Coral responses to temperature, irradiance and acidification stress: linking physiology to satellite remote sensing Robert Allan Burton Mason BSc (Hons), MSc

A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2017 School of Biological Sciences Abstract

The success of the symbiosis of scleractinian corals with dinoflagellates of the genus Symbiodinium is highly dependent on the availability of sufficient, but not excess, light for photosynthesis. After decades of fundamental research into the effect of light on the coral-dinoflagellate symbiosis, an important practical application is emerging in remote monitoring of bleaching at coral reefs. Coral bleaching that originates with the dysfunction of photosynthesis can be either photoacclimatory, a controlled adjustment in response to environmental change, or it can be associated with photodamage, an uncontrolled response to environmental change. It is the latter that tends to lead to severe bleaching events that decrease the rate of carbon fixation, generate excessive oxygen radicals and may ultimately lead to coral death if unfavourable conditions persist. Current best practice methods for the prediction of coral bleaching use water temperature as detected via satellite, and predict the onset of coral bleaching accurately, but not the percent of corals bleached at a reef or the extent of the ensuing mortality.

Due to its central role in causing photodamage, the use of light level (in addition to temperature) as a predictive variable may improve the accuracy of predictions of coral bleaching severity. The rate of photoacclimation affects the duration of elevated stress following an increase or decrease in incident light level and so it is potentially important for predicting the severity of coral bleaching. As coral reef management practitioners aim to predict bleaching and bleaching-induced mortality at entire reef locales, which are typically composed of a multi-species coral community, differences in coral physiological responses are of importance in designing accurate prediction methods. The interaction of high light and high temperature with a third stressor, ocean acidification, may influence coral bleaching, as ocean acidification changes oceanic chemical parameters that are key to cellular mechanisms of homeostasis and to dinoflagellate photosynthesis.

Using multiple-stressor aquarium experiments, I investigated the photoacclimation rate in corals, differences in response to light and temperature stress among coral species, and the interaction of light, temperature and ocean acidification on coral bleaching onset and severity (at the colony scale). My investigation of photoacclimation (via 24 days of exposure to a large increase, a moderate increase, and a large decrease in light) in Acropora muricata and mounding Porites spp. indicated that the direction of light change and the water temperature influenced photoacclimation duration. However, the exact patterns were specific to the variable used to measure photoacclimation (for instance, changes in net areal photosynthesis, or changes in quantum 2 efficiency of photosystem II), with important ramifications for the development of a combined light and temperature bleaching prediction method.

I investigated species-specific physiological responses in A. muricata, mounding Porites spp. and Montipora monasteriata to four water temperatures (25, 27, 29, and 31°C; the latter two exceeding the average temperature of the warmest month) combined with five light levels (µmol quanta·m-2s-1: two below (91, 165), one at (226) and two exceeding (307, 371) the average in situ reef light level). Whilst the combinations of temperature and light change that were required to cause statistically significant symbiont cell loss differed among species, I identified aspects of the bleaching response that were shared between taxa, facilitating the development of bleaching prediction that takes into account biological differences. For instance, mounding Porites spp. and M. monasteriata experienced decreased symbiont densities under high light exposure, whilst symbiont densities in A. muricata did not vary with light when light change was the sole stressor. The suggestion of these results that groups of species with similar responses can be found may save the arduous task of calibrating prediction methods for each individual species at a location.

I performed an experimental exposure of Pocillopora acuta to present day or future ocean acidification combined with average or high light (compared to local in situ reef values) and with optimal or above-optimal temperature (the average of the warmest month minus 2.5°C or plus 2°C). Symbiodinium photosynthetic and population size responses were measured following 39 days of exposure. Ocean acidification (slightly in excess of an RCP8.5 scenario) did not affect Symbiodinium areal densities or areal net photosynthesis nor the severity of thermal bleaching or light change-induced cell loss, but did enhance the increase in dark respiration at elevated temperature. The increase in dark respiration rates might suggest that ocean acidification places increased energy demands on the P. acuta holobiont, with potential long-term ramifications for coral health. A synthesis of results from the literature found that, generally, very high levels of ocean acidification caused statistically-significant symbiont loss in corals. Prediction of coral bleaching under future scenarios of greenhouse gas emissions, an important scientific tool for modelling coral reef futures, may need to take the effect of ocean acidification on bleaching into account.

3 Declaration by author

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

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

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

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

4 Publications during candidature

Peer reviewed papers

Fang, J. K. H., Mason, R. A. B., Schönberg, C. H. L., Hoegh-Guldberg, O. and Dove, S. (2017) Studying interactions between excavating sponges and massive corals by the use of hybrid cores. Marine Ecology 38: e12393.

Skirving W., Enríquez S., Hedley J. D., Dove S., Eakin C. M., Mason R. A. B., De La Cour J. L., Liu G., Hoegh-Guldberg O., Strong A. E., Mumby P. J., and Iglesias-Prieto R. (2018) Remote Sensing of Coral Bleaching Using Temperature and Light: Progress towards an Operational Algorithm. Remote Sensing 10: 18.

Wall, C. B., Mason, R. A. B., Ellis, W. R., Cunning, R. and Gates, R. D. (2017) Elevated pCO2 affects tissue biomass composition, but not calcification, in a reef coral under two light regimes. Royal Society Open Science 4: 170683.

Book sections

Mason, R. (2012) A Student Perspective: Prediction of Coral Bleaching. In: Reid, C., Marshall, J., Logan, D., Kleine, D., Coral Reefs and Climate Change: The Guide for Education and Awareness. Second Edition (ed Angela Dean). Brisbane: CoralWatch, The University of Queensland, pp 228–228.

Conference abstracts

Mason, R. A. B., Cunning, R., Wall, C., Dove, S., Gates, R. (2015) Drivers of coral bleaching: a search for synergistic effects among ocean acidification, heat stress, and irradiance. 89th Annual Australian Coral Reef Society Conference 28th – 31st July 2015 Daydream Island, The Whitsundays: Abstract Book, pp 35–36.

Mason, R. A. B., Skirving, W., Dove, S., Hoegh-Guldberg, O. (2014) Prediction of Coral Bleaching Using Satellite Remote Sensing. 8th Graduate Climate Conference Abstracts University of Washington October 31 – November 2, 2014, pp 45–45.

Mason, R. A. B., Skirving, W., Dove, S. G., Hoegh-Guldberg, O. (2014) Prediction of Coral Bleaching using Remotely Sensed Irradiance and Ocean Temperature. 2014 Ocean Sciences

5 Meeting, 23-28 February 2014, Hawaii Convention Center, Honolulu, Hawaii USA. Abstracts (online): URL: http://www.sgmeet.com/osm2014/viewabstract.asp? AbstractID=15365, accessed 22 October 2017.

Mason, R., Skirving, W., Dove, S., Burgess, T., Laszlo, I., Enriquez, S., Hedley, J., Eakin, M., Mumby, P., Hoegh-Guldberg, O., Iglesias-Prieto, R. (2012) A new bleaching product combining heat stress and light. 12th International Coral Reef Symposium 9-13 July 2012, Cairns, Queensland, Australia: Book of Abstracts, pp 87–87.

Skirving, W., Dove, S., Hoegh-Guldberg, Berkelmans, R., Bainbridge, S., Wachenfield, D., O., Iglesias-Prieto, R., Eakin, M., Enriquez, S., Mason, R. (2012) Next generation satellite tools: understanding environmental stress on coral reefs. 12th International Coral Reef Symposium 9-13 July 2012, Cairns, Queensland, Australia: Book of Abstracts, pp 90–90.

Publications included in this thesis

No publications included

6 Contributions by others to the thesis

Dr Sophie Dove and Dr William Skirving contributed to the experimental design and data interpretation of Chapters 2 and 3 and gave feedback on all parts of the thesis. Dr Robert Iglesias- Prieto suggested the question of the effects of temperature on photoacclimation rate (Chapter 2), Dr David Suggett suggested the use of rapid light curves (Chapter 2), and Dr Simone Blomberg gave advice on dealing with heteroskedastic data sets.

In Chapter 4, the manual work involved in running the experiment was split equally amongst myself, Chris Wall, and Dr Ross Cunning. In addition, Chris Wall performed post-processing of alkalinity measurements in R and performed manual work for a portion of the coral surface area measurements and chlorophyll a determinations, and Dr Ross Cunning contributed an R script that I used for the post-processing of oxygen concentration measurements. I am responsible for the project conception and overall experimental design, whilst Chris Wall, Dr Ross Cunning and Dr Ruth Gates contributed ideas that helped to refine the experimental design. Dr Helen Czerski contributed to the design of the modelling that I performed in Chapter 4 (Appendix 7.6).

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

None

Research Involving Human or Animal Subjects

No animal or human subjects were involved in this research.

7 Acknowledgements

My main supervisors, Associate Professor Sophie Dove and Dr. William Skirving, have been closely involved with my PhD candidature and the research described in this thesis from the beginning. I owe a huge debt of thanks to both of them for their expertise, advice, involvement and support from the word go. My PhD readers, Prof. Stuart Phinn and Prof. Catherine Lovelock, guided me through the milestone assessments. Prof. Ove Hoegh-Guldberg stepped in as a mentor at the milestones and several other points and was invaluable. Dr. Bronte Tilbrook made himself available to help as an advisor for a planned physical oceanographic side of my project.

Many volunteers were involved in my field trips to Heron Island. Emma Zoccola-McFarlane was onboard for my first long stint, then Rob Allen, Natalie Kerr, and Tanya Dodgen during later experiments. Their help made the otherwise complex experiments possible, and involved a significant commitment of their time and energy in a remote location. Robert Dempsey lent his skills to refine an experimental setup. Giovanni Bernal Carrillo, James Fang, Aaron Chai, Matheus Mello Athayde, Kyra Hay, Yuen-Yew Chang and Collette Bagnato lent their time and expertise to assist as divers at Heron Island. The Heron Island Research Station Staff including Liz Perkins, Kyra Hay, Sara Naylor, and many others provided technical, logistical and boating support.

At the Hawaiʻi Institute of Marine Biology, Prof. Ruth Gates provided lab space and project advice for an entire year, and really made my stay there possible. Chris Wall and Ross Cunning were patient collaborators and made possible an experiment that was too large for one person to tackle alone. Maggie Sogin, Hollie Putnam, Amy Eggers and Rafael Ritson-Willians helped with the day- to-day scientifics and, along with Beth Lenz, Laura Nunez-Pons, Danielle Claar, and many University of Hawaiʻi post-grads, attended to my morale. Jason Dulin’s volunteer help was also appreciated. Dr. Helen Czerski at University College London was a great collaborator on seawater carbonate modelling.

Many people at the University of Queensland (UQ) in Brisbane facilitated this research. Annamieke van den Heuval provided assistance as the Coral Reef Ecosystems Lab manager, as did Maya Carmi-Veal early on in my project. Hayley Ware provided vital assistance with UQ forms and financial issues as did Gail Walter, David Ngu, Lyn Lin, Carol Pomfrey, Julie-Ann Harlow, Chris Heaver and others on the School of Biological Sciences Professional Staff team. Jerome Delamare- Deboutteville, Linda Tonk and Virginia Nink advised on lab methods. Mike Phillips assisted throughout the project with diving safety and planning, and Cameron Veal helped on two fronts: dive training and optical advice. Several UQ workshops assisted with fabricating respirometers.

8 Susana Enríquez, Eugenio Méndez, Anastazia Banaszak, Robert Iglesias-Prieto and their collaborating instructors provided training in coral biophysics through their course at Universidad Nacional Autónoma de México. Mark Eakin hosted my multi-day visit to Coral Reef Watch at the NOAA Weather and Climate Centre in Maryland, and his staff contributed to my experience there.

I shared the PhD experience with many other friends at the University of Queensland, including Dorothea Bender, Catalina Reyes-Nivia, Francisco Vidal, James Fang, Norbert Englebert, Matheus Mello Athayde, Veronica Radice, Catherine Kim, Kristen Brown, Michelle Achlatis, Rene van der Zande, Bobby Lust, Pim Bongaerts and Manuel González-Rivero who have been sources of support and wisdom. Aaron Chai and Giovanni Bernal Carrillo, as well as sharing many long months at Heron Island with me, were encyclopaedias of practical aquatic wizardry. There are many other people who have assisted me during this project in some way – your help has been very much appreciated.

My parents have provided enormous support throughout my training, as well as the rest of my family, and I thank you sincerely.

9 Financial Support

This research was supported by an Australian Government Research Training Program Scholarship. Financial support for this research was received from the Australian Research Council (ARC) Linkage Grant LP 110200874 - “Next generation satellite tools for understanding change in coral reef ecosystems due to multiple global and local stressors” with the partner NOAA, as well as from ARC funding (CE140100020) of the ARC Centre of Excellence for Coral Reef Studies. Financial support was also received from a University Grants Award (Australian Wildlife Society), a Fulbright Postgraduate Scholarship and a Gregory Schwartz Enrichment Grant (Australian American Fulbright Commission), a Joyce W. Vickery Scientific Research Grant (Linnean Society of New South Wales), a Paul C. Silva Student Grant (International Phycological Society), a SPIE Optics and Photonics Education Scholarship (Society for Photo-Optical Instrumentation Engineers) and an Endeavour Postgraduate Scholarship (Department of Education, Australian Government). Funds for conference attendance were received from the Graduate Climate Conference (University of Washington), Ocean Sciences Meeting (Association for the Sciences of Limnology and Oceanography), the ARC Centre of Excellence for Coral Reef Studies and the School of Biological Sciences at the University of Queensland.

10 Keywords scleractinian, coral reef, coral bleaching, ocean acidification, ocean warming, photosynthesis, dinoflagellate, symbiosis, remote sensing, satellite.

Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 060601, Animal Physiology – Biophysics, 40% ANZSRC code: 050101, Ecological Impacts of Climate Change, 40% ANZSRC code: 090905, Photogrammetry and Remote Sensing, 20%

Fields of Research (FoR) Classification

FoR code: 0606, Physiology, 40% FoR code: 0501, Ecological Applications, 40% FoR code: 0909, Geomatic Engineering, 20%

11 Table of Contents

List of abbreviations used in the thesis...... 17 1 Introduction and literature review...... 20 1.1 Coral reefs under global change...... 20 1.2 Definition of coral bleaching...... 21 1.3 The need for the prediction of coral bleaching...... 23 1.4 The biological basis of mass coral bleaching...... 25 1.5 Methods for bleaching prediction...... 28 1.6 Which questions about coral bleaching prediction via satellite radiometry are important?...30 1.7 A new bleaching prediction method...... 35 1.8 Directions of this thesis...... 38 2 Water temperature, species differences and the direction of light change may influence photoacclimation rates in two scleractinian corals...... 40 2.1 Abstract...... 40 2.2 Introduction...... 41 2.3 Methods...... 45 2.3.1 Organism collection...... 45 2.3.2 Experimental design...... 46 2.3.3 Spectrometry...... 47 2.3.4 Quantum efficiency of photosystem II and rapid light curves...... 48 2.3.5 Oxygen flux...... 49 2.3.6 Symbiont cell densities, coral skeletal area and volume...... 49 2.3.7 Statistical analysis...... 50 2.4 Results...... 51

net 2.4.1 α and P rates...... 51 2.4.2 Areal symbiont densities...... 52 2.4.3 Photon pressure per symbiont...... 52

2.4.4 Fv/Fm...... 53 2.5 Discussion...... 54 2.5.1 Implications for bleaching prediction...... 60 2.5.2 Conclusions...... 61 3 The relationships of thermal bleaching thresholds to mortality and of light bleaching thresholds to α in three scleractinian corals...... 68

12 3.1 Abstract...... 68 3.2 Introduction...... 69 3.3 Methods...... 73 3.3.1 Organism collection...... 73 3.3.2 Experimental design...... 73 3.3.3 Photophysiology...... 76 3.3.4 Oxygen flux...... 76 3.3.5 Linear extension, symbiont cell counts and coral fragment area...... 77 3.3.6 Water-soluble host protein...... 78 3.3.7 Statistics...... 79 3.3.8 Calculation of α...... 79 3.4 Results...... 80 3.4.1 Control corals...... 80 3.4.2 Parameter levels among the species...... 81 3.4.3 Coral morality, host protein and bleaching...... 81

3.4.4 Qm and SVN...... 83 3.4.5 Physiological dysfunction following bleaching...... 84 3.5 Discussion...... 86 3.5.1 Conclusions...... 92 4 The impacts of ocean acidification on photosynthesis and population size of the dinoflagellate symbiont in scleractinian corals...... 103 4.1 Abstract...... 103 4.2 Introduction...... 104 4.3 Methods...... 108 4.3.1 Published data exploration...... 108 4.3.2 Site description...... 108 4.3.3 Coral collection...... 109 4.3.4 Experimental design...... 109 4.3.5 Respirometry, symbiont density, chlorophyll a and surface area measurements...... 111 4.3.6 Statistical analysis...... 112 4.4 Results...... 113 4.4.1 Published data exploration...... 113 4.4.2 Experimental results...... 113 4.5 Discussion...... 115 4.5.1 Conclusions...... 122

13 5 Conclusions...... 135 5.1 Overview...... 135 5.2 Refinement of the Light Stress Damage algorithm...... 135 5.3 Predicting coral mortality following coral bleaching...... 137 5.4 Biological differences among holobionts...... 138 5.5 Predicting bleaching of whole coral communities...... 139 5.6 Bleaching under acidification...... 140 5.7 Other acidification stress...... 142 5.8 Methodological advances in experimental coral physiology...... 143 5.9 Concluding remarks...... 144 6 References...... 145 7 Appendices...... 172 7.1 Light spectrum of the metal halide lamps used in Chapter 2...... 172 7.2 Determination of correlation type for the chlorophyll fluorescence time-series...... 173 7.3 Probability values for temporal control coral samples used in Chapter 3...... 177 7.4 Graphs of parameters for which statistically significant interactive effects exist...... 178 7.5 Effects of temperature on calcification rate...... 183 7.5.1 A. muricata linear extension...... 183 7.5.2 Mounding Porites spp. linear extension...... 184 7.6 Modelling of future levels of ocean acidification in Kāne‘ohe Bay...... 186

14 List of Figures

Figure 2.1: Schematic of the equipment layout for the optical measurements of reflectance...... 63 Figure 2.2: The slope, α, of the light-limited part of an electron transport rate vs irradiance curve..64 Figure 2.3: Net photosynthesis measured via net oxygen production per unit of surface area...... 65 Figure 2.4: Areal densities of symbiont cells...... 66 Figure 2.5: Estimated photon pressure per symbiont cell within each treatment on days 19-21...... 66 Figure 2.6: Times series of the dark-adapted yield of photosystem II, at (a) 24°C and (b) 29°C...... 67 Figure 3.1: Schematic illustrating the timing and temperature of the four sub-experiments...... 95 Figure 3.2: Symbiont cells per cm² among the temperature levels and light levels...... 96 Figure 3.3: Water-soluble protein per cm² of coral surface...... 97 Figure 3.4: Mortality in each light treatment and species at the end of the 31°C treatment...... 98 Figure 3.5: Maximum excitation pressure over photosystem II among the levels of each factor...... 99 Figure 3.6: Stern-Volmer quenching parameter for non-photochemical quenching...... 100 Figure 3.7: Dark-adapted quantum yield of photosystem II among temperature and light levels.. .101 Figure 3.8: Maximum net photosynthesis per cm² of coral surface...... 102 Figure 4.1: Partial pressure of carbon dioxide over time in the acidified and control treatments....128 Figure 4.2: Histograms summarising published data of acidification impacts on coral symbionts. 129 Figure 4.3: Areal densities of chlorophyll a...... 131 Figure 4.4: Dark respiration...... 132 Figure 4.5: Gross photosynthesis and P/R ratio...... 133 Figure 7.1: Light spectrum of the 10,000 K metal halide lamps from 325-700 nm...... 172 Figure 7.2: Symbionts·cm⁻² in Acropora muricata, with light × temperature interactions shown...178 Figure 7.3: Symbionts·cm⁻² in Montipora monasteriata, with light × temperature interactions...... 179

Figure 7.4: Fv/Fm in mounding Porites spp., with light × temperature interactions shown...... 180

Figure 7.5: Fv/Fm in Montipora monasteriata, with light × temperature interactions shown...... 181

Figure 7.6: Qm in Montipora monasteriata, with light × temperature interactions shown...... 182 Figure 7.7: calcification rates per unit of coral surface area of the control specimens...... 183 Figure 7.8: calcification rates per unit of coral surface area of the experimental specimens...... 184 Figure 7.9: coral surface areas at each sub-experiment’s end of the control specimens...... 185 Figure 7.10: coral surface areas at each sub-experiment’s end of the experimental specimens...... 185 Figure 7.11: Structure of the iterative numerical model used to simulate future levels of partial pressure of carbon dioxide in seawater in Kāne‘ohe Bay...... 188

15 List of Tables

Table 2.1: Probability values for statistical tests...... 61 Table 3.1: Probability values for statistical tests...... 91 Table 4.1: Seawater physical and chemical parameters...... 120 Table 4.2: Summary of the numbers of published instances of thirteen photobiological variables measured in experimental exposures of coral species to acidification...... 121 Table 4.3: Probability values given by ANOVAs performed on mixed effects models...... 122 Table 7.1: ACF results and PACF results for the chlorophyll fluorescence time-series...... 174 Table 7.2: AIC and BIC values for the GLS models fitted to the chlorophyll fluorescence time- series data for days 1-14...... 175 Table 7.3: ANOVA table of physiological parameters compared among the control (22°C) treatments of each of the four sub-experiments...... 177

16 List of abbreviations used in the thesis.

A absorptance α the slope of the light-limited part of a photosynthesis versus irradiance curve acclim PAR the sum of changes in PAR (adjusted for photoacclimation) on all previous days. ACF autocorrelation function AIC Akaike information criterion ANOVA analysis of variance

