloi A 162,373 - 146,737 2 Beloi B 200,904 189,215 144,519 2 Beloi C 210,793 199,210 155,461 3 Beacou A 126,555 199,896 249,989 3 Beacou B 348,560 215,181 - 3 Beacou C 360,750 270,215 295,369 4 Dili Rock A 161,071 118,039 215,629 4 Dili Rock B 173,868 212,808 192,152 4 Dili Rock C 183,409 196,812 208,357 5 Manatuto A 149,733 197,936 165,077 5 Manatuto B 204,562 162,231 125,001 5 Manatuto C - 170,240 160,383 6 Baucau A 158,602 138,030 325,468 6 Baucau B - 188,659 156,413 6 Baucau C 193,537 194,753 161,217 7 Com A 149,278 144,327 419,195 7 Com B 159,933 196,052 105,749 7 Com C 193,169 - 120,827 8 Jaco Island A 198,232 168,504 161,300 8 Jaco Island B - 185,351 165,765 8 Jaco Island C 149,721 155,700 122,200 Total: 4,058,629 4,031,466 4,664,734 Total 12,754,829 Sequences:
200
Supplemental materials
Table S4-1 Summary of Indo-Pacific studies assessing nutrients on reefs.
Study Location Nutrient Concentration [µM] + Smith et al. 2001 west Hawai'i reef slope 12 m NH4 0.716 ± 0.22 Island - NO3 0.38 ± 0.11 3- PO4 0.24 ± 0.07 + enriched NH4 148.80 ± 7.51 - NO3 94.35 ± 19.68 3- PO4 22.38 ± 2.06 Vega Thurber et al. Florida Keys 5-6 m DIN 1.15 ± 0.05 2014 enriched DIN 3.91 ± 1.34 Dinsdale et al. 2008 Kingman 10 - 12 m depth DIN 1.3 ± 0.08 Atoll 3- PO4 0.1 ± 0.003 Kiribati Atoll DIN 3.6 ± 0.1 3- PO4 0.3 ± 0.024 Amato et al. 2016 Maui surface 0.25 m DIN 0.1 ± 0.1-25.6 ± 17.8 3- PO4 0.06 ± 0.06-0.43 ± 0.86 coastal DIN 1.6 ± 1.8-414.9 ± groundwater 37.8 3- PO4 1.01 ± 1.03-4.90 ± 1.06 Kaczmarksy et al. Philippines 1-3 m TN 5.9-15 2011 TP 0.13-0.98 + Osawa et al. 2010 Majuro Atoll reef flat NH4 0.34 ± 0.15 - NO3 0.45 ± 0.45 3- PO4 0.34 ± 0.15 + ground water NH4 2.4-16.9 wells - NO3 49-1060 3- PO4 0.2-22.1
201
Table S4-2 Site and transect GPS points of coral surveys and seawater sampling at four sites (Rural-N, Rural-E, Urban-W, Urban-E) in Timor-Leste. Coral surveys conducted in Nov 2015 and Jul 2017 and seawater was only collected in 2015. Transect 1 Transect 2 Transect 3 Rural 5 8°13'27.56"S, 8°13'26.03"S, 8°13'25.22"S, – N m 125°36'59.53" 125°37'2.03"E 125°37'3.83" 10 8°13'28.51"S, 8°13'26.59"S, 8°13'25.63"S, m 125°36'59.52"E 125°37'2.93"E 125°37'4.25"E Rural- 5 8°28'30.77"S, 8°28'30.02"S, 8°28'29.50"S, E m 125°53'17.01"E 125°53'16.53"E 125°53'16.59"E 10 8°28'31.08"S, 8°28'30.74"S, 8°28'29.71"S, m 125°53'17.36"E 125°53'17.41"E 125°53'17.46"E Urban- 5 8°33'19.74"S, 8°33'18.72"S, 8°33'18.28"S, W m 125°30'0.41"E 125°29'59.46" 125°29'56.95"E 10 8°33'18.85"S, 8°33'17.88"S, 8°33'17.04"S, m 125°30'0.57"E 125°30'0.03"E 125°29'57.27"E Urban- 5 8°31'28.68"S, 8°31'29.82"S, 8°31'31.87"S, E m 125°36'29.00"E 125°36'30.07" 125°36'30.76"E 10 8°31'28.26"S, 8°31'29.80"S, 8°31'31.41"S, m 125°36'28.33"E 125°36'29.32"E 125°36'30.30"E
202
Table S4-3 Description of coral diseases and compromised health found during surveys in Timor-Leste and references citing negative impacts to corals. Superscript letters correspond to images–first three in Figure 4-2 and remaining in Figure 4-3. Disease/Condition Description aWhite Syndrome A class of tissue loss disease producing white symptoms in the Caribbean and the Indo-Pacific have led to the general description White Syndrome (WS) describing corals with clearly defined lesion of exposed coral skeleton from the rapid sloughing of coral tissue ranging from 1.0 to 124.6 cm of tissue loss per day not associated with predation (i.e., Drupella snails or crown-of-thorns seastars [COTS]). In the Indo-Pacific, WS predominantly affects tabulate Acroporids (Bruno et al., 2007; Green and Bruckner, 2000; Hobbs et al., 2015; Willis et al., 2004). bGrowth Anomalies Gross lesions of raised tissue with lighter pigmentation and enlarged variable polyp found on several coral genera (Williams et al., 2011b). Energetically relies on resources from surrounding healthy tissue (Yasuda et al., 2012). cTrematodiasis Pink, swollen nodules caused by infection of a larval trematode in Porites corals which can reduce colony growth (Aeby, 2003, 1992). bBurrowing Worms, barnacles that burrow in or on the surface of corals. Vermetids Invertebrates worms have been shown to change morphology and reduce growth (Zvuloni et al., 2008) and burrowers have been associated with reduced skeletal strength (Scott and Risk, 1988). cFlatworm An infestation of flatworms, typically Waminoa sp., on the surface of Infestation corals. Have been associated with tissue loss (Hoeksema and Farenzena, 2012), reduced heterotrophic feeding (Wijgerde et al., 2012), light-shading (Barneah et al., 2007; Haapkylä et al., 2009a), and experimentally shown to feed on coral mucus (Naumann et al., 2010). dPredation Scars or tissue loss from predation by Drupella snails, COTS, and reef fishes (Rodríguez-Villalobos et al., 2015). Typically, invertebrate predators (Drupella, COTS) will be visible near the predation scar and reef fishes make distinctive lesions. eTissue Loss Unexplained tissue loss (absence of predator, shape of lesion, etc.) and possibly cause by infectious agents, physiologic disorders, or toxins (Rodríguez-Villalobos et al., 2015; Williams et al., 2011b). fCCA overgrowth CCA in competition with living coral causing a reaction or overgrowth. Two species of CCA have been identified to overgrow live coral (Finckh, 1904; Keats et al., 1997) in the Indo-Pacific and one species in Yemen (Benzoni and Basso, 2011). gCyanobacteria Cyanobacterial filaments or filamentous turf algae competing with or and jturf growing on living coral tissue. Cyanobacteria are often associated with overgrowth eutrophication and can smother corals (Smith et al., 2008) and reduce recruitment (Kuffner et al., 2006). Decreased coral function such as decreased zooxanthellae density and tissue thickness have been associated with turf-coral interactions (Cetz-Navarro et al., 2013). Sponge or Sponge overgrowth on live coral tissue can negatively affect corals (Loh htunicate and Pawlik, 2012) with some aggressive species such as Terpios and Overgrowth Cliona killing corals (Rutzler and Muzik, 1993). Colonial tunicates smothering live coral (Bak et al., 1996; Littler and Littler, 1995; Vargas- Ángel et al., 2009). iPigmentation Pigmented edge of a coral lesion-swollen, form bumps, or irregular response shapes. Caused by borers, competitors, breakage, cyanobacteria (Ravindran and Raghukumar, 2006), polychaetes, molluscs (Willis et al., 2004). 203
Table S4-4 Average (± standard error) percent coral cover, diseased corals, and corals exhibiting other signs of compromised health, average number of genera, density of hard corals (colonies/m2), and total number of colonies surveyed per site and depth. % D–percent disease, % Comp–percent compromised health, # Gen–number of genera, Total # Col– Total number of colonies
Site % Coral % Disease % Comp # Gen Density Total # col/m2 Col Rural-N 58.18 1.88 13.56 29.6 8.4 672 5m ± 1 .74 ± 0.18 ± 1.00 ± 4.0 10m 42.54 1.52 16.02 31.6 7.4 759 ± 5.82 ± 0.77 ± 2.44 ± 1.5 Rural-E 20.17 0.21 42.58 28.6 6.0 547 5m ± 3.49 ± 0.21 ± 3.64 ± 2.1 10m 22.10 0.47 25.95 26.3 5.5 502 ± 2.59 ± 0.24 ± 5.78 ± 3.5 Urban- 4.80 0.71 33.48 24.6 3.8 350 W 5m ± 1.78 ± 0.71 ± 4.18 ± 4.2 10m 45.23 0.38 37.44 17.6 6.4 581 ± 8.49 ± 0.38 ± 4.87 ± 4.0 Urban-E 24.43 0.42 27.71 27.3 7.1 647 5m ± 6.35 ± 0.42 ± 5.98 ± 2.5 10 m 12.29 0.23 28.77 25.3 5.4 492 ± 3.24 ± 0.23 ± 1.35 ± 1.5
Table S4-5 Average (± standard error) seawater nutrient values from samples collected in + - triplicate from transects at four sites in Timor-Leste in 2015. DIN is the sum of NH4 , NO2 , - and NO3 . All units are in μM.
+ - - + Site Depth DIN NH4 NO2 and NO3 PO4 Rural -E 5m 1.87 ± 0.31 0.00 ± 0.00 0.80 ± 0.17 0.00 ± 0.00 10m 1.9 ± 0.49 1.35 ± 0.52 0.55 ± 0.06 0.09 ± 0.00 Rural -N 5m 2.19 ± 0.51 1.69 ± 0.49 0.51 ± 0.07 0.10 ± 0.01 10m 3.39 ± 0.62 2.34 ± 0.61 1.05 ± 0.07 0.15 ± 0.01 Urban -E 5m 3.4 ± 0.78 2.69 ± 0.78 0.71 ± 0.07 0.11 ± 0.00 10m 1.78 ± 0.16 1.32 ± 0.17 0.46 ± 0.09 0.10 ± 0.00 Urban -W 5m 2.31 ± 0.66 1.81 ± 0.60 0.50 ± 0.08 0.10 ± 0.01 10m 2.79 ±0.62 2.41 ±0.60 0.37 ± 0.05 0.09 ± 0.00
204
References
Abele, L.G., Patton, W.K., 1976. The Size of Coral Heads and the Community Biology of Associated Decapod Crustaceans. Journal of Biogeography 3, 35–47. https://doi.org/10.2307/3038097 Achlatis, M., Pernice, M., Green, K., de Goeij, J.M., Guagliardo, P., Kilburn, M.R., Hoegh- Guldberg, O., Dove, S., 2019. Single-cell visualization indicates direct role of sponge host in uptake of dissolved organic matter. Proceedings of the Royal Society B: Biological Sciences 286, 20192153. https://doi.org/10.1098/rspb.2019.2153 Acropora Recovery Team, 2015. Recovery Plan Elkhorn Coral (Acropora palmata) and Staghorn Coral (A. cervicornis). National Marine Fisheries Service. ADB, 2014. State of the Coral Triangle: Timor-Leste. Asian Development Bank, Mandaluyong City, Philippines. ADB, 2004. Project Completion Report on The Hera Port Fisheries Facilities Rehabilitation Project (grant-8190-tim[tf]). Asian Development Bank PCR: TIM 25212, Timor-Leste. Adey, W.H., Vassar, J.M., 1975. Colonization, succession and growth rates of tropical crustose coralline algae (Rhodophyta, Cryptonemiales). Phycologia 14, 55–69. https://doi.org/10.2216/i0031-8884-14-2-55.1 Aeby, G.S., 2005. Outbreak of coral disease in the Northwestern Hawaiian Islands. Coral Reefs 24, 481. https://doi.org/10.1007/s00338-005-0493-3 Aeby, G.S., 2003. Corals in the genus Porites are susceptible to infection by a larval trematode. Coral Reefs 22, 216. https://doi.org/10.1007/s00338-003-0310-9 Aeby, G.S., 1992. The potential effect on the ability of a coral intermediate host to regenerate has had on the evolution of its association with a marine parasite., in: Proc 7th Int Coral Reef Symp 2. pp. 809–815. Aeby, G.S., Ross, M., Williams, G.J., Lewis, T.D., Work, T.M., 2010a. Disease dynamics of Montipora white syndrome within Kaneohe Bay, Oahu, Hawaii: distribution, seasonality, virulence, and transmissibility. Diseases of Aquatic Organisms 91, 1–8. https://doi.org/10.3354/dao02247 Aeby, G.S., Williams, G.J., Franklin, E.C., Kenyon, J., Cox, E.F., Coles, S., Work, T.M., 2011. Patterns of coral disease across the Hawaiian archipelago: relating disease to environment. PloS one 6, e20370. https://doi.org/10.1371/journal.pone.0020370 Aeby, G.S., Work, T.M., Bourne, D.G., Wilson, B., 2010b. Coral Diversity and the Severity of Disease Outbreaks: A Cross-Regional Comparison of Acropora White Syndrome in a Species-Rich Region (American Samoa) with a Species-Poor Region (Northwestern Hawaiian Islands). Journal of Marine Biology 2011, 1–8. https://doi.org/10.1155/2011/490198 Akaike, H., 1973. Information Theory as an Extension of the Maximum Likelihood Principle, in: Petrov, B., Csaki, F. (Eds.), Second International Symposium on Information Theory. Budapest: Akademiai Kiado, pp. 267–281. Allen, G.R., 2008. Conservation hotspots of biodiversity and endemism for Indo-Pacific coral reef fishes. Aquatic Conservation: Marine and Freshwater Ecosystems 18, 541–556. https://doi.org/10.1002/aqc.880 Allen, G.R., Erdmann, M.V., 2013. Coral Reef Fishes of Timor-Leste, in: Erdmann, M., Mohan, C. (Eds.), A Rapid Marine Biological Assessment of Timor-Leste. pp. 33–83. Alongi, D.M., Amaral, A., de Carvalho, N., McWilliam, A., Rouwenhorst, J., Tirendi, F., Trott, L., Wasson, R.J., 2012a. River Catchments and Marine Productivity in Timor Leste: Caraulun and Laclo Catchments; South and North Coasts - Final Report (No. Project 205
6 of the Timor Leste Coastal-Marine Habitat Mapping, Tourism and Fisheries Development Project.). Ministry of Agriculture & Fisheries, Government of Timor Leste. Alongi, D.M., Boto, K., Robertson, A., 1992. Nitrogen and phosphorus cycles, in: Robertson, A.I., Alongi, D.M. (Eds.), Tropical Mangrove Ecosystems. American Geophysical Union (AGU), Washington D.C. Alongi, D.M., de Carvalho, N.A., Amaral, A.L., Trott, L., Tirendi, F., 2012b. Uncoupled surface and below-ground soil respiration in mangroves: implications for estimates of dissolved inorganic carbon export. Biogeochemistry 109, 12. https://doi.org/10.1007/s10533-011-9616-9 Alongi, D.M., Manuel da Silva, Rober J. Wasson, Sugiarta Wiransantosa, 2013. Sediment discharge and export of fluvial carbon and nutrients into the Arafura and Timor Seas: A regional synthesis. Marine Geology 343, 13. http://dx.doi.org/10.1016/j.margeo.2013.07.004 Alonso Población, E., 2013. Fisheries and food security in Timor-Leste: the effects of ritual meat exchanges and market chains on fishing. Food Sec. 5, 807–816. https://doi.org/10.1007/s12571-013-0308-2 Al-Rshaidat, M.M.D., Snider, A., Rosebraugh, S., Devine, A.M., Devine, T.D., Plaisance, L., Knowlton, N., Leray, M., 2016. Deep COI sequencing of standardized benthic samples unveils overlooked diversity of Jordanian coral reefs in the Northern Red Sea. Genome 14, gen-2015-0208. https://doi.org/10.1139/gen-2015-0208 Altizer, S., Ostfeld, R.S., Johnson, P.T.J., Kutz, S., Harvell, C.D., 2013. Climate change and infectious diseases: from evidence to a predictive framework. Science (New York, N.Y.) 341, 514–9. https://doi.org/10.1126/science.1239401 Alvarez-Filip, L., Côté, I.M., Gill, J.A., Watkinson, A.R., Dulvy, N.K., 2011. Region-wide temporal and spatial variation in Caribbean reef architecture: Is coral cover the whole story? Global Change Biology 17, 2470–2477. https://doi.org/10.1111/j.1365- 2486.2010.02385.x Alvarez-Filip, L., Dulvy, N.K., Gill, J.A., Côté, I.M., Watkinson, A.R., 2009. Flattening of Caribbean coral reefs: region-wide declines in architectural complexity. Proc R Soc B 276, 3019–25. https://doi.org/10.1098/rspb.2009.0339 Amaro, C., Biosca, E.G., 1996. Vibrio vulnificus biotype 2, pathogenic for eels, is also an opportunistic pathogen for humans. Appl. Environ. Microbiol. 62, 1454–1457. Amato, D.W., Bishop, J.M., Glenn, C.R., Dulai, H., Smith, C.M., 2016. Impact of Submarine Groundwater Discharge on Marine Water Quality and Reef Biota of Maui. PLoS One 11. https://doi.org/10.1371/journal.pone.0165825 Anderson, M., Gorley, R.N., Clarke, K., 2008. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. PRIMER-E Ltd, Plymouth, UK. Anderson, M.J., 2001. Permutation tests for univariate or multivariate analysis of variance and regression. Can. J. Fish. Aquat. Sci. 58, 626–639. https://doi.org/10.1139/f01- 004 Anderson, R.M., May, R.M., 1979. Population biology of infectious diseases: Part I. Nature 280, 361–367. https://doi.org/10.1038/280361a0 Andersson, A.J., Gledhill, D., 2013. Ocean Acidification and Coral Reefs: Effects on Breakdown, Dissolution, and Net Ecosystem Calcification. Annu. Rev. Mar. Sci. 5, 321–348. https://doi.org/10.1146/annurev-marine-121211-172241 Andréfouët, S., Guillaume, M.M.M., Delval, A., Rasoamanendrika, F.M.A., Blanchot, J., Bruggemann, J.H., 2013. Fifty years of changes in reef flat habitats of the Grand Récif of Toliara (SW Madagascar) and the impact of gleaning. Coral Reefs 32, 757– 768. https://doi.org/10.1007/s00338-013-1026-0
206
Aronson, R.B., Macintyre, I.G., Wapnick, C.M., O’Neill, M.W., 2004. Phase shifts, alternative states, and the unprecedented convergence of two reef systems. Ecology 85, 1876– 1891. https://doi.org/10.1890/03-0108 Aronson, R.B., Precht, W.F., 2006. Conservation, precaution, and Caribbean reefs. Coral Reefs 25, 10. Aronson, R.B., Precht, W.F., 2001. White-band disease and the changing face of Caribbean coral reefs. Hydrobiologia 460, 25–38. Arya, S., Mount, D., Kemp, S.E., Jerreris, G., Jefferis, M., 2019. Package “RANN.” Audley-Charles, M.G., 2004. Ocean trench blocked and obliterated by Banda forearc collision with Australian proximal continental slope. Tectonophysics 389, 65–79. Ayling, A.M., Ayling, A.L., Edyvane, K.S., Penny, S., de Carvalho, N., Fernandes, A., Amaral, A.L., 2009. Preliminary biological resource survey of fringing reefs in the proposed Nino Konis Santana Marine Park, Timor-Leste. Report to the Northern Territory Department of Natural Resources, Environment, and the Arts. Palmerston, Northern Territory 830. Azovsky, A.I., 2002. Size-dependent species-area relationships in benthos: Is the world more diverse for microbes? Ecography 25, 273–282. https://doi.org/10.1034/j.1600- 0587.2002.250303.x Bailey-Brock, J., Brock, R., Kam, A., Fukunaga, A., Akiyama, H., 2007. Anthropogenic Disturbance on Shallow Cryptofaunal Communities in a Marine Life Conservation District on Oahu, Hawaii. Internat. Rev. Hydrobiol. 92, 291–300. https://doi.org/10.1002/iroh.200610958 Baird, A.H., Marshall, P.A., 2002. Mortality, growth and reproduction in scleractinian corals following bleaching on the Great Barrier Reef. Marine Ecology Progress Series 237, 133–141. https://doi.org/10.3354/meps237133 Bak, R., DYM, L., Joenje, M., Nieuwland, G., Van Veghel, M., 1996. Long-term changes on coral reefs in booming populations of a competitive colonial ascidian. Marine Ecology Progress Series 133, 303–306. Baker, D.M., MacAvoy, S.E., Kim, K., 2007. Relationship between water quality, δ15N, and aspergillosis of Caribbean sea fan corals. Mar. Ecol. Prog. Ser. 343, 123–130. https://doi.org/10.3354/meps06937 Bakus, G.J., 1966. Some Relationships of Fishes to Benthic Organisms on Coral Reefs. Nature 210, 280–284. https://doi.org/10.1038/210280a0 Barbier, E.B., Hacker, S.D., Kennedy, C., Koch, E.W., Stier, A.C., Silliman, B.R., 2011. The value of estuarine and coastal ecosystem services. Ecological Monographs 81, 169– 193. https://doi.org/10.1890/10-1510.1 Barbosa, M., Booth, S., 2009. Timor-Leste’s fisheries catches (1950-2009): Fisheries under different regimes, in: Zeller, D., Harper, S. (Eds.), Fisheries Catch Reconstructions: Islands, Part I. Fisheries Centre, University of British Columbia, Vancouver, Canada, pp. 39–52. Barneah, O., Brickner, I., Hooge, M., Weis, V.M., LaJeunesse, T.C., Benayahu, Y., 2007. Three party symbiosis: acoelomorph worms, corals and unicellular algal symbionts in Eilat (Red Sea). Mar Biol 151, 1215–1223. https://doi.org/10.1007/s00227-006- 0563-2 Barnett, J., Dessai, S., Jones, R.N., 2007. Vulnerability to Climate Variability and Change in East Timor 36, 8. Bartley, R., Bainbridge, Z.T., Lewis, S.E., Kroon, F.J., Wilkinson, S.N., Brodie, J.E., Silburn, D.M., 2014. Relating sediment impacts on coral reefs to watershed sources, processes and management: A review. Science of the Total Environment 468–469, 1138–1153. https://doi.org/10.1016/j.scitotenv.2013.09.030
207