APAR average absorptance in the photosynthetically active spectrum

ĀPAR average APAR over all samples in a treatment AR autoregressive ARMA autoregressive moving average ATP adenosine triphosphate BIC Bayesian information criterion ca. circa °C degrees Celcius chl chlorophyll cm centimetre

CO2 carbon dioxide d.f. degrees of freedom DGGE denaturing gradient gel-electrophoresis DHD degree heating days DHM degree heating months DHW degree heating weeks DIC dissolved inorganic carbon δDIC change in dissolved inorganic carbon DNA deoxyribonucleic acid DR dark respiration ed, eds editor, editors EDTA ethylenediaminetetraacetic acid EEE excess excitation energy

Ek sub-saturation irradiance

Ek-ETR sub-saturation irradiance level determined from an ETR versus irradiance curve. ETR electron transport rate

ETRmax maximum electron transport rate ɛ photoacclimation rate

Fm chlorophyll fluorescence in the evening, with fully reduced electron acceptors

Fmʹ chlorophyll fluorescence at daytime, with fully reduced electron acceptors FSC-A forward scatter area

Fv/Fm dark-adapted quantum efficiency of charge separation at photosystem II

ΔF/Fmʹ quantum efficiency of photosystem II under daytime illumination

17 GCM general circulation model GHG greenhouse gas GLS generalised least squares

H2O2 hydrogen peroxide hr hour ITS2 internal transcribed spacer between the 5.8S and large subunit structural rRNA genes K Kelvin

Kd diffuse attenuation coefficient km kilometre kW kilowatt l litre LEDR light enhanced dark respiration LSD Light Stress Damage bleaching prediction algorithm m metre M moles per litre MA moving average matm milliatmospheres min minute ml millilitre mm millimetre MMM maximum monthly mean mol mole MPSA mean positive summer anomaly mV millivolt n sample size

N2 diatomic nitrogen gas NaCl sodium chloride NADPH reduced nicotinamide adenine dinucleotide phosphate ND neutral density nm nanometre NO nitric oxide NOAA National Oceanic and Atmospheric Administration NPQ non-photochemical quenching

O2 diatomic oxygen gas – ·O2 superoxide 1 O2 singlet oxygen ·OH hydroxyl radical p probability value pp pages

P vs E photosynthesis versus irradiance P/R ratio ratio of gross photosynthesis to respiration

18 PACF partial autocorrelation function PAM pulsed amplitude modulated fluorometer PAR photosynthetically active radiation pCO2 partial pressure of carbon dioxide

δpCO2 change in partial pressure of carbon dioxide PCR polymerase chain reaction Pgross gross photosynthesis

Pmax maximum rate of photosynthesis pmol picomole Pnet net photosynthesis POAMA predictive ocean atmosphere model for Australia ppm parts per million PsbA the D1 protein, which forms one half of the reaction core of photosystem II PSI photosystem I PSII photosystem II PSU photosynthetic unit PVC polyvinyl chloride plastic

Qm maximum excitation pressure over photosystem II R reflectance r.c.f. relative centrifugal force RCP8.5 representative concentration pathway 8.5 RLC rapid light curve ROS reactive oxygen species s second SE sub-experiment s.d. standard deviation s.e.m. standard error of the mean sig. significant sp. species (singular) spp. species (plural) SST sea surface temperature

SVN Stern-Volmer quenching parameter for non-photochemical quenching temp. temperature tanh hyperbolic tangent function µatm microatmosphere µl microlitre µm micrometre µmol micromole UV ultraviolet radiation UVA ultraviolet radiation between 320-400 nm W watt

19 1 Introduction and literature review

1.1 Coral reefs under global change

Coral reefs are ecosystems of high cultural, environmental and economic value, yet are one of the planet’s most threatened ecosystems. Coral reefs support a level of biodiversity (species·km2) that exceeds that of tropical rainforests (Reaka-Kudla 1997; Small et al. 1998). Reefs play important roles in global ecology, biogeochemical cycles, and in providing services to human societies. They protect coasts from erosion, provide nursery habitat for fish recruits, filter some pollutants from marine waters, and play globally important roles in nitrogen fixation and calcium precipitation (Moberg and Folke 1999). They support commercial and artisanal fishing, tourism, the aquarium trade and many other biodiversity-based human industries (Moberg and Folke 1999; Cesar 2000). Most coral reefs are at risk from local impacts such as over-exploitation and marine pollution, but all are threatened by the global impacts of greenhouse gas (GHG) emissions to an extent that their continued existence is in doubt (Hoegh-Guldberg 1999; Hoegh-Guldberg et al. 2007).

Several impacts of greenhouse emissions on reef corals are recognised: (1) increases in sea temperature (Hoegh-Guldberg 1999); (2) ocean acidification (Kleypas et al. 1999; Fabricius et al. 2011); and (3) in some areas, changes in the intensity of the light reaching the benthos (Sheppard 1999; Masiri et al. 2008; Fischer and Jones 2012). The incidence of sea temperature anomalies above the normal climatological range is increasing due to GHG emissions. The take up of additional atmospheric CO2 by the ocean has resulted in an oceanic pH decrease and the decreased availability of carbonate ions for calcifying marine organisms. The predicted trajectory of ocean acidification will place the calcification mechanism and other physiological processes of corals under high levels of stress (Kleypas et al. 1999). Climate change will change the pattern of cloud cover over reefs; a decrease in cloud cover has already been observed along the southern Great Barrier Reef (Masiri et al. 2008). Changes in patterns of landbased runoff due to climate change will affect turbidity above coral reefs. Changes in light level received by reefs as a result of changes in cloud cover and turbidity could impact on the coral-dinoflagellate symbiotic relationship (Mumby et al. 2001b). Importantly, changes in sea temperature, light levels, and ocean pH are expected to interact, synergistically enhancing the negative effects on corals of the individual stressors (Vogel et al. 2015; Prada et al. 2017).

20 The effects of GHG emissions on reef corals are occurring in the context of a suite of other human impacts that have already triggered long-term trajectories of decline on many reefs (Pandolfi et al. 2003; Bellwood et al. 2004). Increased sediment load due to land use practices causes decreased light availability in the water column as well as the smothering of corals via particulate accumulation on tissue surfaces (Fabricius and Wolanski 2000). Pesticides within terrestrial runoff cause toxicity to corals and dinoflagellates (Kühlmann 1988; Fabricius and De’ath 2004). Additional phosphates and nitrogen from fertilisers or road base cause nutrient imbalances in the reef ecosystem (Paton-Walsh et al. 2011), leading to eutrophication and changing the competitive interactions among reef organisms. General degradation of many reefs has resulted from extractive industries, including the practices of overfishing (Pandolfi et al. 2003), limestone mining (Brown 2011), and removal of live specimens for the aquarium trade (Hughes et al. 2003). These stressors will exacerbate the effects of GHG emissions on corals (Carpenter et al. 2008).

In this era of global change, very large areas of reef are susceptible to coral bleaching, leaving reef managers reliant upon remote-sensing forecasts of coral bleaching for estimates of when and where bleaching will occur (Marshall and Schuttenberg 2006). Coral bleaching prediction methods have been in use for some time (Liu et al. 2006). Whilst accurate at predicting the onset of coral bleaching, they are not accurate at forecasting other outcomes of thermal stress, including coral mortality (McClanahan et al. 2007c; Eakin et al. 2010a). Development of better bleaching prediction products is hampered by a lack of ecophysiological knowledge on many aspects of the coral bleaching process. These include the way that light and temperature interact to produce stress in coral tissues, recovery or mortality following bleaching, differences in the stress responses of different coral taxa, and how these differences affect the responses of heterogeneous coral communities. This thesis will report on experiments that were designed to provide information that will facilitate the ongoing development of algorithms for bleaching and mortality prediction under thermal and light stress.

1.2 Definition of coral bleaching.

Corals consist of a symbiosis of the coral animal (the host), single-celled dinoflagellates of the genus Symbiodinium (the symbiont) that are enclosed within the coral animal cells, and a bacterial community (the microbiome) (Knowlton and Rohwer 2003). The three components, living together in one association, are known as the holobiont (Rohwer et al. 2002). The phenomenon of “coral bleaching” is the loss of dinoflagellate cells from coral tissue (Yonge and Nicholls 1931; Goreau

21 1964), the loss of photosynthetic pigmentation from the dinoflagellate cells, or both (Kleppel et al. 1989). The phenomenon of mass coral bleaching (of entire reefs at a large geographic scale) typically involves the loss of dinoflagellates with or without the loss of pigments from dinoflagellate cells (Hoegh-Guldberg 1999). Loss of dinoflagellates and/or loss of chlorophyll does occur as a necessary part of physiological adjustments made in response to natural cycles, such as seasonal changes in temperature or light, but the severity of the response tends not to be as extreme as during a bleaching event (Fagoonee et al. 1999; Fitt et al. 2000; Secord and Muller-Parker 2005). Coral bleaching (at a moderate to severe level) is often a response to a factor inducing physiological stress in the coral host and/or dinoflagellate cells that exceeds the acclimation capacity of the organism. These stressors can include herbicide pollution, cyanide fishing, low salinity, darkness, large increases in visible light (photosynthetically active radiation), high UV radiation and substantially elevated temperature (Baker et al. 2008). This thesis refers to bleaching caused by elevated water temperature as “thermally induced bleaching”, and bleaching caused by dramatic increases or decreases in photosynthetically active radiation as “light change-induced bleaching”.

Dinoflagellate cells can be lost from coral tissue in numerous ways. This includes the detachment of host cells that contain dinoflagellates from the coral endoderm (Gates et al. 1992), autophagy of symbiont cells by host cells (Downs et al. 2009), or necrosis of host cells that contain dinoflagellates with either the release of intact dinoflagellate cells into the coelenteron (facilitating symbiont expulsion) or the necrosis and programmed cell death of dinoflagellate cells (Dunn et al. 2002). Thus, dinoflagellate cell loss often occurs in synchrony with host cell loss (sometimes visible to the naked eye as tissue sloughing). Furthermore, thinning of and apoptosis within the host tissue layers may precede large-scale symbiont cell loss (Ainsworth et al. 2008). Degradation of host cell mitochondria and changes in symbiont mitochondrial function may occur (Dunn et al. 2012; Hawkins and Warner 2017).

When measuring coral bleaching, different proxies are used, depending on the scale of the observations. When a reef is observed by scuba or snorkel diving, several methods are in use, such as the visual assessment of coral colour compared to a colour scale on a reference card (Siebeck et al. 2006), or photographic assessment (Johnson and Goulet 2007; Winters et al. 2009). In aquaria experiments on pieces of live coral, bleaching is usually measured as a statistically significant decline in Symbiodinium cell density and/or chlorophyll content (per unit area or per symbiont cell). At a cellular level, processes that are attendant to coral bleaching, such as structural changes involved in cellular or organelle degradation, are often measured (Gates et al. 1992; Dunn et al. 2002). In this thesis’s experiments, coral bleaching in experimental treatments will be measured as

22 any statistically significant decrease in symbiont cell densities per unit area of coral surface, compared to corals in a control treatment.

Definitions of coral bleaching “severity” also involve different levels of resolution, depending on the scale of observations. Coral bleaching severity at a reef scale is often defined in a whole-of- benthos sense, such as the percent of corals that are totally white (Maynard et al. 2009), or the percent of corals displaying whiteness on at least part of the colony (Wooldridge and Done 2004). In aquaria experiments, coral bleaching “severity” may instead refer to the proportion by which symbiont density, averaged over all fragments within one treatment, declines compared to a control treatment (Glynn and D’Croz 1990). As the proxy for bleaching changes depending on the scale of observations, it can be difficult to apply results discovered at one scale to further the understanding of bleaching at a different scale. Nevertheless, the study and monitoring of coral bleaching require the synthesis of information obtained at different scales because of the huge geographic extent of reefs affected by bleaching and the fact that molecular or organism-scale processes can influence larger-scale patterns.

1.3 The need for the prediction of coral bleaching

Coral bleaching at the level of individual colonies or small patches of reef has been noted for over 100 years, whilst bleaching events involving large areas of coral reef were observed for the first time in the 1980s (Williams and Bunkley-Williams 1990), and have been observed regularly since then (Baker et al. 2008). During the 1980s, mass coral bleaching was first linked to El Niño events that caused high water temperature anomalies (Glynn 1984). That climate change effects mass coral bleaching, via long-term ocean warming and via exacerbation of meteorological events that cause acute ocean temperature increases, has since been conclusively demonstrated (Hoegh-Guldberg 1999).

Coral bleaching has a broad array of impacts on coral reef ecosystems. During bleaching, corals receive fewer photosynthates from their symbionts (Porter et al. 1989), skeletogenesis is disrupted (Goreau and Macfarlane 1990), tissue biomass decreases (Mendes and Woodley 2002), tissue repair ceases (Meesters and Bak 1993), and mucus production (after an initial increase) may decline (Baker et al. 2008). Various degrees of coral mortality may result (Baird and Marshall 2002). Surviving corals may recover their dinoflagellate populations after a period of non-stressful conditions (Szmant and Gassman 1990). Following bleaching, corals may suffer reduced gamete

23 production (Ward et al. 2000), reduced fertilisation success (Omori et al. 2001), reduced reef-scale recruitment due to Allee effects (Knowlton 2001), decreased energy stores (Grottoli et al. 2004) and higher susceptibility to infectious diseases (Rogers et al. 2008). Effects on other reef animals may be equally severe. Obligate corallivorous species may die soon after a mass coral mortality event, and fish and invertebrates that utilise the high rugosity provided by the three-dimensional framework of corals may decline (Iglesias Prieto et al. 2003; Pratchett et al. 2009). Prevalence of bioeroders may increase due to the abundance of dead coral skeletons (Hutchings and Hoegh- Guldberg 2008), and fish communities may restructure as the benthos shifts from live coral- dominated to algae-covered limestone (Graham et al. 2008).

Several management options exist to reduce the effects of coral bleaching, which depend, at differing spatial and temporal scales, on knowledge of bleaching extent and severity. As local actions cannot reduce the warming associated with climate change, management actions instead focus on local factors that affect coral bleaching severity, and on supporting coral recovery and long-term survival following bleaching events (Marshall and Schuttenberg 2006). Chronic elevation of dissolved inorganic nitrogen and soluble reactive phosphorous (at, respectively, 3 × and 9 × the background concentration at the experiment location), to a level within the range seen at anthropogenically polluted reefs globally, was found to increase bleaching severity (the proportion of colonies exhibiting colour loss) (Vega Thurber et al. 2014). A reduction in these pollutants may thus improve coral resilience to bleaching. Increases in reef resilience can be achieved by reducing local stressors, such as the over-exploitation of herbivorous fish, which may assist corals to recover from thermal stress events by enabling the control of algal overgrowth (Hughes et al. 2003; Marshall and Schuttenberg 2006). As pollution abatement and other local or regional actions take many years, long-term modelling of bleaching impacts or analyses of previous bleaching events can help build a case for management interventions at these scales.

Other management responses to bleaching occur shortly before, during, or in the months after a bleaching event, and thus rely directly on knowledge of the extent and duration of that bleaching episode (McClanahan et al. 2007a). Restricting activities that physically injure corals during the bleaching episode helps to mitigate the lowered capacity that bleached corals have to heal (Marshall and Schuttenberg 2006). These activities include boat anchoring, anthropogenic sources of siltation such as dredging, the use of fishing gear types that pose a high risk of contact with the benthos, and some tourism activities that risk harming coral (Marshall and Schuttenberg 2006).

24 To provide these responses, reef managers need to conduct operations that require accurate predictions of coral bleaching onset, mortality or recovery. Surveys (via aircraft, diving, satellite photogrammetry, citizen science or otherwise) of coral bleaching are an essential response to an event, and advance forecasts of bleaching onset will spur managers to commit resources to these activities (Maynard et al. 2009). The difficulties of undertaking reconnaissance at regional scales (Baker et al. 2008) means that accurate predictions greatly improve the efficiency of survey effort. Predictions of the timing and spatial extent of mortality enable managers to plan the timing of reef surveys that conclusively link any mortality to the thermal anomaly. Surveys conducted too early may not observe mortality, whilst surveys conducted too long after mortality occurs are affected by the uncertainty that the observed mortality may have another cause (Marshall and Schuttenberg 2006). Assessment of recovery from bleaching is important for determining the length of time that a reef is more sensitive to local anthropogenic stresses following a bleaching event, such as mechanical injury. Coral bleaching predictions may also aid managers in assessing the socio- economic impacts of bleaching, and for the mitigation of socioeconomic impacts caused by management interventions. For instance, temporary bans on fishing during a period of coral bleaching and recovery may necessitate temporary compensation of fishers or tourism operators (Marshall and Schuttenberg 2006).

Beyond the reef management sector, predictions of coral bleaching are used by scientists and by the broader community. Coral bleaching predictions are used by biologists to contextualise physiological experiments on corals (Ainsworth et al. 2008). Coral bleaching predictions over a historic period have been used to identify that coral reefs had adapted partially to recent ocean warming (Logan et al. 2014). Coral bleaching predictions under various climate scenarios are used in understanding the impacts of climate change and have played an important role in building a case for climate action (Veron et al. 2009). With sufficient time, businesses and communities affected by coral bleaching and mortality may be able to adapt (Marshall and Schuttenberg 2006), and coral bleaching predictions may increasingly play a role in providing the advance notice required for adaptation planning.

1.4 The biological basis of mass coral bleaching

Environmental stressors appear to cause coral bleaching through direct impacts on the coral host, or through causing the dinoflagellate symbionts to (a) produce toxic substances (Lesser 1997) or (b) switch from a symbiotic to a parasitic role (Iglesias-Prieto 1995). In this review, I focus on the

25 stressors prevalent during the meteorological conditions that are typical during mass coral bleaching events: ocean warming, increased UV light, and increased visible light. Ocean warming that exceeds temperature thresholds can occur during high-pressure systems, characterised by low wind and little cloud (MacKellar and McGowan 2010). Clear skies permit large quantities of solar insolation to reach the ocean surface, thermally heating the water (Falter et al. 2014). The accompanying high visible and UV light may penetrate to greater ocean depths as the low wind conditions promote thermal stratification, and increased clarity, of the water column (Strong et al. 2006; Heron et al. 2008). The cyclical climate phenomena of El Niño and La Niña events, Pacific Decadal Oscillation and the Indian Ocean Dipole lead to heat and light stress at large scales (Glynn 1984; Wellington et al. 2001; Jokiel and Brown 2004; McClanahan et al. 2007b). For instance, El Niño causes drops in sea level (leading to higher subsurface UV and visible light levels) in the western Pacific (Glynn 1984).

Bleaching can result when heat or UV light has a direct impact on the coral animal. Through its effect on respiration in the host mitochondria, thermal stress may cause increased reactive oxygen species (ROS) production in the host (Nii and Muscatine 1997). Elevated temperatures trigger apoptosis (programmed cell death) in the host and/or the symbionts (Dunn et al. 2002), or the

- shedding of coral endodermal cells (Gates et al. 1992) as a result of reactive oxygen species (O2 ,

1 H2O2, O2, ·OH) production (Tchernov et al. 2011). Apoptosis occurs at a “background” level in normal coral tissue, but is upregulated in response to stressful temperatures, and is highest at the time of symbiont loss during bleaching (Dunn et al. 2004), resulting in the destruction of all dinoflagellates within apoptotic host tissue (Dunn et al. 2002). Thermally induced apoptosis can lead to the mortality of the coral, depending on whether or not the particular coral species possesses the ability to down-regulate the programmed cell-death cascade (Tchernov et al. 2011). Apoptosis in corals may be activated at an early stage of thermal stress (Ainsworth et al. 2008). UV penetration to the benthos causes the production of reactive oxygen species and damage to coral DNA (Lesser et al. 1990; Ferrier-Pages et al. 2007), thus retaining the potential to trigger apoptosis pathways.

Elevated temperatures, high visible light or high UV light cause changes to the photosynthetic machinery of the dinoflagellate symbiont that can result in the production of toxins and subsequent bleaching (Coles and Jokiel 1978; Lesser et al. 1990). Elevated temperatures can suppress the CO2 fixation mechanism (Calvin-Benson cycle) of the symbionts, resulting in an over-reduced electron transport chain, a reduction in the ability of photosystem II (PSII) to photochemically utilise incoming photons, and production of ROS as a result (Jones et al. 1998; Buxton et al. 2012;

26 Bhagooli 2013; but see Leggat et al. 2004). Elevated temperatures may cause damage to the thylakoid membranes (Iglesias-Prieto et al. 1992), which may become energetically uncoupled but still able to evolve oxygen, some of which is converted into ROS by photosystem I (PSI) (Tchernov et al. 2004). Elevated temperatures may also damage PSII directly, inhibiting the protein synthesis- based repair of photo-damage to PSII and causing photoinhibition (Iglesias-Prieto 1995; Warner et al. 1996, 1999; Takahashi et al. 2004). PSII may be damaged by high light at certain wavelengths, most strongly by UV, but also by the blue, yellow and red visible bandwidths (relative efficiencies of damage, respectively: 2–8, 1.9, 2.5 and 1) (Nishiyama et al. 2006; Takahashi et al. 2010). Elevated temperatures may also increase the production of nitric oxide synthase, and therefore nitric oxide (NO), by the symbionts (Buxton et al. 2002; Trapido-Rosenthal et al. 2005). ROS or NO produced by the above pathways causes damage to both the coral host and symbionts (Lesser et al. 1990; Trapido-Rosenthal et al. 2005). Bleaching occurs because toxin damage to PSII decreases the pigment concentration in dinoflagellate cells (Takahashi et al. 2004), or because the coral destroys or expels the dinoflagellates to prevent continued damage to its tissue (Hoegh-Guldberg and Smith 1989; Dunn et al. 2002).

Damage to any part of the photosynthetic pathway downstream from the antennae will impair the photochemical quenching of excited chlorophyll. Thus in a heat-, UV- or visible light-damaged photosystem, elevations in photosynthetically active radiation will, by increasing the excited-state chlorophyll population, increase ROS production (Hoegh-Guldberg 1999; Lesser and Farrell 2004). Put another way, stressors such as elevated heat and UV reduce the visible light threshold for photoinhibition of the dinoflagellate symbionts (Hoegh-Guldberg 1999). This highlights an important difference between the impacts of UV light and visible (photosynthetically active) light. Whilst UV can damage the proteins involved in photosynthesis as well as in other sites of the holobiont, it is the inability of damaged photosystems to quench high levels of visible light energy that is the cause of photoinhibition-induced coral bleaching (Hoegh-Guldberg 1999).

Damage to the host directly or to the dinoflagellate photosystems may also cause bleaching through impairing the usual supply of substances from host to symbiont or vice versa. It has been theorised that thermal stress may decrease the host-mediated supply of photosynthetic cofactors such as Ca2+ or photosynthetic substrate (CO2), leading to photoinhibition (Dove and Hoegh-Guldberg 2006; Dove et al. 2006), or even directly triggering an apoptosis mechanism in the symbionts (Dunn et al. 2002). Damage to components of the dinoflagellate photosynthetic apparatus by temperature, visible light or UV reduces the capacity for carbon fixation. Reduced photosynthesis due to substrate or cofactor limitation or to photosystem damage may directly reduce translocation of

27 photosynthate from symbiont to the host. At this point, the dinoflagellate cells may become parasitic (Iglesias-Prieto 1995), as they are facultative heterotrophs with the capability to subsist on host-supplied organic matter (Steen 1987; Banaszak et al. 2013) and this mode of survival is detrimental to the host (Steen 1986). The host cells may actively dissociate parasitic dinoflagellates from their tissue, causing bleaching. On the other hand, the ability (when present) of a coral to switch to heterotrophy, combined with a sufficient supply of food in the water column, may alleviate bleaching (Borell and Bischof 2008; Wooldridge 2014a), and even assist in the repair of photosystems (Hoogenboom et al. 2012).

1.5 Methods for bleaching prediction

Computational methods (“products”) for predicting the global occurrence of coral bleaching from remotely-sensed data have been in use for 15 years. Currently, radiometers on eight different satellites provide a daily, 0.05° (ca. 5 km) resolution sea surface temperature (SST) dataset that covers the entire globe (Liu et al. 2017). Using this dataset, current, daily predictions of coral bleaching by the National Oceanic and Atmospheric Administration (NOAA) are made via the product Degree Heating Weeks (Wellington et al. 2001), which in turn relies on another product, HotSpot (Strong et al. 1997).

Temperature processing algorithms for bleaching prediction rely on simplified models of coral bleaching responses under thermal stress. Degree Heating Weeks (DHW) has a model based on three assumptions: (1) temperatures of 1°C or more above the maximum monthly mean (MMM), which is the mean temperature of the warmest month in the climatology of a location, will begin causing physiological stress; (2) the ability of any temperature ≥1°C above MMM to cause coral bleaching is dose dependent; and (3) the cumulative dose of all stressful temperatures within the previous 12 weeks is the determinant of bleaching (Gleeson and Strong 1995; Strong et al. 2006). The MMM is derived from night-time satellite-detected sea-surface temperatures for the years 1985-1993 (Liu et al. 2006). The HotSpot is the number of degrees or the fractions of a degree that the sea-surface temperature is above the MMM at a particular location, during a particular week (Liu et al. 2006). The DHW index is the cumulative total of the weekly HotSpot values that are ≥ 1, over the previous twelve weeks (Liu et al. 2006). Field observations indicate that a DHW of 4 or more correlates with the onset of mass bleaching one to three weeks later and that a DHW of 8 is suggestive of the onset of coral mortality (Eakin et al. 2010b).

28 ReefTemp, an alternative temperature processing algorithm for the Great Barrier Reef, uses the amount of heat stress accumulated, and additionally, the rate of heating, to calculate bleaching likelihood. For any day in the Austral summer period (December to March, inclusive), ReefTemp calculates Degree Heating Days (DHD), which is the sum, up to that date, of the positive anomalies of daily average SST (the baseline for determining anomalies is the 2002-2011 mean summer temperature) (Garde et al. 2014). ReefTemp then calculates the Mean Positive Summer Anomaly (MPSA), a measure of heating rate, which is the average rate of accumulation of Degree Heating Days during the summer period. The DHD and MPSA indices are individually used to predict the position of a reef on a graded scale of bleaching severity, for instance, the bleaching of 10-20% of coral colonies is predicted by a DHD between 70-78 or an MPSA between 1.5-1.7. The graded scales of bleaching percentages against DHD and MPSA values were calibrated using a satellite SST time-series and diver bleaching surveys for the Great Barrier Reef in 2002 (Maynard et al. 2008b).

Other temperature processing algorithms for bleaching prediction use ocean-atmosphere models to predict sea-surface temperatures. The Australian Bureau of Meteorology developed the Predictive Ocean Atmosphere Model for Australia (POAMA), an ocean-atmosphere computational model that predicts sea-surface temperatures several months in advance (Spillman and Alves 2009). These projections are made on a monthly rather than a weekly timeframe, so the near-future bleaching risk is calculated from these projections using Degree Heating Months (DHM) (Spillman et al. 2011). Using the fraction of an observed monthly average temperature that is above the MMM, the DHM algorithm operates by summing these fractions over a rolling three-month window and predicts bleaching onset when the total exceeds 1 (Donner et al. 2005). Another use of DHM is in long-term predictions of coral bleaching, between now and 2099, which have been made using SST data from General Circulation Models (GCM; models of the Earth’s climate during future decades that are calculated for particular greenhouse gas emissions scenarios) (Donner et al. 2005, 2007; Hoegh- Guldberg et al. 2014; van Hooidonk et al. 2015, 2016). Degree Heating Months are calculated using the SST predicted by one of these models, summed over rolling periods of 3 months. The Degree Heating Months are then converted to Degree Heating Weeks using a multiplication factor of 4.35 (Donner et al. 2005). Due to the coarse resolution of GCMs (1 × 1°), such predictions can inform debate but not provide the detail needed for conservation planning, except when post-processing techniques are used to downscale GCMs to a finer (e.g. 4 × 4 km) scale (van Hooidonk et al. 2016)

To inform conservation planning, other predictive approaches have been used to estimate the long- term bleaching sensitivity of distinct areas within reef systems or regions. The technique of

29 Bayesian Belief Networks has been used to identify the parameters in multidimensional datasets that best predict bleaching. On the Great Barrier Reef, the percent of coral colonies displaying bleaching during 2002 was best predicted by the parameters of highest 3-day summer SST, acclimatisation temperature (the summertime characteristic pattern of maximum SST), capacity for cooling through tidal mixing, and coral community type (Wooldridge and Done 2004). Once selected through a Bayesian Belief Network, the parameters that best predict past levels of bleaching and mortality across in a reefscape could then be used to identify areas of resilience for inclusion in protected area networks (Wooldridge and Done 2004). Another approach is to map the hydrological characteristics of a reef system, and use physical modelling to identify marine areas of high and low thermal capacitance (those areas that will, respectively, heat slowly or quickly when heat is added to the system) (Skirving et al. 2006a, 2010). Modelling of this type has been used to improve the design of the marine protected area networks (Reef Resilience Network 2014).

1.6 Which questions about coral bleaching prediction via satellite radiometry are important?

Given that a range of methods for the prediction of coral bleaching exists, why is further development of these techniques required? There are several compelling answers to this question.

Incomplete physiological foundation underlying bleaching prediction methods

The algorithms behind the various bleaching prediction methods only partially capture the physiological processes underlying coral bleaching. The discordance between physiology and the prediction methods falls into several areas: (1) issues with the definition of temperature thresholds for physiological stress and temperature–duration thresholds for bleaching onset, and a lack of knowledge of temperature–irradiance–duration relationships for bleaching onset; (2) no use of the known relationship between temperature history and thresholds for the onset of bleaching; and (3) a lack of a formalised relationship between light history and bleaching onset/severity thresholds.

Whilst climatological temperature thresholds for thermal stress are well established, the temperature–duration relationships for bleaching onset may not be well defined, and temperature– irradiance–duration relationships for bleaching onset remain poorly known. Prior to the modern era of mass coral bleaching, the reef-specific temperature for bleaching onset was identified as being 1- 2°C above the annual summer maxima (Coles et al. 1976; Goreau and Hayes 1994). Several authors went one step further to recognise that the number of degrees of heating above a temperature

30 threshold (the anomaly), combined with the time spent at this anomaly, could explain bleaching better than thermal threshold exceedance alone (Gleeson and Strong 1995). The DHW and DHM algorithms recognise this through use of the combination of thermal anomaly and duration of exposure to predict coral bleaching. However, the relationship among bleaching onset, degrees of temperature above the stress threshold and the duration of exposure is not rigorously defined. This is because data types available for establishing the combined thermal anomaly and duration thresholds at the time of the development of DHW were informally reported, subjective measurements of bleaching (Liu et al. 2003; Skirving et al. 2006b). A number of confounding factors may affect these measurements, such as the effect of species differences and the survey analysis methods for assessing bleaching at reef scales. A further conceptual advance came when Jokiel (2004) put forward a model in which bleaching thresholds are identified by a function of temperature anomaly, light anomaly, and the duration of exposure to both. However, the characterisation of such relationships using field observations, satellite data, mesocosm experiments or aquaria experiments has not been widely performed.

Heating rate, temperature history and seasonal variation are known to influence the thermal threshold for the onset of coral bleaching. An effect of heating rate on bleaching thresholds was identified by Middlebrook et al. (2010): Acropora muricata heated at 1°C day-1 attained the same level of symbiont loss at half the DHW of corals heated at 0.5°C day-1. Heating rate has been incorporated into the ReefTemp bleaching prediction method (Maynard et al. 2008b) but is not a component of DHW or DHM. Bleaching onset is also influenced by recent thermal history, particularly the occurrence of prior short-term temperature exposures that are slightly above maximum monthly averages, followed by a recovery period (lower temperatures) before the onset of a high-temperature event (Ainsworth et al. 2016). Middlebrook et al. (2008) identified a protective effect of temporary high heat exposure (“pre-heating”) in two groups of A. aspera corals, one heated to 31°C briefly, and the other not. The pre-heated group experienced no reduction in symbiont densities during to a subsequent 6-day exposure to 34°C, whilst the second group experienced a 40% decline. A second study confirmed the protective effect of pre-heating on thermal bleaching thresholds in A. aspera (Ainsworth et al. 2016). Similar studies have documented both the presence (Bellantuono et al. 2012) and absence (Middlebrook et al. 2012) of a protective effect of pre-heating on bleaching thresholds in A. millepora. The effect of pre-heating is not yet incorporated into any bleaching prediction algorithms. Lastly, evidence suggests that the temperature thresholds for bleaching are 1-2°C lower in winter months than in summer months (Berkelmans and Willis 1999), although other experiments have documented the opposite effect (summer thresholds < winter thresholds) (Fisher et al. 2012). Discarding a fixed threshold

31 (summertime maximum monthly mean) in favour of a seasonally varying threshold has been found to improve the predictive accuracy of DHW (Weeks et al. 2008).

Another area where bleaching prediction algorithms do not align with physiological mechanisms is that light history (prior periods of high light exposure) may influence thresholds for the onset of coral bleaching. Exposure to elevated visible light levels in the month prior to high water temperatures appeared to confer a protective effect (a rise in the thermal threshold for bleaching) to reef corals in the Andaman sea (Dunne and Brown 2001). Coles and Jokiel (1978) investigated the effect of pre-acclimation to various combinations of temperature and light on subsequent mortality of Montipora verrucosa during an exposure to 32.5°C. Among corals pre-acclimated at 28°C, pre- acclimation at higher light resulted in a trend for lower mortality at 32.5°C than pre-acclimation at lower light, though this trend was not seen at some cooler pre-acclimation temperatures.

In summary, some aspects of coral stress responses are well modelled by one or more coral bleaching prediction methods, including: the use of temperature and duration of exposure to model bleaching thresholds (DHW), the use of local climatology (DHW), seasonally varying temperature thresholds (ReefTemp) and thresholds varying by heating-rate (ReefTemp) (Gleeson and Strong 1995; Maynard et al. 2008b). Several coral stress responses have not been included in bleaching prediction methods, but have nonetheless been characterised empirically in some species, including the effects of pre-heating on varying the thermal bleaching threshold, and the effects of exposure to previous light levels on thermal mortality thresholds (Coles and Jokiel 1978; Middlebrook et al. 2008; Ainsworth et al. 2016). However, variation in these coral stress responses among coral taxonomic groups is unknown.

Coral bleaching prediction algorithms lack a physiological model that incorporates time- temperature-light relationships, effects of temperature history, and effects of light history. The physiological information needed to incorporate these elements into bleaching prediction is also not complete in some respects. Furthermore, evidence (discussed next) demonstrates that the simple temperature-duration relationships employed by DHM and DHW accurately predict some, but not all, aspects of coral bleaching.

Accurate prediction of bleaching occurrence, but not severity, mortality or recovery.

The temperature-duration relationships utilised by the DHW and DHM algorithms accurately predict the onset of coral bleaching, but not the severity of nor the speed of recovery (the regaining

32 of symbiont populations and pigmentation) following bleaching events. The correlation between the DHW index and the intensity of bleaching at a location is often low (McClanahan et al. 2007c). Similarly, the ability of the DHW index to accurately and precisely predict coral mortality is somewhat limited. The best empirically-establised guideline is that DHW values below 8 correspond with a low likelihood of widespread mortality at a reef location, whilst values above 8 correspond to a 1 in 3 risk of widespread mortality at a location (Eakin et al. 2010a).

A better understanding of when and why coral mortality follows bleaching will help to address why, in some coral species that are regarded as being robust to bleaching-induced mortality, high mortality nevertheless sometimes occurs (Mumby et al. 2001a). A further unresolved aspect of coral bleaching is, to what extent do observed changes in coral physiology during bleaching, such as

Fv/Fm and symbiont densities, correlate with actual mortality (Suggett and Smith 2011)? Physiological changes in the host, such as host cell loss, may have a direct mechanistic link to coral mortality (Dove and Hoegh-Guldberg 2006), and thus the prediction of host physiological responses under thermal and light anomalies may be useful for predicting mortality.

Knowledge of the speed of coral recovery following bleaching events allows the planning of reef management response to bleaching (section 1.3), but is not provided by current bleaching prediction methods. Recovery speed is linked to heating rate in at least one coral species. A. muricata corals heated at 0.5°C day-1 to 33°C recovered 80 % of their original symbiont density after 1 week at non- stressful temperature, whilst those heated at 1°C day-1 to 33°C experienced a further ~40% decline during the recovery period (Middlebrook et al. 2010). Hydrodynamic factors (rate of turnover of water or flow speed) may also play a large role in recovery speed (Cunning et al. 2016).

Lack of capacity to predict differences among species and reef communities

Current bleaching prediction methods (DHW, DHM and ReefTemp) do not produce predictions tailored to specific reef types or coral communities, nor provide an indication of variation among species within communities. Substantial evidence exists that the relationship between temperature anomaly, duration of exposure and bleaching onset is not, as assumed by DHW and DHM, the same at all reef locations. Based on surveys of bleaching, site-specific bleaching onset thresholds (as a function of temperature and duration of exposure) have been calculated for many sites on the Great Barrier Reef (Berkelmans 2002) and in Florida (Manzello et al. 2007), and vary substantially among locations within both regions. Such differences among sites may be due, in part, to variation

33 in coral community composition and differences among species in their susceptibility to bleaching and bleaching-associated mortality (Marshall and Baird 2000).

Not only do different genera of corals appear to display differences in their susceptibility to coral bleaching and mortality, but within a genus these traits can vary among regions (McClanahan et al. 2004). It is possible that such differences are due to undocumented variability in the way that species respond to a heating event with regard to the present light levels as well as the heat and light history leading up to the event.

Is acclimation an issue?

Some authors have proposed that an increase in the thermal tolerance of reef corals to warm temperatures could occur following a bleaching event (Maynard et al. 2008a; Grottoli et al. 2014). Evidence for this idea includes the fact that the observed frequency of bleaching over the past 30 years is lower than that predicted using DHM and SST datasets for the same period (Logan et al. 2014). An ability for micro-evolutionary adaptation by the coral host to higher temperature has not been indicated in quantitative genetic experiments designed for that hypothesis (Csaszar et al. 2010). Switching to a more thermotolerant Symbiodinium type by the coral host (“adaptive bleaching”) has also been proposed (Buddemeier and Fautin 1993; Baker 2001). However, instances of a coral host switching to a new dominant Symbiodinium type after an environmental change have only been demonstrated in a small number of studies (Goulet 2006; Grottoli et al. 2014), and likely involve symbiont shuffling as opposed to the uptake of a new symbiont type from the surrounding environment (McGinley et al. 2012; Sampayo et al. 2016).

An alternative plausible mechanism for increased thermal bleaching thresholds for whole reefs following past bleaching events is the selective mortality of thermally sensitive coral species, genotypes or those hosting thermally sensitive Symbiodinium types (Sampayo et al. 2008; Pratchett et al. 2013). Selective mortality may change the functions of reefs: in addition to the direct ecological impacts of a reduction in coral biodiversity, susceptibility to bleaching may correlate with other phenotypic traits (Edmunds 1994). Should this be the case, then the function or structure of coral reefs may be affected when those traits decline, whilst other traits that correlate with bleaching resilience may become prevalent. Therefore, selective mortality following bleaching needs further attention due to its implications for coral reef bleaching prediction, and also as a factor that may be altering the ecological services that reefs provide.

34 The impact of ocean acidification on bleaching risk, severity and recovery.

Algorithms such as DHW and DHM are currently used to predict coral bleaching towards the end of this century, a time at which ocean acidification may have substantially altered the carbonate chemistry of the ocean (Kleypas et al. 1999). Experimental evidence suggests that increased partial pressures of carbon dioxide in seawater can induce bleaching in corals (Anthony et al. 2008), although the effect is debated (Wall et al. 2014). Should the agency of acidification to cause bleaching be conclusively demonstrated, then long-term 21st-century bleaching projections will need to take this effect into account (as per Kwiatkowski et al. 2015). Furthermore, remote sensing of global oceanic pCO2 concentrations is becoming possible (Eakin et al. 2010b; Land et al. 2015), which will enable acidification to be factored into current bleaching prediction methods, should the magnitude of near-future acidification levels be found to elevate the risk of bleaching.

1.7 A new bleaching prediction method

Algorithms for the prediction of coral bleaching have incomplete models of bleaching physiology, a limited ability to predict severity, mortality and recovery, and no representation of bleaching susceptibility differences among coral communities and species within communities. At the same time, the factors that influence coral bleaching are many, and it is likely that no single bleaching algorithm could explicitly include all of these variables. A recent algorithm for the prediction of coral bleaching has been designed to incorporate some of these missing variables, whilst still remaining simple enough to be practically implemented. This algorithm, Light Stress Damage (LSD), incorporates a model of bleaching that includes photosynthetically active radiation (PAR) and temperature, and using this model it attempts to predict bleaching-induced mortality and recovery time following bleaching with increased accuracy (Skirving et al. 2018).

Light levels are difficult to measure over large areas via fixed sensors, and so the LSD algorithm has been made possible by the explicit development of a remote sensing method to measure reef light levels. Traditionally, light intensity has been measured from field station-based sensors (Hansen et al. 2002), limiting the scope for light intensity to be accurately measured over large areas of the oceans for the prediction of coral bleaching. However, approaches have recently been developed to retrieve global visible light levels from geostationary environmental satellites, specifically for the purpose of including light intensity in coral bleaching prediction methods (Laszlo et al. 2008; Eakin et al. 2010b).

35 The LSD bleaching prediction algorithm (Skirving et al. 2018) is based on a physiological model of coral bleaching. The LSD algorithm uses the sum of the change in relative Fv/Fm (defined as the rise or decline in the relative quantum yield of photosystem II) to derive an index of physiological stress in corals. Numerous physiological processes, termed photoacclimation, occur following a change in light level to optimise the efficiency with which the coral symbiosis utilises the available photosynthetically active radiation (Anthony and Hoegh-Guldberg 2003a). In the LSD product,

-1 relative Fv/Fm is determined by (a) excess excitation energy (EEE) in mol quanta·day , approximated as photosynthetically active radiation (PAR) today minus PAR in previous days adjusted for photoacclimation that has occurred; and (b) summer SST anomaly (°C above MMM). The daily EEE is combined with the SST anomaly to produce an approximation of the change in

Fv/Fm. Both EEE and SST anomaly can be derived from satellite maps of light intensity and temperature in near-real-time compared to the measurements of light over the preceding months and the climatological baseline of temperature for the location. To determine the relative Fv/Fm from both of these factors, physiological experiments were performed to measure the actual Fv/Fm in corals at many points along a continuum of anomalous temperatures and light levels (Scheufen et al. 2017). In the LSD algorithm, these experimental values are used as reference data to estimate the relative Fv/Fm for a given satellite-derived temperature anomaly and light level (Skirving et al. 2018).