Bayer, F.M., 1957. Recent Octocorals, in: Geological Society of America Memoirs. Geological Society of America, pp. 1105–1108. https://doi.org/10.1130/MEM67V1- p1105 Beeden, R., Willis, B.L., Page, C.A., Weil, E., 2008. Underwater Cards for Assessing Coral Health on Indo-Pacific Reefs. Beger, M., McKenna, S.A., Possingham, H.P., 2007. Effectiveness of Surrogate Taxa in the Design of Coral Reef Reserve Systems in the Indo-Pacific. Conservation Biology 21, 1584–1593. https://doi.org/10.1111/j.1523-1739.2007.00795.x Beijbom, O., Edmunds, P.J., Kline, D.I., Mitchell, B.G., Kriegman, D., 2012. Automated annotation of coral reef survey images. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1170–1177. https://doi.org/10.1109/CVPR.2012.6247798 Beijbom, O., Edmunds, P.J., Roelfsema, C., Smith, J., Kline, D.I., Neal, B.P., Dunlap, M.J., Moriarty, V., Fan, T.Y., Tan, C.J., Chan, S., Treibitz, T., Gamst, A., Mitchell, B.G., Kriegman, D., 2015. Towards automated annotation of benthic survey images: Variability of human experts and operational modes of automation. PLoS ONE 10, 1–22. https://doi.org/10.1371/journal.pone.0130312 Bejarano, S., Mumby, P.J., Sotheran, I., 2011. Predicting structural complexity of reefs and fish abundance using acoustic remote sensing (RoxAnn). Mar Biol 158, 489–504. https://doi.org/10.1007/s00227-010-1575-5 Bell, J.J., Smith, D., 2004. Ecology of sponge assemblages (Porifera) in the Wakatobi region, south-east Sulawesi, Indonesia: richness and abundance. J. Mar. Biol. Ass. 84, 581–591. https://doi.org/10.1017/S0025315404009580h Bellwood, D.R., Hughes, T.P., 2001. Regional-Scale Assembly Rules and Biodiversity of Coral Reefs. Science 292, 1532–1535. https://doi.org/10.1126/science.1058635 Bellwood, D.R., Hughes, T.P., Folke, C., Nyström, M., 2004. Confronting the coral reef crisis. Nature 429, 827–833. https://doi.org/10.1038/nature02691 Benzoni, F., Basso, D., 2011. Hydrolithon spp. (Rhodophyta, Corallinales) overgrow live corals (Cnidaria, Scleractinia) in Yemen. Marine Biology 158, 2419–2428. https://doi.org/10.1007/s00227-011-1743-2 Bergman, K.C., Öhman, M.C., Svensson, S., 2000. Influence of Habitat Structure on Pomacentrus sulfureus, A Western Indian Ocean Reef Fish. Environmental Biology of Fishes 59, 243–252. https://doi.org/10.1023/A:1007610023865 Bertzky, M., Stoll-Kleemann, S., 2009. Multi-level discrepancies with sharing data on protected areas: What we have and what we need for the global village. Journal of Environmental Management 90, 8–24. https://doi.org/10.1016/j.jenvman.2007.11.001 Beukers, J.S., Jones, G.P., 1998. Habitat complexity modifies the impact of piscivores on a coral reef fish population. Oecologia 114, 50–59. https://doi.org/10.1007/s004420050419 Beurmann, S., Ushijima, B., Videau, P., Svoboda, C.M., Smith, A.M., Rivers, O.S., Aeby, G.S., Callahan, S.M., 2017. Pseudoalteromonas piratica strain OCN003 is a coral pathogen that causes a switch from chronic to acute Montipora white syndrome in Montipora capitata. PLoS One 12. https://doi.org/10.1371/journal.pone.0188319 Beyer, H.L., Kennedy, E.V., Beger, M., Chen, C.A., Cinner, J.E., Darling, E.S., Eakin, C.M., Gates, R.D., Heron, S.F., Knowlton, N., Obura, D.O., Palumbi, S.R., Possingham, H.P., Puotinen, M., Runting, R.K., Skirving, W.J., Spalding, M., Wilson, K.A., Wood, S., Veron, J.E., Hoegh‐Guldberg, O., 2018. Risk-sensitive planning for conserving coral reefs under rapid climate change. Conservation Letters 11, e12587. https://doi.org/10.1111/conl.12587
208
Birkeland, C., Rowley, D., Randall, R.H., 1981. Coral recruitment patterns at Guam, in: Proc 4th Int Coral Reef Symp. pp. 339–344. Bista, I., Carvalho, G.R., Walsh, K., Seymour, M., Hajibabaei, M., Lallias, D., Christmas, M., Creer, S., 2017. Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity. Nat Commun 8, 14087. https://doi.org/10.1038/ncomms14087 Blaxter, M., Mann, J., Chapman, T., Thomas, F., Whitton, C., Floyd, R., Abebe, E., 2005. Defining operational taxonomic units using DNA barcode data. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 360, 1935– 1943. https://doi.org/10.1098/rstb.2005.1725 Blois, J.L., Williams, J.W., Fitzpatrick, M.C., Jackson, S.T., Ferrier, S., 2013. Space can substitute for time in predicting climate-change effects on biodiversity. PNAS 110, 9374–9379. https://doi.org/10.1073/pnas.1220228110 BMRC, 2003. Effect of El Niño on East Timor Rainfall (Dili). Bureau of Meteorology Research Centre. Boggs, G., Edyvane, K., de Carvalho, N., Penny, S., Rouwenhorst, J., Brocklehurst, P., Cowie, I., Barreto, C., Amaral, A., Monteiro, J., Pinto, P., Mau, R., Smit, N., Amaral, J., Fernandes, L., 2012. Marine and Coastal Habitat Mapping in Timor Leste (North Coast) - Final Report (Project 1 of the Timor Leste Coastal-Marine Habitat Mapping, Tourism and Fisheries Development Project). Ministry of Agriculture & Fisheries, Government of Timor Leste. Bokulich, N.A., Subramanian, S., Faith, J.J., Gevers, D., Gordon, J.I., Knight, R., Mills, D.A., Caporaso, J.G., 2013. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods 10, 57–59. https://doi.org/10.1038/nmeth.2276 BOM, 2019. Southern Hemisphere Tropical Cyclone Data Portal [WWW Document]. Bureau of Meterology Commonwealth of Australia. URL http://www.bom.gov.au/cyclone/history/tracks/ Borrelli, P., Robinson, D.A., Fleischer, L.R., Lugato, E., Ballabio, C., Alewell, C., Meusburger, K., Modugno, S., Schütt, B., Ferro, V., Bagarello, V., Oost, K.V., Montanarella, L., Panagos, P., 2017. An assessment of the global impact of 21st century land use change on soil erosion. Nature Communications 8, 2013. https://doi.org/10.1038/s41467-017-02142-7 Bouchet, P., 2006. The magnitude of marine biodiversity, in: The Exploration of Marine Biodiversity: Scientific and Technological Challenges. Fundación BBVA, Madrid, p. 32. Bouchet, P., Ng, P.K.L., Largo, D., Tan, S.H., 2009. Panglao 2004 – Investigations of the marine species richness in the Pilippines (No. 20), The Raffles Bulletin of Zoology. National University of Singapore. Bouma, G.A., Kobryn, H.T., 2004. Change in vegetation cover in East Timor, 1989–1999. Natural Resources Forum 28, 1–12. https://doi.org/10.1111/j.0165- 0203.2004.00067.x Bourlat, S.J., Borja, A., Gilbert, J., Taylor, M.I., Davies, N., Weisberg, S.B., Griffith, J.F., Lettieri, T., Field, D., Benzie, J., Glöckner, F.O., Rodríguez-Ezpeleta, N., Faith, D.P., Bean, T.P., Obst, M., 2013. Genomics in marine monitoring: New opportunities for assessing marine health status. Marine Pollution Bulletin 74, 19–31. https://doi.org/10.1016/j.marpolbul.2013.05.042 Bourne, D.G., Morrow, K.M., Webster, N.S., 2016. Insights into the Coral Microbiome: Underpinning the Health and Resilience of Reef Ecosystems. Annual Review of Microbiology 70, 317–340. https://doi.org/10.1146/annurev-micro-102215-095440
209
Bradbury, R., Reichelt, R., 1983. Fractal Dimension of a Coral Reef at Ecological Scales. Mar. Ecol. Prog. Ser. 10, 169–171. https://doi.org/10.3354/meps010169 Brodnicke, O.B., Bourne, D.G., Heron, S.F., Pears, R.J., Stella, J.S., Smith, H.A., Willis, B.L., 2019. Unravelling the links between heat stress, bleaching and disease: fate of tabular corals following a combined disease and bleaching event. Coral Reefs 38, 591–603. https://doi.org/10.1007/s00338-019-01813-9 Brown, K.T., Bender-Champ, D., Bryant, D.E.P., Dove, S., Hoegh-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, 33–40. https://doi.org/10.1016/j.jembe.2017.09.006 Brown, W.M., 1985. The mitochondrial genome of animals. Molecular evolutionary genetics. Bruce, A.J., 1976. Shrimps and prawns of coral reefs, with special reference to commensalism.(pp 37-94) In: Jones OA and Endean R (eds) Biology and Geology of Coral Reefs, Vol. 3. Academic Press, New York. Bruno, J.F., 2015. The coral disease triangle. Nature Clim Change 5, 302–303. https://doi.org/10.1038/nclimate2571 Bruno, J.F., Bertness, M.D., 2001. Positive interactions, facilitations and foundations species. Marine community ecology 201–218. Bruno, J.F., Petes, L.E., Harvell, C.D., Hettinger, A., 2003. Nutrient enrichment can increase the severity of coral diseases. Ecology Letters 6, 1056–1061. https://doi.org/10.1046/j.1461-0248.2003.00544.x Bruno, J.F., Selig, E.R., 2007. Regional Decline of Coral Cover in the Indo-Pacific: Timing, Extent, and Subregional Comparisons. PLOS ONE 2, e711. https://doi.org/10.1371/journal.pone.0000711 Bruno, J.F., Selig, E.R., Casey, K.S., Page, C.A., Willis, B.L., Harvell, C.D., Sweatman, H., Melendy, A.M., 2007. Thermal stress and coral cover as drivers of coral disease outbreaks. PLoS Biology 5, 1220–1227. https://doi.org/10.1371/journal.pbio.0050124 Bruno, J.F., Valdivia, A., 2016. Coral reef degradation is not correlated with local human population density. Sci Rep 6, 29778. https://doi.org/10.1038/srep29778 Bryant, D.E.P., Rodriguez-Ramirez, A., Phinn, S., González-Rivero, M., Brown, K.T., Neal, B.P., Hoegh-Guldberg, O., Dove, S., 2017. Comparison of two photographic methodologies for collecting and analyzing the condition of coral reef ecosystems. Ecosphere e01971. https://doi.org/10.1002/ecs2.1971 Burgett, J., 2012. Summary of Algal Community Changes Observed on the Southwest Arm of Rose Atoll from 1995-2002. USFWS, Honolulu. Burke, L., Reytar, K., Spalding, M., Perry, A., 2012. Reefs at Risk Revisited in the Coral Triangle. Burke, L., Reytar, K., Spalding, M., Perry, A., 2011. Reefs at Risk Revisited. World Resources Institute, Washington D.C. Burkepile, D.E., Hay, M.E., 2008. Herbivore species richness and feeding complementarity affect community structure and function on a coral reef. Proceedings of the National Academy of Sciences 105, 16201–16206. https://doi.org/10.1073/pnas.0801946105 Burnham, K.P., Anderson, D.R., 2004. Multimodel Inference. Sociological Methods & Research 33, 261–304. https://doi.org/10.1177/0049124104268644 Burt, J., Bartholomew, A., Bauman, A., Saif, A., Sale, P.F., 2009. Coral recruitment and early benthic community development on several materials used in the construction of artificial reefs and breakwaters. Journal of Experimental Marine Biology and Ecology 373, 72–78. https://doi.org/10.1016/j.jembe.2009.03.009
210
Buss, L.W., Jackson, J.B.C., 1981. Planktonic food availability and suspension-feeder abundance: Evidence of in situ depletion. Journal of Experimental Marine Biology and Ecology 49, 151–161. https://doi.org/10.1016/0022-0981(81)90067-8 Buss, L.W., Jackson, J.B.C., 1979. Competitive Networks: Nontransitive Competitive Relationships in Cryptic Coral Reef Environments. The American Naturalist 113, 223–234. https://doi.org/10.1086/283381 Caley, M.J., Buckley, K.A., Jones, G.P., 2001. Separating ecological effects of habitat fragmentation, degradation, and loss on coral commensals. Ecology 82, 14. Cardinoza, M., 2005. Reviving Traditional NRM Regulations (Tara Bandu) as a Community- based Approach of Protecting Carbon Stocks and Securing Livelihoods, in: Murdiyarso, D., Herawati, H. (Eds.), Carbon Forestry: Who Will Benefit?: Proceedings of Workshop on Carbon Sequestration and Sustainable Livelihoods. Presented at the Workshop on Carbon Sequestration and Sustainable Livelihoods, Center for International Forestry Research, Sindang Barang, Bogor, Indonesia. Carleton, J.H., Sammarco, P.W., 1987. Effects of Substratum Irregularity on Success of Coral Settlement: Quantification by Comparative Geomorphological Techniques. Bulletin of Marine Science 40, 85–98. Castro, P., 1976. Brachyuran Crabs Symbiotic with Scleractinian Corals: A Review of their Biology. p. 12. Cervino, J.M., Hayes, R.L., Polson, S.W., Polson, S.C., Goreau, T.J., Martinez, R.J., Smith, G.W., 2004. Relationship of Vibrio Species Infection and Elevated Temperatures to Yellow Blotch/Band Disease in Caribbean Corals. Appl. Environ. Microbiol. 70, 6855– 6864. https://doi.org/10.1128/AEM.70.11.6855-6864.2004 Cesar, H., Burke, L., Pet-Soede, L., 2003. The economics of worldwide coral reef degradation. Cesar Environmental Economics Consulting, Arnhem, and WWF- Netherlands. Cetz-Navarro, N., Espinoza-Avalos, J., Hernandez-Arana, H., Carricart-Ganivet, J., 2013. Biological Responses of the Coral Montastraea annularis to the Removal of Filamentous Turf Algae. PLoS ONE 8, 1–9. https://doi.org/10.1371/journal.pone.0054810 Chandra, A., Dargusch, P., McNamara, K.E., 2016. How might adaptation to climate change by smallholder farming communities contribute to climate change mitigation outcomes? A case study from Timor-Leste, Southeast Asia. Sustain Sci 11, 477–492. https://doi.org/10.1007/s11625-016-0361-9 Chappell, J., 1980. Coral morphology, diversity and reef growth. Nature 286, 249–252. Chappell, J., Veeh, H.H., 1978. Late Quaternary tectonic movements and sea-level changes at Timor and Atauro Island. Geological Society of America Bulletin 89, 356–368. Cheal, A.J., MacNeil, M.A., Emslie, M.J., Sweatman, H., 2017. The threat to coral reefs from more intense cyclones under climate change. Global Change Biology 23, 1511–1524. https://doi.org/10.1111/gcb.13593 Chennu, A., Faeber, P., De’ath, G., De Beer, D., Fabricius, K.E., 2017. A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats. https://doi.org/10.1038/s41598-017-07337-y Chesson, P., Huntly, N., 1997. The Roles of Harsh and Fluctuating Conditions in the Dynamics of Ecological Communities Author ( s ): Peter Chesson and Nancy Huntly Source : The American Naturalist , Vol . 150 , No . 5 ( November 1997 ), pp . 519- 553 Published by : The University of Chi. The American Naturalist 150, 519–553. Choi, D.R., Ginsburg, R.N., 1983. Distribution of coelobites (cavity-dwellers) in coral rubble across the Florida Reef Tract. Coral Reefs 2, 165–172. https://doi.org/10.1007/BF00336723
211
Chollett, I., Mumby, P.J., 2012. Predicting the distribution of Montastraea reefs using wave exposure. Coral Reefs 31, 493–503. https://doi.org/10.1007/s00338-011-0867-7 Chollett, I., Mumby, P.J., Cortés, J., 2010. Upwelling areas do not guarantee refuge for coral reefs in a warming ocean. Marine Ecology Progress Series 416, 47–56. https://doi.org/10.3354/meps08775 Cinelli, F., Fresi, E., Mazzella, L., Pansini, M., Pronzato, R., Svoboda, A., 1977. Distribution of benthic phyto-and zoocoenoses along a light gradient in a superficial marine cave, in: Biology of Benthic Organisms. Elsevier, pp. 173–183. Cinner, J.E., McClanahan, T.R., Daw, T.M., Graham, N.A.J., Maina, J., Wilson, S.K., Hughes, T.P., 2009. Linking Social and Ecological Systems to Sustain Coral Reef Fisheries. Current Biology 19, 206–212. https://doi.org/10.1016/j.cub.2008.11.055 Clarke, K.R., Gorley, R.N., 2015. Getting started with PRIMER v7. PRIMER-E, Plymouth, UK. Cleary, D.F.R., Suharsono, Hoeksema, B.W., 2006. Coral diversity across a disturbance gradient in the Pulau Seribu reef complex off Jakarta, Indonesia, in: Hawksworth, D.L., Bull, A.T. (Eds.), Marine, Freshwater, and Wetlands Biodiversity Conservation. Springer Netherlands, Dordrecht, pp. 285–306. https://doi.org/10.1007/978-1-4020- 5734-2_19 Clifton, J., 2009. Science, funding and participation: key issues for marine protected area networks and the Coral Triangle Initiative. Envir. Conserv. 36, 91–96. https://doi.org/10.1017/S0376892909990075 Coker, D.J., Pratchett, M.S., Munday, P.L., 2009. Coral bleaching and habitat degradation increase susceptibility to predation for coral-dwelling fishes. Behavioral Ecology 20, 1204–1210. https://doi.org/10.1093/beheco/arp113 Coleman, B.D., 1981. On random placement and species-area relations. Mathematical Biosciences 54, 191–215. https://doi.org/10.1016/0025-5564(81)90086-9 Coles, S.L., 1980. Species Diversity of Decapods Associated with Living and Dead Reef Coral Pocillopora meandrina. Marine Ecology Progress Series 2, 281–291. Coles, S.L., Coles, P.L., 1977. Effects of temperature on photosynthesis and respiration in hermatypic corals. Marine Biology 43, 209–216. https://doi.org/10.1007/BF00402313 Connell, J.H., 1985. The consequences of variation in initial settlement vs. post-settlement mortality in rocky intertidal communities. Journal of Experimental Marine Biology and Ecology 93, 11–45. https://doi.org/10.1016/0022-0981(85)90146-7 Connell, J.H., 1978. Diversity in Tropical Rain Forests and Coral Reefs. Science 199, 1302– 1310. Cook, D., 2000. Mission Report: Rapid Appraisal of the Small-Sacle Fisheries Sector of East Timor. Food and Agriculture Organization, Viale delle Terme di Caracalla, 00100 Rome, Italy. Costanzo, S.D., O’Donohue, M.J., Dennison, W.C., Loneragan, N.R., Thomas, M., 2001. A New Approach for Detecting and Mapping Sewage Impacts. Marine Pollution Bulletin 42, 149–156. https://doi.org/10.1016/S0025-326X(00)00125-9 Costanzo, S.D., Udy, J., Longstaff, B., Jones, A., 2005. Using nitrogen stable isotope ratios (δ15N) of macroalgae to determine the effectiveness of sewage upgrades: changes in the extent of sewage plumes over four years in Moreton Bay, Australia. Marine Pollution Bulletin, Catchment to Reef: Water Quality Issues in the Great Barrier Reef Region 51, 212–217. https://doi.org/10.1016/j.marpolbul.2004.10.018 Costin, G., Powell, B., 2006. Situation Analysis Report Timor-Leste. Australian Water Research Facility. Couch, C.S., Garriques, J.D., Barnett, C., Preskitt, L., Cotton, S., Giddens, J., Walsh, W., 2014. Spatial and temporal patterns of coral health and disease along leeward
212
Hawai’i Island. Coral Reefs 33, 693–704. https://doi.org/10.1007/s00338-014-1174- x CREP, 2018. Assessing cryptic reef diversity of colonizing marine invertebrates using autonomous reef monitoring structures (ARMS) deployed at coral reef sites in Timor- Leste from 2012-10-15 to 2014-10-19 (NCEI Accession 0169338) Version 1.1. NOAA National Centers for Environmental Information. Dataset. Coral Reef Ecosystem Program; Pacific Islands Fisheries Science Center, NOAA National Centers for Environmental Information. Dataset. Crowder, L.B., Cooper, W.E., 1982. Habitat Structural Complexity and the Interaction Between Bluegills and Their Prey. Ecology 63, 1802–1813. https://doi.org/10.2307/1940122 Cruz, R., Harasawa, H., Lal, M., Wu, S., Anokhin, Y., Pusalmaa, B., 2007. Climate change 2007: Impacts, vulnerabilities, and adaptation., in: Parry, M., Canziani, O., Palutikof, J., van der Linden, P., Hanson, C. (Eds.), Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cam, pp. 469–506. da Costa, H., da Silva, A., Freitas, J., Gomes, R., Knudsen, B., McInerney, C., Serra, A., Teixeira, J., 2002. East Timor National Human Development Report. United Nations, Development Programme, Dili. da Costa, M.D.J., Lopes, M., Ximenes, A., Ferreira, A.R., Spyckerelle, L., Williams, R., Nesbitt, H., Erskine, W., 2013. Household food insecurity in Timor-Leste. Food Sec. 5, 83–94. https://doi.org/10.1007/s12571-012-0228-6 Dai, C., 1990. Interspecific competition in Taiwanese corals with special reference to interactions between alcyonaceans and scleractinians. Mar. Ecol. Prog. Ser. 60, 291–297. https://doi.org/10.3354/meps060291 Dailer, M.L., Knox, R.S., Smith, J.E., Napier, M., Smith, C.M., 2010. Using δ15N values in algal tissue to map locations and potential sources of anthropogenic nutrient inputs on the island of Maui, Hawai‘i, USA. Marine Pollution Bulletin 60, 655–671. https://doi.org/10.1016/j.marpolbul.2009.12.021 Dailer, M.L., Ramey, H.L., Saephan, S., Smith, C.M., 2012. Algal δ15N values detect a wastewater effluent plume in nearshore and offshore surface waters and three- dimensionally model the plume across a coral reef on Maui, Hawai‘i, USA. Marine Pollution Bulletin 64, 207–213. https://doi.org/10.1016/j.marpolbul.2011.12.004 Darling, E.S., Graham, N.A.J., Januchowski-Hartley, F.A., Nash, K.L., Pratchett, M.S., Wilson, S.K., 2017. Relationships between structural complexity, coral traits, and reef fish assemblages. Coral Reefs 36, 561–575. https://doi.org/10.1007/s00338-017- 1539-z Dayrat, B., 2005. Towards integrative taxonomy. Biological Journal of the Linnean Society 85, 407–415. https://doi.org/10.1111/j.1095-8312.2005.00503.x de Goeij, J.M., van Oevelen, D., Vermeij, M.J.A., Osinga, R., Middelburg, J.J., de Goeij, A.F.P.M., Admiraal, W., 2013. Surviving in a Marine Desert: The Sponge Loop Retains Resources Within Coral Reefs. Science 342, 108–110. https://doi.org/10.1126/science.1241981 de Sousa, L.S.-C., 2001. Some Facts and Comments on the East Timor 2001 Constituent Assembly Election. Lusotopie 299–311. De’ath, G., Fabricius, K.E., Sweatman, H., Puotinen, M., 2012. The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proceedings of the National Academy of Sciences 109, 17995–17999. https://doi.org/10.1073/pnas.1208909109 De’ath, G., Lough, J.M., Fabricius, K.E., 2009. Declining Coral Calcification on the Great Barrier Reef. Science 323, 116–119. https://doi.org/10.1126/science.1165283