Fv/Fm was chosen as the model output of the LSD algorithm as this parameter has a mechanistic link to, and is often a good proxy for, the onset of coral bleaching (Warner et al. 1996, 1999; Jones et al. 1998). In this sense, the LSD algorithm is a conceptual advance over previous algorithms, which do not explicitly model a physiological variable that is mechanistically linked to bleaching onset. As

Fv/Fm is the quantum efficiency of photosystem II, direct damage to PSII or the over-reduction of the electron transport chain (symptoms of heat- or light change-induced damage in the symbiont cells that presage bleaching) will register as a decline in Fv/Fm (Jones et al. 1998; Warner et al.

1999). On the other hand, declines in Fv/Fm can also indicate processes that are photoprotective, such as the dissipation, as heat, of excess light energy by aggregations of antenna complexes that are not linked to the electron transport chain (Matsubara and Chow 2004; Takahashi and Badger

2011). Thus, the decline of Fv/Fm in corals that are faced with thermal or light stress may not necessarily indicate physiological damage that will lead to coral bleaching. Furthermore, Fv/Fm does not have a mechanistic link to physiological mechanisms for the onset of bleaching that originate in the host (Gates et al. 1992), and it is unclear whether the onset of host damage will always lead to declines in Fv/Fm (Ainsworth et al. 2008). Nevertheless, Fv/Fm often correlates with symbiont cell density declines in coral bleaching experiments (Warner et al. 1996; Jones et al. 1998), supporting

36 its use as a bleaching proxy in the LSD algorithm. A challenge for the further development of the

LSD algorithm is to identify, for different species, the threshold of Fv/Fm decline that indicates the onset of symbiont physiological damage that will lead to bleaching and to identify the situations when Fv/Fm decline is indicative of photoprotection rather than sub-bleaching damage.

Initial testing suggests that the LSD approach could also provide better prediction of mortality following coral bleaching. In the LSD algorithm, mortality is predicted by the “LSD index”, which integrates, through time, the degree to which Fv/Fm is below the physiological damage threshold. The performance of the LSD and DHW indices has been compared for three summertime water temperature anomalies (in 2002, 2004, and 2006), each separated by two years of no anomaly, at the Acropora-dominated Keppel archipelago on the Great Barrier Reef. The LSD index demonstrated good predictive power of coral mortality, demonstrated by a close match between the ratio of surveyed coral mortality in 2006 vs 2002, 1.35, and the ratio of the LSD index in 2006 vs 2002, 1.36. The same ratio of the DHW index, 1.14, demonstrates worse predictive power when compared to the ratio of coral mortality in 2006 vs 2002. In addition, the DHW index predicted the occurrence of bleaching during 2004, whilst the LSD index was close to zero (predicting no bleaching occurrence), which matches field survey results (no coral bleaching detected). Graphical analysis for each of the three years suggested that the better prediction of bleaching severity by the LSD algorithm may be due to the combined use of light and temperature anomalies (Skirving et al. 2018).

The LSD algorithm also provides the possibility of prediction of the speed of coral recovery. In the algorithm, the slope of the linear relationship between excess excitation energy and relative Fv/Fm becomes more negative as thermal anomaly increases (Skirving et al. 2018). Some evidence suggests that, after the cessation of heat stress, this slope retains its steeper gradient for approximately 30 days, that is, the rate of repair (or repigmentation) of coral tissues is temporarily upregulated following heat stress (Rodríguez-Román et al. 2006; DeSalvo et al. 2010). Maintaining a more negative relationship between EEE and relative Fv/Fm for a temporary period following a return to non-stressful water temperatures may help provide realistic estimates of the recovery rate of bleached corals (Skirving et al. 2018).

Further development of the LSD bleaching index.

To incorporate past light changes into the LSD index, these changes are scaled by a coefficient representing the daily light acclimation rate; the academic debate surrounding this rate requires

37 resolution through empirical investigation (Anthony and Hoegh-Guldberg 2003a; Skirving et al. 2018). A coefficient representing the daily light acclimation rate is used to calculate the component of light changes on all previous days that the coral has not yet fully acclimated to, which is then used to calculate EEE. (Skirving et al. 2018). However, changes of light in one direction may take longer to photoacclimate to than changes in the opposite direction. This is because changes in one direction may involve the building up of particular photosynthetic structures (e.g. pigment-protein complexes), whilst changes in the other direction may involve reducing the size or number of the same structures (Falkowski and Raven 2007). The limited experimentation that has been done in this area has confirmed that light acclimation takes a different period of time to complete depending on the direction of light change (Falkowski and Dubinsky 1981; Gattuso and Jaubert 1984). However, the same studies (both using the same coral species) conflict as to whether increases in light require more time to acclimate to than decreases, or vice versa. Further questions include whether the magnitude of the light change (e.g. +1 versus +5 mol quanta·m-2·day-1) or the water temperature influences photoacclimation rate. The resolution of these questions is required in order to determine whether the acclimation rate used in the LSD algorithm must vary depending on the nature of the change in light intensity, or the water temperature.

In the LSD algorithm, the magnitude of the stress-causing ability of elevated heat on corals is modified by the daily change in light level, a feature that requires further refinement. In the algorithm, daily increases in light levels amplify the heat stress, whilst daily decreases in light levels diminish the heat stress (Skirving et al. 2018). Thus, recovery from a thermal anomaly event can, in this model, be induced by a series of sustained decreases in light level. However, cases exist in the scientific literature where a decrease in light level, during a time of a thermal anomaly, causes physiological damage: e.g. high mortality was observed among coral fragments at Heron Island experimentally exposed to a decrease in light level at high heat (Banks 2015). Thus, the investigation of whether a decrease in light level has a recovery effect in the majority of corals facing heat stress requires further validation.

1.8 Directions of this thesis

The overarching aim of this thesis was to provide physiological information that will advance the development of methods for the prediction of the onset and severity of coral bleaching and subsequent mortality. As the above review aimed to be comprehensive, not all knowledge gaps identified above will be addressed, such as the effect of prior high light exposure on thermal

38 thresholds (Dunne and Brown 2001). In the experimental investigations to follow, I focus in particular on physiological knowledge gaps that are pertinent to the Light Stress Damage bleaching prediction method. This thesis also outlines findings that are useful to any method aiming to predict differences in bleaching response among coral taxa. Results relevant to the prediction of coral bleaching under distant future scenarios, in which substantial ocean acidification may be present in addition to both thermal and light change, are presented.

Chapter 2 investigates whether the photoacclimation rate in scleractinian corals is constant, or varies depending on the magnitude of visible light change, the direction of visible light change, and the water temperature.

Chapter 3 investigates the light-dependence of thermal thresholds for coral bleaching, the exceedance of the same thermal thresholds on host mortality, and differences in response among coral species. The mechanistic causes of mortality are investigated through addressing whether a loss of symbiont cells simultaneously results in a loss of host protein and whether one or both of these events correlate with mortality of the entire coral specimen. The biological reasons underlying some differences among several coral species in their photosynthetic responses and bleaching responses to elevated light are investigated, using photophysiological techniques.

Chapter 4 investigates whether ocean acidification alters the temperature thresholds and light change thresholds for coral bleaching, and whether ocean acidification causes changes in other physiological parameters, such as photosynthesis and holobiont respiration.

In the concluding chapter, I identify how the new findings on photoacclimation rate and the visible light dependence of thermal thresholds suggest modifications to the LSD bleaching prediction algorithm. I also discuss the implications of observed coral species differences, and the physiological impacts of acidification, for the development of coral bleaching prediction methods.

39 2 Water temperature, species differences and the direction of light change may influence photoacclimation rates in two scleractinian corals.

2.1 Abstract

1. The success of the symbiosis of scleractinian corals with dinoflagellates of the genus Symbiodinium is highly dependent the availability of sufficient, but not excess, light for photosynthesis. The speed of physiological adjustment (photoacclimation) following a change in incident light level determines the amount of time that the holobiont remains maladjusted to the new light intensity. The speed of photoacclimation is potentially important for predicting coral bleaching and is best studied under controlled conditions that enable various confounding factors encountered during field-based experimentation to be eliminated.

2. I studied photosynthetic variables relevant to photoacclimation at two time points over 24 days in two scleractinian corals (mounding Porites spp. and branching Acropora muricata). Coral specimens were exposed to three light levels (117, 218 and 282 µmol quanta∙m-2∙s-1 for 13 hr 46 min per day) at two temperatures (control, 24°C or MMM–3.12°C, and mildly stressful, 29°C or MMM+1.88°C). Prior to this exposure, corals had acclimated at 24°C and 171 µmol quanta∙m-2∙s-1 averaged over a 13 hr daylength. Measurements were taken of the slope (α) of the light-limited part of a photosynthesis vs irradiance curve, the areal rate of net photosynthesis (Pnet), areal symbiont cell densities, Fv/Fm and the photon flux pressure per symbiont cell. Following a change in light, a physiological marker was considered to have reached a state of complete photoacclimation once it exhibited stabilisation through time.

3. The direction of light change influenced the photoacclimation rate in mounding Porites spp., which achieved photoacclimation of Pnet per cm2 most rapidly when light intensity decreased, whilst symbiont densities per cm2 photoacclimated most rapidly when light intensity increased. Elevated temperature extended the duration required for photoacclimation of Fv/Fm, but not of any other variable, in mounding Porites. There was no evidence of differences in photoacclimation rates among treatments for any variables in A. muricata. Increased light level induced symbiont loss at both 24°C and 29°C in mounding Porites spp., but not A. muricata.

40 4. This study provides some evidence that different temperatures alter the photoacclimation rate of

Fv/Fm. This helps to address a long-standing open question in coral biology of what impact temperature has on photoacclimation duration. Any coral bleaching prediction method that uses light intensity and temperature to predict the bleaching proxy Fv/Fm may need to apply a photoacclimation rate of Fv/Fm appropriate to the measured SST and coral taxon. Of relevance to understanding differences in bleaching susceptibility among coral species, mounding Porites spp. displayed the beginnings of a classic photoinhibition bleaching response at 29°C, whilst A. muricata displayed signs of light undersaturation under the same conditions.

2.2 Introduction

Light is a key abiotic driver of photosymbiotic coral distributions and colony health (Achituv and Dubinsky 1990; Iluz and Dubinsky 2015). In the symbiotic partnership of dinoflagellates and corals, the holobiont – the macro-organism comprising the symbiont and host as a whole – is optimised to capture light to drive dinoflagellate photosynthesis that provides nutrition to both partners. Changes (over hours, days and weeks) in light conditions are accommodated through the suite of physiological changes known as photoacclimation. These mechanisms include longer-term changes such as alterations in the number of photosynthetic units (a PSII reaction centre and the light harvesting complexes that are attached to it) and the size of the photosynthetic units (LHCs can be added to or removed from each photosynthetic unit) (Prézelin 1987; Mauzerall and Greenbaum 1989). The extent of the ability of an alga to make such changes is largely determined by its genetic code (Raven and Geider 2003). Mechanisms that enable short-term biochemical and physiological changes also exist. These include changes in quantity of xanthophyll cycle pigments involved in heat dissipation (Iglesias-Prieto and Trench 1997), islands of PSII reaction centres delinked from the electron transport chain that quench light as heat (Matsubara and Chow 2004), photochemical quenching through alternative electron transport pathways (Reynolds et al. 2008) and upregulation of rates of repair of photodamage (Jeans et al. 2013).

After decades of fundamental research into coral photoacclimation, an important practical application is emerging in the remote monitoring of coral reefs. Satellite methods for predicting coral bleaching use sea-surface temperature, but have recently undergone further development to take into account sea-surface light levels (Eakin et al. 2010b). The recently-developed Light Stress Damage (LSD) bleaching prediction algorithm (Skirving et al. 2018) uses summer sea surface temperature anomaly (°C above the maximum monthly mean, MMM) and excess excitation energy

41 (in moles of quanta per m2 per day), approximated as photosynthetically active radiation (PAR) today minus PAR in previous days adjusted for photoacclimation that has occurred, to produce an index of coral photophysiology that is correlated with bleaching. The photoacclimation rate (i.e. 1 divided by the number of days required for photochemical parameters to stabilise following a change in light) moderates the capacity of excess excitation energy to cause photodamage, and thereby bleaching (Skirving et al. 2018). Improvement to the scant data available on coral photoacclimation rates will thus contribute to a better bleaching prediction algorithm.

The rate of photoacclimation has, thus far, been determined in only two scleractinian coral species, through the use of photosynthesis versus irradiance (P vs E) curves. In a P vs E curve, α is the slope of the initial linear (light-limited) part of the P vs E relationship, and Ek is the sub-saturation irradiance, near to which the increase in photosynthesis per unit increase in light intensity begins to taper off until the maximum rate of photosynthesis (Pmax) is reached (Chalker 1981). Within a particular species, α, Ek and Pmax take on different values depending on whether the coral colony is high-light or low-light adapted (Wethey and Porter 1976; Falkowski and Dubinsky 1981; Porter et al. 1984). The photoacclimation rate can be measured by sampling one or more of α, Ek or Pmax through time, following a change in light intensity, and identifying the period of time required for the variable(s) to stabilise. Using this technique, photoacclimation was measured to completion within 10 days in Turbinaria mesenterina (Anthony and Hoegh-Guldberg 2003a). Photoacclimation was faster after movement from low to high light compared to movement in the opposite direction, in Stylophora pistillata, (Falkowski and Dubinsky 1981). However, another study of S. pistillata found the opposite, i.e. that adaptation to high light was slower than adaptation to low light (Gattuso and Jaubert 1984). Falkowski and Dubinsky (1981) suggested, based on evidence from a diatom (Hitchcock 1980a, 1980b), that such differences may be due to the water temperature rather than the direction of light change. An elevation in temperature could, hypothetically, increase the photoacclimation rate by increasing the kinetic rate of reactions in the biochemical pathways involved (Sheridan et al. 2012). However, in the ensuing three decades (to my knowledge), the effect of temperature on photoacclimation rate in corals has not yet been tested.

The effect of direction of light change on photoacclimation rate also remains to be clarified. Anthony and Hoegh-Guldberg (2003a) found no statistically significant effect of an increase versus a decrease in light, nor of large versus small changes in light, on photoacclimation rate. However, modelling of the photoacclimation rates required to maintain observed patterns of photophysiology over an annual cycle of seasons suggests that photoacclimation to a decrease in light should be faster than photoacclimation to an increase in light (Skirving et al. 2018). Furthermore, a trend for

42 photoacclimation time to be shorter in response to a larger magnitude change in light was present in Anthony and Hoegh-Guldberg’s (2003a) study, and thus the effect of the magnitude of light change on photoacclimation rate also remains to be resolved (an aim of the following experiment). I suggest that photoacclimation rate could be more rapid in response to a larger magnitude light change because the risk of negative effects of the new light level on a non-acclimated photosystem may be more pronounced, or because the physiological induction of acclimation may be more effective when a larger external signal is present. For instance, in green algae a highly reduced state of the plastoquinone pool triggers the repression of the transcription of light-harvesting complex apoproteins associated with photosystem II, hinting at a direct link between light intensity and the speed of photoacclimative changes to the light harvesting complex (Escoubas et al. 1995). An oxidised plastoquinone pool can also act as a direct quencher of chlorophyll a minimum fluorescence (Hohmann-Marriott et al. 2010).

To investigate the influence of temperature, direction of light change and magnitude of light change on photoacclimation rate, this chapter focuses on two taxa that are dominant on reefs in Eastern Australia and elsewhere in the Indo-Pacific, Acropora muricata and mounding Porites species (Veron and Wallace 1984; Forsman et al. 2009). These taxa are juxtaposed in their response to adverse conditions, e.g. mounding Porites spp. appears to be more resilient to bleaching-induced mortality, and occurs at a wider range of turbidity, and light and temperature variability, than Acropora spp. (Marshall and Baird 2000; Hennige et al. 2010).

To measure the photoacclimation rate, I made measurements of α (and other parameters) at several time points following a change in light. I inferred that photoacclimation of a parameter was complete when that parameter had ceased to change. Here, α was determined using electron transport rate (ETR) which has a linear relationship to gross photosynthesis in corals within the low ETR range at which α is calculated (see Figure 5 in Rodolfo-Metalpa et al. 2008). As coral colony absorptance can vary under abiotic stressors due to changes in pigment density (Enríquez et al. 2005) and varying absorptance can influence relative ETR (Warner 2005), I made measurements of colony absorptance using spectrometry so as to correct for this effect. As a second measure of photoacclimation rate, the rate of net photosynthesis (Pnet) per cm2 of coral surface area at ambient light levels, was used. At a certain irradiance threshold (e.g. 150 µmol quanta∙m-2∙s-1 in the alga Phaeodactylum tricornutum), ETR rates in marine algae begin to depart from actual photosynthesis, probably due to the induction of a Mehler-type alternative electron transport pathway (Geel et al. 1997). As the ambient light levels under investigation were of this magnitude, Pnet was measured

43 using the rate of oxygen evolution, a good approximator (though with some positive bias) of the carbon fixation rate (Gattuso and Jaubert 1990).

The coral host may have some control over photoacclimation, through exercising an influence over symbiont population sizes. The host can partially control symbiont populations by modulating the amounts of nitrogen or carbon it actively supplies to the symbionts, or by expelling a fraction of the symbionts (Stimson and Kinzie 1991). Thus, change in the size of the symbiont population has been used as a method of inferring photoacclimative changes, and the rate thereof (Titlyanov et al. 2001a, 2001b). Changes in symbiont population size alter the photon pressure (amount of light) per symbiont residing in the host tissue. Symbiont density changes may thus exert further pressure on the remaining symbionts to induce their own photoacclimative mechanisms (Enríquez et al. 2005), or alternatively, may return the photon pressure to the level that the symbionts were exposed to prior to a change in incident light, thereby negating the need for some other photoacclimative mechanisms. I measured changes in symbiont population size through time and the resultant changes in photon pressure per symbiont.

As photoacclimation rate coefficients have an applied use in coral bleaching prediction, I compared the above measurements to dark-adapted Fv/Fm, a parameter that is predicted by the LSD algorithm and used in this algorithm as a bleaching proxy (Skirving et al. 2018). The aim, in doing so, was to determine if the use of photoacclimation rate(s) determined from the above markers to predict changes in Fv/Fm (following a change in light) is conceptually sound. In corals, Fv/Fm displays indications of a photoacclimation response, for instance, Fv/Fm decreases with decreasing depth (Warner et al. 2002; Fitt et al. 2000; Lesser and Gorbunov 2001). However, the interpretation of

Fv/Fm data to infer photoacclimation is complicated by the fact that, whilst a depressed Fv/Fm can indicate a dysfunction of actual photosynthesis, in other circumstances Fv/Fm can be depressed and yet actual photosynthesis is still occurring at normal rates (Dimond et al. 2013). This is because some photosynthetic organisms possess photoprotective mechanisms that result in depressed Fv/Fm but enable actual photosynthesis to continue unimpaired under light levels that would be otherwise damaging (Matsubara and Chow 2004).

This chapter describes a laboratory-based time-course investigation of the photoacclimation in response to a long-term (24 day) change in light intensity. Samples of A. muricata and mounding Porites spp. were taken from prior spring field conditions of 24°C and 171 µmol quanta∙m-2∙s-1 (averaged over a 13 hr 46 min daylength) and exposed for a 13 hr daylength indoors to three light levels (low, 117 µmol quanta∙m-2∙s-1; medium, 218 µmol quanta∙m-2∙s-1; and peak, 282 µmol

44 quanta∙m-2∙s-1) crossed with two water temperatures (24°C and 29°C). The former is within the normal springtime seawater temperature range at the study site, whilst 29°C is 1.88°C above the MMM for the region (NOAA Coral Reef Watch 2017a), a thermal anomaly of a magnitude that is considered to be physiologically stressful to corals (Goreau and Hayes 1994). Existing evidence suggests that the bulk of photoacclimatory changes likely occur within the first ten days following a change in light level (Anthony and Hoegh-Guldberg 2003a). Therefore, any extension of the time required to photoacclimate beyond this period may incur physiological costs due to the extended time spent in a state of ill-adjustment to the environmental conditions. Hence, I concentrated on the period beyond the first 10 days; physiological markers were sampled and resampled at ~15 and ~24 days following the movement of corals to changed light conditions. I hypothesised that, where an extended photoacclimation period (a lack of stabilisation between days 15 and 24) is seen at one level of an independent variable but not the other(s), that level will be of the following:

(1) the control temperature (24°C) as opposed to elevated temperature (29°C),

(2) an increase in light as opposed to a decrease in light.

(3) a smaller-magnitude change in light (e.g. from 171 to 218 µmol quanta∙m-2∙s-1) as opposed to a larger-magnitude change in light (e.g. from 171 to 282 µmol quanta∙m-2∙s-1).

2.3 Methods

2.3.1 Organism collection

The assemblage of large mounding Porites species on the Australian east coast and elsewhere are not readily identified to species in situ (Forsman et al. 2009). Other investigators have approached this uncertainty by reporting only the genus for Porites specimens used experimentally in the Red Sea (Krief et al. 2010) and the tropical Pacific (Edmunds 2012). Following this precedent, this study focuses on the mounding Porites assemblage at Heron Island as a group rather than choosing one particular species. Within the Capricorn Bunker Group, Great Barrier Reef, cores of mounding Porites spp. (4 cm diameter) and A. muricata branch tips (3-5 cm) were collected from, respectively, North Wistari Reef, and Coral Gardens at Heron Reef, at a depth of 5-9 m, over 14-20 August 2012. Samples were secured to racks and left to recover for 3 months at 9 m below datum at Harry’s Bomme at Heron Reef (23.5ºC [annual mean], mean instantaneous photon flux 69.8 to 210.25 µmol quanta∙m-2∙s-1 [winter to summer] (Sampayo et al. (2016) values extrapolated to 9 m 45 water depth using Kd from Michael et al. (2012)). The coral specimens were collected under permit number G11/34549.1 issued by the Great Barrier Reef Marine Park Authority.

2.3.2 Experimental design

The experimental design consisted of 24 clear glass aquaria (39 l), randomly divided among 6 treatments (three light levels crossed with two temperature levels). The installation consisted of four central systems (Escobal 1996) of one sump and six aquaria each, modified for flow through (exiting water was not recirculated back into the system). Water was drawn from the Heron Reef flat, and the water temperature was modified to the desired levels using Seachill TR-60 heater/chillers (TECO, Ravenna, Italy). The flow rate of new water entering each experimental aquarium was 811 ml∙min-1 (± 70 s.e.m.). Residence time (the duration required to turn over 99% of aquarium water: Escobal 1996) was 221 minutes. Each aquarium received an estimated 0.47g of particular organic carbon per day, measured through the ash-free dry weight of GF/F filters (n = 9) used to filter flow-through water (Whatman, Maidstone, England). The two water temperature treatments were control: 24.25°C (± 0.0032 s.e.m., n = 36573), and elevated: 28.57°C (± 0.0036 s.e.m., n = 36573).

A 13:11 hr (light:dark) cycle was delivered during the experiment (5 am-6 pm) by 10,000 K metal halide 250 W lamps (Aqua Medic, Bissendorf, Germany), each fitted with a polymethylmethacrylate prismatic light diffuser (K12) (Pierlite, Padstow, Australia). Three light levels were administered: low (mean and max = 117 (± 9 s.e.m.) µmol quanta∙m-2∙s-1, 5.5 mol quanta∙m-2∙day-1), medium (218 (± 9) µmol quanta∙m-2∙s-1, 10.2 mol quanta∙m-2∙day-1), and peak (282 (± 15) µmol quanta∙m-2∙s-1, 13.2 mol quanta∙m-2∙day-1). These light levels were measured with an LI- 192 quantum sensor and an LI-1400 logger (LI-COR, Lincoln NE, USA). These compare to in-field values for light dose of 8.5 mol quanta∙m-2∙day-1, instantaneous flux of 171 µmol quanta∙m-2∙s-1, and daily maximum of 378 µmol quanta∙m-2∙s-1 that the coral specimens were exposed to at 9 m depth on the reef, averaged over the 14 days prior to day 0 of the experiment (measured with an Odyssey PAR logger, 10 minute logging interval, (Dataflow Systems, Christchurch, New Zealand)). The reef water temperatures over this period averaged 24.16°C (range 23.06 to 25.09°C).

Coral samples were retrieved from the reef on 21st November 2012 (day 0) and placed in experimental conditions that evening; the experiment was terminated on day 24. Sample mortality of 2.5% (mounding Porites spp.) and 15% (Acropora muricata) had occurred during the three-

46 month in-field recovery period. One core from each mounding Porites spp. colony was placed into one randomly allocated aquarium in each treatment (n = 8 per aquarium) on a horizontal surface. The higher mortality caused an uneven loss of nubbins among the sampled A. muricata colonies, preventing the systematic distribution of colony fragments amongst the experimental treatments. The remaining samples were simply randomly allocated to aquaria (n = 7 per aquarium), and suspended in a vertical orientation with monofilament fishing line. Mortality throughout the experiment was low ( ≤ 2 fragments per species per combination of light and temperature).

2.3.3 Spectrometry

At two time points (days 12-13 and 19-21), reflectance spectrometry was performed on 4 corals of each species per aquarium with an S2000 spectrometer and VIS/NIR optical fibre (in-air acceptance angle of 25.4°) (Ocean Optics, Dunedin, USA). Samples were immersed in a shallow layer of water contained in a black tray and illuminated at 6500 K with a colour temperature-adjustable light source (Dicon Fibreoptics Inc., Richmond CA, USA). For a white reference, a GSWP nitrocellulose membrane was used (Millipore, Tullagreen, Ireland). Reflectance was measured from 400-700 nm in steps of 0.37-0.38 nm and averaged to obtain a resolution of 1 nm; three replicate spectra per coral were taken. Due to the short path length, differing absorption by the seawater at different wavelengths was ignored. To take into account the multiple tissue-clad surfaces (lower and upper) of A. muricata nubbins, two geometries of measurement were used for A. muricata. The 1st geometry of measurement, illustrated in Figure 2.1, primarily measured the underside of the corallites. The 2nd geometry of measurement primarily targeted the upper side of the corallites. Only measurements made in the first geometry were used to calculate absorptance (described later).

As a measure of the degree of photon pressure per symbiont, calculations were made of the amount of reflected light per symbiont cell at the second time point (days 19-21). To do so, the spectrum of the metal halide lamps from 400-700 nm (Appendix 7.1) was used to calculate the percentage of PAR emitted at each nm and multiplied by the integrated PAR (measured with a quantum sensor) to obtain the photon flux emitted at each nm in each treatment. Then, this spectrally-resolved photon flux was multiplied by the reflectance of each coral sample and divided by the symbiont density of each treatment (averaged over both time points) to obtain an estimate of the amount of reflected light per symbiont cell in each coral fragment, which I used as a proxy for photon pressure per symbiont cell.

47 The assumption was made that the quantum sensor had an equal response at every nanometer (in truth, there are some minor deviations from a completely equal response). Secondly, an assumption was made that the spectrum of the metal halide lamp (Appendix 7.1) is representative of all metal halide lamps used in the experiment (in truth, lamps of the same model vary in their spectra due to slight manufacturing variation and ageing).

For use in calculating ETR, the absorptance, A, as a function of wavelength, λ, was calculated from reflectance, R (expressed as a proportion), using the equation A(λ) = 1 – R(λ). The assumption was made that the reflectance measurements captured both reflection and scattering. Absorptance was then averaged over all wavelengths between 400-700 nm, giving mean absorptance in the photosynthetically active spectrum, APAR. Average APAR over all samples in a treatment, ĀPAR, was used in the ETR calculation below. In applying absorptance to the ETR calculation, I assumed that the photosynthetic antennae are responsible for the fraction of downwelling PAR absorbed by the coral; possible absorption by non-photosynthetic pigments or structures was ignored.

2.3.4 Quantum efficiency of photosystem II and rapid light curves

Every evening, Fv/Fm was measured with a Diving PAM (Walz, Effeltrich, Germany) on four corals of each species per aquarium, 1.5-2.5 hours after lights-out. The same location on each fragment was sampled each evening, at a fixed distance from the surface (3 mm). On days 15-16 and 22-23, α was measured during daylight hours via rapid light curves (RLC) performed with a Diving PAM, with the instrument settings of light-curve starting intensity: 2, and actinic light factor: 0.6. The array of consecutive, increasing PAR levels were 0.2, 0.63, 5.69, 7.01, 9.06, 15.54, 24.1 and 43.11 µmol quanta∙m-2∙s-1, with an exposure time of 10 s per light level. Each RLC contained only the light-limited part, as ΔF/Fm’ had a purely linear response to these increasing light levels. Coral samples were measured in situ inside their respective experimental aquarium. Samples were illuminated with the built-in halogen lamp of the diving PAM (providing white light with little input from wavelengths above 700 nm; Heinz Walz GmbH 1998). Interference from the metal halide lamps was prevented by baffling the perimeter of the path between the fibre optic and the coral with black pantyhose. For each sample, electron transport rate at photosystem II was calculated with the following equation, which assumes that half of all absorbed light is sent to PSII:

ETR = PAR × ĀPAR × ΔF/Fm’ × 0.5 (Schreiber et al. 1997) The parameter α (with the units: electrons·photon-1 of downwelling PAR) was calculated by taking the slope of ETR versus PAR level.

48 2.3.5 Oxygen flux

Dissolved-oxygen measurements of photosynthesis were made on 8 coral specimens (two per replicate aquarium) of each species from each treatment, on days 15-16 and 22-23 (A. muricata) and days 16-17 and 23-24 (mounding Porites spp.), electrical failure prevented measurements at an earlier time point (i.e. days 7-9). Measurements were conducted in the evening or early morning (pre-dawn) hours. Each sample was sealed into a transparent Perspex chamber (volume of 242 ml for mounding Porites spp., 79.5 ml for A. muricata) containing filtered sea water (0.22 μm) with a magnetic stir bar to circulate the water using a nine-position stirring plate (Cole-Parmer, Vernon Hills, USA). Prior to incubations, chamber water was partially stripped of oxygen by bubbling with

N2. Tissue-free skeletal bases of mounding Porites spp. were wrapped in Teflon plumbing tape and Parafilm to minimise respiration and photosynthesis from endoliths. Dissolved oxygen in chamber water was measured with an Oxy-10 optode (PreSens, Regensburg, Germany) every 15 seconds, during 20 minutes of light (measuring net photochemistry, Pnet; corals were exposed to the same light level as in their treatment). The internal chamber temperature was controlled to the temperature in the treatment from which corals came, using a Perspex water jacket surrounding the chambers and supplied with water by an F33-ME Refrigerated/Heating Circulator (Julabo, Seelbach, Germany). At the conclusion, corals were snap-frozen in liquid nitrogen.

2.3.6 Symbiont cell densities, coral skeletal area and volume.

Tissue was removed from thawed corals with an airbrush ejecting dibasic potassium phosphate buffer (0.12 M) at pH 6.8. After centrifugation (4500 r.c.f., 4˚C, 5 min) pellets of symbiont cells were resuspended in 1× phosphate buffered saline at pH 7.4 and syringe-filtered through Nitex 38 µm mesh (Sefar, Thal, Switzerland). Symbiont cell counts were performed on an LSRII flow cytometer (BD, Franklin Lakes, USA), with AccuCount Ultra Rainbow 3.8 µm Particles (Spherotech, Green Oaks, USA) used as a counting standard in each sample. Surface area was determined using double wax dipping for A. muricata (Vytopil and Willis 2001) and single wax dipping for mounding Porites spp. (Stimson and Kinzie 1991). Coral volumes were determined to the nearest 0.01 ml using the suspension technique of Hughes (2005). There was one time × treatment combination missing for cell counts and fragment area from each species, due to sample loss in freezers (days 23-24 at 24ºC peak light for A. muricata, days 23-24 at 29ºC medium light for mounding Porites spp.). Therefore, peak light was excluded from statistical tests of the variables

49 involving symbiont cell densities for A. muricata and medium light excluded for mounding Porites. For area-standardised variables, the average surface area of all specimens within a species was used for the missing surface area data (for mounding Porites spp.: 23.31 cm2 (± 4.51 s.d.); for A. muricata: 10.44 cm2 (± 3.19 s.d.)).

2.3.7 Statistical analysis

Analyses were performed in R version 3.3.1. (R Core Team 2016), in the nlme package, chosen as it enables the modelling of heteroskedasticity. Differences between aquaria (“tank effects”) were dealt with by averaging the coral replicates from within each aquarium within each treatment, providing n = 4 averaged values per variable per treatment at each time point. The median per aquarium instead of mean per aquarium was used for Fv/Fm as an initial data exploration had indicated some

net skew to the Fv/Fm distributions. I modelled the Fv/Fm time-series, α, P , symbiont cell density and photon pressure per symbiont using generalised least squares (GLS) models. Models were factorial and fully crossed, the fixed-effects model structures were temperature × light × day (repeated measures) for Fv/Fm, temperature × light for photon pressure per symbiont, and temperature × light × sampling time point for all other variables. Repeated measures models for the latter variables were not used as their measurements were destructive and therefore different samples were assayed at each time point. Heteroskedasticity between groups was included in all models. For Fv/Fm, to choose the correlation type for repeated measures, I examined ACF and PACF plots and also compared AIC scores for models using a range of different correlation types (described in Appendix 7.2). Where stated, an additional mixed-effects model (lme) with heteroskedastic variances was performed by grouping days into two groups (middle and end of experiment), with the model structure temperature × light × group as fixed effects and “day of experiment” as a nested random effect. An ANOVA was performed for each modelled variable, ANOVA assumptions were checked with quantile-quantile and residuals versus fitted values plots, and least-square means with Tukey’s test (the lsmeans R package: Lenth 2016) were used for post hoc tests. The differences in treatment means identified through statistical tests are expressed using “=” to signify no statistical difference between two treatments, and “>” (or “<”) to signify that the mean of the treatment to the left of the sign is greater than (or less than) the mean of the treatment to the right with statistical significance.

50 2.4 Results

net 2.4.1 α and P rates

Mounding Porites spp. and A. muricata displayed very different patterns in the slope (α) of the light limited part of an ETR versus irradiance curve. Ranges of the treatment means of α were 0.28-0.30 (A. muricata) and 0.15-0.25 (mounding Porites spp.). In A. muricata, α was lower with statistical significance at 29°C versus 24°C and a main effect of time was present, with α being higher with statistical significance at the second time point (Table 2.1, Figure 2.2 a, e). Therefore, α did not achieve stabilisation between days 15 and 24 in all treatments for A. muricata. Light level had a non-significant effect on α in A. muricata (F2,36 = 2.9, p = 0.07), but a slight downward trend in α with increasing light was seen (Figure 2.2 c). In mounding Porites spp., α decreased with increasing light level (Tukey's test: Low > Medium > Peak), and with increasing temperature, whilst time point had no effect (Table 2.1, Figure 2.2 b, d, f). Therefore, α was stable between days 15 and 24 in all treatments for mounding Porites. For α in both species, there were no two- or three-way interactions among any of temperature, light and time point.

Pnet displayed some similarities as well as differences between mounding Porites spp. and A.

net muricata. In both species, under the main effect of each factor, P was significantly greater at 29°C versus 24°C, significantly lower at low light (Tukey's test: Low < Medium = Peak) and significantly lower at the first compared to second time point (Table 2.1, Figure 2.3 a, b). As there was no significant difference between Pnet measured at the medium and peak light levels, the photosynthesis-saturating irradiances for both species were probably lower than the medium light level. Pnet in the two taxa differed in terms of interactions of time point with either or both of temperature and light. There was no interaction of time with temperature or with light, nor a three- way interaction of all three factors, in A. muricata (Table 2.1). In other words, Pnet failed to stabilise between day 15 and 24 across all treatments in A. muricata. In mounding Porites spp., there was a two-way interaction of time point with light (letters in Figure 2.3 c) and a three-way interaction of temperature, light and timepoint (Table 2.1). This was because peak light displayed an increase with increasing time at 24°C and a decrease with increasing time at 29°C (at peak light, 24°C was not significantly different from 29°C at days 15-17, however at days 22-24, 24°C significantly exceeded 29°C, p = 0.0326). For both temperatures, there was no difference between time points at low light, and a significant increase through time at medium light (24°C: p = 0.0055; 29°C: p = 0.002). Thus, in mounding Porites spp., Pnet displayed stability between day 15 and day 24 at low light, but changed between day 15 and 24 at medium and peak light.

51 2.4.2 Areal symbiont densities

Areal symbiont densities within A. muricata (at low and medium light) and within mounding Porites spp. (at low and peak light) statistically differed with temperature (24°C > 29°C) and light (A. muricata: low < medium; mounding Porites spp.: low > peak). There was no effect of time point or interaction of time point with temperature or with light in A. muricata (Table 2.1). Thus, areal symbiont densities were apparently stable between day 15 and day 24 in all treatments in A. muricata. Time point had a significant two-way interaction with temperature and a significant three- way interaction with temperature and light in mounding Porites spp. (Table 2.1). In the three-way interaction, at peak light, areal symbiont densities showed no significant difference between the two time points at both temperature levels and at low light, 24°C and 29°C did not significantly differ at days 15-17, however, at days 22-24, 24°C significantly exceeded 29°C (p = 0.0198) – thus areal cell densities increased with time at 24°C and did the opposite at 29°C (Figure 2.4 a, b). Therefore, in mounding Porites spp., areal symbiont densities were stable between day 15 and 24 at peak light but changed between day 15 and 24 at low light.

2.4.3 Photon pressure per symbiont

Measurements of photon pressure (approximated as the amount of reflected and scattered light) per symbiont remaining in the host tissue were made over days 19-21. In A. muricata, the photon pressure per symbiont on the corallite undersides (the 1st geometry of measurement) was affected by significant main effects of temperature (24°C < 29°C) and light (Tukey's test: medium > low). In A. muricata, light level had a significant main effect on the photon pressure per symbiont on the corallite tops (the 2nd geometry of measurement) (Table 2.1), but subsequent post hoc tests for this main effect were non-significant. Measurements of photon pressure per symbiont cell taken from either the top or the underside of the corallites were significantly different (underside > top). However, both corallite areas displayed the same overall pattern (Figure 2.5 a).

For mounding Porites spp., there was a significant temperature × light interaction (Table 2.1); all treatments were significantly different from one another by Tukey's post hoc tests (Figure 2.5 b). The difference in reflected photon pressure per symbiont (in the 1st geometry) was much higher amongst treatments as light and temperature increased in mounding Porites spp. versus A. muricata: for instance, the peak light 29°C treatment was ~20 fold higher than the low light 24°C treatment in mounding Porites spp., compared to ~4 fold higher for the same comparison in A. muricata.

52 2.4.4 Fv/Fm

For both A. muricata and mounding Porites spp., the best fit to the Fv/Fm time-series data was a combined autoregressive and moving average model with both components of order 1 (ARMA(1)) allowing for heteroskedastic variances (Appendix 7.2).

Increases in light level caused decreases in Fv/Fm in both species (Tukey's test: low > medium > peak), except from medium to peak light at 29°C in A. muricata (Tukey's test p > 0.05). Increased temperature caused differences in Fv/Fm in A. muricata at low and medium light (Tukey's test: 24°C > 29°C), but not peak light (Tukey's test p > 0.05).

Water temperature caused a significant difference in average Fv/Fm in mounding Porites spp. (24°C > 29°C). An interactive effect of temperature with light was observed (Table 2.1), but post hoc tests found no significant difference in the pairwise comparisons of 24°C low light with 29°C low light, 24°C medium light with 29°C medium light, and 24°C peak light with 29°C peak light. There was a significant interaction of temperature with time point in mounding Porites spp. (Table 2.1).

Temperature did have a visible effect on the change of Fv/Fm through time in mounding Porites spp. with Fv/Fm displaying a downward trend through time at 29°C but not at 24°C (Figure 2.6 a, b).

Because of high Fv/Fm variation from day to day in some treatments, it was not feasible to choose particular days as “midpoints” and “endpoints” to determine whether Fv/Fm had stabilised within each treatment by the end of the experiment. This could have resulted in these comparisons being driven by high inter-day variation rather than being driven by the overall tendency of Fv/Fm through time. Instead, I determined whether samples had stabilised by the end of the experiment by pooling the Fv/Fm data into a midpoint group (days 10-14) and an endpoint group (days 17-21), and testing for a difference between the two groups with a mixed-effects model. A significant main effect of mid/endpoint (p < 0.0001; not reported in Table 2.1) in A. muricata revealed a significant decrease in Fv/Fm towards the end of the experiment (Tukey's test: p = 0.0006). A significant interaction of temperature with mid/endpoint in mounding Porites spp. (p = 0.0274; not reported in Table 2.1) did not yield any significant differences between midpoint and endpoint at either temperature (Tukey's test: p > 0.05). Therefore, Fv/Fm may indeed have achieved photoacclimation in mounding Porites spp. by day 15 whilst a continuation of photoacclimative change of Fv/Fm in A. muricata beyond day 15 of the experiment is suggested.

53 In addition to long-term trends, there were rapid changes in Fv/Fm soon after the beginning of exposure to the experimental light treatments. A. muricata experienced an increase in Fv/Fm from the first to the second day in all treatments (Table 2.1, Tukey's test: day 1 = day 12 < days 2-11, 13-

14). In contrast, at both temperatures, Fv/Fm of mounding Porites spp. at low light increased between the first and second day, stayed more-or-less stable between the first and the second day at medium light, and decreased between the first and the second day at peak light (Figure 2.6 a, b).

2.5 Discussion

This study exposed two coral taxa to three changes in light level, combined with two temperature levels, to investigate the duration of photoacclimation, and whether this is influenced by temperature level, direction of light change, or magnitude of light change. This was achieved through examining the slope (α) of the light-limited part of an electron transport rate (ETR) versus

net 2 irradiance curve, as well as using P per cm , symbiont cell densities and Fv/Fm. In an advance over most prior measurements of electron transport in corals (e.g. Ralph et al. 1999; Rodolfo-Metalpa et al. 2006, 2008; Langlois and Hoogenboom 2014), I used measurements of coral absorptance to derive ETR measurements that were corrected for the impacts of changing areal photosynthetic pigment densities.

The variables used in this study reflect different physiological processes that may change as a part of a photoacclimation response. The parameter α, as calculated using ETR in this study, is a measurement of the efficiency with which the downwelling light is absorbed and then utilised to drive the electron transport chain (Falkowski and Raven 2007). It is therefore an estimate of the efficiency with which incident light is utilised to power photochemical processes that can include carbon fixation (Harland and Davies 1994). This efficiency, and therefore α, can be enhanced by increasing the number of photon-trapping pigments contributing to each photosynthetic unit (Herron and Mauzerall 1972; Falkowski and Owens 1980; Falkowski and Dubinsky 1981). If the

net 2 light level is above saturation (e.g. in the “light increase” treatments), P per cm can be optimised to ambient light levels by changing one or more of the turnover time of PSII, the number of photosynthetic units per symbiont cell (Falkowski and Raven 2007), and the number of symbiont

net 2 cells. If the light level is below saturation (e.g. in the “light decrease” treatment), P per cm can be optimised by changing the aforementioned factors and α. Fv/Fm can identify photoacclimatory changes in the processes that repair or manufacture PSII. Decoupled PSII (delinked due to photodamage or photoprotection) will fluoresce and dissipate some energy as heat, but cannot

54 engage in photochemical quenching, and hence Fv/Fm will decrease during the day. However, if repair mechanisms can keep up, Fv/Fm will recover to its previous maximum level during the evening (Vass et al. 1992). Finally, symbiont population densities may alter due to host regulation or self-regulation of symbiont populations (Davy et al. 2012; Weston et al. 2015). Thus, photoacclimation can occur through one or more of a number of mechanisms. It stands to reason that the ecological and organismal consequences of a particular photoacclimation rate may depend on the mechanism(s) involved.