213
Denny, M., 1994. Extreme Drag Forces and the Survival of Wind- and Water-Swept Organisms. The Journal of experimental biology 194, 97–115. https://doi.org/citeulike-article-id:6043941 Depczynski, M., Bellwood, D.R., 2005. Wave energy and spatial variability in community structure of small cryptic coral reef fishes. Marine Ecology Progress Series 303, 283– 293. DeVantier, L., Turak, E., Allen, G., 2008. Lesser Sunda Ecoregional Planning Coral Reef Stratification Reef-and Seascapes of the Lesser Sunda Ecoregion. Report to The Nature Conservancy, Jl. Pengembak. Diaz-Pulido, G., McCook, L.J., 2004. Effects of live coral, epilithic algal communities and substrate type on algal recruitment. Coral Reefs 23, 225–233. https://doi.org/10.1007/s00338-004-0370-5 Dinsdale, E.A., Pantos, O., Smriga, S., Edwards, R.A., Angly, F., Wegley, L., Hatay, M., Hall, D., Brown, E., Haynes, M., Krause, L., Sala, E., Sandin, S.A., Thurber, R.V., Willis, B.L., Azam, F., Knowlton, N., Rohwer, F., 2008. Microbial Ecology of Four Coral Atolls in the Northern Line Islands. PLoS ONE 3, e1584. https://doi.org/10.1371/journal.pone.0001584 DNMG, BOM, CSIRO, 2015. Current and future climate of Timor-Leste. Timor-Leste National Directorate of Meteorology and Geophysics. Dollar, S., 1982. Wave Stress and Coral Community Structure in Hawaii. Coral Reefs 1, 71– 81. https://doi.org/10.1016/B978-012374473-9.00090-4 Done, T.J., 1992a. Phase shifts in coral reef communities and their ecological significance. Hydrobiologia 247, 121–132. Done, T.J., 1992b. Effects of tropical cyclone waves on ecological and geomorphological structures on the Great Barrier Reef. Continental Shelf Research 12, 859–872. https://doi.org/10.1016/0278-4343(92)90048-O Done, T.J., 1982. Patterns in the distribution of coral communities across the central Great Barrier Reef. Coral Reefs 1, 95–107. https://doi.org/10.1007/BF00301691 Dornelas, M., Connolly, S.R., Hughes, T.P., 2006. Coral reef diversity refutes the neutral theory of biodiversity. Nature 440, 80–82. https://doi.org/10.1038/nature04534 Dove, S.G., Kline, D.I., Pantos, O., Angly, F.E., Tyson, G.W., Hoegh-Guldberg, O., 2013. Future reef decalcification under a business-as-usual CO2 emission scenario. Proceedings of the National Academy of Sciences 110, 15342–15347. https://doi.org/10.1073/pnas.1302701110 Dutra, L.X.C., Taboada, M.B., 2006. Preliminary survey of the coral reefs from Beloi (Atauro Island, Timor-Leste): alternatives for sustainable use. National Directorate of Fisheries & Aquaculture, Ministry of Agriculture, Forestry and Fisheries, Timor-Leste. Eakin, C.M., Liu, G., Gomez, A.M., De La Cour, J.L., Heron, S.F., Skirving, W.J., Geiger, E.F., Marsh, B.L., Tirak, K.V., Strong, A.E., 2017. Ding, Dong, The Witch is Dead (?) - Three Years of Global Coral Bleaching 2014-2017. Reef Encounter 32, 33–38. Edgar, R.C., 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. https://doi.org/10.1093/bioinformatics/btq461 Edgar, R.C., Flyvbjerg, H., 2015. Error filtering, pair assembly and error correction for next- generation sequencing reads. Bioinformatics 31, 3476–3482. https://doi.org/10.1093/bioinformatics/btv401 Edwards, A., Emberton, H., 1980. Crustacea associated with the scleractinian coral, Stylophora pistillata (Esper), in the Sudanese Red Sea. Journal of Experimental Marine Biology and Ecology 42, 225–240. https://doi.org/10.1016/0022- 0981(80)90178-1
214
Edyvane, K., de Carvalho, N., Penny, S., Fernandes, A., de Cunha, C., Amaral, A., Mendes, M., Pinto, P., 2012. Conservation Values, Issues and Planning in the Nino Konis Santana Marine Park, Timor-Leste - Final Report (Project 4 of the Timor Leste Coastal-Marine Habitat Mapping, Tourism and Fisheries Development Project). Ministry of Agriculture & Fisheries, Government of Timor Leste. Elbrecht, V., Vamos, E.E., Meissner, K., Aroviita, J., Leese, F., 2017. Assessing strengths and weaknesses of DNA metabarcoding‐based macroinvertebrate identification for routine stream monitoring. Methods Ecol Evol 8, 1265–1275. https://doi.org/10.1111/2041-210X.12789 Elton, C.S., 1958. The Biology of Invasions by Animals and Plants. John Wiley and Sons, New York. Emslie, M.J., Cheal, A.J., Johns, K.A., 2014. Retention of Habitat Complexity Minimizes Disassembly of Reef Fish Communities following Disturbance: A Large-Scale Natural Experiment. PLOS ONE 9, e105384. https://doi.org/10.1371/journal.pone.0105384 Emslie, M.J., Cheal, A.J., Sweatman, H., Delean, S., 2008. Recovery from disturbance of coral and reef fish communities on the Great Barrier Reef, Australia. Marine Ecology Progress Series 371, 177–190. https://doi.org/10.3354/meps07657 English, S., Wilkinson, C., Baker, V., 1997. Survey manual for tropical marine resources. Enochs, I.C., 2012. Motile cryptofauna associated with live and dead coral substrates: implications for coral mortality and framework erosion. Mar Biol 159, 709–722. https://doi.org/10.1007/s00227-011-1848-7 Enochs, I.C., 2010. Motile cryptofauna of an eastern Pacific coral reef: biodiversity and trophic contribution. Open Access Dissertations 497, Paper 497. Enochs, I.C., Hockensmith, G., 2008. Effects of coral mortality on the community composition of cryptic metazoans associated with Pocillopora damicornis., in: Proc 11th Int Coral Reef Symp. Vol. 26. Enochs, I.C., Manzello, D.P., 2012a. Species richness of motile cryptofauna across a gradient of reef framework erosion. Coral Reefs 31, 653–661. https://doi.org/10.1007/s00338-012-0886-z Enochs, I.C., Manzello, D.P., 2012b. Responses of Cryptofaunal Species Richness and Trophic Potential to Coral Reef Habitat Degradation. Diversity 4, 94–104. https://doi.org/10.3390/d4010094 Enochs, I.C., Toth, L.T., Brandtneris, V.W., Afflerbach, J.C., Manzello, D.P., 2011. Environmental determinants of motile cryptofauna on an eastern Pacific coral reef. Marine Ecology Progress Series 438, 105–118. https://doi.org/10.3354/meps09259 Erdmann, M.V., Mohan, C. (Eds.), 2013. A rapid marine biological assessment of Timor- Leste, RAP Bulletin of Biological Assessment 66. Coral Triangle Support Partnership, Conservation International, Dili, Timor-Leste. Evans, M.C., 2015. Clear consideration of costs, condition and conservation benefits yields better planning outcomes. Biological Conservation 12. Eyre, B., 1994. Nutrient Biogeochemistry in the Tropical Moresby River Estuary System North Queensland, Australia. Estuarine, Coastal and Shelf Science 39, 15–31. Fabricius, K.E., 2005. Effects of terrestrial runoff on the ecology of corals and coral reefs: Review and synthesis. Marine Pollution Bulletin 50, 125–146. https://doi.org/10.1016/j.marpolbul.2004.11.028 Fabricius, K.E., 1995. Slow population turnover in the soft coral genera Sinularia and Sarcophyton on mid- and outer-shelf reefs of the Great Barrier Reef. Mar. Ecol. Prog. Ser. 126, 145–152. https://doi.org/10.3354/meps126145 Fabricius, K.E., De’ath, G., Noonan, S.H.C., Uthicke, S., 2014. Ecological effects of ocean acidification and habitat complexity on reef-associated macroinvertebrate
215
communities. Proceedings of the Royal Society B: Biological Sciences 281, 20132479. https://doi.org/10.1098/rspb.2013.2479 FAO, 2010. Global Forest Resource Assessment 2010 (No. 163). Food and Agriculture Ogranization. Fautin, D., Dalton, P., Incze, L.S., Leong, J.-A.C., Pautzke, C., Rosenberg, A., Sandifer, P., Sedberry, G., Tunnell, J.W., Abbott, I., Brainard, R.E., Brodeur, M., Eldredge, L.G., Feldman, M., Moretzsohn, F., Vroom, P.S., Wainstein, M., Wolff, N., 2010. An Overview of Marine Biodiversity in United States Waters. PLoS ONE 5, e11914. https://doi.org/10.1371/journal.pone.0011914 Feely, R.A., Sabine, C.L., Hernandez-Ayon, J.M., Ianson, D., Hales, B., 2008. Evidence for Upwelling of Corrosive “Acidified” Water onto the Continental Shelf. Science 320, 1490–1492. https://doi.org/10.1126/science.1155676 Ferrari, R., Bryson, M., Bridge, T., Hustache, J., Williams, S.B., Byrne, M., Figueira, W., 2016. Quantifying the response of structural complexity and community composition to environmental change in marine communities. Global Change Biology 22, 1965– 1975. https://doi.org/10.1111/gcb.13197 Field, S.N., Glassom, D., Bythell, J., 2007. Effects of artificial settlement plate materials and methods of deployment on the sessile epibenthic community development in a tropical environment. Coral Reefs 26, 279–289. https://doi.org/10.1007/s00338-006- 0191-9 Finckh, A.E., 1904. Biology of the reef-forming organisms at Funafuti Atoll, in: The Atoll of Funafuti. Royal Society London, pp. 125–150. Firstater, F.N., Hidalgo, F.J., Lomovasky, B.J., Tarazona, J., Flores, G., Iribarne, O.O., 2010. Coastal upwelling may overwhelm the effect of sewage discharges in rocky intertidal communities of the Peruvian coast. Mar. Freshwater Res. 61, 309. https://doi.org/10.1071/MF09102 Fitzhardinge, R.C., Bailey-Brock, J.H., 1989. Colonization of Artificial Reef Materials by Corals and Other Sessile Organisms. Bulletin of Marine Science 44, 567–579. Folmer, O., Black, M., Hoeh, W., Lutz, R., Vrijenhoek, R., 1994. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology 3, 294–299. Fonseca, A.C., Dean, H.K., Cortés, J., 2006. Non-colonial coral macro-borers as indicators of coral reef status in the South Pacific of Costa Rica. Rev. Biol. Trop. 54, 101–115. Fourqurean, J.W., Moore, T.O., Fry, B., Hollibaugh, J.T., 1997. Spatial and temporal variation in C:N:P ratios, δ15N, and δ13C of eelgrass Zostera marina as indicators of ecosystem processes, Tomales Bay, California, USA. Marine Ecology Progress Series 157, 147–157. Fox, H.E., Caldwell, R.L., 2006. Recovery from blast fishing on coral reefs: a tale of two scales. Ecological Applications 16, 1631–1635. https://doi.org/10.1890/1051- 0761(2006)016[1631:RFBFOC]2.0.CO;2 Fox, J., Weisberg, S., 2019. An {R} Companion to Applied Regression. Sage, Thousand Oaks, CA. France, S.C., Hoover, L.L., 2002. DNA sequences of the mitochondrial COI gene have low levels of divergence among deep-sea octocorals (Cnidaria: Anthozoa). Hydrobiologia 471, 149–155. https://doi.org/10.1023/a:1016517724749 Fraschetti, S., Giangrande, A., Terlizzi, A., Boero, F., 2002. Pre- and post-settlement events in benthic community dynamics. Oceanologica Acta 25, 285–295. https://doi.org/10.1016/S0399-1784(02)01194-5
216
Fraser, S.B., Sedberry, G.R., 2008. Reef Morphology and Invertebrate Distribution at Continental Shelf Edge Reefs in the South Atlantic Bight. sena 7, 191–206. https://doi.org/10.1656/1528-7092(2008)7[191:RMAIDA]2.0.CO;2 Freestone, A.L., Osman, R.W., Ruiz, G.M., Torchin, M.E., 2011. Stronger predation in the tropics shapes species richness patterns in marine communities. Ecology 92, 983– 993. https://doi.org/10.1890/09-2379.1 Friedlander, A.M., Brown, E.K., Jokiel, P.L., Smith, W.R., Rodgers, K.S., 2003. Effects of habitat, wave exposure, and marine protected area status on coral reef fish assemblages in the Hawaiian archipelago. Coral Reefs 22, 291–305. https://doi.org/10.1007/s00338-003-0317-2 Friedlander, A.M., Parrish, J.D., 1998. Habitat characteristics affecting fish assemblages on a Hawaiian coral reef. Journal of Experimental Marine Biology and Ecology 224, 1– 30. https://doi.org/10.1016/S0022-0981(97)00164-0 Fry, B., Baltz, D.M., Benfield, M.C., Fleeger, J.W., Gace, A., Haas, H.L., Quiñones-Rivera, Z.J., 2003. Stable isotope indicators of movement and residency for brown shrimp (Farfantepenaeus aztecus) in coastal Louisiana marshscapes. Estuaries 26, 82–97. https://doi.org/10.1007/BF02691696 Gardner, T.A., Côté, I.M., Gill, J.A., Grant, A., Watkinson, A.R., 2005. Hurricanes and Caribbean coral reefs: impacts, recovery patterns, and role in long-term decline. Ecology 86, 174–184. https://doi.org/10.1890/04-0141 Gardner, T.A., Côté, I.M., Gill, J.A., Grant, A., Watkinson, A.R., 2003. Long-term region- wide declines in Caribbean corals. Science (New York, N.Y.) 301, 958–60. https://doi.org/10.1126/science.1086050 Garpe, K.C., Yahya, S.A.S., Lindahl, U., Öhman, M.C., 2006. Long-term effects of the 1998 coral bleaching event on reef fish assemblages. Marine Ecology Progress Series 315, 237–247. https://doi.org/10.3354/meps315237 Gartner, A., Lavery, P., Smit, A.J., 2002. Use of δ15N signatures of different functional forms of macroalgae and filter-feeders to reveal temporal and spatial patterns in sewage dispersal. Marine Ecology Progress Series 235, 63–73. https://doi.org/10.3354/meps235063 Geller, J., Meyer, C., Parker, M., Hawk, H., 2013. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Molecular Ecology Resources 13, 851–861. https://doi.org/10.1111/1755-0998.12138 Gillett, R., 2009. Risheries in the economies of the Pacific island countries and territories. Asia Development Bank. Gischler, E., Ginsburg, R.N., 1996. Cavity dwellers (coelobites) under coral rubble in southern Belize barrier and atoll reefs. Bulletin of Marine Science 58, 570–589. Glynn, P.W., 1996. Coral reef bleaching: Facts, hypotheses and implications. Global Change Biology 2, 495–509. https://doi.org/10.1111/j.1365-2486.1996.tb00063.x Godfrey, J.S., 1989. A sverdrup model of the depth-integrated flow for the world ocean allowing for island circulations. Geophysical & Astrophysical Fluid Dynamics 45, 89– 112. https://doi.org/10.1080/03091928908208894 Golbuu, Y., Fabricius, K., Victor, S., Richmond, R.H., 2008. Gradients in coral reef communities exposed to muddy river discharge in Pohnpei, Micronesia. Estuarine, Coastal and Shelf Science 76, 14–20. https://doi.org/10.1016/j.ecss.2007.06.005 Golbuu, Y., van Woesik, R., Richmond, R.H., Harrison, P., Fabricius, K.E., 2011. River discharge reduces reef coral diversity in Palau. Marine Pollution Bulletin 62, 824– 831. https://doi.org/10.1016/j.marpolbul.2010.12.015
217
González-Rivero, M., Beijbom, O., Rodriguez-Ramirez, A., Bryant, D.E.P., Ganase, A., Gonzalez-Marrero, Y., Herrera-Reveles, A., Kennedy, E.V., Kim, C.J.S., Lopez- Marcano, S., Markey, K., Neal, B.P., Osborne, K., Reyes-Nivia, C., Sampayo, E.M., Stolberg, K., Taylor, A., Vercelloni, J., Wyatt, M., Hoegh-Guldberg, O., 2020. Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach. Remote Sensing 12, 489. https://doi.org/10.3390/rs12030489 González-Rivero, M., Beijbom, O., Rodriguez-Ramirez, A., Holtrop, T., González-Marrero, Y., Ganase, A., Roelfsema, C., Phinn, S., Hoegh-Guldberg, O., 2016. Scaling up Ecological Measurements of Coral Reefs Using Semi-Automated Field Image Collection and Analysis. Remote Sensing 8, 30. https://doi.org/10.3390/rs8010030 González-Rivero, M., Bongaerts, P., Beijbom, O., Pizarro, O., Friedman, A., Rodriguez- Ramirez, A., Upcroft, B., Laffoley, D., Kline, D., Bailhache, C., Vevers, R., Hoegh- Guldberg, O., 2014. The Catlin Seaview Survey - kilometre-scale seascape assessment, and monitoring of coral reef ecosystems. Aquatic Conservation: Marine and Freshwater Ecosystems 24, 184–198. https://doi.org/10.1002/aqc.2505 Gordon, A.L., 2005. The Indonesian Seas. Oceanography 18, 14–27. Gordon, A.L., Sprintall, J., Van Aken, H.M., Susanto, D., Wijffels, S., Molcard, R., Field, A., Pranowo, W., Wirasantosa, S., 2010. The Indonesian throughflow during 2004–2006 as observed by the INSTANT program. Dynamics of Atmospheres and Oceans 50, 115–128. https://doi.org/10.1016/j.dynatmoce.2009.12.002 Goreau, T., McClanahan, T., Hayes, R., Strong, A., 2000. Conservation of Coral Reefs after the 1998 Global Bleaching Event. Conservation Biology 14, 5–15. Goreau, T.F., 1959. The physiology of skeleton formation in corals. I. A method for measuring the rate of calcium deposition by corals under different conditions. The Biological Bulletin 116, 59–75. https://doi.org/10.2307/1539156 Goreau, T.F., Hartman, W.D., 1963. Boring sponges as controlling factors in the formation and maintenance of coral reefs. Gotelli, N.J., Colwell, R.K., 2001. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters 4, 379–391. https://doi.org/10.1046/j.1461-0248.2001.00230.x Graham, N.A.J., Jennings, S., MacNeil, M.A., Mouillot, D., Wilson, S.K., 2015. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518, 94– 97. https://doi.org/10.1038/nature14140 Graham, N.A.J., Nash, K.L., 2013. The importance of structural complexity in coral reef ecosystems. Coral Reefs 32, 315–326. https://doi.org/10.1007/s00338-012-0984-y Graham, N.A.J., Wilson, S.K., Jennings, S., Polunin, N.V.C., Bijoux, J.P., Robinson, J., 2006. Dynamic fragility of oceanic coral reef ecosystems. PNAS 103, 8425–8429. https://doi.org/10.1073/pnas.0600693103 Grantham, H., 2010. National Ecological Gap Assessment for Timor-Leste 2010. United Nations Development Programme and the Department of Protected Areas and National Parks of Timor-Leste by CNTM Solutions Pty Ltd, Byron Bay, New South Wales. Grassle, J.F., Jones, O.A., Endean, R., 1973. Variety in coral reef communities. Biology and geology of coral reefs 2, 247–270. Grassle, J.F., Maciolek, N.J., 1992. Deep-sea species richness: regional and local diversity estimates from quantitative bottom samples. The American Naturalist 139, 313–341. Graus, R.R., Chamberlain, J.A., Boker, A.M., 1977. Structural Modification of Corals in Relation to Waves and Currents: Reef Biota, in: Frost, S.H., Weiss, M.P., Saunders, J.B. (Eds.), Reefs and Related Carbonates - Ecology and Sedimentology. Am Ass Petr Geol, Tulsa, Oklahoma, pp. 135–153.
218
Graus, R.R., Macintyre, I.G., 1989. The zonation patterns of Caribbean coral reefs as controlled by wave and light energy input, bathymetric setting and reef morphology: computer simulation experiments. Coral Reefs 8, 9–18. https://doi.org/10.1007/BF00304687 Green, E.P., Bruckner, A.W., 2000. The significance of coral disease epizootiology for coral reef conservation. Biological Conservation 96, 347–361. https://doi.org/10.1016/S0006-3207(00)00073-2 Grigg, R.W., Dollar, S.J., 2005. Reassessing U.S. Coral Reefs. Science 308, 5. https://doi.org/10.1126/science.308.5729.1740c Guidetti, P., Sala, E., 2007. Community-wide effects of marine reserves in the Mediterranean Sea. Mar. Ecol. Prog. Ser. 335, 43–56. https://doi.org/10.3354/meps335043 Guillemot, N., Chabanet, P., Kulbicki, M., Vigliola, L., Léopold, M., Jollit, I., Le Pape, O., 2014. Effects of fishing on fish assemblages in a coral reef ecosystem: From functional response to potential indicators. Ecological Indicators 43, 227–235. https://doi.org/10.1016/j.ecolind.2014.02.015 Guzmán, H.M., Holst, I., 1993. Effects of chronic oil-sediment pollution on the reproduction of the Caribbean reef coral Siderastrea siderea. Marine Pollution Bulletin 26, 276– 282. https://doi.org/10.1016/0025-326X(93)90068-U Haapkylä, J., Seymour, A.S., Barneah, O., Brickner, I., Hennige, S., Suggett, D., 2009a. Association of Waminoa sp. (Acoela) with corals in the Wakatobi Marine Park , South- East Sulawesi, Indonesia 1021–1027. https://doi.org/10.1007/s00227-009-1145-x Haapkylä, J., Seymour, A.S., Trebilco, J., Smith, D., 2007. Coral disease prevalence and coral health in the Wakatobi Marine Park , south-east Sulawesi , Indonesia 403–414. https://doi.org/10.1017/S0025315407055828 Haapkylä, J., Unsworth, R.K.F., Seymour, A.S., Jessica, M.T., Flavell, M., Willis, B.L., Smith, D.J., 2009b. Spatio-temporal coral disease dynamics in the Wakatobi Marine National Park, South-East Sulawesi, Indonesia. Diseases of Aquatic Organisms 87, 105–115. https://doi.org/10.3354/dao02160 Hadley Wickham, 2016. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, New York. Hall, R., 2001. Cenozoic reconstructions of SE Asia and the SW Pacific: changing patterns of land and sea. Faunal and floral migrations and evolution in SE Asia–Australasia 35–56. Harborne, A.R., Rogers, A., Bozec, Y.-M., Mumby, P.J., 2017. Multiple Stressors and the Functioning of Coral Reefs. Annu. Rev. Mar. Sci. 9, 445–468. https://doi.org/10.1146/annurev-marine-010816-060551 Harriott, V.J., Fisk, D.A., 1987. A comparison of settlement plate types for experiments on the recruitment of scleractinian corals. Mar Ecol Prog Ser 37, 201–208. Harvell, C.D., Jordan-Dahgren, E., Merkel, S., Rosenberg, E., Raymundo, L., Smith, G., Weil, E., Willis, B., 2007. Coral Disease, Environmental Drivers, and the Balance Between. Oceanography 20, 172–195. Harvell, C.D., Kim, K., Burkholder, J.M., Colwell, R.R., Epstein, P.R., Grimes, D.J., Hofmann, E.E., Lipp, E.K., Osterhaus, A.D., Overstreet, R.M., Porter, J.W., Smith, G.W., Vasta, G.R., 1999. Emerging marine diseases–climate links and anthropogenic factors. Science 285, 1505–10. Harvell, C.D., Mitchell, C.E., Ward, J.R., Altizer, S., Dobson, A.P., Ostfeld, R.S., Samuel, M.D., 2002. Climate warming and disease risks for terrestrial and marine biota. Science 296, 2158–62. https://doi.org/10.1126/science.1063699
219
Harvell, C.D., Montecino-Latorre, D., Caldwell, J.M., Burt, J.M., Bosley, K., Keller, A., Heron, S.F., Salomon, A.K., Lee, L., Pontier, O., Pattengill-Semmens, C., Gaydos, J.K., 2019. Disease epidemic and a marine heat wave are associated with the continental- scale collapse of a pivotal predator (Pycnopodia helianthoides). Sci. Adv. 5, eaau7042. https://doi.org/10.1126/sciadv.aau7042 Hearn, C., Atkinson, M., Falter, J., 2001. A physical derivation of nutrient-uptake rates in coral reefs: effects of roughness and waves. Coral Reefs 20, 347–356. https://doi.org/10.1007/s00338-001-0185-6 Heaton, T.H.E., 1986. Isotopic Studies of nitrogen pollution in the hydroshpere and atomosphere: a review. Isotope Geoscience Section 59, 87–102. Hebert, P.D.N., Cywinska, A., Ball, S.L., de Waard, J.R., 2003a. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B 270, 313–321. https://doi.org/10.1098/rspb.2002.2218 Hebert, P.D.N., Ratnasingham, S., de Waard, J.R., 2003b. Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proc. R. Soc. Lond. B 270. https://doi.org/10.1098/rsbl.2003.0025 Heck, K.L., Wetstone, G.S., 1977. Habitat Complexity and Invertebrate Species Richness and Abundance in Tropical Seagrass Meadows. Journal of Biogeography 4, 135– 142. https://doi.org/10.2307/3038158 Heenan, A., Williams, I.D., 2013. Monitoring Herbivorous Fishes as Indicators of Coral Reef Resilience in American Samoa. PLoS One 8. https://doi.org/10.1371/journal.pone.0079604 Heron, S.F., Eakin, C.M., Maynard, J.A., van Hooidonk, R., 2016a. 3.8 Impacts and effects of ocean warming on coral reefs. Heron, S.F., Maynard, J.A., van Hooidonk, R., Eakin, C.M., 2016b. Warming Trends and Bleaching Stress of the World’s Coral Reefs 1985–2012. Sci Rep 6, 38402. https://doi.org/10.1038/srep38402 Heron, S.F., Willis, B.L., Skirving, W.J., Eakin, C.M., Page, C.A., Miller, I.R., 2010. Summer hot snaps and winter conditions: modelling white syndrome outbreaks on Great Barrier Reef corals. PloS one 5, e12210. https://doi.org/10.1371/journal.pone.0012210 Hill, J.K., Wheeler, P.A., 2002. Organic carbon and nitrogen in the northern California current system: comparison of offshore, river plume, and coastally upwelled waters. Progress in Oceanography, Physical and Biological Conditions and Processes in the Northeasts Pacific Ocean 53, 369–387. https://doi.org/10.1016/S0079- 6611(02)00037-X Hixon, M.A., Beets, J.P., 1993. Predation, Prey Refuges, and the Structure of Coral-Reef Fish Assemblages. Ecological Monographs 63, 77–101. https://doi.org/10.2307/2937124 Hobbs, J.-P.A., Frisch, A.J., Newman, S.J., Wakefield, C.B., 2015. Selective Impact of Disease on Coral Communities: Outbreak of White Syndrome Causes Significant Total Mortality of Acropora Plate Corals. PLoS ONE 10, e0132528. https://doi.org/10.1371/journal.pone.0132528 Hoegh-Guldberg, O., 1999. Climate change, coral bleaching and the future of the world’s coral reefs. Mar. Freshwater Res. 50, 839–866. https://doi.org/10.1071/mf99078 Hoegh-Guldberg, O., Bruno, J.F., 2010. The impact of climate change on the world’s marine ecosystems. Science (New York, N.Y.) 328, 1523–8. https://doi.org/10.1126/science.1189930 Hoegh-Guldberg, O., Fine, M., 2004. Low temperatures cause coral bleaching. Coral Reefs 23, 444–444. https://doi.org/10.1007/s00338-004-0401-2
220
Hoegh-Guldberg, O., Kennedy, E.V., Beyer, H.L., McClennen, C., Possingham, H.P., 2018. Securing a Long-term Future for Coral Reefs. Trends in Ecology & Evolution 33, 936– 944. https://doi.org/10.1016/j.tree.2018.09.006 Hoegh-Guldberg, O., Mumby, P.J., Hooten, A.J., Steneck, R.S., Greenfield, P., Gomez, E., Harvell, H., C. Drew, Sale, P.F., Edwards, A.J., Caldeira, K., Knowlton, N., Eakin, C.M., Iglesias-Prieto, R., Muthiga, N., Bradbury, R.H., Dubi, A., Hatziolos, M.E., 2007. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737– 42. https://doi.org/10.1126/science.1152509 Hoegh-Guldberg, O., Smith, G.J., 1989. The effect of sudden changes in temperature, light and salinity on the population density and export of zooxanthellae from the reef corals Stylophora pistillata Esper and Seriatopora hystrix Dana. Journal of Experimental Marine Biology and Ecology 129, 279–303. https://doi.org/10.1016/0022- 0981(89)90109-3 Hoeksema, B.W., Farenzena, Z., 2012. Tissue loss in corals infested by acoelomorph flatworms (Waminoa sp.). Coral Reefs 31, 869. https://doi.org/10.1007/s00338-012- 0919-7 Holling, C.S., 1992. Cross-Scale Morphology, Geometry, and Dynamics of Ecosystems. Ecological Monographs 62, 447–502. https://doi.org/10.2307/2937313 Holling, C.S., 1973. Resilience and Stability of Ecological Systems. Annual Review of Ecology and Systematics 4, 1–23. https://doi.org/10.1146/annurev.es.04.110173.000245 Hortal, J., Borges, P.A.V., Gaspar, C., 2006. Evaluating the performance of species richness estimators: sensitivity to sample grain size. Journal of Animal Ecology 75, 274–287. https://doi.org/10.1111/j.1365-2656.2006.01048.x Hothorn, T., Bretz, F., Westfall, P., 2008. Simultaneous Inference in General Parametric Models. Biometrical Journal 50, 346–363. Huang, D., Meier, R., Todd, P.A., Chou, L.M., 2008. Slow mitochondrial COI sequence evolution at the base of the metazoan tree and its implications for DNA barcoding. Journal of Molecular Evolution 66, 167–174. https://doi.org/10.1007/s00239-008- 9069-5 Huang, H., Li, X.B., Titlyanov, E.A., Ye, C., Titlyanova, T.V., Guo, Y.P., Zhang, J., 2013. Linking macroalgal δ15N-values to nitrogen sources and effects of nutrient stress on coral condition in an upwelling region. Botanica Marina 56. https://doi.org/10.1515/bot-2012-0223 Hughes, T.P., 1994. Shifts , Catastrophes , Phase Large-Scale Degradation Reef Coral Caribbean. Science 265, 1547–1551. Hughes, T.P., Anderson, K.D., Connolly, S.R., Heron, S.F., Kerry, J.T., Lough, J.M., Baird, A.H., Baum, J.K., Berumen, M.L., Bridge, T.C., Claar, D.C., Eakin, C.M., Gilmour, J.P., Graham, N.A.J., Harrison, H., Hobbs, J.-P.A., Hoey, A.S., Hoogenboom, M., Lowe, R.J., McCulloch, M.T., Pandolfi, J.M., Pratchett, M., Schoepf, V., Torda, G., Wilson, S.K., 2018. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83. https://doi.org/10.1126/science.aan8048 Hughes, T.P., Baird, A.H., Bellwood, D.R., Card, M., Connolly, S.R., Folke, C., Grosberg, R., Hoegh-Guldberg, O., Jackson, J.B.C., Kleypas, J., Lough, J.M., Marshall, P., Nyström, M., Palumbi, S.R., Pandolfi, J.M., Rosen, B., Roughgarden, J., 2003. Climate Change, Human Impacts, and the Resilience of Coral Reefs. Science, New Series 301, 929–933. Hughes, T.P., Barnes, M.L., Bellwood, D.R., Cinner, J.E., Cumming, G.S., Jackson, J.B.C., Kleypas, J., van de Leemput, I.A., Lough, J.M., Morrison, T.H., Palumbi, S.R., van
221
Nes, E.H., Scheffer, M., 2017a. Coral reefs in the Anthropocene. Nature 546, 82–90. https://doi.org/10.1038/nature22901 Hughes, T.P., Connell, J.H., 1999. Multiple Stressors on Coral Reefs: A Long-Term Perspective. Limnology and Oceanography 44, 932–940. Hughes, T.P., Kerry, J.T., Álvarez-Noriega, M., Álvarez-Romero, J.G., Anderson, K.D., Baird, A.H., Babcock, R.C., Beger, M., Bellwood, D.R., Berkelmans, R., Bridge, T.C., Butler, I.R., Byrne, M., Cantin, N.E., Comeau, S., Connolly, S.R., Cumming, G.S., Dalton, S.J., Diaz-Pulido, G., Eakin, C.M., Figueira, W.F., Gilmour, J.P., Harrison, H.B., Heron, S.F., Hoey, A.S., Hobbs, J.P.A., Hoogenboom, M.O., Kennedy, E.V., Kuo, C.Y., Lough, J.M., Lowe, R.J., Liu, G., McCulloch, M.T., Malcolm, H.A., McWilliam, M.J., Pandolfi, J.M., Pears, R.J., Pratchett, M.S., Schoepf, V., Simpson, T., Skirving, W.J., Sommer, B., Torda, G., Wachenfeld, D.R., Willis, B.L., Wilson, S.K., 2017b. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377. https://doi.org/10.1038/nature21707 Hunt, H.L., Scheibling, R.E., 1997. Role of early post-settlement mortality in recruitment of benthic marine invertebrates. Marine Ecology Progress Series 155, 269–301. Hurley, K.K.C., Timmers, M.A., Godwin, L.S., Copus, J.M., Skillings, D.J., Toonen, R.J., 2016. An assessment of shallow and mesophotic reef brachyuran crab assemblages on the south shore of O‘ahu, Hawai‘i. Coral Reefs 35, 103–112. https://doi.org/10.1007/s00338-015-1382-z Hutchings, P.A., 1986. Biological destruction of coral reefs. Coral Reefs 4, 239–252. Hutchings, P.A., 1985. Cryptofaunal communities of coral reefs. Acta Oceanol Sin 5, 603– 613. Hutchings, P.A., Weate, P.D., 1977. Distribution and abundance of cryptofauna from Lizard Island, Great Barrier Reef. Marine Research in Indonesia 17, 99–112. Idjadi, J.A., Edmunds, E., Peter J., 2006. Scleractinian corals as facilitators for other invertebrates on a Caribbean reef. Mar. Ecol. Prog. Ser. 319, 117–127. https://doi.org/10.3354/meps319117 IPCC, 2018. Global Warming of 1.5°C Summary for Policymakers. Jackson, J.B.C., 2001. Historical Overfishing and the Recent Collapse of Coastal Ecosystems. Science 293, 629–637. https://doi.org/10.1126/science.1059199 Jackson, J.B.C., 1985. Distribution and ecology of clonal and aclonal benthic invertebrates., in: Buss, L.W., Jackson, J.B.C., Cook, R.E. (Eds.), Population Biology and Evolution of Clonal Organisms. Yale University Press, New Haven, CT, pp. 297–355. Jackson, J.B.C., Goreau, T.F., Hartman, W.D., 1971. Recent Brachiopod-Coralline Sponge Communities and Their Paleoecological Significance. Science, New Series 173, 623–625. Jackson, J.B.C., Winston, J.E., 1982. Ecology of cryptic coral reef communities. I. Distribution and abundance of major groups of encrusting organisms. Journal of Experimental Marine Biology and Ecology 57, 135–147. https://doi.org/10.1016/0022-0981(82)90188-5 Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T., 2014. Caffe, in: Proceedings of the ACM International Conference on Multimedia - MM ’14. ACM Press, New York, New York, USA, pp. 675–678. https://doi.org/10.1145/2647868.2654889 JICA, 2010. The Study on Community-based Integrated Watershed Management in Laclo and Comoro River Basins in the Democratic Republic of Timor-Leste. Japan International Cooperation Agency. http://www.royalcommission.vic.gov.au/finaldocuments/summary/PF/VBRC_Summ ary_PF.pdf
222
Johan, O., Bengen, D.G., Zamani, N.P., Suharsono, Sweet, M.J., 2015. The Distribution and Abundance of Black Band Disease and White Syndrome in Kepulauan Seribu, Indonesia. HAYATI Journal of Biosciences 22, 105–112. https://doi.org/10.1016/j.hjb.2015.09.001 Jokiel, P.L., 1978. Effects of water motion on reef corals. Journal of Experimental Marine Biology and Ecology 35, 87–97. https://doi.org/10.1016/0022-0981(78)90092-8 Jokiel, P.L., Brown, E.K., Friedlander, A., Rodgers, S.K., Smith, W.R., 2004. Hawai’i Coral Reef Assessment and Monitoring Program: Spatial Patterns and Temporal Dynamics in Reef Coral Communities. Pacific Science 58, 159–174. https://doi.org/10.1353/psc.2004.0018 Jokiel, P.L., Coles, S.L., 1977. Effects of Temperature on the Mortality and Growth of Hawaiian Reef Corals. Marine Biology 43, 201–208. https://doi.org/10.1007/BF00402312 Jones, C.G., Lawton, J.H., Shachak, M., 1996. Organisms as Ecosystem Engineers, in: Samson, F.B., Knopf, F.L. (Eds.), Ecosystem Management: Selected Readings. Springer, New York, NY, pp. 130–147. https://doi.org/10.1007/978-1-4612-4018- 1_14 Jones, C.M., Grutter, A.S., 2008. Reef-based micropredators reduce the growth of post- settlement damselfish in captivity. Coral Reefs 27, 677–684. https://doi.org/10.1007/s00338-008-0383-6 Jouffray, J.B., Nyström, M., Norström, A. V., Williams, I.D., Wedding, L.M., Kittinger, J.N., Williams, G.J., 2015. Identifying multiple coral reef regimes and their drivers across the Hawaiian archipelago. Philosophical Transactions of the Royal Society B: Biological Sciences 370, 1–8. https://doi.org/10.1098/rstb.2013.0268 Kaczmarsky, L., Richardson, L.L., 2010. Do elevated nutrients and organic carbon on Philippine reefs increase the prevalence of coral disease? Coral Reefs 30, 253–257. https://doi.org/10.1007/s00338-010-0686-2 Kaczmarsky, L.T., 2006. Coral disease dynamics in the central Philippines. Diseases of Aquatic Organisms 69, 9–21. https://doi.org/10.3354/dao069009 Kantor Statistik, 1997. East Timor Dalam Angaka (in Figures). Propinsi Timor Timur. Karig, D.E., Barber, A.J., Charlton, T.R., Klemperer, S., Hussong, D.M., 1987. Nature and distribution of deformation across the Banda Arc–Australian collision zone at Timor. Geological Society of America Bulletin 98, 18–32. Keats, D.W., Chamberlain, Y.M., Baba, M., 1997. a Widespread Indo-Pacific Non- Geniculate Coralline Alga that Overgrows and Kills Live Coral 40, 263–279. Keefer, G.D., 2000. Report on Restoration of Meteorological Network-Timor Loro Sae. United Nation Translation Administration in East Timor. Keep, M., Barber, L., Haig, D., 2009. Deformation of the Cablac Mountain Range, East Timor: An overthrust stack derived from an Australian continental terrace. Journal of Asian Earth Sciences 35, 150–166. Kench, P.S., Mann, T., 2017. Reef Island Evolution and Dynamics: Insights from the Indian and Pacific Oceans and Perspectives for the Spermonde Archipelago. Front. Mar. Sci. 4. https://doi.org/10.3389/fmars.2017.00145 Kennedy, E.V., Ordoñez, A., Lewis, B.E., Diaz-Pulido, G., 2017. Comparison of recruitment tile materials for monitoring coralline algae responses to a changing climate. Mar. Ecol. Prog. Ser. 569, 129–144. https://doi.org/10.3354/meps12076 Kennedy, E.V., Perry, C.T., Halloran, P.R., Iglesias-Prieto, R., Schönberg, C.H.L., Wisshak, M., Form, A.U., Carricart-Ganivet, J.P., Fine, M., Eakin, C.M., Mumby, P.J., 2013. Avoiding Coral Reef Functional Collapse Requires Local and Global Action. Current Biology 23, 7. http://dx.doi.org/10.1016/j.cub.2013.04.020
223
Kerry, J.T., Bellwood, D.R., 2015. Do tabular corals constitute keystone structures for fishes on coral reefs? Coral Reefs 34, 41–50. https://doi.org/10.1007/s00338-014-1232-4 Kingsbury, D., Soares, D.B., Harris, V., Fox, J.J., Bateman, S., Bergin, A., 2011. A Reliable Partner: Strengthening Australia – Timor-Leste Relations. Australian Strategic Policy Institute Limited 68. Kirsteuer, E., 1969. Qualitative and quantitative aspects of nemertean fauna in coral reefs of the Indian Ocean, in: Proceedings of the Symposium on Corals and Coral Reefs. The Marine Biological Association of India, Cochin. pp. 367–71. Kleypas, J.A., Buddemeier, R.W., Gattuso, J.-P., 2001. The future of coral reefs in an age of global change. Int J Earth Sci 90, 426–437. https://doi.org/10.1007/s005310000125 Knowlton, N., 2001. Sea urchin recovery from mass mortality: new hope for Caribbean coral reefs? Proceedings of the National Academy of Sciences of the United States of America 98, 4822–4824. https://doi.org/10.1073/pnas.091092998 Knowlton, N., 2000. Molecular genetic analyses of species boundaries in the sea, in: Solé- Cava, A.M., Russo, C.A.M., Thorpe, J.P. (Eds.), Marine Genetics. Springer Netherlands, Dordrecht, pp. 73–90. https://doi.org/10.1007/978-94-017-2184-4_8 Knowlton, N., 1993. Sibling Species in the Sea. Annual Review of Ecology and Systematics 24, 189–21630. Knowlton, N., Brainard, R.E., Fisher, R., Moews, M., Plaisance, L., Caley, M.J., 2010. Coral reef biodiversity, in: Life in the World’s Oceans: Diversity Distribution and Abundance. Wiley-Blackwell, pp. 65–74. Knowlton, N., Jackson, J.B.C., 2008. Shifting Baselines, Local Impacts, and Global Change on Coral Reefs. PLoS Biol 6, e54. https://doi.org/10.1371/journal.pbio.0060054 Knudsen, J.W., 1967. Trapezia and Tetralia (Decapoda, Brachyura, Xanthidae) as Obligate Ectoparasites of Pocilloporid and Acroporid Corals’ : 2. Pacific Science 21, 51–57. Kohler, K.E., Gill, S.M., 2006. Coral Point Count with Excel extensions (CPCe): A Visual Basic program for the determination of coral and substrate coverage using random point count methodology. Computers & Geosciences 32, 1259–1269. https://doi.org/10.1016/J.CAGEO.2005.11.009 Kohn, A.J., Leviten, P.J., 1976. Effect of habitat complexity on population density and species richness in tropical intertidal predatory gastropod assemblages. Oecologia 25, 199–210. https://doi.org/10.1007/BF00345098 Kolasa, J., Manne, L.L., Pandit, S.N., 2012. Species-area relationships arise from interaction of habitat heterogeneity and species pool. Hydrobiologia 685, 135–144. https://doi.org/10.1007/s10750-011-0846-6 Kovalenko, K.E., Thomaz, S.M., Warfe, D.M., 2012. Habitat complexity: approaches and future directions. Hydrobiologia 685, 1–17. https://doi.org/10.1007/s10750-011-0974- z Kuffner, I.B., Walters, L.J., Becerro, M.A., Paul, V.J., Ritson-Williams, R., Beach, K.S., 2006. Inhibition of coral recruitment by macroalgae and cyanobacteria. Mar Ecol Prog Ser 323, 107–117. Lafferty, K.D., Holt, R.D., 2003. How should environmental stress affect the population dynamics of disease? Ecology Letters 6, 654–664. https://doi.org/10.1046/j.1461- 0248.2003.00480.x Lam, V.Y.Y., Doropoulos, C., Mumby, P.J., 2017. The influence of resilience-based management on coral reef monitoring: A systematic review. PLOS ONE 12, e0172064. https://doi.org/10.1371/journal.pone.0172064