Due to increased kinetic rates of biochemical reactions, I suggested (in the introduction) that the rate of photoacclimation would be faster at increased temperature. In this study, temperature affected the time to stabilisation (following a light change) of Fv/Fm in mounding Porites. An interactive effect of temperature with time was detected in mounding Porites spp., with its Fv/Fm time-series (Figure 2.6 a, b) indicating a downward trend between days 6-14 at 29°C but not 24°C.

Thus, full photoacclimation of Fv/Fm in mounding Porites spp. appeared to be reached sooner at the lower temperature rather than the elevated temperature, running contrary to part 1 of my hypothesis.

In contrast, Fv/Fm in A. muricata in this study and in low light adapted-Favia favus did not display greater change through time at elevated temperature (29-31°C) compared to 25°C, but did in F. favia adapted to high light (Kuguru et al. 2007).

Fv/Fm is known to be a very temperature sensitive variable. This may be because the quantum efficiency of PSII is affected by several heat-sensitive processes, such as the rate of repair of photodamage to the PsbA protein of the reaction centre in PSII (Takahashi et al. 2004). Some photoprotective processes that are enacted at elevated temperature, including PSII that are delinked from the electron transport chain and dissipate light as heat (“inactive PSII”), also cause a decrease in Fv/Fm (Warner et al. 1996). Thus, assuming that one or both processes are capable of operating in the symbionts of mounding Porites spp., the fact that Fv/Fm takes longer to photoacclimate in mounding Porites spp. at elevated temperature is mechanistically understandable. The fact that

Fv/Fm does not take longer to photoacclimate in A. muricata at elevated temperature raises a hypothesis that in this species, PsbA damage and/or “inactive PSII” do not differ substantially between 24°C and 29°C. Indeed, expression of PsbA in another Acropora species (A. aspera) does not significantly differ between heat-stressed and control coral specimens (Gierz et al. 2016), whereas a greater proportion of PSII in A. nobilis are inactive under heat stressed conditions than in species of other genera (Hill and Ralph 2006).

55 There are several reasons why, in mounding Porites spp., Pnet per cm2 may not display the faster photoacclimation at the control temperature seen for Fv/Fm. Pulsed Amplitude Modulated fluorometry measures the responses of the surface symbiont cells (Schreiber 2004) (which receive

net direct incident light) whilst O2 evolution (this study’s proxy for P ) integrates the photosynthetic response over all layers of the tissue (over which the range of light microenvironments may vary; Wangpraseurt et al. 2012). Secondly, the symbiont population could compensate for any factors that cause a reduction in quantum efficiency of PSII (such as a decrease in PSII turnover time) by increasing the numbers of photosynthetic units per symbiont cell (Falkowski and Raven 2007). Thirdly, at the holobiont level, an increase in the symbiont population size could also compensate for any reductions in photosynthesis per symbiont cell due to reduced PSII quantum efficiency (Hoogenboom et al. 2010). Fourthly, Pnet is directly affected by symbiont and coral respiration, whereas Fv/Fm is not (though an indirect effect could be present: Harland and Davies 1995). Only the holobiont respiration rate is usually measured, due to difficulties in making separate measurements of host respiration and symbiont respiration (Muscatine et al. 1981). In past experiments, changes in light and temperature have affected holobiont respiration rates (e.g. Edmunds and Davies 1988; Dunn et al. 2012; Baker et al. 2018), and so my observed changes in

net P might be caused, to some extent, by such a change. These phenomena, due to which Fv/Fm may fail to reflect actual Pnet per cm2, may help explain the mismatch between the photoacclimation timeframes of the two parameters in mounding Porites.

Other than Fv/Fm in mounding Porites spp., no response variables in either species displayed different patterns of stabilisation between the two temperatures (ie. stability between day 15 and 24 at one temperature, but not at the other). This observation does not rule out differences in photoacclimation rate between the temperatures before this period. Due to the impact of temperature on the kinetic rates of biological reactions (Gillooly et al. 2001), one would expect that temperature would impact the photoacclimation rate. However, as this is the first study, to my knowledge, to compare coral photoacclimation rates at different temperatures, there is not yet evidence that this is the case. This caveat is applicable to symbiont densities in A. muricata, and

net symbiont densities at peak light, P at low light and α in mounding Porites spp., which were stable between day 15 and 24. Alternatively, a lack of any photoacclimatory changes being made at all is another possibility (Chang et al. 1983), though this could only have applied to one level of any independent variable as, in most cases, the response variables must have changed from some original value as they differ in value among the treatments. Some other response variables continued to change through to the end of the experiment in both temperature treatments, e.g. α and Pnet in A. muricata, Pnet at medium and peak light and symbiont densities at low light in mounding

56 Porites. A comparable result has been found in Stylophora pistillata, where Km (the irradiance at

net which Pnet = Pmax /2) did not stabilise until 45 days or longer after the incident light intensity was permanently changed (Gattuso and Jaubert 1984). For these variables, the sampling regime would not have detected evidence of differences in photoacclimation rate that manifested following the end of the experiment (e.g. stabilisation after 30 days in one treatment and 40 days in a different treatment). Similar caveats are applicable to the following discussions of differences in photoacclimation rates between light treatments.

Modelling evidence suggests that corals exposed to a decrease in incident light will take less time to photoacclimate than those exposed to an increase in incident light (Skirving et al. 2018), a concept formalised in the second part of my hypothesis. This study did not produce evidence to support this modelling in A. muricata, nor for any variables except Pnet per cm2 in mounding Porites spp., though the results in the latter species are more nuanced. In A. muricata, symbiont densities and Pnet per cm2 exhibited no change between day 15 and 24, suggesting that either change in these variables had been completed within 14 days in all treatments, or that no photoacclimatory changes occurred. α in A. muricata continued to photoacclimate between day 15 and day 24 in all treatments, and thus there was no evidence to distinguish rate of photoacclimation in the “light increase” versus “light decrease” treatments. In mounding Porites spp., α had stabilised within 14 days in all treatments. For the other response variables in mounding Porites spp., the primary influence of the direction of light change was to elicit the completion of photoacclimation within 14 days for symbiont cell densities in the “light increase” treatments, which contributed to Pnet per cm2 stabilising within 14 days in the “light decrease” treatment.

Because of the greater signal provided by a larger abiotic change, or more negative ramifications of delayed photoacclimation for the organism under such a change, larger magnitude changes in light may incur faster photoacclimation rates than smaller magnitude changes in light (referred to in part 3 of my hypothesis). I tested this concept by looking for differences between the 218 µmol quanta∙m-2∙s-1 treatment versus the 282 µmol quanta∙m-2∙s-1 treatment regarding whether stabilisation was seen between day 15 and 24 in each variable. The level of 218 µmol quanta∙m-2∙s-1 represents a smaller magnitude light change (of +47 µmol quanta∙m-2∙s-1 compared to pre-experiment levels) whilst 282 µmol quanta∙m-2∙s-1 represents a larger magnitude light change (of +111 µmol quanta∙m- 2∙s-1). However, these light treatments did not display any difference in stabilisation for any parameter, providing no evidence to suggest that photoacclimation was more rapid at the peak light level than at the medium level (or the converse), in either species.

57 The behaviour of α in A. muricata could reflect that of a light-limited plant, a suggestion corroborated by the low level of photon pressure per symbiont cell in this species. The increase of α through time over all treatments in A. muricata implies that the efficiency of photochemical quenching at PSII per unit light increased through time. Such an increase in efficiency of light utilisation is an expected response in corals acclimating to low light (Falkowski and Dubinsky

1981). A trend of decreases in α at the higher light levels was seen in A. muricata (Figure 2.2 c), which may have been an indication that the rate of change in α had varied among the light treatments. A main effect of increase in Pnet through time was also observed (Figure 2.3 a), suggesting that A. muricata were becoming more efficient at using incident light, regardless of their treatment light level. Whilst A. muricata were exposed to the same light levels as mounding Porites spp., photon pressure per A. muricata symbiont cell in all light treatments (ca. 0.2-0.7) was of the same order of magnitude as that at low light in mounding Porites spp. (ca. 0.5-1) (Figure 2.5 a, b). Thus, the local light micro-environment of A. muricata symbionts in all treatments could have been a light limited environment, perhaps explaining the species’ increase in α through time. In a study of the coral species Madracis pharensis, differences in α were found to be strongly associated with differences in long-term light intensity (Frade et al. 2008).

Increased time for stabilisation of Pnet per cm2 at medium and peak light in mounding Porites spp. is paralleled by changes in the amount of photon pressure per symbiont cell in this taxon. Treatments that increased their Pnet per cm2 with time – medium light at both temperatures and peak light at 24°C – experienced elevated photon pressure per symbiont cell (Figure 2.5 b) compared to those

net 2 treatments – low light at both temperatures – that experienced stable P per cm through time. Peak light (282 µmol quanta∙m-2∙s-1) at 29°C, which saw a decrease in Pnet per cm2 with time, experienced extremely high photon pressure per symbiont cell compared to all other treatments (Figure 2.5 b).

net 2 This suggests that photoinhibition was reducing P per cm through time in this treatment, whilst treatments experiencing moderate levels of available light per symbiont cell upregulated their areal rate of photosynthesis through time. Wangpraseurt et al. (2017) also found that photon pressure per

net 2 symbiont increased five-fold and P per cm decreased in bleached Pocillopora damicornis, however Pgross per cell increased, suggesting that photoinhibition was not occurring. Therefore, rates of Pgross per cell would be required to determine if mounding Porites spp. were photoinihibited in this treatment.

Areal symbiont densities could have been driven by photoinhibition in mounding Porites spp., but by other factors in A. muricata. At 24°C, mounding Porites spp. lost symbiont cells with increasing light level (Figure 2.4 a), whereas A. muricata cell densities remained the same at all light levels

58 (Tukey's test p = 0.99). In mounding Porites spp., cell loss with increasing light levels under non- stressful temperature suggests that the cause of cell loss is photoinhibition. In accord with the photoinhibition model of bleaching (Iglesias-Prieto 1995; Hoegh-Guldberg 1999), there was a trend for cell loss in Porites spp. to be aggravated by increased temperature (Table 2.1, Figure 2.4 a, b). Symbiont cell densities in A. muricata also decreased with elevated temperature (Figure 2.4 b).

However, in A. muricata, a much greater decrease in symbiont density at high temperature occurred for low light than medium or peak light, suggesting that an additional mechanism rather than only photo-inhibition was driving this change. To my knowledge, this is the first factorial light × temperature experiment on corals that, at an elevated temperature, has found a greater decline in symbiont density at low light intensity compared to higher intensities. Elevated temperature causes an increase in the kinetic rate of biochemical reactions (Sheridan et al. 2012) and therefore increases the metabolic rate of each symbiotic partner (Gillooly et al. 2001). Symbiodinium are known to be mixotrophic, capable of deriving nutrition from both the host and from photosynthesis (Steen 1987; Banaszak et al. 2013). One hypothesis for the decline in symbiont density in A. muricata at 29°C and low light is that the symbiont population may have been reduced to mitigate against the symbionts changing from a mixotrophic to a heterotrophic mode of feeding (becoming parasitic) (Dimond and Carrington 2008). A second hypothesis is that the symbiont population may have been thinned to reduce self-shading, increasing the photosynthetic performance of the remaining symbiont cells. Other hypotheses are that the combination of low light and elevated temperature induced symbiont apopotosis, or induced some host cell apoptosis or necrosis that involved degradation of symbiont cells. Determination of the mechanism of symbiont loss (e.g. through histological examination and stains for apoptosis) could help to explain why the greatest symbiont loss occurred at 29°C and low light in A. muricata.

net The main effect of increased areal P with time in A. muricata also suggests that photoinhibition was a low risk at high temperature. Experiments on a taxonomically diverse group of other coral species show that those that don’t experience a high increase in photon-pressure per symbiont with increasing temperature (like A. muricata: Figure 2.5a) experience reduced photophysiological dysfunction compared to those that do (like mounding Porites spp.: Figure 2.5b) (Swain et al. 2016). In A. muricata, metabolic needs may instead have been the driving force of areal symbiont density changes and thermally induced bleaching may originate from a site of damage in A. muricata that is outside of the dinoflagellate photosystems.

59 The Symbiodinium type in A. muricata in this study is actually known to be thermally sensitive, whilst the probable Symbiodinium phylotypes (i.e. putative Symbiodinium taxonomic units) in mounding Porites spp. are known to be thermally tolerant. The A. muricata colonies at Heron Island exhibit the C3 Symbiodinium phylotype (Tonk et al. 2013), whilst mounding Porites spp. in the southern Great Barrier Reef have exhibited phylotypes C1, C1c, C15, and C3 (Fisher et al. 2012; Tonk et al. 2013). There is some evidence that C1 and C15 are thermally tolerant phylotypes. In A. millepora, some colonies found surviving after a bleaching event contained a predominance of either phylotype C1 or clade D symbionts (Jones et al. 2008). P. cylindrica and P. lutea containing phylotype C15 have a substantially better photosynthetic performance at high temperature than other host-symbiont combinations (Fitt et al. 2009; Fisher et al. 2012). In contrast, compared to corals hosting C15, those hosting C3 have reduced Fv/Fm (but not reduced cell densities) at 31-34°C (Fisher et al. 2012). That the C3-associating A. muricata in my study did not have reduced performance in comparison to mounding Porites spp. at 29°C adds the nuance that C3-hosting corals may have advantages in performance at temperatures that are intermediate between optimal and highly-stressful.

2.5.1 Implications for bleaching prediction

The findings, in mounding Porites spp., of a difference in photoacclimation rate of Fv/Fm between the two temperatures, and of Pnet per cm2 between the two directions of light change, have implications for coral bleaching prediction. The use of Fv/Fm in remote sensing bleaching prediction is now occurring: prediction algorithms use empirical relationships between light, temperature and

Fv/Fm to predict Fv/Fm from remotely sensed data, and then predict bleaching onset based on characterised thresholds of Fv/Fm for bleaching (Skirving et al. 2018). Declines in areal or colony Pnet typically reflect stress associated with coral bleaching (Hoegh-Guldberg and Smith 1989;

Middlebrook et al. 2010; Hoogenboom et al. 2012), whereas Fv/Fm decline may in some circumstances reflect processes that actually are photoprotective (Matsubara and Chow 2004). The

net 2 slightly different physiological processes that Fv/Fm and P per cm reflect manifested as

net 2 inconsistencies between the photoacclimation responses of Fv/Fm and P per cm under the experimental treatments. For instance, in mounding Porites spp., elevated temperature reduced the

net 2 photoacclimation rate of Fv/Fm but not that of P per cm . If photoacclimation rate is determined using Fv/Fm, then prediction algorithms must take into account that this photoacclimation rate may differ, under elevated temperature in some corals species, from variables such as Pnet per cm2 that typically reflect actual changes in photosynthesis or respiration in the holobiont that occur during

60 bleaching (Hoegh-Guldberg and Smith 1989). Potentially, this could be accounted for by adjusting the Fv/Fm bleaching threshold during periods of Fv/Fm photoacclimation. If photoacclimation rate is determined through measurement of Pnet per cm2 (photoacclimation rates determined using Pnet per cm2 are currently used in the LSD algorithm: Skirving et al. 2018) then algorithm users must be aware that the modelled photoacclimation rate may depart from the true photoacclimation rate of

Fv/Fm at some temperatures.

The apparently slower photoacclimation rate in “light increase” versus “light decrease” treatments

net 2 for P per cm also may have implications for coral bleaching prediction. The apparently faster photoacclimation rate of Pnet per cm2 under light decrease matches the result from modelling by Skirving et al. (2018) suggesting that photoacclimation rate should be faster at light decrease than at light increase. However, as Fv/Fm did not display the same delayed pattern of faster acclimation under light decrease, this further underlines that caution in the use of photoacclimation coefficients

net 2 determined with either Fv/Fm or with P per cm must be exercised in the LSD (and in future) bleaching prediction methods. In addition, it must be borne in mind that photoacclimation rates of Pnet per cm2 in mounding Porites spp. were likely driven in part by the photoacclimation rates of symbiont densities per cm2.

2.5.2 Conclusions

In this study of coral photoacclimation rates in response to changed light intensity, I have emphasised the theoretical underpinning that there are a wide range of physiological processes that undergo photoacclimation. Through my experimental results, I have demonstrated that the photoacclimation rates differ among some of these physiological processes. Furthermore, my evidence shows that different aspects of light change (i.e. light increase versus decrease, light change under elevated versus optimal water temperature) could influence the photoacclimation rates of some but not other physiological processes. The determination of photoacclimation rates for use in bleaching prediction will rely on careful consideration of the parameter used to measure photoacclimation. In the case of the LSD algorithm, further investigation may be needed to determine how to adjust the predicted Fv/Fm when some other physiological variable has been used to determine photoacclimation rate, or how to adjust Fv/Fm thresholds for coral bleaching when

Fv/Fm has been used to determine photoacclimation rate.

61 Table 2.1: Probability values for statistical tests.

The number above every F statistic is the corresponding p value. The peak light level (282 μmol quanta·m-2·s-1) was not included for symbiont cells per cm2 for A. muricata. The medium light level (218 μmol quanta·m-2·s-1) was not included for mounding Porites spp. for symbiont cells per cm2.

The ANOVA of Fv/Fm was performed on a GLS autoregressive moving average model of order 1 allowing for heteroskedasticity. The intercept had a probability value of p < 0.0001 for all ANOVAs. Probability values that are less than 0.05 are highlighted in bold.

Photon pressure per Photon pressure per net 2 2 α P per cm Cells per cm symbiont cell, 1st geometry symbiont cell, 2nd geometry Fv/Fm A. muricata Temp < 0.01 < 0.01 < 0.01 0.01 0.83 < 0.01

F1,36 = 9.45 F1,36 = 24.8 F1,24 = 45.4 F1,12 = 9.21 F1,12 = 0.05 F1,252 = 141 Light 0.07 < 0.01 < 0.01 < 0.01 0.02 < 0.01

F2,36 = 2.90 F2,36 = 150 F1,24 = 20.9 F1,12 = 21.1 F1,12 = 7.72 F2,252 = 1922 Time point < 0.01 < 0.01 0.6 < 0.01

F1,36 = 8.93 F1,36 = 28 F13,252 = 47 F1,24 = 0.29 Temp×Light 0.15 < 0.01 < 0.05 0.28 0.83 < 0.01

F1,12 = 1.27 F1,12 = 0.05 F2,252 = 79 F2,36 = 2.01 F2,36 = 8.12 F1,24 = 4.48 Temp×Time 0.38 0.27 0.72 < 0.01

point F13,252 = 4 F1,36 = 0.79 F1,36 = 1.25 F1,24 = 0.13 Light×Time 0.23 0.58 0.39 0.21 point F2,36 = 1.54 F2,36 = 0.55 F1,24 = 0.75 F26,252 = 1 Temp×Light 0.69 0.29 0.62 0.27 ×Time point F2,36 = 0.38 F2,36 = 1.27 F1,24 = 0.26 F26,252 = 1 Porites spp. Temp < 0.05 < 0.01 < 0.01 < 0.01 < 0.01

F1,36 = 4.12 F1,36 = 68.6 F1,24 = 24.3 F1,12 = 45.3 F1,252 = 89.5 Light < 0.01 < 0.01 < 0.01 < 0.01 < 0.01

F2,36 = 101 F2,36 = 29.1 F1,24 = 37.5 F1,12 = 211 F2,252 = 1298 Time point 0.92 < 0.01 0.8 < 0.01

F1,36 = 18.23 F1,24 = 0.063 F13,252 = 25.7 F1,36 = 0.01 Temp×Light 0.86 0.12 0.18 < 0.01 < 0.01

F1,12 = 34 F2,252 = 6.74 F2,36 = 0.16 F2,36 = 2.22 F1,24 = 1.87 Temp×Time 0.67 0.72 0.04 < 0.01

point F13,252 = 2.54 F1,36 = 0.19 F1,36 = 0.13 F1,24 = 4.53 Light×Time 0.30 < 0.01 0.61 < 0.01

point F2,36 = 9.98 F26,252 = 4.60 F2,36 = 1.24 F1,24 = 0.27 Temp×Light 0.64 0.04 < 0.05 0.22 ×Time point F2,36 = 0.45 F2,36 = 3.41 F1,24 = 4.47 F26,252 = 1.22

62 Depicted is the 1st geometry of measurement (used for both A. muricata and mounding Porites spp.), illustrating the orientations of and distances between the light source (upper left), coral sample (Acropora nubbin) (lower centre) and optical fibre measurement sensor (centre, above coral). The light source and the measurement sensor were angled at 45º to the horizontal. In the 1st geometry of measurement, the A. muricata fragment was orientated so that the corallite undersides were facing towards the measurement sensor. In the 2nd geometry of measurement (not illustrated), the A. muricata fragment was horizontally rotated 180° compared to the fragment position in the 1st geometry of measurement, so that the corallite tops were pointing towards the measurement sensor, and the measurement light was positioned directly above the fragment at a fixed distance.