224
Lamb, J.B., Willis, B.L., 2011. Using coral disease prevalence to assess the effects of concentrating tourism activities on offshore reefs in a tropical marine park. Conservation Biology. https://doi.org/10.1111/j.1523-1739.2011.01724.x Lamb, J.B., Willis, B.L., Fiorenza, E.A., Couch, C.S., Howard, R., Rader, D.N., True, J.D., Kelly, L.A., Ahmad, A., Jompa, J., Harvell, C.D., 2018. Plastic waste associated with disease on coral reefs. Science 359, 460–462. https://doi.org/10.1126/science.aar3320 Lamb, K., Swart, P., Altabet, M., 2012. Nitrogen and Carbon Isotopic Systematics of the Florida Reef Tract. Bulletin of Marine Science 88, 119–146. https://doi.org/10.5343/bms.2010.1105 Lang, J.C., Chornesky, E.A., 1990. Competition between scleractinian reef corals-a review of mechanisms and effects. Ecosystems of the world 25, 209–252. Lapointe, B.E., 1997. Nutrient thresholds for bottom-up control of macroalgal blooms on coral reefs in Jamaica and southeast Florida. Limnol. Oceanogr. 42, 1119–1131. https://doi.org/10.4319/lo.1997.42.5_part_2.1119 Lapointe, B.E., Barile, P.J., Littler, M.M., Littler, D.S., 2005. Macroalgal blooms on southeast Florida coral reefs: II. Cross-shelf discrimination of nitrogen sources indicates widespread assimilation of sewage nitrogen. Harmful Algae 4, 1106–1122. https://doi.org/10.1016/j.hal.2005.06.002 Lapointe, B.E., Barile, P.J., Matzie, W.R., 2004. Anthropogenic nutrient enrichment of seagrass and coral reef communities in the Lower Florida Keys : discrimination of local versus regional nitrogen sources. https://doi.org/10.1016/j.jembe.2004.01.019 Lawton, J.H., Brown, V.K., 2012. Redundancy in Ecosystems, in: Schulze, E.-D., Mooney, H.A. (Eds.), Biodiversity and Ecosystem Function. Springer, Berlin, pp. 255–268. Lee, J.K.M., 2016. Geochemistry of pillow lavas in Timor: Implications for supra-subduction zone magmatism and the evolution of the Banda Arc. Presented at the Goldschmidt Conference Abstracts. Lee, S.C., 2006. Habitat complexity and consumer-mediated positive feedbacks on a Caribbean coral reef. Oikos 112, 442–447. https://doi.org/10.1111/j.0030- 1299.2006.14247.x Lee, S.Y., 2008. Mangrove macrobenthos: Assemblages, services, and linkages. Journal of Sea Research, Mangrove Macrobenthos Special Issue 59, 16–29. https://doi.org/10.1016/j.seares.2007.05.002 Lee, S.Y., Primavera, J.H., Dahdouh‐Guebas, F., McKee, K., Bosire, J.O., Cannicci, S., Diele, K., Fromard, F., Koedam, N., Marchand, C., Mendelssohn, I., Mukherjee, N., Record, S., 2014. Ecological role and services of tropical mangrove ecosystems: a reassessment. Global Ecology and Biogeography 23, 726–743. https://doi.org/10.1111/geb.12155 Leichter, J.J., Genovese, S.J., 2006. Intermittent upwelling and subsidized growth of the scleractinian coral Madracis mirabilis on the deep fore-reef slope of Discovery Bay, Jamaica. Marine Ecology Progress Series 316, 95–103. https://doi.org/10.3354/meps316095 Leichter, J.J., Stewart, H.L., Miller, S.L., 2003. Episodic nutrient transport to Florida coral reefs. Limnol. Oceanogr. 48, 1394–1407. https://doi.org/10.4319/lo.2003.48.4.1394 Leichter, J.J., Wankel, S., Paytan, A., Hanson, K., Miller, S., Altabet, M.A., 2007. Nitrogen and oxygen isotopic signatures of subsurface nitrate seaward of the Florida Keys reef tract. Limnol. Oceanogr. 52, 1258–1267. https://doi.org/10.4319/lo.2007.52.3.1258 Lenth, R., 2020. emmeans: Estimated Marginal Means, aka Least-Squares Means. Leray, M., Knowlton, N., 2015. DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity. Proceedings of the National Academy of
225
Sciences of the United States of America 112, 2076–2081. https://doi.org/dx.doi.org/10.5883/DS-ARMS Leray, M., Yang, J.Y., Meyer, C.P., Mills, S.C., Agudelo, N., Ranwez, V., Boehm, J.T., Machida, R.J., 2013. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front Zool 10, 34. https://doi.org/10.1186/1742-9994-10-34 Leslie, H.M., McLeod, K.L., 2007. Confronting the Challenges of Implementing Marine Ecosystem-Based Management. Frontiers in Ecology and the Environment 5, 540– 548. Lesser, M.P., Bythell, J.C., Gates, R.D., Johnstone, R.W., Hoegh-Guldberg, O., 2007. Are infectious diseases really killing corals? Alternative interpretations of the experimental and ecological data. Journal of Experimental Marine Biology and Ecology 346, 36–44. https://doi.org/10.1016/j.jembe.2007.02.015 Levin, S.A., 1992. The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur Award Lecture. Ecology 73, 1943–1967. https://doi.org/10.2307/1941447 Li, X., Wang, D., Huang, H., Zhang, J., Lian, J., Yuan, X., Yang, J., Zhang, G., 2015. Linking benthic community structure to terrestrial runoff and upwelling in the coral reefs of northeastern Hainan Island. Estuarine, Coastal and Shelf Science, Changes to processes in estuaries and coastal waters due to intense multiple pressures 156, 92– 102. https://doi.org/10.1016/j.ecss.2014.09.021 Lin, H.J., Wu, C.Y., Kao, S.J., Kao, W.Y., Meng, P.J., 2007. Mapping anthropogenic nitrogen through point sources in coral reefs using δ15N in macroalgae. Marine Ecology Progress Series 335, 95–109. https://doi.org/10.3354/meps335095 Linkous, D.A., Oliver, J.D., 1999. Pathogenesis of Vibrio vulnificus. FEMS Microbiol Lett 174, 207–214. https://doi.org/10.1111/j.1574-6968.1999.tb13570.x Littler, M.M., Littler, D.S., 1995. A colonial tunicate smothers corals and corraline algae on the Great Astrolabe Reef, Fiji. Coral Reefs 14, 148–149. Littler, M.M., Littler, D.S., Brooks, B.L., 2006. Harmful algae on tropical coral reefs: Bottom- up eutrophication and top-down herbivory. Harmful Algae 5, 565–585. https://doi.org/10.1016/j.hal.2005.11.003 Liu, G., Rauenzahn, J.L., Heron, S., Eakin, C.M., Skirving, W.J., Christensen, T.R.L., Strong, A.E., Li, J., 2013. NOAA Coral Reef Watch 50 km Satellite Sea Surface Temperature- Based Decision Support System for Coral Bleaching Management (NOAA Technical Report NESDIS 143). Washington D.C. Liu, G., Strong, A.E., Skirving, W., Arzayus, L.F., 2006. Overview of NOAA Coral Reef Watch Program’s Near-Real- Time Satellite Global Coral Bleaching Monitoring Activities, in: Proceedings of 10th International Coral Reef Symposium, 1783-1793. pp. 1783–1793. Logan, A., Mathers, S.M., Thomas, M.L.H., 1984. Sessile invertebrate coelobite communities from reefs of Bermuda: Species composition and distribution. Coral Reefs 2, 205–213. https://doi.org/10.1007/BF00263574 Loh, T., Pawlik, J.R., 2012. Friend or foe? No evidence that association with the sponge Mycale laevis provides a benefit to corals of the genus Montastraea. Marine Ecology Progress Series 465, 111–117. https://doi.org/10.3354/meps09904 Lomolino, M.V., 2000. Ecology’s most general, yet protean 1 pattern: the species-area relationship. Journal of Biogeography 27, 17–26. https://doi.org/10.1046/j.1365- 2699.2000.00377.x Loreau, M., 2000. Biodiversity and ecosystem functioning: recent theoretical advances. Oikos 91, 3–17. https://doi.org/10.1034/j.1600-0706.2000.910101.x
226
Loya, Y., Sakai, K., Yamazato, K., Nakano, Y., Sambali, H., van Woesik, R., 2001. Coral bleaching: the winners and the losers. Ecology Letters 4, 122–131. https://doi.org/10.1046/j.1461-0248.2001.00203.x Luckhurst, B.E., Luckhurst, K., 1978. Analysis of the influence of substrate variables on coral reef fish communities. Mar. Biol. 49, 317–323. https://doi.org/10.1007/BF00455026 MacArthur, R.H., MacArthur, J.W., 1961. On Bird Species Diversity. Ecology 42, 594–598. https://doi.org/10.2307/1932254 Macaulay, J., 2003. Timor Leste: Newest and Poorest of Asian Nations. Geography 88, 40– 46. Macdonald, A.M., 1993. Property fluxes at 30°S and their implications for the Pacific-Indian throughflow and the global heat budget. Journal of Geophysical Research: Oceans 98, 6851–6868. https://doi.org/10.1029/92JC02964 Maina, J., Moel, H. de, Zinke, J., Madin, J., McClanahan, T., Vermaat, J.E., 2013. Human deforestation outweighs future climate change impacts of sedimentation on coral reefs. Nature Communications 4, 1986. https://doi.org/10.1038/ncomms2986 Mallela, J., Milne, B.C., Martinez-Escobar, D., 2017. A comparison of epibenthic reef communities settling on commonly used experimental substrates: PVC versus ceramic tiles. Journal of Experimental Marine Biology and Ecology 486, 290–295. https://doi.org/10.1016/j.jembe.2016.10.028 Mangi, S.C., Roberts, C.M., 2007. Factors influencing fish catch levels on Kenya’s coral reefs. Fisheries Management and Ecology 14, 245–253. https://doi.org/10.1111/j.1365-2400.2007.00549.x Margules, C.R., Pressey, R.L., 2000. Systematic conservation planning. Nature 405, 11. Marshall, P.A., Baird, A.H., 2000. Bleaching of corals on the Great Barrier Reef: differential susceptibilities among taxa. Coral Reefs 19, 155–163. https://doi.org/10.1007/s003380000086 Martin, M., 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17.1 10–123. Matias, M.G., Underwood, A.J., Hochuli, D.F., Coleman, R.A., 2010. Independent effects of patch size and structural complexity on diversity of benthic macroinvertebrates. Ecology 91, 1908–1915. https://doi.org/10.1890/09-1083.1 May, J.A., Macintyre, I.G., Perkins, R.D., 1982. Distribution of microborers within planted substrates along a barrier reef transect, Carrie Bow Cay, Belize. Smith Contr Mar Sci 12, 93–107. May, R.M., 1994. Biological diversity: differences between land and sea. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 343, 105– 111. May, R.M., 1988. How Many Species are There on Earth? Science, New Series 241, 9. Maynard, J., van Hooidonk, R., Eakin, C.M., Puotinen, M., Garren, M., Williams, G., Heron, S.F., Lamb, J., Weil, E., Willis, B., Harvell, C.D., 2015a. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nature Climate Change. https://doi.org/10.1038/nclimate2625 Maynard, J., van Hooidonk, R., Eakin, C.M., Puotinen, M., Garren, M., Williams, G., Heron, S.F., Lamb, J.B., Weil, E., Willis, B., Harvell, C.D., 2015b. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nature Climate Change. https://doi.org/10.1038/nclimate2625 McArdle, B.H., Anderson, M.J., 2001. Fitting Multivariate Models to Community Data: A Comment on Distance-Based Redundancy Analysis. Ecology 82, 290–297. https://doi.org/10.1890/0012-9658(2001)082[0290:FMMTCD]2.0.CO;2
227
McClanahan, T.R., 1999. Predation and the control of the sea urchin Echinometra viridisand fleshy algae in the patch reefs of Glovers Reef, Belize. Ecosystems 2, 511–523. McClanahan, T.R., 1997. Primary succession of coral-reef algae: Differing patterns on fished versus unfished reefs. Journal of Experimental Marine Biology and Ecology 218, 77–102. https://doi.org/10.1016/S0022-0981(97)00069-5 McClanahan, T.R., Obura, D., 1997. Sedimentation effects on shallow coral communities in Kenya. Journal of Experimental Marine Biology and Ecology 209, 103–122. https://doi.org/10.1016/S0022-0981(96)02663-9 McClanahan, T.R., Polunin, N., Done, T., 2002. Ecological States and the Resilience of Coral Reefs. CE 6, art18. https://doi.org/10.5751/ES-00461-060218 McClanahan, T.R., Shafir, S.H., 1990. Causes and consequences of sea urchin abundance and diversity in Kenyan coral reef lagoons. Oecologia 83, 362–370. https://doi.org/10.1007/BF00317561 McClanahan, T.R., Weil, E., Maina, J., 2009. Strong relationship between coral bleaching and growth anomalies in massive Porites. Global Change Biology 15, 1804–1816. https://doi.org/10.1111/j.1365-2486.2008.01799.x McCloskey, L.R., 1970. The Dynamics of the Community Associated with a Marine Scleractinian Coral. Internationale Revue der gesamten Hydrobiologie und Hydrographie 55, 13–81. https://doi.org/10.1002/iroh.19700550103 McCoy, E.D., Bell, S.S., 1991. Habitat structure: The evolution and diversification of a complex topic, in: Bell, Susan S., McCoy, Earl D., Mushinsky, H.R. (Eds.), Habitat Structure: The Physical Arrangement of Objects in Space, Population and Community Biology Series. Springer Netherlands, Dordrecht, pp. 3–27. https://doi.org/10.1007/978-94-011-3076-9_1 McCulloch, M., Fallon, S., Wyndham, T., Hendy, E., Lough, J., Barnes, D., 2003. Coral record of increased sediment flux to the inner Great Barrier Reef since European settlement. Nature 421, 727–730. https://doi.org/10.1038/nature01380.1. McDevitt-Irwin, J.M., Baum, J.K., Garren, M., Vega Thurber, R.L., 2017. Responses of Coral-Associated Bacterial Communities to Local and Global Stressors. Front. Mar. Sci. 4. https://doi.org/10.3389/fmars.2017.00262 Mcmanus, J.W., 1988. Coral Reefs of the ASEAN Region: Status and Management. Ambio 17, 189–193. McWilliam, A., 2003. New beginnings in East Timorese forest management. Journal of Southeast Asian Studies 307–327. McWilliam, A., 2002. Perspectives on customary marine tenures in East Timor. The Asia Pacific Journal of Anthropology 3, 6–32. https://doi.org/10.1080/14442210210001706266 McWilliams, J.P., Côté, I.M., Gill, J.A., Sutherland, W.J., Watkinson, A.R., 2005. Accelerating impacts of temperature-induced coral bleaching in the Caribbean. Ecology 86, 2055–2060. https://doi.org/10.1890/04-1657 Meesters, E., Knijn, R., Willemsen, P., Pennartz, R., Roebers, G., van Soest, R.W.M., 1991. Sub-rubble communities of Curaçao and Bonaire coral reefs. Coral Reefs 10, 189– 197. https://doi.org/10.1007/BF00336773 Mellin, C., Delean, S., Caley, J., Edgar, G., Meekan, M., Pitcher, R., Przeslawski, R., Williams, A., Bradshaw, C., 2011. Effectiveness of Biological Surrogates for Predicting Patterns of Marine Biodiversity: A Global Meta-Analysis. PLoS ONE 6, e20141. https://doi.org/10.1371/journal.pone.0020141 Menge, B.A., 2000. Recruitment Vs. Postrecruitment Processes as Determinants of Barnacle Population Abundance. Ecological Monographs 70, 265–288. https://doi.org/10.1890/0012-9615(2000)070[0265:RVPPAD]2.0.CO;2
228
Menge, B.A., 1991. Relative importance of recruitment and other causes of variation in rocky intertidal community structure. Journal of Experimental Marine Biology and Ecology 146, 69–100. https://doi.org/10.1016/0022-0981(91)90255-U Mera, H., Bourne, D.G., 2018. Disentangling causation: complex roles of coral-associated microorganisms in disease. Environmental Microbiology 20, 431–449. https://doi.org/10.1111/1462-2920.13958 meteoblue, n.d. Climate Dili [WWW Document]. URL https://www.meteoblue.com/en/weather/forecast/modelclimate/dili_timor- leste_1645457 Meyer, C.P., Paulay, G., 2005. DNA Barcoding: Error Rates Based on Comprehensive Sampling. PLoS Biol 3, e422. https://doi.org/10.1371/journal.pbio.0030422 Meyers, G., 1996. Variation of Indonesian throughflow and the El Niño‐Southern Oscillation. Journal of Geophysical Research. Miller, J., Muller, E., Rogers, C., Waara, R., Atkinson, A., Whelan, K.R.T., Patterson, M., Witcher, B., 2009. Coral disease following massive bleaching in 2005 causes 60% decline in coral cover on reefs in the US Virgin Islands. Coral Reefs 28, 925. https://doi.org/10.1007/s00338-009-0531-7 Miller, K.J., 1994. Morphological variation in the coral genus taxonomic implications. Marine Ecology Progress Series 110, 19–28. Mills, D.J., Tilley, A., Pereira, M., Hellebrandt, D., Pereira Fernandes, A., Cohen, P.J., 2017. Livelihood diversity and dynamism in Timor-Leste; insights for coastal resource governance and livelihood development. Marine Policy 82, 206–215. https://doi.org/10.1016/j.marpol.2017.04.021 Milton, D.L., Norqvist, A., Wolf-Watz, H., 1992. Cloning of a metalloprotease gene involved in the virulence mechanism of Vibrio anguillarum. Journal of Bacteriology 174, 7235– 7244. https://doi.org/10.1128/jb.174.22.7235-7244.1992 Minton, D., Lundgren, I., Pakenham, A., 2007. A Two-year Study of Coral Recruitment and Sedimentation in Asan Bay, Guam (Final report prepared for the National Park Service, Guam). Mitchell, A.W., Bramley, R.G.V., Johnson, A.K.L., 1997. Export of nutrients and suspended sediment during a cyclone-mediated flood event in the Herbert River catchment, Australia. Mar. Freshwater Res. 48, 79. https://doi.org/10.1071/MF96021 Miyazawa, N., 2013. Customary law and community-based natural resource management in post-conflict Timor-Leste. Land and post-conflict peacebuilding 511–532. Moberg, F., Folke, C., 1999. Ecological goods and services of coral reef ecosystems. Ecological Economics 29, 215–233. https://doi.org/10.1016/S0921-8009(99)00009-9 Moberly, R., Chamberlain, T., 1964. Hawaiian beach systems. Institute of Geophysics, University of Hawaii. Mokady, O., Loya, Y., Lazar, B., 1998. Ammonium contribution from boring bivalves to their coral host--a mutualistic symbiosis? Mar. Ecol. Prog. Ser. 169, 295–301. https://doi.org/10.3354/meps169295 Montoya, J.P., Carpenter, E.J., Capone, D.G., 2002. Nitrogen fixation and nitrogen isotope abundances in zooplankton of the oligotrophic North Atlantic. Limnology and Oceanography 47, 1617–1628. https://doi.org/10.4319/lo.2002.47.6.1617 Mora, C., 2008. A Clear Human Footprint in the Coral Reefs of the Caribbean. Proceedings: Biological Sciences 275, 767–773. Mora, C., Aburto-Oropeza, O., Ayala-Bocos, A., Ayotte, P.M., Banks, S., Bauman, A.G., Beger, M., Bessudo, S., Booth, D.J., Brokovich, E., Brooks, A., Chabanet, P., Cinner, J.E., Cortés, J., Cruz-Motta, J.J., Cupul-Magaña, A., DeMartini, E.E., Edgar, G.J., Feary, D.A., Ferse, S.C.A., Friedlander, A.M., Gaston, K.J., Gough, C., Graham, N.
229 A.J., Green, A., Guzman, H., Hardt, M., Kulbicki, M., Letourneur, Y., Ĺopez-Pérez, A., Loreau, M., Loya, Y., Martinez, C., Mascareñas-Osorio, I., Morove, T., Nadon, M.O., Nakamura, Y., Paredes, G., Polunin, N.V.C., Pratchett, M.S., Reyes Bonilla, H., Rivera, F., Sala, E., Sandin, S.A., Soler, G., Stuart-Smith, R., Tessier, E., Tittensor, D.P., Tupper, M., Usseglio, P., Vigliola, L., Wantiez, L., Williams, I., Wilson, S.K., Zapata, F. a., 2011. Global human footprint on the linkage between biodiversity and ecosystem functioning in reef fishes. PLoS Biology 9, e1000606. https://doi.org/10.1371/journal.pbio.1000606 Moran, D.P., Reaka, M.L., 1988. Bioerosion and availability of shelter for benthic reel organisms. Mar. Ecol. Prog. Ser. 15. Moran, D.P., Reaka-Kudla, M.L., 1991. Effects of disturbance: disruption and enhancement of coral reef cryptofaunal populations by hurricanes. Coral Reefs 9, 215–224. https://doi.org/10.1007/BF00290425 Moreno-Forero, S.K., Navas S., G.R., Solano, O.D., 1998. Cryptobiota associated to dead Acropora palmata (Scleractinia: Acroporidae) coral, Isla Grande, colombian Caribbean. Revista de Biología Tropical 46, 229–236. Mori, N., Yasuda, T., Mase, H., Tom, T., Oku, Y., 2010. Projection of Extreme Wave Climate Change under Global Warming. Hydrological Research Letters 4, 15–19. https://doi.org/10.3178/hrl.4.15 Morrongiello, J.R., Thresher, R.E., 2015. A statistical framework to explore ontogenetic growth variation among individuals and populations: a marine fish example. Ecological Monographs 85, 93–115. https://doi.org/10.1890/13-2355.1 Moynihan, M. a, Baker, D.M., Mmochi, A.J., 2012. Isotopic and microbial indicators of sewage pollution from Stone Town, Zanzibar, Tanzania. Marine pollution bulletin 64, 1348–55. https://doi.org/10.1016/j.marpolbul.2012.05.001 Muller, E.M., Raymundo, L.J., Willis, B.L., Haapkylä, J., Yusuf, S., Wilson, J.R., Harvell, D.C., 2012. Coral Health and Disease in the Spermonde Archipelago and Wakatobi, Sulawesi 1, 13. Muller, E.M., van Woesik, R., 2012. Caribbean coral diseases: primary transmission or secondary infection? Glob Change Biol 18, 3529–3535. https://doi.org/10.1111/gcb.12019 Mumby, P.J., Skirving, W., Strong, A.E., Hardy, J.T., LeDrew, E.F., Hochberg, E.J., Stumpf, R.P., David, L.T., 2004. Remote sensing of coral reefs and their physical environment. Marine Pollution Bulletin 48, 219–228. https://doi.org/10.1016/j.marpolbul.2003.10.031 Mundy, C.N., 2000. An appraisal of methods used in coral recruitment studies. Coral Reefs 19, 124–131. https://doi.org/10.1007/s003380000081 Myers, R.L., Raymundo, L.J., 2009. Coral disease in Micronesian reefs: A link between disease prevalence and host abundance. Diseases of Aquatic Organisms 87, 97– 104. https://doi.org/10.3354/dao02139 Naeem, S., 1998. Species Redundancy and Ecosystem Reliability. Conservation Biology 12, 39–45. https://doi.org/10.1111/j.1523-1739.1998.96379.x Nash, K.L., Graham, N.A.J., Wilson, S.K., Bellwood, D.R., 2013. Cross-scale Habitat Structure Drives Fish Body Size Distributions on Coral Reefs. Ecosystems 16, 478– 490. https://doi.org/10.1007/s10021-012-9625-0 Naumann, M.S., Mayr, C., Struck, U., Wild, C., 2010. Coral mucus stable isotope composition and labeling : experimental evidence for mucus uptake by epizoic acoelomorph worms 2521–2531. https://doi.org/10.1007/s00227-010-1516-3
230 Navas, G.R., Moreno-Forero, S.K., Solano, O.D., Diaz-Pulido, G., 1998. Ensamblajes arrecifales epiliticos del coral Acropora palmata muerto, isla Grande, Islas del Rosario, Caribe Colombiano. Caribbean Journal of Science 34, 58–66. NBSAP, 2011. The National Biodiversity Strategy and Action Plan of Timor-Leste (2011 - 2020). Ministry of Economy and Development, United Nations Development Programme, Global Environment Facility. Ninio, R., Meekan, M., 2002. Spatial patterns in benthic communities and the dynamics of a mosaic ecosystem on the Great Barrier Reef, Australia. Coral Reefs 21, 95–104. https://doi.org/10.1007/s00338-001-0202-9 NOAA Coral Reef Watch, 2018. NOAA Coral Reef Watch Version 3.0 Daily Global 5-km Satellite Virtual Station Time Series Data for Timor-Leste, Aug. 1, 2015 - Aug. 31, 2017. [WWW Document]. NOAA Coral Reef Watch. URL https://coralreefwatch.noaa.gov/vs/data.php (accessed 11.14.17). Noonan, S.H.C., Jones, G.P., Pratchett, M.S., 2012. Coral size, health and structural complexity: effects on the ecology of a coral reef damselfish. Marine Ecology Progress Series 456, 127–137. https://doi.org/10.3354/meps09687 Norberg, J., Swaney, D.P., Dushoff, J., Lin, J., Casagrandi, R., Levin, S.A., 2001. Phenotypic diversity and ecosystem functioning in changing environments: A theoretical framework. Proceedings of the National Academy of Sciences 98, 11376–11381. https://doi.org/10.1073/pnas.171315998 Nowell, A.R.M., Jumars, P.A., 1984. Flow environments of aquatic benthos. Annual review of ecology and systematics 15, 303–328. NSD & UNFPA, 2011. Population and Housing Census of Timor-Leste, 2010 Volume 2: Population Distribution by Administrative Areas. National Statistics Directorate, United Nations Population Fund. NSD Timor-Leste, 2015. Timor-Leste Population and Housing Census–Data Sheet. Dili: Timor-Leste National Statistics Directorate. Nyström, M., 2006. Redundancy and Response Diversity of Functional Groups: Implications for the Resilience of Coral Reefs. AMBIO: A Journal of the Human Environment 35, 30–35. https://doi.org/10.1579/0044-7447-35.1.30 Nyström, M., Folke, C., 2001. Spatial resilience of coral reefs. Ecosystems 4, 406–417. Ogden, J.C., 1977. Carbonate-Sediment Production by Parrot Fish and Sea Urchins on Caribbean Reefs1, in: Reefs and Related Carbonates—Ecology and Sedimentology. American Association of Petroleum Geologists, pp. 281–288. https://doi.org/10.1306/St4393C21 Ogden, J.C., 1976. Some aspects of herbivore-plant relationships on Caribbean reefs and seagrass beds. Aquatic Botany 2, 103–116. Ogle, D.H., Wheeler, P., Dinno, A., 2020. FSA: Fisheries Stock Analysis. Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E., Wagner, H., 2016. vegan: Community Ecology Package. Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E., Wagner, H., 2019. vegan: Community Ecology Package. R Foundation for Statistical Computing. Osawa, Y., Fujita, K., Umezawa, Y., Kayanne, H., Ide, Y., Nagaoka, T., Miyajima, T., Yamano, H., 2010. Human impacts on large benthic foraminifers near a densely populated area of Majuro Atoll, Marshall Islands. Marine Pollution Bulletin 60, 1279– 1287. https://doi.org/10.1016/j.marpolbul.2010.03.014
231
Patton, W.K., 1966. Decapod Crustacea Commensal With Queensland Branching Corals. Crustaceana 10, 271–295. https://doi.org/10.1163/156854066X00180 Paulay, G., Kropp, R., Ng, P.K.L., Eldredge, L.G., 2003. The crustaceans and pycnogonids of the Mariana Islands 58. Pearman, J.K., Leray, M., Villalobos, R., Machida, R.J., Berumen, M.L., Knowlton, N., Carvalho, S., 2018. Cross-shelf investigation of coral reef cryptic benthic organisms reveals diversity patterns of the hidden majority. Sci Rep 8, 8090. https://doi.org/10.1038/s41598-018-26332-5 Pepper, A., 2009. GREMO : A GIS-based generic model for estimating relative wave exposure 1964–1970. Perry, C.T., Morgan, K.M., 2017. Bleaching drives collapse in reef carbonate budgets and reef growth potential on southern Maldives reefs. Scientific Reports 7, 1–9. https://doi.org/10.1038/srep40581 Perry, C.T., Murphy, G.N., Kench, P.S., Smithers, S.G., Edinger, E.N., Steneck, R.S., Mumby, P.J., 2013. Caribbean-wide decline in carbonate production threatens coral reef growth. Nat Commun 4, 1402. https://doi.org/10.1038/ncomms2409 Pielke, R.A., Bluestein, H.B., Enfield, D.B., Wells, N.C., Cenedese, C., Daives, R., Waggoner, P.E., Gentilli, J., Smith, P.J., Arnfield, A.J., Mason, B.J., Krishnamurti, T.N., Hayden, B.P., Loewe, F.P., 2019. Climate. Encyclopedia Britannica. PIFSC, 2017. Interdisciplinary baseline ecosystem assessment surveys to inform ecosystem-based management planning in Timor-Leste: Final Report. (PIFSC Special Publications No. SP-17-02). NOAA Pacific Islands Fisheries Science Center. Pile, A.J., Patterson, M.R., Savarese, M., Chernykh, V.I., Fialkov, V.A., 1997. Trophic effects of sponge feeding within Lake Baikal’s littoral zone. 2. Sponge abundance, diet, feeding efficiency, and carbon flux. Limnol. Oceanogr. 42, 178–184. https://doi.org/10.4319/lo.1997.42.1.0178 Pineda, J., Porri, F., Starczak, V., Blythe, J., 2010. Causes of decoupling between larval supply and settlement and consequences for understanding recruitment and population connectivity. Journal of Experimental Marine Biology and Ecology 392, 9– 21. https://doi.org/10.1016/j.jembe.2010.04.008 Pinheiro, J., Bates, D., Debroy, S., Sarkar, D., R Core Team, 2020. nlme: Linear and Nonlinear Mixed Effects Models. Pinheiro, J.C., Bates, D.M., 2000. Mixed-Effects Models in S and S-Plus. Springer, New York, NY. Plaisance, L., Caley, M.J., Brainard, R.E., Knowlton, N., 2011a. The diversity of coral reefs: What are we missing? PLoS ONE 6. https://doi.org/10.1371/journal.pone.0025026 Plaisance, L., Caley, M.J., Brainard, R.E., Knowlton, N., 2011b. Using DNA barcoding and standardized sampling to compare geographic and habitat differentiation of crustaceans: A Hawaiian Islands example. Diversity 3, 581–591. https://doi.org/10.3390/d3040581 Plaisance, L., Caley, M.J., Brainard, R.E., Knowlton, N., 2011c. The diversity of coral reefs: What are we missing? PLoS ONE 6, e25026. https://doi.org/10.1371/journal.pone.0025026 Plaisance, L., Knowlton, N., Paulay, Gustav, G., Meyer, C.P., 2009. Reef-associated crustacean fauna: biodiversity estimates using semi-quantitative sampling and DNA barcoding. Coral Reefs 28, 977–986. https://doi.org/10.1007/s00338-009-0543-3 Plank, J.E., 1963. Plant Diseases-Epidemics and Control Academic Press New York. USA. Pollock, F.J., Lamb, J.B., Field, S.N., Heron, S.F., Schaffelke, B., Shedrawi, G., Bourne, D.G., Willis, B.L., 2014. Sediment and turbidity associated with offshore dredging
232
increase coral disease prevalence on nearby reefs. PLoS ONE 9. https://doi.org/10.1371/journal.pone.0102498 Pomponi, S.A., 1980. Cytological Mechanisms of Calcium Carbonate Excavation by Boring Sponges, in: Bourne, G.H., Danielli, J.F. (Eds.), International Review of Cytology. Academic Press, pp. 301–319. https://doi.org/10.1016/S0074-7696(08)61963-4 Porter, J.W., Dustan, P., Jaap, W., Patterson, K., Kosmynin, V., Meier, O., Patterson, M., Parsons, M., 2001. Patterns of spread of coral disease in the Florida Keys. Hydrobiologia 460, 1–24. Poteet, M., 2006. Shifting roles of abiotic and biotic regulation of a multi-host parasite following disturbance, in: Disease Ecology: Community Structure and Pathogen Dynamics. OUP Oxford. Potemra, J.T., Schneider, N., 2007. Interannual variations of the Indonesian throughflow. Journal of Geophysical Research: Oceans 112. https://doi.org/10.1029/2006JC003808 Pratchett, M.S., 2001. Influence of coral symbionts on feeding preferences of crown-of- thorns starfish Acanthaster planci in the western Pacific. Marine Ecology Progress Series 214, 111–119. https://doi.org/10.3354/meps214111 Pratchett, M.S., Munday, P.L., Wilson, S.K., Graham, N.A.J., Cinner, J.E., Bellwood, D.R., Jones, G.P., Polunin, N.V.C., McClanahan, T.R., 2008. Effects of climate-induced coral bleaching on coral-reef fishes — ecological and economic consequences. Oceanography and Marine Biology: An Annual Review 46, 254–296. Preston, N., Doherty, P., 1994. Cross-shelf patterns in the community structure of coral- dwelling Crustacea in the central region of the Great Barrier Reef. 11. Cryptofauna. Mar. Ecol. Prog. Ser. 104, 27–38. https://doi.org/10.3354/meps104027 Price, S.A., Holzman, R., Near, T.J., Wainwright, P.C., 2011. Coral reefs promote the evolution of morphological diversity and ecological novelty in labrid fishes. Ecology Letters 14, 462–469. https://doi.org/10.1111/j.1461-0248.2011.01607.x Radice, V.Z., Hoegh‐Guldberg, O., Fry, B., Fox, M.D., Dove, S.G., 2019. Upwelling as the major source of nitrogen for shallow and deep reef‐building corals across an oceanic atoll system. Funct Ecol 33, 1120–1134. https://doi.org/10.1111/1365-2435.13314 Ralph, C.J., 1985. Habitat association patterns of forest and steppe birds of northern Patagonia, Argentina. The Condor 87, 471–483. Ransome, E., Geller, J.B., Timmers, M.A., Leray, M., Mahardini, A., Collins, A.G., Meyer, C.P., 2017. The importance of standardization for biodiversity comparisons : A case study using autonomous reef monitoring structures (ARMS) and metabarcoding to measure cryptic diversity on Mo’orea coral reefs , French Polynesia 12, 1–19. Rasheed, M., Badran, M.I., Richter, C., Huettel, M., 2002. Effect of reef framework and bottom sediment on nutrient enrichment in a coral reef of the Gulf of Aqaba, Red Sea. Marine Ecology Progress Series 239, 277–285. https://doi.org/10.3354/meps239277 Ravindran, J., Raghukumar, C., 2006. Pink-line syndrome, a physiological crisis in the scleractinian coral Porites lutea 347–356. https://doi.org/10.1007/s00227-005-0192- 1 Raymundo, L.J., Couch, C.S., Bruckner, A.W., Harvell, C.D., 2008. Coral Disease Handbook Guidelines for Assessment: Guidelines for Assessment Monitoring and Management. Currie Communications, Melbourne, Australia. Raymundo, L.J., Halford, A.R., Maypa, A.P., Kerr, A.M., 2009. Functionally diverse reef-fish communities ameliorate coral disease. Proceedings of the National Academy of Sciences of the United States of America 106, 17067–70. https://doi.org/10.1073/pnas.0900365106
233
Raymundo, L.J.H., Harvell, C.D., Reynolds, T.L., 2003. Porites ulcerative white spot disease: Description, prevalence, and host range of a new coral disease affecting Indo-Pacific reefs. Diseases of Aquatic Organisms 56, 95–104. https://doi.org/10.3354/dao056095 RDTL, 2015. Population and Housing Census 2015 Preliminary Results. Democratic Republic of Timor-Leste (RDTL). RDTL, 2011. Timor-Leste Strategic Development Plan 2011 - 2030. Republica Democratica de Timor-Leste, Dili, Timor-Leste. Reaka, M.L., 1987. Adult-Juvenile Interactions in Benthic Reef Crustaceans. Bull Mar Sci 41, 27. Reaka, M.L., 1985. Interactions between fishes and motile benthic invertebrates on reefs: the significance of motility vs. defensive adaptations., in: Proceedings of the Fifth International Coral Reef Congress. pp. 429–444. Reaka-Kudla, M.L., 1997. The global biodiversity of coral reefs: a comparison with rain forests. Biodiversity II: Understanding and protecting our biological resources 2, 551. Redding, J.E., Myers-Miller, R.L., Baker, D.M., Fogel, M., Raymundo, L.J., Kim, K., 2013. Link between sewage-derived nitrogen pollution and coral disease severity in Guam. Marine Pollution Bulletin 73, 57–63. https://doi.org/10.1016/j.marpolbul.2013.06.002 Reichelt, R.E., 1982. Space: A non-limiting resource in the niches of some abundant coral reef gastropods. Coral Reefs 1, 3–11. https://doi.org/10.1007/BF00286534 Richter, C., Wunsch, M., 1999. Cavity-dwelling suspension feeders in coral reefs - a new link in reef trophodynamics. Mar. Ecol. Prog. Ser. 188, 105–116. https://doi.org/10.3354/meps188105 Richter, C., Wunsch, M., Rasheed, M., Kötter, I., Badran, M.I., 2001. Endoscopic exploration of Red Sea coral reefs reveals dense populations of cavity-dwelling sponges. Nature 413, 726–730. https://doi.org/10.1038/35099547 Risk, M.J., 1972. Fish diversity on a coral reef in the Virgin Islands. Atoll Research Bulletin. Risk, M.J., Lapointe, B.E., Sherwood, O. a, Bedford, B.J., 2009. The use of delta(15)N in assessing sewage stress on coral reefs. Marine pollution bulletin 58, 793–802. https://doi.org/10.1016/j.marpolbul.2009.02.008 Rixen, T., Jiménez, C., Cortés, J., 2015. Impact of upwelling events on the sea water carbonate chemistry and dissolved oxygen concentration in the Gulf of Papagayo (Culebra Bay), Costa Rica: Implications for coral reefs. RBT 60, 187. https://doi.org/10.15517/rbt.v60i2.20004 Robertson, A.I., Dixon, P., Alongi, D.M., 1998. The Influence of Fluvial Discharge on Pelagic Production in the Gulf of Papua, Northern Coral Sea. Estuarine, Coastal and Shelf Science 46, 319–331. https://doi.org/10.1006/ecss.1997.0270 Robson, B.J., Barmuta, L.A., Fairweather, P.G., 2005. Methodological and conceptual issues in the search for a relationship between animal body-size distributions and benthic habitat architecture. Mar. Freshwater Res. 56, 1–11. https://doi.org/10.1071/MF04210 Rodríguez-Villalobos, J.C., Work, T.M., Calderon-Aguilera, L.E., Reyes-Bonilla, H., Hernández, L., 2015. Explained and unexplained tissue loss in corals from the Tropical Eastern Pacific 116, 121–131. https://doi.org/10.3354/dao02914 Rodriquez-Ramirez, A., Gonzalez-Rivero, M., Beijbom, O., Bailhache, C., Bongaerts, P., Brown, K., Bryant, D., Dalton, P., Dove, S., Ganase, A., Kennedy, E.V., Kim, C.J., Lopez-Marcano, S., Neal, B.P., Radice, V.Z., Vercelloni, J., Beyer, H., Hoegh- Guldberg, O., 2020. A contemporary baseline record of the world’s coral reefs. Scientific Data.