63 (a) 0.3 * * (b) 0.3

0.25 0.25 * 0.2 0.2 *

0.15 0.15

0.1 0.1 of downwelling of PAR) 0.05 downwellingof PAR) 0.05 α (electrons·photonˉ¹α α (electrons·photonˉ¹α

0 0 24°C 29°C 24°C 29°C temperature (°C) temperature (°C)

(c) 0.3 (d) 0.3

0.25 0.25 *

0.2 0.2 * * 0.15 0.15

0.1 0.1

of downwellingof PAR) 0.05 downwelling of PAR) 0.05 α (electrons·photonˉ¹ α (electrons·photonˉ¹ α

0 0 117 218 282 117 218 282 light level (µmol photons·mˉ²·sˉ¹) light level (µmol photons·mˉ²·sˉ¹)

(e) 0.3 * * (f) 0.3

0.25 0.25

0.2 0.2

0.15 0.15

0.1 0.1

of downwellingof PAR) 0.05 of downwellingof PAR)

0.05 (electrons·photonˉ¹ α α (electrons·photonˉ¹ α

0 0 Days 15-16 Days 22-23 Days 15-16 Days 22-23 timepoint timepoint Figure 2.2: The slope, α, of the light-limited part of an electron transport rate vs irradiance curve in A. muricata (a, c, e) and mounding Porites spp. (b, d, f). Symbols (*) indicate treatments that are significantly different from one another (p < 0.05) in the post hoc test. Error bars are standard error of the mean. The value at each level of a factor is averaged over all the levels of the other factors.

64 (a) (b) cg bf 1.2 1.2 dh * ae 1 * 1 ₂ 0.8 ₂ 0.8 ab ef cd gh 0.6 0.6

0.4 0.4

0.2 0.2 Netphotosynthesis (µmol O ·cmˉ²·hrˉ¹) 0 Netphotosynthesis (µmol O ·cmˉ²·hrˉ¹) 0 days 15-17 days 22-24 24°C, 117 24°C, 218 24°C, 282 29°C, 117 29°C, 218 29°C, 282

timepoint temperature (°C), light level (µmol quanta·mˉ²·sˉ¹)

(c) 1.2 cek pq Days 16-17 Days 23-24 dgh 1 ijm

fl 0.8

₂ no

ab 0.6 hi jk cd efg 0.4 ai mnp oq bhj kl

Net photosynthesis (µmol O (µmol photosynthesis Net ·cmˉ²·hrˉ¹) 0.2

0 24°C, 117 24°C, 218 24°C, 282 29°C, 117 29°C, 218 29°C, 282

temperature (°C), light level (µmol quanta·mˉ²·sˉ¹)

Figure 2.3: Net photosynthesis measured via net oxygen production per unit of surface area in (a) A. muricata, at both time points averaged across all treatments; (b) A. muricata, in all treatments averaged across both time points; (c) mounding Porites. Treatments annotated with the same alphabetic letter or the symbol (*) were significantly different (p < 0.05) in post hoc tests. Error bars are standard error of the mean. 65 Depicted are the symbiont densities at (a) 24°C, and (b) 29°C. The line identifies of both plots are shown at the base of panel (a). Error bars are standard error of the mean.

for (a) A. muricata and (b) mounding Porites. Symbols (*) represent treatments that were significantly different from one another (p < 0.05) in post hoc tests. Error bars are standard error of the mean. The graph without symbols is that for which there was no significant interaction of light with temperature.

66 Figure 2.6: Times series of the dark-adapted yield of photosystem II, at (a) 24°C and (b) 29°C.

In both plots, the lines (from top to bottom) are: A. muricata (abbreviated A) at low light, medium light and peak light; mounding Porites spp. (abbreviated P) at low light, medium light and peak light. All data points are the means (± s.e.m.) of the aquarium medians (n = 4) of each treatment.

67 3 The relationships of thermal bleaching thresholds to mortality and of light bleaching thresholds to α in three scleractinian corals.

3.1 Abstract

1. Global change entails, among other impacts, increased water temperature around coral reefs and alterations to light intensity, factors that facilitate the prediction of coral bleaching via satellite radiometry. There is a need to predict coral mortality following coral bleaching, to understand the differences in bleaching thresholds amongst different species, and to better understand the biological mechanisms that underpin these differences.

2. This chapter proposed the hypotheses that mortality following thermally induced bleaching will correlate with the quantity of host protein lost during coral bleaching, that species that bleach at lower temperature or light thresholds will subsequently incur less physiological damage, and that susceptibility to decline in α (the slope of the light-limited part of an ETR vs irradiance curve) under high light would correlate with an increased susceptibility to light change-induced bleaching. These hypotheses were investigated in three dominant reef coral taxa, mounding Porites spp., Acropora muricata, and Montipora monasteriata, using twelve or more ecologically relevant combinations of light intensity and water temperature.

3. I found that mortality in M. monasteriata at 31°C was correlated with host protein loss, but this host protein loss may have been due to the direct effects of heat on the host rather than due to symbiont cell loss (bleaching). Mortality in A. muricata appeared to be related to a dramatic loss of symbiont cells, and there was no mortality in mounding Porites spp.; thus the organism-level events that are the preludes to mortality, following coral bleaching, are species specific. However, at 31°C, when light was increased or decreased, substantial symbiont loss in A. muricata occurred, whilst symbiont loss was the least severe when the elevated temperature was not accompanied by light change. In mounding Porites spp., a similar pattern was seen with host protein loss.

4. The energetic cost of photoacclimation to low light levels may explain why exposure to low light has aggravated bleaching or host protein loss at high temperature, in these cases. The species that experienced a reduction in α under high light (M. monasteriata and mounding Porites spp.) were found to have greater maximum excitation pressure over PSII and greater light change-induced

68 bleaching, compared to the species that maintained a higher α under elevated light (A. muricata). Species that bleached at lower temperature thresholds did not incur less physiological damage, as

net 2 measured through Fv/Fm and Pmax per cm . I explore the implications of my results for the prediction of bleaching of entire coral communities. These results also identify previously unforeseen technical issues with the measurement, via chlorophyll fluorescence at midday and evening hours, of Qm and NPQ.

3.2 Introduction.

Greenhouse gas emissions are contributing to the degradation of coral reefs, through ocean warming and acidification. High-temperature thermal anomalies and concomitant bleaching events have been a regular occurrence in tropical oceans since 1980 (Williams and Bunkley-Williams 1990; Baker et al. 2008). The frequency and intensity of these events are predicted to increase as climate change worsens (Hoegh-Guldberg et al. 2014; Hughes et al. 2017a).

Coral bleaching can have a range of ramifications for the coral. Bleaching through symbiont loss can be part of a photoacclimation response to changed light conditions (Piggot et al. 2009), and in this manifestation, bleaching could be advantageous to long-term coral health, particularly if the biomass of the coral does not decline. In other circumstances, bleaching through symbiont loss can involve host cell loss or energetic deficits, possibly predisposing the coral to mortality (Gates et al. 1992; Middlebrook et al. 2010). The outcomes for corals exposed to prolonged high temperatures vary, and include bleaching followed by prolonged survival without mortality (Yonge and Nicholls 1931), bleaching followed rapidly by mortality (Mason, R., unpublished observations), and mortality without bleaching (Banks 2015). Thus, coral reef managers ideally need to understand when bleaching actually has negative long-term consequences rather than being a part of adjustments (e.g. photoacclimation) that a coral is making to its environment.

In light of this fact – that the onset of coral bleaching does not predict long-term decline or mortality in all cases – there is a need to develop bleaching prediction methods that predict coral mortality accurately. Symbiont loss during coral bleaching can occur through several pathways, some that involve the loss of host cells, and some that preserve host cells (Gates et al. 1992; Dunn et al. 2002; Downs et al. 2009). It is possible that the differences in mortality among species following bleaching may have to do with whether substantial loss of host tissue occurs during coral

69 bleaching. This study will explore whether coral bleaching severity (measured as the percent of symbionts lost) or loss of host cells (measured as host protein) are predictive of coral mortality.

Furthermore, due to the varying consequences of coral bleaching for long-term coral health and mortality, the biological reasons behind why some corals bleach in response to particular conditions, whilst others do not, is of major interest in coral reef studies (Loya et al. 2001; van Woesik et al. 2011). A case study of bleaching in response to different conditions is provided by the comparison of Acropora muricata and mounding Porites spp. in Chapter 2. When exposed to high light at a non-stressful temperature, A. muricata did not suffer symbiont loss, but at an elevated temperature lost more symbionts at low light compared to elevated light levels. In contrast, when exposed to high light at a non-stressful temperature, mounding Porites spp. did suffer symbiont loss, and symbiont loss at high light was further aggravated by elevated temperature. A biological explanation behind these responses is suggested by the patterns of α (the slope of the light-limited part of an ETR vs irradiance curve) in the two species. The parameter α is an approximation of the efficiency with which incident light is utilised to power photochemical processes that can include carbon fixation (Harland and Davies 1994). A. muricata demonstrated an increase in α (and net photosynthesis [Pnet] per cm2) through time at both high and low light whilst mounding Porites spp. rapidly reduced its α at higher light levels. I interpreted this evidence to hypothesise that A. muricata experiences light limitation even at high light, whilst at the same light levels mounding Porites spp. is at or above its saturating irradiance.

This study will explore this hypothesis using information on the biological state of photosystem II provided by measurements of the maximum excitation pressure over photosystem II (Qm) and non- photochemical quenching (NPQ). Qm is the ratio, subtracted from 1, of ΔF/Fmʹ to Fv/Fm. (Iglesias-

Prieto et al. 2004). ΔF/Fmʹ is the quantum efficiency of photosystem II under daily maximum illumination, whilst Fv/Fm is the dark-adapted quantum efficiency of photosystem II. Values of Qm range between 0 and 1; low values indicate that most of the PSII reaction centres remain open under daily maximum light (the level at which ΔF/Fmʹ was measured), suggesting that that the corals are acclimated to a much greater light level than their current conditions. High values indicate that most reaction centres are closed under daily maximum light, suggesting that this light level is indeed close to the saturation irradiance (Iglesias-Prieto et al. 2004). The proportion of reaction centres that stay open at a particular light intensity is determined by the amount of NPQ and by the capacity for photochemical quenching through either carbon fixation or alternative electron transport pathways (Warner et al. 2006). In addition, as the size of the photon-trapping pigment bed attached to each reaction centre (photosynthetic unit [PSU] size) directly affects the quantity of excitation energy

70 flowing to both the reaction centre and to NPQ mechanisms, PSU size will affect Qm (Mauzerall and Greenbaum 1989).

Non-photochemical quenching (NPQ) is the dissipation of absorbed light energy by conversion to heat that occurs in a photosynthetic unit, and may be of two forms: “basal” mechanisms (instantaneous processes such as heat emission via carotenoid triplets), and “inducible” mechanisms (Falkowski and Raven 2007; Murchie and Harbinson 2014). “Inducible mechanisms” are upregulated in response to light exposure, showing a marked increase under levels of light energy that approach the saturating irradiance of the electron transport chain (Gorbunov et al. 2001). In symbiotic dinoflagellates, one mechanism of inducible NPQ is the conversion of the pigment diadinoxanthin to diatoxanthin, releasing heat (Goss and Jakob 2010). Inducible NPQ can be measured from chlorophyll fluorescence determinations of Fm and Fmʹ using the Stern-Volmer quenching parameter (SVN) for NPQ, given by SVN = (Fm – Fmʹ)/Fmʹ (Bilger and Björkman 1990; Maxwell and Johnson 2000). This equation is based on a general equation for the kinetics of a fluorescence quenching reaction (Desilets et al. 1987). Together, Qm indicates how well the photochemical apparatus (both photochemical and non-photochemical processes: Warner et al. 2006) is optimised to quench the excitation energy that is captured at the incident light level, and

SVN indicates the degree of photoprotection through inducible NPQ.

The differing bleaching responses of A. muricata and mounding Porites spp. also provide an opportunity to explore what the consequences of each type of bleaching response are. The lower light thresholds for symbiont loss in mounding Porites spp. (Chapter 2), and the known robustness of the same taxon to mortality (Marshall and Baird 2000), suggest that there could be a tradeoff between the level of light or temperature that induces bleaching and the physiological consequences of bleaching. I hypothesise that under light change-induced bleaching (bleaching caused by increases or decreases in irradiance), species that bleach at a lower light threshold will have less

net severe reductions in photosynthetic performance, such as Fv/Fm and P per unit area. By extension, I hypothesise that bleaching at high light in A. muricata (should it occur) may have more serious

net physiological consequences, such as more severe declines in Fv/Fm and P per unit area. This chapter examines this hypothesis by pushing A. muricata to light levels (307 and 371 µmol quanta·m-2·s-1) that are higher than the highest level used in Chapter 2 (282 µmol quanta·m-2·s-1) in an attempt to precipitate light change-induced bleaching. I also examine if the same species-specific physiological responses may result under thermally induced bleaching (i.e. a lower temperature threshold for, but also lower physiological impacts of, bleaching in mounding Porites spp. compared to A. muricata).

71 An understanding of the differences among species in bleaching response, and the underlying biological cause(s), has an applied use in predicting the bleaching responses of heterogeneous coral communities. To increase the taxonomic spread of these experiments with this goal in mind, I introduce a third species M. monasteriata, a plating coral species, into the experiments. M. monasteriata is an informative counterpoint as it shares some physiological affinities to both mounding Porites spp. and A. muricata. M. monasteriata is in the same family (Acroporidae) as A. muricata, but has a planar surface, which is morphologically closer to the gently rounded morphology of mounding Porites than to the branching morphology of A. muricata (Veron 1986).

To address my hypotheses, corals were exposed to four temperatures combined with five light levels. To facilitate this quantity of experimental treatments, I performed four sub-experiments at four different temperatures (with a temperature control at each) (Figure 3.1). Corals were raised to temperature slowly at the rate of 0.244°C∙day-1, close to thermal anomaly heating rates that occur near the study site (0.07-0.11°C∙day‾1: Weeks et al. 2008). In summary, my aims and hypotheses were:

(1) To investigate coral mortality following thermal stress and its correlation with symbiont loss (bleaching) and host tissue loss (measured as loss of host protein). Hypothesis:

Mortality following thermally induced coral bleaching will correlate with the amount of host protein that is lost during the process of coral bleaching.

(2) To elucidate photophysiological differences between coral species and their relationship to light change-induced bleaching susceptibility. Hypothesis:

Species that maintain their α at high light will display signatures of robustness to high light (reduced maximum excitation pressure over PSII, the absence of light change-induced symbiont density loss) compared to those that decrease their α at high light.

(3) To explore the consequences of species differences in bleaching onset thresholds. Hypothesis:

Under (a) thermally induced bleaching and (b) light change-induced bleaching,

physiological dysfunction (declines in Fv/Fm, decreased photosynthesis per unit surface area) will be more severe in species that bleach at a higher thermal threshold or light change threshold.

72 3.3 Methods

3.3.1 Organism collection

The coral specimens were collected under permit number G11/34549.1 issued by the Great Barrier Reef Marine Park Authority. Collections occurred at 6-10 m depth in the Capricorn Bunker Group, Great Barrier Reef, in 2013. Mounding Porites spp. (cores 3 cm in diameter) were collected at reef slope locations at north Wistari Reef, over 27 July – 1 August. Montipora monasteriata (pieces with a mean area of 5.21 cm2, ± 0.27 s.e.m.) were collected at reef slope locations at Heron Reef, on 3 August. All samples were attached to gridded racks at 8 m depth (Harry’s Bomme, Heron Reef) to recover for a period of 59 days. Branch tips (length 5 cm) of Acropora muricata were taken at 8-9 m depth at Coral Gardens, Heron Reef, on 4 August and 29 September, and allowed to recover at 8 m depth (August collection) or in outdoor aquaria (September collection). The specimens of all species were translocated from the reef to outdoor holding aquaria 12 days prior to the start of the experiment. Mortality over the recovery period was negligible for mounding Porites spp., M. monasteriata, and the September collection of A. muricata but high for the August collection of A. muricata. The number of coral colonies from which pieces were taken and the number of coral pieces used in the experiment, respectively, were, in A muricata: 37 and 328; in M. monasteriata: 25 and 293; and in mounding Porites spp.: 21 and 177.

At the collection location, A. muricata associates with Symbiodinium phylotype C3 (Tonk et al. 2013), whilst mounding Porites spp. may associate with phylotypes C1, C1c, C15, and C3 (Fisher et al. 2012; Tonk et al. 2013). Montipora monasteriata has a number of colour morphs at Heron Reef and the nearby Wistari Reef (tan, blue (purple), brown, green and red) (Dove et al. 2008). At the reef slope of Wistari Reef, the tan morph and the blue (purple) morph host phylotype C17 whilst the brown morph hosts phylotype C21 (Dove et al. 2006). I predominantly sampled the blue (purple) morph (which is an open habitat specialist) but also a few brown morph or tan morph colonies (tan occurs in the open and brown occurs in the shaded habitats) (Dove et al. 2008). Thus the majority of the M. monasteriata specimens contained C17.

3.3.2 Experimental design

Four treatment temperature levels (below) and a temperature control (22.49°C ± 0.0021 s.e.m., n = 20809), each combined with five light levels (91, 165, 226, 307, and 371 µmol quanta·m-2·s-1), were

73 administered in indoor aquaria, providing 20 treatment combinations and five control combinations. Treatment temperatures were 25.35°C (± 0.0027 s.e.m., n = 15624), 27.02°C (± 0.007 s.e.m., n = 13020), 29.56°C (± 0.0034, n = 15279), and 31.33°C (± 0.0065, n = 15624). The daily light doses were 4.2, 7.6, 10.3, 14.1 and 17 mol quanta·m-2·day-1, compared to 10.1 mol quanta·m-2·day-1 – the mean (over 9 Aug. – 21 Sept. 2013) at the field recovery location. Of the light levels, 226 µmol quanta·m-2·s-1 was the control light level, which, over a full day, closely approximated the average cumulative daily light dose at the coral recovery location adjusted to the day length used indoors. For water temperature, 22ºC was the approximate water temperature in situ on the reef during the recovery period. 25°C is slightly warmer than the long-term (10-year) average water temperature at Heron Island, 23.95°C (Australian Institute of Marine Science 2017), whilst 27°C is close to the monthly mean of the hottest month, 27.12°C (NOAA Coral Reef Watch 2017a). As they exceed this threshold, 29°C and 31°C are progressively more stressful temperatures for coral reefs at Heron Island.

To render this large experiment practical, four sub-experiments were performed, each a factorial combination of two temperatures with five light levels. During each sub-experiment (length: 9 days), one of the temperatures (22°C) was repeated to act as a temporal control, whilst the other temperature (from here on, the “treatment temperature”) was consecutively chosen as 25°C, 27°C, 29°C and 31°C. Four replicate 39 l glass aquaria were used per combination of “treatment temperature” and light level, whilst one aquarium replicate was used per combination of “control temperature” and light level, per sub-experiment (culminating in 4 replicate aquaria per combination of “control temperature” and light level by the study’s close).

To avoid sudden temperature shock, corals were gradually heated outside to the required temperature in a 1782 l plastic aquarium (0.24°C∙day-1), and then moved to the glass aquaria indoors once the relevant treatment temperature had been reached (Figure 3.1). The control corals were held outdoors in a 200 l plastic aquarium (flow rate of new water into the aquarium: 6 l·min-1) at 21.5ºC, with a subset of these corals moved into the indoor control aquaria at the start of each sub- experiment. In both outdoor aquaria, sunlight levels were controlled by placing two layers of polyester film filter (131 – Marine Blue and 0.15 Neutral Density) over aquaria (Lee Filters, Andover, England). Average in-aquarium instantaneous PAR from dawn to dusk was 238 µmol quanta·m-2·s-1 and the average daily dose was 11 mol quanta·m-2·24hr-1. Corals acclimated to this light level for at least 12 days prior to being moved indoors. The outdoors control aquarium was held at a mean of 21.54°C (±1.44 s.d., 12 measurements·hr-1), using a chiller (5 kW cooling and 5.2 kW heating capacity: TECO, Ravenna, Italy) with water supplied from a mesocosm that simulated

74 present-day reef-slope levels of carbonate chemistry and water temperature. The outdoors treatment aquarium was raised slowly from a base level of 22°C to 31°C over 31 days with a chiller (5.8 kW cooling and 5.9 kW heating capacity: TECO), using filtered water pumped from the Heron Island reef flat. The average rate of change in both daily mean and daily maximum was +0.244°C∙day-1. Movement of corals from outdoors to indoors occurred during the evening, under dim artificial lighting. Corals in each sub-experiment experienced a change in temperature on the evening they were brought inside by the following amount (calculations use daily mean (and in brackets, maximum) temperature outdoors): sub-experiment 1: +2.61°C (+2°C); sub-experiment 2: +1.45°C (-0.57°C); sub-experiment 3: +1.72°C (+0.74°C); sub-experiment 4: +1.71°C (+0.54°C). This primarily occurred because the chiller consistently maintained the outdoor treatment aquarium water temperature at a level slightly below the desired set-point programmed into the chiller.

N = 3 coral fragments per species per aquarium were used in each sub-experiment, resulting in n = 12 corals per species per treatment. For mounding Porites spp., three rather than five light levels were used due to sample number limitations (resulting in 12 treatment temperature × light combinations and 3 control temperature × light combinations). Mounding Porites spp. and M. monasteriata corals were placed onto one petri dish in each aquarium. Dishes were rotated 90°, twice a day, to overcome within-aquarium light variance. A. muricata branches were suspended mid-way in the aquarium water column on monofilament fishing line. Glass aquaria were illuminated by 21,000 K metal halide 250 W lamps (Giesemann aquaristic, Nettetal, Germany) in OceanLight or aqua star light housing (Aqua Medic, Bissendorf, Germany), with the illumination from each lamp filtered through a polymethylmethacrylate prismatic light diffuser (K12) (Pierlite, Padstow, Australia). Lights were timed to turn on at 4.47am and turn off at 5.30pm, coinciding with actual dawn and sunset at the calendar date of the beginning of sub-experiment 1. The experimental light treatments were administered to corals for 9 days, with the start of the first light period commencing at circa 7 hours after the corals were moved indoors. The allocation of light treatments amongst aquaria was randomised. Polyester neutral density filters – untinted or laser-printer tinted plain paper copier transparency film (Nobo by ACCO Brands, Lake Zurich, USA), and 0.15 ND or 0.3 ND Lee Filters – were affixed to aquarium lids and sides to adjust light intensity.