234
Roff, G., Chollett, I., Doropoulos, C., Golbuu, Y., Steneck, R.S., Isechal, A.L., van Woesik, R., Mumby, P.J., 2015. Exposure-driven macroalgal phase shift following catastrophic disturbance on coral reefs. Coral Reefs 34, 715–725. https://doi.org/10.1007/s00338-015-1305-z Roff, G., Hoegh-Guldberg, O., Fine, M., 2006. Intra-colonial response to Acroporid “white syndrome” lesions in tabular Acropora spp. (Scleractinia). Coral Reefs 25, 255–264. https://doi.org/10.1007/s00338-006-0099-4 Rogers, A., Blanchard, J.L., Mumby, P.J., 2014. Vulnerability of Coral Reef Fisheries to a Loss of Structural Complexity. Current Biology 24, 1000–1005. https://doi.org/10.1016/j.cub.2014.03.026 Rogers, A.D., Laffoley, D., 2013. Introduction to the special issue: The global state of the ocean; interactions between stresses, impacts and some potential solutions. Synthesis papers from the International Programme on the State of the Ocean 2011 and 2012 workshops. Marine Pollution Bulletin, The Global State of the Ocean; Interactions Between Stresses, Impacts and Some Potential Solutions. Synthesis papers from the International Programme on the State of the Ocean 2011 and 2012 Workshops 74, 491–494. https://doi.org/10.1016/j.marpolbul.2013.06.057 Rogers, C.S., 1990. Responses of coral reefs and reef organisms to sedimentation. Marine Ecology Progress Series 62, 185–202. https://doi.org/10.3354/meps062185 Rosenberg, E., Ben Haim, Y., 2002. Microbial disease of corals and global warming. Environmental Microbiology 4, 318–326. Rothans, T.C., Miller, A.C., 1991. A link between biologically imported particulate organic nutrients and the detritus food web in reef communities. Mar Biol 110, 145–150. https://doi.org/10.1007/BF01313101 Rotjan, R.D., Lewis, S.M., 2008. Impact of coral predators on tropical reefs. Mar. Ecol. Prog. Ser. 367, 73–91. https://doi.org/10.3354/meps07531 Roxburgh, S.H., Shea, K., Wilson, J.B., 2004. The intermediate disturbance hypothesis: patch dynamics and mechanisms of species coexistence. Ecology 85, 359–371. https://doi.org/10.1890/03-0266 Russell, B.C., 1977. Population and standing crop estimates for rocky reef fishes of North‐ Eastern New Zealand. New Zealand Journal of Marine and Freshwater Research 11, 23–36. https://doi.org/10.1080/00288330.1977.9515659 Rutzler, K., Muzik, K., 1993. Terpios hoshinota, a new cyanobacteriosponge threatening Pacific reefs. Sci Mar 57:395–403. Sci Mar 57, 395–403. Sabdono, A., Radjasa, O.K., Ambariyanto, Trianto, A., Wijayanti, D.P., Pringgenies, D., Munasik, 2014. An early evaluation of coral disease prevalence on panjang island, Java sea, Indonesia. International Journal of Zoological Research 10, 20–29. https://doi.org/10.3923/ijzr.2014.20.29 Sala, E., Knowlton, N., 2006. Global Marine Biodiversity Trends. Annu. Rev. Environ. Resour. 31, 93–122. https://doi.org/10.1146/annurev.energy.31.020105.100235 Salinas-de-León, P., Costales-Carrera, A., Zeljkovic, S., Smith, D.J., Bell, J.J., 2011. Scleractinian settlement patterns to natural cleared reef substrata and artificial settlement panels on an Indonesian coral reef. Estuarine, Coastal and Shelf Science 93, 80–85. https://doi.org/10.1016/j.ecss.2011.02.016 Sanderson, E., Jaiteh, M., Levy, M., Redford, K., Wannebo, A., Woolmer, G., 2002. The human footprint and the last of the wild. Bioscience 52, 891–904. Sandlund, O.T., Bryceson, I., de Carvalho, D., Rio, N., da Silva, J., Silva, M.I., 2001. Assessing Environmental Needs and Priorities in Timor-Leste. United Nations Development Programme (UNDP) commissioned paper. New York: UNDP.
235
Sanyu Consultants Inc., 2001. The study on integrated agricultural development of East Timor: progress report. A cooperation between the East Timor Transitional Administration (ETTA)/United Nations Transitional Administration in East Timor (UNTAET) and Japan International Cooperation. Savage, C., Elmgren, R., 2004. MACROALGAL (FUCUS VESICULOSUS) δ15N VALUES TRACE DECREASE IN SEWAGE INFLUENCE. Ecological Applications 14, 517– 526. https://doi.org/10.1890/02-5396 Saxby, T., Dennison, W., Hoegh-Guldberg, O., 2003. Photosynthetic responses of the coral Montipora digitata to cold temperature stress. Marine Ecology Progress Series 248, 85–97. https://doi.org/10.3354/meps248085 Scheffers, S.R., Van Soest, R.W.M., Nieuwland, G., Bak, R.P.M., 2010. Coral reef framework cavities: Is functional similarity reflected in composition of the cryptic macrofaunal community? Atoll Research Bulletin. Schönberg, C.H.L., Fang, J.K.-H., Carballo, J.L., 2017. Bioeroding Sponges and the Future of Coral Reefs, in: Carballo, J.L., Bell, J.J. (Eds.), Climate Change, Ocean Acidification and Sponges: Impacts Across Multiple Levels of Organization. Springer International Publishing, Cham, pp. 179–372. https://doi.org/10.1007/978-3-319- 59008-0_7 Schutte, V.G.W., Selig, E.R., Bruno, J.F., 2010. Regional spatio-temporal trends in Caribbean coral reef benthic communities. Marine Ecology Progress Series 402, 115–122. https://doi.org/10.3354/meps08438 Scott, P.J.B., Risk, M.J., 1988. The effect of Lithophaga (Bivalvia: Mytilidae) boreholes on the strength of the coral Porites lobata. Coral Reefs 7, 145–151. https://doi.org/10.1007/BF00300974 Sebens, K.P., 1991. Habitat structure and community dynamics in marine benthic systems, in: Bell, S.S., McCoy, E.D., Mushinsky, H.R. (Eds.), Habitat Structure: The Physical Arrangement of Objects in Space. Springer Netherlands, Dordrecht, pp. 211–234. https://doi.org/10.1007/978-94-011-3076-9_11 Servis, J.A., Reid, B.N., Timmers, M.A., Stergioula, V., Naro-Maciel, E., 2020. Characterizing coral reef biodiversity: genetic species delimitation in brachyuran crabs of Palmyra Atoll, Central Pacific. Mitochondrial DNA Part A 31, 178–189. https://doi.org/10.1080/24701394.2020.1769087 Shea, K., Roxburgh, S.H., Rauschert, E.S.J., 2004. Moving from pattern to process: Coexistence mechanisms under intermediate disturbance regimes. Ecology Letters 7, 491–508. https://doi.org/10.1111/j.1461-0248.2004.00600.x Shearer, T., van Oppen, M.J.H., Romanos, S.L., Worheide, G., 2002. Slow mitochondrial DNA sequence evolution in the Anthozoa (Cnidaria). Molecular Ecology 11, 2475– 2487. https://doi.org/10.1046/j.1365-294X.2002.01652.x Sheppard, C., Dixon, D.J., Gourlay, M., Sheppard, A., Payet, R., 2005. Coral mortality increases wave energy reaching shores protected by reef flats: Examples from the Seychelles. Estuarine, Coastal and Shelf Science 64, 223–234. https://doi.org/10.1016/j.ecss.2005.02.016 Sheppard, C.R.C., 1982. Coral Populations on Reef Slopes and Their Major Controls. Marine Ecology Progress Series 7, 83–115. https://doi.org/10.3354/meps007083 Sheridan, C., Grosjean, Ph., Leblud, J., Palmer, C.V., Kushmaro, A., Eeckhaut, I., 2014. Sedimentation rapidly induces an immune response and depletes energy stores in a hard coral. Coral Reefs 33, 1067–1076. https://doi.org/10.1007/s00338-014-1202-x Shirayama, Y., Horikoshi, M., 1982. A new method of classifying the growth form of corals and its application to a field survey of coral-associated animals in Kabira Cove,
236
Ishigaki Island. Journal of the Oceanographical Society of Japan 38, 193–207. https://doi.org/10.1007/BF02111102 Siddall, M., Rohling, E.J., Almogi-Labin, A., Hemleben, C., Meischner, D., Schmelzer, I., Smeed, D.A., 2003. Sea-level fluctuations during the last glacial cycle. Nature 423, 853. Sigman, D.M., Altabet, M.A., McCorkle, D.C., Francois, R., Fischer, G., 2000. The δ15N of nitrate in the Southern Ocean: Nitrogen cycling and circulation in the ocean interior. J. Geophys. Res. 105, 19599–19614. https://doi.org/10.1029/2000JC000265 Skirving, W., Marsh, B., De La Cour, J., Liu, G., Harris, A., Maturi, E., Geiger, E., Eakin, C.M., 2020. CoralTemp and the Coral Reef Watch Coral Bleaching Heat Stress Product Suite Version 3.1. Remote Sensing 12, 3856. https://doi.org/10.3390/rs12233856 Slezak, M., 2016. Atauro Island: scientist discover the most biodiverse waters in the world [WWW Document]. The Guardian. URL https://www.theguardian.com/world/2016/aug/17/atauro-island-timor-leste-the-push- to-protect-the-most-biodiverse-waters-in-the-world (accessed 9.1.18). Small, A., Adey, W.H., Spoon, D., 1998. Are current estimates of coral reef biodiversity too low? The view through the window of a microcosm. Atoll Research Bulletin 458, 1– 20. https://doi.org/10.5479/si.00775630.458.1 Smith, J., Smith, C., Hunter, C., 2001. An experimental analysis of the effects of herbivory and nutrient enrichment on benthic community dynamics on a Hawaiian reef. Coral Reefs 19, 332–342. https://doi.org/10.1007/s003380000124 Smith, J.E., Brainard, R., Carter, A., Grillo, S., Edwards, C., Harris, J., Lewis, L., Obura, D., Rohwer, F., Sala, E., Vroom, P.S., Sandin, S., Smith, J.E., 2016. Re-evaluating the health of coral reef communities : baselines and evidence for human impacts across the central Pacific. Smith, J.E., Runcie, J.W., Smith, C.M., 2005. Characterization of a large-scale ephemeral bloom of the green alga Cladophora sericea on the coral reefs of West Maui, Hawai’i. Marine Ecology Progress Series 302, 77–91. https://doi.org/10.3354/meps302077 Smith, J.E., Shaw, M., Edwards, R.A., Obura, D., Pantos, O., Sala, E., Sandin, S.A., Smriga, S., Hatay, M., Rohwer, F.L., 2006. Indirect effects of algae on coral: algae-mediated, microbe-induced coral mortality. Ecology Letters 9, 835–845. https://doi.org/10.1111/j.1461-0248.2006.00937.x Smith, N., 2001. Spatial heterogeneity in recruitment of larval trematodes to snail intermediate hosts. Oecologia 127, 115-122 LA-English. https://doi.org/10.1007/s004420000560 Smith, S.V., 1978. Coral-reef area and the contributions of reefs to processes and resources of the world’s oceans. Nature 273, 225–226. Smith, T.B., Nemeth, R.S., Blondeau, J., Calnan, J.M., Kadison, E., Herzlieb, S., 2008. Assessing coral reef health across onshore to offshore stress gradients in the US Virgin Islands. Marine Pollution Bulletin 56, 1983–1991. https://doi.org/10.1016/j.marpolbul.2008.08.015 Smithson, M., Verkuilen, J., 2006. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychological Methods 11, 54–71. https://doi.org/10.1037/1082-989X.11.1.54 Spies, T.A., 1982. Forest structure; a key to the ecosystem. Northwest science 72, 34–36. Spotte, S., 1996. Supply of regenerated nitrogen to sea anemones by their symbiotic shrimp. Journal of Experimental Marine Biology and Ecology 198, 27–36. https://doi.org/10.1016/0022-0981(95)00169-7
237
Sprintall, J., Gordon, A.L., Koch-Larrouy, A., Lee, T., Potemra, J.T., Pujiana, K., Wijffels, S., 2014. The Indonesian seas and their role in the coupled ocean–climate system. Nature Geoscience 7, 487–492. Steneck, R.S., 2001. Functional Groups, in: Levin, S.A. (Ed.), Encyclopedia of Biodiversity. Elsevier, New York, pp. 121–139. https://doi.org/10.1016/B0-12-226865-2/00133-4 Steneck, R.S., Watling, L., 1982. Feeding capabilities and limitation of herbivorous molluscs: A functional group approach. Mar. Biol. 68, 299–319. https://doi.org/10.1007/BF00409596 Stewart, H., Price, N., Holbrook, S., Schmitt, R., Brooks, A., 2013. Determinants of the onset and strength of mutualistic interactions between branching corals and associate crabs. Mar. Ecol. Prog. Ser. 493, 155–163. https://doi.org/10.3354/meps10525 Stewart, H.L., Holbrook, S.J., Schmitt, R.J., Brooks, A.J., 2006. Symbiotic crabs maintain coral health by clearing sediments. Coral Reefs 25, 609–615. https://doi.org/10.1007/s00338-006-0132-7 Stimson, J., 1990. Stimulation of fat-body production in the polyps of the coral Pocillopora damicornis by the presence of mutualistic crabs of the genus Trapezia. Mar. Biol. 106, 211–218. https://doi.org/10.1007/BF01314802 Stoddart, D.R., 1969. Ecology and Morphology of Recent Coral Reefs. Biological Reviews 44, 433–498. https://doi.org/10.1111/j.1469-185X.1969.tb00609.x Stoner, D.S., 1990. Recruitment of a Tropical Colonial Ascidian: Relative Importance of Pre- Settlement vs. Post-Settlement Processes. Ecology 71, 1682–1690. https://doi.org/10.2307/1937577 Storlazzi, C.D., Brown, E.K., Field, M.E., Rodgers, K., Jokiel, P.L., 2005. A model for wave control on coral breakage and species distribution in the Hawaiian Islands. Coral Reefs 24, 43–55. https://doi.org/10.1007/s00338-004-0430-x Storlazzi, C.D., Field, M.E., Dykes, J.D., Jokiel, P.L., Brown, E., 2002. Wave Control on Reef Morphology and Coral Distribution: Molokai, Hawaii, in: Fourth International Symposium on Ocean Wave Measurement and Analysis. pp. 1–10. Strong, A.E., Liu, G., Skirving, W., Eakin, C.M., 2011. NOAA’s Coral Reef Watch program from satellite observations. Annals of GIS 17, 83–92. https://doi.org/10.1080/19475683.2011.576266 Sullivan, T.P., Sullivan, D.S., 2001. Influence of variable retention harvests on forest ecosystems. II. Diversity and population dynamics of small mammals. Journal of Applied Ecology 38, 1234–1252. https://doi.org/10.1046/j.0021-8901.2001.00674.x Susanto, R.D., Gordon, A.L., Sprintall, J., 2007. Observations and proxies of the surface layer throughflow in Lombok Strait. Journal of Geophysical Research: Oceans. https://doi.org/10.1029/[email protected]/(ISSN)2169-9291.CHINASEAS1 Sussman, M., Willis, B.L., Victor, S., Bourne, D.G., 2008. Coral Pathogens Identified for White Syndrome ( WS ) Epizootics in the Indo-Pacific 3. https://doi.org/10.1371/journal.pone.0002393 Sutherland, K.P., Berry, B., Park, A., Kemp, D.W., Kemp, K.M., Lipp, E.K., Porter, J.W., 2016. Shifting white pox aetiologies affecting Acropora palmata in the Florida Keys, 1994–2014. Phil. Trans. R. Soc. B 371, 20150205. https://doi.org/10.1098/rstb.2015.0205 Sutherland, K.P., Porter, J.W., Torres, C., 2004. Disease and immunity in Caribbean and Indo-Pacific zooxanthellate corals. Marine Ecology Progress Series 266, 273–302. Sutherland, K.P., Porter, J.W., Turner, J.W., Thomas, B.J., Looney, E.E., Luna, T.P., Meyers, M.K., Futch, J.C., Lipp, E.K., 2010. Human sewage identified as likely source of white pox disease of the threatened Caribbean elkhorn coral, Acropora palmata.
238
Environmental microbiology 12, 1122–31. https://doi.org/10.1111/j.1462- 2920.2010.02152.x Szmant-Froelich, A., 1983. Functional Aspects of Nutrient Cycling on Coral Reefs, in: The Ecology of Deep Shallow Coral Reefs, NOAA Symp Ser Undersea Res., Edited by: Rosenstiel School of Marine and Atmospheric Science University of Miami, Rockville, MD, NOAA Undersea Research Program. pp. 133–139. Tautz, D., Arctander, P., Minelli, A., Thomas, R.H., Vogler, A.P., 2003. A plea for DNA taxonomy. Trends in Ecology & Evolution 18, 70–74. https://doi.org/10.1016/S0169- 5347(02)00041-1 Teh, L.S.L., Teh, L.C.L., Sumaila, U.R., 2013. A Global Estimate of the Number of Coral Reef Fishers. PLoS One 8. https://doi.org/10.1371/journal.pone.0065397 Temmerman, S., Meire, P., Bouma, T.J., Herman, P.M.J., Ysebaert, T., De Vriend, H.J., 2013. Ecosystem-based coastal defence in the face of global change. Nature 504, 79–83. https://doi.org/10.1038/nature12859 Tews, J., Brose, U., Grimm, V., Tielbörger, K., Wichmann, M.C., Schwager, M., Jeltsch, F., 2004. Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures: Animal species diversity driven by habitat heterogeneity. Journal of Biogeography 31, 79–92. https://doi.org/10.1046/j.0305- 0270.2003.00994.x Thacker, R.W., Ginsburg, D.W., Paul, V.J., 2001. Effects of herbivore exclusion and nutrient enrichment on coral reef macroalgae and cyanobacteria. Coral Reefs 19, 318–329. https://doi.org/10.1007/s003380000122 The R Core Team, 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Thornber, C.S., Kinlan, B.P., Graham, M.H., Stachowicz, J.J., 2004. Population ecology of the invasive kelp Undaria pinnatifida in California: environmental and biological controls on demography. Marine Ecology Progress Series 268, 69–80. https://doi.org/10.3354/meps268069 Thorson, G., 1966. Some factors influencing the recruitment and establishment of marine benthic communities. Netherlands Journal of Sea Research 3, 267–293. https://doi.org/10.1016/0077-7579(66)90015-9 Tilley, A., Burgos, A., Duarte, A., Dos Reis Lopes, J., Eriksson, H., Mills, D.J., 2020. Contribution of women’s fisheries substantial, but overlooked, in Timor-Leste. https://dx.doi.org/10.1007/s13280-020-01335-7 Tilley, A., Hunnam, K.J., Mills, D.J., Steenbergen, D.J., Govan, H., Alonso-Poblacion, E., Roscher, M., Pereira, M., Rodrigues, P., Amador, T., Duarte, A., Gomes, M., Cohen, P.J., 2019. Evaluating the Fit of Co-management for Small-Scale Fisheries Governance in Timor-Leste. Front. Mar. Sci. 6. https://doi.org/10.3389/fmars.2019.00392 Titlyanov, E.A., Kiyashko, S.I., Titlyanova, T.V., Huyen, P.V., Yakovleva, I.M., 2011. Identifying nitrogen sources for macroalgal growth in variously polluted coastal areas of southern Vietnam. Botanica Marina 54. https://doi.org/10.1515/bot.2011.041 Tokeshi, M., Arakaki, S., 2012. Habitat complexity in aquatic systems: fractals and beyond. Hydrobiologia 685, 27–47. https://doi.org/10.1007/s10750-011-0832-z Tomascik, T., 1991. Settlement patterns of Caribbean scleractinian corals on artificial substrata along a eutrophication gradient, Barbados, West Indies. Mar. Ecol. Prog. Ser. 77, 261–269. https://doi.org/10.3354/meps077261 Tscharntke, T., Klein, A.M., Kruess, A., Steffan‐Dewenter, I., Thies, C., 2005. Landscape perspectives on agricultural intensification and biodiversity – ecosystem service
239
management. Ecology Letters 8, 857–874. https://doi.org/10.1111/j.1461- 0248.2005.00782.x Tucker, J., Sheats, N., Giblin, A.E., Hopkinson, C.S., Montoya, J.P., 1999. Using stable isotopes to trace sewage-derived material through Boston Harbor and Massachusetts Bay. Marine Environmental Research 48, 353–375. https://doi.org/10.1016/S0141-1136(99)00069-0 Turak, E., Devantier, L., 2013. Reef-Building Corals in Timor-Leste. A Rapid Marine Biological Assessment of Timor-Leste 85. https://doi.org/10.1896/054.066.0104 Umezawa, Y., Miyajima, T., Kayanne, H., Koike, I., 2002. Significance of groundwater nitrogen discharge into coral reefs at Ishigaki Island, southwest of Japan. Coral Reefs 21, 346–356. https://doi.org/10.1007/s00338-002-0254-5 UNFCCC, 2014. Report on the meeting on available tools for the use of indigenous and traditional knowledge and practices for adaptation, needs of local and indigenous communities and the application of gender-sensitive approaches and tools for adaptation. United Nations Framework Convention on Climate Change, Bonn. Ushijima, B., Smith, A., Aeby, G.S., Callahan, S.M., 2012. Vibrio owensii Induces the Tissue Loss Disease Montipora White Syndrome in the Hawaiian Reef Coral Montipora capitata. PLOS ONE 7, e46717. https://doi.org/10.1371/journal.pone.0046717 Ushijima, B., Videau, P., Burger, A.H., Shore-Maggio, A., Runyon, C.M., Sudek, M., Aeby, G.S., Callahan, S.M., 2014. Vibrio coralliilyticus Strain OCN008 Is an Etiological Agent of Acute Montipora White Syndrome. Appl. Environ. Microbiol. 80, 2102–2109. https://doi.org/10.1128/AEM.03463-13 van der Meij, S.E.T., Suharsono, Hoeksema, B.W., 2010. Long-term changes in coral assemblages under natural and anthropogenic stress in Jakarta Bay (1920-2005). Marine Pollution Bulletin 60, 1442–1454. https://doi.org/10.1016/j.marpolbul.2010.05.011 van der Zande, R.M., Achlatis, M., Bender‐Champ, D., Kubicek, A., Dove, S., Hoegh‐ Guldberg, O., 2020. Paradise lost: End-of-century warming and acidification under business-as-usual emissions have severe consequences for symbiotic corals. Global Change Biology 26, 2203–2219. https://doi.org/10.1111/gcb.14998 van Ofwegen, L.P., 2000. Status and knowledge of the Indo-Pacific soft coral genus Sinularia May, 1898 (Anthozoa: Octocorallia), in: Proceedings 9th International Coral Reef Symposium. Bali, Indonesia. Vargas-Ángel, B., Godwin, L., Brainard, R., 2009. Invasive didemnid tunicate spreading across coral reefs at remote Swains Island, American Samoa. Coral Reefs 28, 53. https://doi.org/10.1007/s00338-008-0428-x Vega Thurber, R.L., Burkepile, D.E., Fuchs, C., Shantz, A.A., McMinds, R., Zaneveld, J.R., 2014. Chronic nutrient enrichment increases prevalence and severity of coral disease and bleaching. Glob Change Biol 20, 544–54. https://doi.org/10.1111/gcb.12450 Vercammen, A., McGowan, J., Knight, A.T., Pardede, S., Muttaqin, E., Harris, J., Ahmadia, G., Estradivari, Dallison, T., Selig, E., Beger, M., 2019. Evaluating the impact of accounting for coral cover in large-scale marine conservation prioritizations. Diversity and Distributions 25, 1564–1574. https://doi.org/10.1111/ddi.12957 Vercelloni, J., Kennedy, E.V., Castro-Sanguino, C., González-Rivero, M., Rodriguez- Ramirez, A., Puotinen, M., Pearse, A., Lopez-Marcano, S., Stinger, A., Bryant, D.E.P., Ganase, A., Brown, K.T., Kim, C.J.S., Radice, V.Z., Mora, C., Peterson, E.E., Mengersen, K., Brown, C.J., Caley, M.J., Hoegh-Guldberg, O., 2020a. Global patterns of community assembly on coral reefs. Preprint in https://www.researchgate.net/publication/339972863_Global_patterns_of_communi ty_assembly_on_coral_reefs.
240
Vercelloni, J., Liquet, B., Kennedy, E.V., González‐Rivero, M., Caley, M.J., Peterson, E.E., Puotinen, M., Hoegh‐Guldberg, O., Mengersen, K., 2020b. Forecasting intensifying disturbance effects on coral reefs. Global Change Biology 26, 2785–2797. https://doi.org/10.1111/gcb.15059 Vermeij, M.J.A., van Moorselaar, I., Engelhard, S., Hörnlein, C., Vonk, S.M., Visser, P.M., 2010. The effects of nutrient enrichment and herbivore abundance on the ability of turf algae to overgrow coral in the Caribbean. PLoS ONE 5, 1–8. https://doi.org/10.1371/journal.pone.0014312 Veron, J.E.N., 2000. Corals of the World. Australian Institute of Marine Science, Townsville, Australia. Veron, J.E.N., Devantier, L.M., Turak, E., Green, A.L., Kininmonth, S., Stafford-Smith, M., Peterson, N., 2009. Delineating the Coral Triangle. Journal of Coral Reef Studies 11, 91–100. https://doi.org/10.3755/galaxea.11.91 Voss, J.D., Richardson, L.L., 2006. Nutrient enrichment enhances black band disease progression in corals. Coral Reefs 25, 569–576. https://doi.org/10.1007/s00338-006- 0131-8 Vytopil, E., Willis, B., 2001. Epifaunal community structure in Acropora spp. (Scleractinia) on the Great Barrier Reef: implications of coral morphology and habitat complexity. Coral Reefs 20, 281–288. https://doi.org/10.1007/s003380100172 Wagner, D.E., Kramer, P., Woesik, R. van, 2010. Species composition, habitat, and water quality influence coral bleaching in southern Florida. Marine Ecology Progress Series 408, 65–78. https://doi.org/10.3354/meps08584 Walker, B.H., 1992. Biodiversity and Ecological Redundancy. Conservation Biology 6, 18– 23. https://doi.org/10.1046/j.1523-1739.1992.610018.x Wallace, C., 1999. Staghorn corals of the world: a revision of the genus Acropora. CSIRO publishing. Ward, J.R., Kim, K., Harvell, C.D., 2007. Temperature affects coral disease resistance and pathogen growth. Marine Ecology Progress Series 329, 115–121. https://doi.org/10.3354/meps329115 Ward, T.J., Vanderklift, M.A., Nicholls, A.O., Kenchington, R.A., 1999. Selecting marine reserves using habitat and species assemblages as surrogates for biological diversity. Ecological Applications 9, 691–698. https://doi.org/10.1890/1051- 0761(1999)009[0691:SMRUHA]2.0.CO;2 Wedding, L.M., Friedlander, A.M., McGranaghan, M., Yost, R.S., Monaco, M.E., 2008. Using bathymetric lidar to define nearshore benthic habitat complexity: Implications for management of reef fish assemblages in Hawaii. Remote Sensing of Environment 112, 4159–4165. https://doi.org/10.1016/j.rse.2008.01.025 Wedding, L.M., Lecky, J., Gove, J.M., Walecka, H.R., Donovan, M.K., Williams, G.J., Jouffray, J.-B., Crowder, L.B., Erickson, A., Falinski, K., Friedlander, A.M., Kappel, C.V., Kittinger, J.N., McCoy, K., Norström, A., Nyström, M., Oleson, K.L.L., Stamoulis, K.A., White, C., Selkoe, K.A., 2018. Advancing the integration of spatial data to map human and natural drivers on coral reefs. PLOS ONE 13, 29. Weeks, R., Aliño, P.M., Atkinson, S., II, P.B., Binson, A., Campos, W.L., Djohani, R., Green, A.L., Hamilton, R., Horigue, V., Jumin, R., Kalim, K., Kasasiah, A., Kereseka, J., Klein, C., Laroya, L., Magupin, S., Masike, B., Mohan, C., Pinto, R.M.D.S., Vave- Karamui, A., Villanoy, C., Welly, M., White, A.T., 2014. Developing Marine Protected Area Networks in the Coral Triangle: Good Practices for Expanding the Coral Triangle Marine Protected Area System. Coastal Management 42, 183–205. https://doi.org/10.1080/08920753.2014.877768
241
Weil, E., 2004. Coral Reef Diseases in the Wider Caribbean, in: Rosenberg, E., Loya, Y. (Eds.), Coral Health and Disease. Springer Berlin Heidelberg, pp. 35–68. https://doi.org/10.1007/978-3-662-06414-6 Weil, E., Croquer, A., Urreiztieta, I., 2009. Temporal variability and impact of coral diseases and bleaching in La Parguera, Puerto Rico from 2003-2007. Caribbean journal of science 45, 221–246. Weil, E., Irikawa, A., Casareto, B., Suzuki, Y., 2012. Extended geographic distribution of several Indo-Pacific coral reef diseases. Dis. Aquat. Org. 98, 163–170. https://doi.org/10.3354/dao02433 Weil, E., Smith, G., Gil-Agudelo, D.L., 2006. Status and progress of coral reef disease research. Diseases of Aquatic Organisms 69, 1–7. Weil, E., Torres, J.L., Ashton, M., 2005. Population characteristics of the sea urchin Diadema antillarum in La Parguera, Puerto Rico, 17 years after the mass mortality event. Rev. Biol. Trop. 53, 15. Weiss, S., Xu, Z.Z., Peddada, S., Amir, A., Bittinger, K., Gonzalez, A., Lozupone, C., Zaneveld, J.R., Vázquez-Baeza, Y., Birmingham, A., Hyde, E.R., Knight, R., 2017. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27. https://doi.org/10.1186/s40168-017-0237-y Wellington, G.M., 1982. Depth Zonation of Corals in the Gulf of Panama: Control and Facilitation by Resident Reef Fishes. Ecological Monographs 52, 223–241. https://doi.org/10.2307/2937329 West, J.M., Salm, R.V., 2003. Resistance and Resilience to Coral Bleaching: Implications for Coral Reef Conservation and Management. Conservation Biology 17, 956–967. https://doi.org/10.1046/j.1523-1739.2003.02055.x Wever, L., 2008. Assessing Management Challenges and Options in the Coastal Zone of Timor-Leste 83. Whalan, S., Abdul Wahab, M.A., Sprungala, S., Poole, A.J., de Nys, R., 2015. Larval Settlement: The Role of Surface Topography for Sessile Coral Reef Invertebrates. PLoS ONE 10, e0117675. https://doi.org/10.1371/journal.pone.0117675 White, A.T., Aliño, P.M., Cros, A., Fatan, N.A., Green, A.L., Teoh, S.J., Laroya, L., Peterson, N., Tan, S., Tighe, S., Venegas-Li, R., Walton, A., Wen, W., 2014. Marine Protected Areas in the Coral Triangle: Progress, Issues, and Options. Coastal Management 42, 87–106. https://doi.org/10.1080/08920753.2014.878177 Wiens, J.A., 1989. Spatial Scaling in Ecology. Functional Ecology 3, 385–397. https://doi.org/10.2307/2389612 Wijgerde, T., Schots, P., Onselen, E. Van, Janse, M., Karruppannan, E., Verreth, J.A.J., Osinga, R., 2012. Epizoic acoelomorph flatworms impair zooplankton feeding by the scleractinian coral Galaxea fascicularis. https://doi.org/10.1242/bio.20122741 Wild, C., Hoegh-Guldberg, O., Naumann, M.S., Colombo-Pallotta, M.F., Ateweberhan, M., Fitt, W.K., Iglesias-Prieto, R., Palmer, C., Bythell, J.C., Ortiz, J.-C., Loya, Y., van Woesik, R., 2011. Climate change impedes scleractinian corals as primary reef ecosystem engineers. Mar. Freshwater Res. 62, 205. https://doi.org/10.1071/MF10254 Wild, C., Huettel, M., Klueter, A., Kremb, S.G., Rasheed, M.Y.M., Jørgensen, B.B., 2004. Coral mucus functions as an energy carrier and particle trap in the reef ecosystem. Nature 428, 66–70. https://doi.org/10.1038/nature02344 Wilkinson, S.P., Davy, S.K., Bunce, M., Stat, M., 2018. Taxonomic identification of environmental DNA with informatic sequence classification trees. PeerJ PrePrints; San Diego. http://dx.doi.org.ezproxy.library.uq.edu.au/10.7287/peerj.preprints.26812v1
242
Will, K.W., Mishler, B.D., Wheeler, Q.D., 2005. The perils of DNA barcoding and the need for integrative taxonomy. Syst Biol 54, 844–851. https://doi.org/10.1080/10635150500354878 Williams, C.B., 1943. Area and number of species. Nature 152, 264–267. https://doi.org/10.1038/152264a0 Williams, G.J., Graham, N.A.J., Jouffray, J.-B., Norström, A.V., Nyström, M., Gove, J.M., Heenan, A., Wedding, L.M., 2019. Coral reef ecology in the Anthropocene. Functional Ecology 33, 1014–1022. https://doi.org/10.1111/1365-2435.13290 Williams, G.J., Knapp, I.S., Maragos, J.E., Davy, S.K., 2011a. Proximate environmental drivers of coral communities at Palmyra Atoll : Establishing baselines prior to removing a WWII military causeway. Marine Pollution Bulletin 62, 1842–1851. https://doi.org/10.1016/j.marpolbul.2011.05.002 Williams, G.J., Smith, J.E., Conklin, E.J., Gove, J.M., Sala, E., Sandin, S.A., 2013. Benthic communities at two remote Pacific coral reefs: effects of reef habitat, depth, and wave energy gradients on spatial patterns. PeerJ 1, e81. https://doi.org/10.7717/peerj.81 Williams, G.J., Work, T.M., Aeby, G.S., Knapp, I.S., Davy, S.K., 2011b. Gross and microscopic morphology of lesions in Cnidaria from Palmyra Atoll , Central Pacific. Journal of Invertebrate Pathology 106, 165–173. https://doi.org/10.1016/j.jip.2010.08.002 Williams, I.D., Couch, C.S., Beijbom, O., Oliver, T.A., Vargas-Ángel, B., Schumacher, B.D., Brainard, R.E., 2019. Leveraging Automated Image Analysis Tools to Transform Our Capacity to Assess Status and Trends of Coral Reefs. Front Mar Sci 6. https://doi.org/10.3389/fmars.2019.00222 Williams, I.D., Polunin, N.V.C., 2000. Differences between protected and unprotected reefs of the western Caribbean in attributes preferred by dive tourists. Environmental Conservation 27, 382–391. https://doi.org/10.1017/S0376892900000436 Willis, B.L., Page, C.A., Dinsdale, E.A., 2004. Coral disease on the Great Barrier Reef. Coral Health and Disease 69–104. Wilson, S.K., Graham, N.A.J., Polunin, N.V.C., 2007. Appraisal of visual assessments of habitat complexity and benthic composition on coral reefs. Mar Biol 151, 1069–1076. https://doi.org/10.1007/s00227-006-0538-3 Wilson, S.K., Robinson, J.P.W., Chong-Seng, K., Robinson, J., Graham, N.A.J., 2019. Boom and bust of keystone structure on coral reefs. Coral Reefs 38, 625–635. https://doi.org/10.1007/s00338-019-01818-4 Wobeser, G., 1981. Diseases of Wild Waterfowl. Plenum Press, New York. Wolanski, E., 1994. Physical oceanographic processes of the Great Barrier Reef. CRC Press. Wolff, N.H., Wong, A., Vitolo, R., Stolberg, K., Anthony, K.R.N., Mumby, P.J., 2016. Temporal clustering of tropical cyclones on the Great Barrier Reef and its ecological importance. Coral Reefs 35, 613–623. https://doi.org/10.1007/s00338-016-1400-9 Wong, L.S., Chou, L.M., 2004. Status of Coral Reefs on North-east Atauro Island, Timor- Leste, Based on Surveys Conducted in November 2004 (Technical Report No. 7). Woodland, D.J., Hooper, J., 1977. The Effect of Human Trampling on Coral Reefs. Biol Conserv 11, 1–4. Wooldridge, S.A., Done, T.J., 2009. Improved water quality can ameliorate effects of climate change on corals. Ecological Applications 19, 1492–1499. https://doi.org/10.1890/08- 0963.1 Work, T.M., Aeby, G.S., 2006. Systematically describing gross lesions in corals. Diseases of aquatic organisms 70, 155–60. https://doi.org/10.3354/dao070155
243
Work, T.M., Russell, R., Aeby, G.S., 2012. Tissue loss (white syndrome) in the coral Montipora capitata is a dynamic disease with multiple host responses and potential causes 4334–4341. https://doi.org/10.1098/rspb.2012.1827 Wunsch, M., Al-Moghrabi, S.M., Kötter, I., 2003. Communities of coral reef cavities in Jordan, Gulf of Aqaba (Red Sea). Feeding ecology of coral reef sponges 40. Yachi, S., Loreau, M., 1999. Biodiversity and ecosystem productivity in a fluctuating environment: The insurance hypothesis. PNAS 96, 1463–1468. https://doi.org/10.1073/pnas.96.4.1463 Yasuda, N., Nakano, Y., Yamashiro, H., Hidaka, M., 2012. Skeletal structure and progression of growth anomalies in Porites australiensis in Okinawa, Japan 97, 237– 247. https://doi.org/10.3354/dao02408 Yoder, L.S.M., 2005. Custom, codification, collaboration: Integrating the legacies of land and forest authorities in Oecusse Enclave, East Timor. Yoshioka, R.M., Kim, C.J.S., Tracy, A.M., Most, R., Harvell, C.D., 2016. Linking sewage pollution and water quality to spatial patterns of Porites lobata growth anomalies in Puako, Hawaii. Marine Pollution Bulletin 104. https://doi.org/10.1016/j.marpolbul.2016.01.002 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M., Andrews, F., 2019. Package “zoo.” Zhang, J., Kobert, K., Flouri, T., Stamatakis, A., 2014. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620. https://doi.org/10.1093/bioinformatics/btt593 Zimmerman, T.L., Martin, J.W., 2004. Artificial Reef Matrix Structures (Arms): An Inexpensive and Effective Method for Collecting Coral Reef-Associated Invertebrates. GCR 16. https://doi.org/10.18785/gcr.1601.08 Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V., 2018. Learning Transferable Architectures for Scalable Image Recognition. Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697–8710. Zundelevich, A., Lazar, B., Ilan, M., 2007. Chemical versus mechanical bioerosion of coral reefs by boring sponges - lessons from Pione cf. vastifica. Journal of Experimental Biology 210, 91–96. https://doi.org/10.1242/jeb.02627 Zuur, A., Ieno, E., Walker, N., Saveliev, A., Smith, G., 2009. Mixed Effects Models and Extensions in Ecology with R. Springer-Verlag, New York. Zvuloni, A., Armoza-Zvuloni, R., Loya, Y., 2008. Structural deformation of branching corals associated with the vermetid gastropod Dendropoma maxima. Marine Ecology Progress Series 363, 103–108. https://doi.org/10.3354/meps07473 Zychaluk, K., Bruno, J.F., Clancy, D., McClanahan, T.R., Spencer, M., 2012. Data-driven models for regional coral-reef dynamics. Ecology Letters 15, 151–158. https://doi.org/10.1111/j.1461-0248.2011.01720.x
244