For the indoor sub-experiments, seawater from the Heron Island Reef flat was piped into sumps (200 l), and distributed by 7000 l∙h-1 pumps (AquaBee Aquarientechnik, Zerbst, Germany) from sumps to a TECO Seachill TR-60 aquarium chiller and then to experiment aquaria. The flow rate of new water entering aquaria was circa 357 ml∙min-1∙aquarium-1. One Koralia Nano 900 l·h-1 pump (Hydor, Bassano del Grappa, Italy) was placed in each aquarium to circulate water.

75 3.3.3 Photophysiology

The dark-adapted quantum efficiency of charge separation at photosystem II (Fv/Fm) was measured on each coral at circa 10 pm, after 4.5 hours of dark acclimation, using a diving PAM (Walz Heinz GmbH, Effeltrich, Germany) on day 1, 2, 3, 5 and 6 of each sub-experiment. The quantum efficiency of charge separation at photosystem II in the light (ΔF/Fmʹ) was measured (without 1 minute of dark acclimation) on each coral in each aquarium at noon on day 1, 2, 3, 5 and 6 of sub- experiments 1-3 and day 1 of sub-experiment 4. Using the noon and evening fluorometry measurements (within each day), the physiological parameters of maximum excitation pressure over photosystem II (Qm) and the Stern-Volmer quenching parameter for NPQ (SVN ) were calculated as

Qm = 1 – (ΔF/Fmʹ)/(Fv /Fm) and SVN = (Fm – Fmʹ)/Fmʹ. The fibre-optic probe was separated from the coral surface by a short (3 mm) tube of clear plastic. Values of ΔF/Fmʹ could not be consistently measured after day 1 of exposure to 31°C due to low biological signal (presumably, a response to the high temperature), therefore Qm and SVN values are not reported for 31°C. A caveat of these measurements is that the “block” light regime used in the experiments (i.e. the light level did not vary with the hours of daylight) occasioned that the daily maximum light in aquaria was far lower than the daily maximum light that corals would experience on the reef for an equivalent daily light dose. Therefore, it is likely that the values of ΔF/Fmʹ measured in this study were less than those that might be experienced under the same daily light dose on the reef. This could lead to a lower

Qm, and possibly SVN, in aquaria than in the field under equivalent daily light doses.

Chlorophyll a fluorescence measurements were performed in all temperature treatments; whereas respirometry, cell counts and protein measurements (described below) were performed in all temperature treatments except 25ºC due to logistical constraints.

3.3.4 Oxygen flux

Net photosynthesis was measured once on each fragment on days 7-10 of sub-experiments 2, 3 and 4. Corals were placed into filtered seawater (0.22 µm) with all air excluded in sealed Perspex chambers (123 ml) placed over a magnetic stirrer for circulation. Chambers were surrounded by a water jacket supplied with water at the correct temperature (22°C, 27°C, 29°C or 31°C) by a Seachill TR-60 aquarium chiller. Corals were exposed to 15 minutes of darkness, 15 minutes of light at 520 µmol quanta∙m-2 s-1 (using same equipment used to illuminate aquaria) and then 10-20 minutes of darkness. Dissolved oxygen (% air-saturation) was measured in each chamber using

76 optodes (PreSens, Regensburg, Germany) and converted to molar quantities (John and Huber 2005, equations 17, 20 and 28). The atmospheric pressure was approximately that at sea level as the laboratory was only several metres in altitude. Measurements were performed at both daytime and night-time, however, each treatment was measured in four different lots randomised for the start- time of measurement. This minimised the possible confounding effect of diel fluctuations in respiration and photosynthesis (Chalker and Taylor 1978). Following this assay, each coral fragment was snap frozen in liquid nitrogen and stored at -80°C. Because the measurement light level used was close to the average daily maximum at the reef slope recovery location, 544 µmol quanta·m-2·s-1, and exceeded the level of even the highest light treatment, photosynthesis was

net probably saturated and so is denoted Pmax . At the time of oxygen flux measurements, the number of dead corals of each species in each aquarium was recorded.

Seven seawater blanks were assayed under identical conditions to the coral specimens at each temperature. The seawater blanks contained a noticeable signal that was consistent among replicate blanks within each temperature, despite containing seawater filtered to 0.22µm, which should exclude most microbial cells. This signal could be due to temperature changes associated with the metal halide lamp used during respirometry. The measured signals for all coral specimens were adjusted by subtracting the average blank signal for dark respiration and net photosynthesis at the appropriate temperature. In some cases, this led to positive oxygen flux for the dark respiration phases and negative oxygen flux values for Pgross. To avoid this artefact, the average blank signal was not subtracted from the respirometry results.

3.3.5 Linear extension, symbiont cell counts and coral fragment area

To check that dinoflagellate density was not affected by different rates of linear extension at different temperatures, linear extension was determined via calcification measurement (alkalinity anomaly) in A. muricata and by surface area measurement in mounding Porites (the latter technique could be used in any species with a standardised nubbin size i.e. mounding Porites spp. but not A. muricata or M. monasteriata). Alkalinity anomaly measurement (Smith and Key 1975) was performed on nine A. muricata specimens per treatment, on day 4-5 of each sub-experiment. I did not correct for changes in seawater non-cabonate ion concentrations following the observation that such corrections are usually smaller than the typical measurement error of potentiometric alkalinity titration (Chisholm and Gattuso 1991). Corals were immersed in 140 ml Perspex chambers containing filtered seawater and incubated for 2 hours temperature levels of their respective

77 treatment, and at light levels very close to those of their respective treatment (80 µmol m-2 s-1, 240 µmol m-2 s-1 or 360 µmol m-2 s-1). A sample of each lot of immersion water at the start of each incubation, and the water from each chamber at the end of each incubation, were bottled with no headspace, and kept at 4ºC. Alkalinity was determined within several days of collection using a T50 Titrator (Mettler Toledo, Greifensee, Switzerland) and certified reference material (Marine Physical Laboratory at University of California, San Diego, USA), with multiple titrations performed per sample until two replicate titrations were within 0.003 alkalinity units of one another.

For symbiont counts, tissue was removed from sacrificed coral specimens using an airbrush expelling filtered seawater (Whatman GF/F filter, Maidstone, England) at 4°C. The blastate was centrifuged (4000 r.c.f., 5 min) and then frozen. The supernatant (containing the host fraction) was later used for protein determinations. The pelleted dinoflagellates were resuspended in 1 × phosphate buffered saline (pH 7.4), syringed through Nitex 38 µm filter (Sefar, Thal, Switzerland) and mixed with Sphero AccuCount Ultra-rainbow fluorescent particles (3.8 ± 0.3 µm) (Spherotech, Green Oaks, USA) to a final concentration of 101.86 particles∙µl-1. Preparations were assayed on an LSRII cytometer (Becton Dickinson, Franklin Lakes, USA). AccuCount particles were visualised using a scatter plot of the optical gates of FSC-A versus 660/20 Red-A, or FSC-A versus APC-A. Dinoflagellate cells were visualised via chlorophyll fluorescence using a scatter plot of the optical gates of 660/20 Red-A versus 605/12 Violet-A, or Qdot 665-A versus Indo-1 (Violet)-A. Dinoflagellate cell numbers were calculated via the ratio of AccuCount particles to dinoflagellate cells in each sample.

Surface areas of A. muricata coral fragments were determined following the double wax dipping method (Vytopil and Willis 2001), using a beaker of paraffin wax heated to 61˚C and stirred continuously on a magnetic stirring hotplate. M. monasteriata were determined using single wax dipping (Stimson and Kinzie 1991). Surface areas of mounding Porites sppׅ were determined following the aluminium foil wrapping method (Marsh 1970). Volumes of coral fragments were determined by the suspension method (Hughes 2005). Mounding Porites sppׅ and M. monasteriata were soaked in water first to prevent sponging by the skeleton affecting the volume determination, this was not needed with A. muricata as the entirety of each skeleton was wax sealed.

3.3.6 Water-soluble host protein

The coral blastate host fractions were diluted 1:4 (in artificial seawater), and water-soluble protein concentration was measured using absorbance at 235 nm and 280 nm via the Whitaker and Granum

78 (1980) method. As some absorbance readings at 235 nm or 280 nm exceeded 1 absorbance unit, protein concentrations were corrected to a standard curve made with Bovine Serum Albumin (0.0125 – 5 mg∙ml-1). In perforate coral skeletons such as those of mounding Porites spp., much of the host tissue is trapped within the upper layer of skeleton, meaning that the measurements of host protein made here are likely to be underestimates.

3.3.7 Statistics

For all parameters, the values within each aquarium were averaged to obtain a single value per aquarium, to negate any “tank effects”. All parameters were analysed with a generalised least- squares model containing all factorial combinations (“×”) of temperature × light. Heteroskedasticity was modelled in all models, compartmentalised to every temperature × light combination. This sample protocol was followed for analysis of the temperature control experiment, except that values were not averaged within aquaria, and the model consisted of all factorial combinations of sub- experiment × light. Analyses were performed using the gls or lme functions (nlme package) and lsmeans function (lsmeans package) in R (R Core Team 2016). Quantile-quantile plots and plots of residuals versus fitted values were examined prior to performing an ANOVA on every model and post hoc analyses were performed with Tukey’s test on least-squares means.

3.3.8 Calculation of α

For M. monasteriata, I calculated α (the slope of the light-limited part of an ETR vs irradiance curve) using values of maximum apparent electron transport rate (ETRmax) and sub-saturation irradiance (Ek-ETR) (both reported in Anthony and Hoegh-Guldberg 2003b) and the relation: apparent

ETR = ETRmax·tanh(irradiance/Ek-ETR), adapted here from Chalker (1981). Values of α in A. muricata and mounding Porites spp. are known from Chapter 2. ETR in Anthony and Hoegh- Guldberg (2003b) was not adjusted to account for the portion of incident light reflected by the coral, whereas ETR in Chapter 2 was; however the effect of not adjusting for this is expected to be slight.

79 3.4 Results

3.4.1 Control corals

The corals at 22ºC in each sub-experiment (SE) acted as temporal controls. The SE from which the control corals came had a statistically significant effect on Fv/Fm measured on the first day in all three taxa, and on first day SVN and first day Qm in A. muricata (p values are in Appendix 7.3). However, Tukey’s post hoc tests did not reveal any specific differences amongst sub-experiments for first day Fv/Fm in all three taxa nor for first day SVN and first day Qm in A. muricata (only the

net 2 first day was analysed due to no measurements of ΔF/Fmʹ on days 2-6 of SE4). Pmax per cm was not affected by SE in A. muricata or mounding Porites spp. but was in M. monasteriata. In the

net 2 -2 -1 latter species, SE2 had a significantly lower Pmax per cm (1.08 µmol O2·cm ·hr ) compared to

-2 -1 -2 -1 net SE3 (1.44 µmol O2·cm ·hr ) and SE4 (1.39 µmol O2·cm ·hr ) (no Pmax measurements were taken

net in SE1). There was no main effect of SE on Pmax per symbiont cell in all three taxa. Host protein per cm2 was not significantly affected by SE in A. muricata or mounding Porites spp. but was significantly affected in M. monasteriata, with SE4 (6.31 mg·ml-1) being significantly greater (Tukey's test p < 0.05) than SE2 (3.48 mg·ml-1) or SE3 (3.98 mg·ml-1). SE did not have an effect on symbiont cells per cm2 in A. muricata. In mounding Porites spp, SE had a significant main effect on symbiont cells per cm2; SE2 (0.9×106 cells∙cm-2) was significantly lower (Tukey's test p < 0.05) than SE4 (2×106 cells∙cm-2), but there was no significant difference between SE2 and SE3 (1.1×106 cells∙cm-2) nor SE3 and SE4. In M. monasteriata, SE had a significant main effect on symbiont cells per cm2 (Appendix 7.3) but no significant differences between sub-experiments were revealed in post hoc tests.

In summary, there were effects of SE in mounding Porites spp. for symbiont cells per cm2 (SE4 > SE2, SE2 = SE3, SE3 = SE4) and in M. monasteriata for host protein per cm2 (SE4 > SE3 = SE2)

net 2 and Pmax per cm (SE4 = SE3 > SE2). In the results to follow there was a significant main effect of temperature (29°C > 27°C = 31°C) on host protein in M. monasteriata (Table 3.1). As the SE effect (SE4 > SE3 = SE2) was contrary to the main effect of temperature, there was little danger of confounding the effect of SE with the effect of temperature for this variable. Likewise, as the effect

net 2 of temperature on Pmax per cm in mounding M. monasteriata (31°C > 29°C > 27°C) was contrary to the main effect of SE (SE4 = SE3 > SE2), there is little danger of confounding the effect of SE with the effect of temperature on this variable. The effect of temperature on symbiont cells in mounding Porites spp. (SE4 > SE2) could have masked a decrease in symbiont cells at 31°C compared to 27°C, as no significant difference between these temperature was found for this

80 variable. In the results to follow, symbiont loss in Porites spp. at 31°C is calculated based on a baseline of symbiont density at 29°C, avoiding any potential confounding effect of SE.

3.4.2 Parameter levels among the species

net 2 The mean over all treatments of Pmax per cm was on par in A. muricata (0.555 ± 0.080 µmol

-2 -1 -2 -1 O2·cm ·hr , mean ± s.e.m, n = 33) and mounding Porites spp. (0.513 ± 0.058 µmol O2·cm ·hr , n

-2 -1 = 36), and one fold higher in M. monasteriata (1.121 ± 0.078 µmol O2·cm ·hr , n = 60). Mean values in A. muricata of Qm and SVN, at -0.004 ± 0.008 and -0.033 ± 0.015 (n = 59), respectively, were lower compared to those in mounding Porites sppׅ (0.096 ± 0.019, 0.197 ± 0.056, n = 36) and M. monasteriata (0.173 ± 0.017, 0.370 ± 0.05, n = 59).

Mean values of Fv/Fm over all treatments were higher in A. muricata (0.61 ± 0.003, n = 59) than M. monasteriata (0.56 ± 0.007, n = 59), and mounding Porites spp. (0.54 ± 0.008, n = 36). Mean symbiont cell densities were on par in all species: A. muricata (1.02 ± 0.06 × 106 cells·cm-2, n = 149), mounding Porites spp (1 ± 0.07 ×106 cells·cm-2, n=129), M. monasteriata (1.09 ± 0.16 ×106 cells·cm-2, n = 216). Mean water-soluble host protein levels in A. muricata (1.5 ± 0.11 mg·cm-2, n = 31) and mounding Porites spp (1.25 ± 0.09 mg·cm-2, n = 32) were between half to two-thirds lower than those in M. monasteriata (3.18 ± 0.15 mg·cm-2, n = 56).

To characterise the effect, if any, of the potential of multiple species within the mounding Porites sample, the standard errors of the three species were compared within each response variable. Higher variance in performance measures could indicate physiological heterogeneity within a group. A qualitative examination of the graphs of the response variables (Figures 3.2, 3.3 and 3.5 to 3.8) did not reveal a trend for standard error to be higher in mounding Porites spp. compared to the other two species. This suggests that heterogeneity of physiological response was no greater in mounding Porites spp. than in A. muricata or M. monasteriata, though does not rule out physiologically heterogeneous genotypes existing simultaneously in all three groups.

3.4.3 Coral morality, host protein and bleaching.

Mortality at 27°C and 29°C was negligible in all three species, with a maximum of 1 coral fragment (out of 12) lost per species in any one combination of light level and temperature. At 31°C,

81 mortality in A. muricata, M. monasteriata, and mounding Porites spp. was, respectively, 67%, 23% and 0% of coral samples. Whilst there was a clear relationship of mortality to high temperature, any patterns of mortality at 31°C that correlated with increases in light level were difficult to discern (Figure 3.4), and may or may not have been present.

There was an interactive effect of temperature and light on areal symbiont densities in A. muricata and M. monasteriata. In A. muricata, at 27°C and 29°C, all cell densities at light levels were of same order of magnitude (Figure 7.2). At 31°C, the areal symbiont density at the mid light level was about 18 × that of any other light level; all other light levels were of same approximate symbiont density. In M. monasteriata, at 27°C and 29°C, symbiont densities decreased linearly with increasing light (significant post hocs at 27°C: 91 quanta∙m-2∙s-1 > 307, 91 > 371, 165 > 307, 165 > 371; at 29°C: 91 > 307, 91 > 371) (Figure 7.3). At 31°C, this pattern was lost with no significant differences (but large variation of standard errors) in areal symbiont density found among light levels (Figure 7.3).

The three species studied displayed a variety of patterns of how host protein did or did not reflect thermally induced bleaching patterns and mortality patterns. In A. muricata, symbiont densities declined by 83% between 29°C and 31°C (Table 3.1, Figure 3.2 a) whilst host protein levels showed a trend to decline by a smaller fraction, 25% (Figure 3.3 a, not statistically significant). Declines in symbiont densities and host protein levels were not caused by increased linear extension of A. muricata at elevated temperature, as calcification declined with increasing temperature (Appendix 7.5.1). Substantial mortality of A. muricata (67%) was seen at 31°C. Thus, the severity of symbiont loss was matched by a high degree of mortality but was accompanied by a less-than- commensurate loss of host protein.

In mounding Porites spp., bleaching and host protein loss were similar: symbiont loss at 31°C was 43% (when compared to 29°C) (Figure 3.2 c), with 30% loss of protein (Figure 3.3 c), but no mortality was seen (Figure 3.4). Declines in symbiont densities and host protein levels were not caused by an effect of increased temperature on linear extension, as no such effect was found (Appendix 7.5.2). In mounding Porites spp. there was an interactive effect of light and temperature on host protein loss, with 371 µmol quanta·m-2·s-1 inducing host protein loss compared to the lower light levels at 29°C, and both 371 µmol quanta·m-2·s-1 and 91 µmol quanta·m-2·s-1 inducing host protein loss compared to 226 µmol quanta·m-2·s-1 at 31°C (Figure 3.3 d).

82 In M. monasteriata, symbiont densities decreased by 33% between 27°C and 29°C (Figure 3.2 e), whilst host protein increased by 38% between these temperatures (Figure 3.3 e), and there was no substantial mortality at 29°C (Figure 3.4). Host protein levels decreased at 31°C by 27% (when compared to 29°C (Figure 3.3 e) and at the same temperature mortality of 23% occurred (Figure 3.4), whilst no significant changes in cell densities occurred at 31°C (Figure 3.2 e), and thus mortality and host protein loss were correlated. Host protein loss was not induced at any light levels in A. muricata (Figure 3.3 b) and M. monasteriata (Figure 3.3 f). In summary, symbiont cell loss correlated with mortality in A. muricata whilst host protein loss correlated with mortality in M. monasteriata, and symbiont loss actually correlated with host protein gain in the latter species. Whilst such information could not be derived in mounding Porites spp as mortality thresholds were not reached, symbiont loss correlated with host protein loss. Therefore, my first hypothesis, that coral mortality following bleaching will correlate with loss of host protein, was supported in M. monasteriata, however, this loss of host protein was not attendant to the loss of symbionts.

3.4.4 Qm and SVN

I hypothesised that species that display no significant decrease in the slope of the light-limited part of an ETR vs irradiance curve (α) when transplanted from mid to high light levels will exhibit lower

Qm at increased light compared to species whose α decreases rapidly in response to transplantation to higher light levels. First, I reviewed which behaviour of α in response to changed light is displayed by each of the three species. According to Chapter 2, in A. muricata, α does not decrease substantially when transplanted from moderate to high light (from 8.5 to 10.2 or 13.2 mol quanta∙m- 2∙day-1), whilst in mounding Porites spp., α decreases rapidly in response to transplantation to the same higher light levels. Patterns of change in α with changes in light were determined in M. monasteriata by reexamination of published data for this species. In Anthony and Hoegh-Guldberg (2003b), specimens of M. monasteriata were taken from within caves at 4-6 m depth (light level: 0.2-0.9 mol quanta∙m-2∙day-1) and transplanted to beneath overhangs (6.0-7.3 mol quanta∙m-2∙day-1) and into deep caves (light level unreported). Separately, specimens of M. monasteriata were taken from the open reef slope at 3-5 m depth (14.3 mol quanta∙m-2∙day-1) and transplanted to overhangs (6.0-7.3 mol quanta∙m-2∙day-1) and to the reef crest at 1-3 m depth (light level unreported). After extracting the parameter α from ETR versus irradiance curves in Anthony and Hoegh-Guldberg (2003b), I found that movement of M. monasteriata to lower light did not appear to cause any change in α (cave: 0.22, cave to deep cave transplants: 0.22; slope: 0.27, slope to overhang transplants: 0.28). However, movement to higher light induced a decrease in α (cave: 0.22, cave to

83 overhang transplants: 0.20; slope: 0.27, slope to crest transplants: 0.23). This result is similar to mounding Porites spp., (i.e. in both species the value of α was negatively-correlated with the light intensity that corals received).

Thus, my hypothesis was that mounding Porites spp. and M. monasteriata will display increased Qm as light levels increase, compared to A. muricata. Whilst an interactive effect of temperature and light on Qm was present in M. monasteriata, the effect seemed to be slight with no pattern of interaction present graphically (Figure 7.6). The above hypothesis was upheld by the main effects of light. In both mounding Porites spp. and M. monasteriata, light level had a significant impact upon

Qm and non-photochemical quenching (SVN) (Table 3.1), which both increased with each consecutively higher light level (Figures 3.5 d, f, and 3.6 d, f). In A. muricata, light also had a significant impact upon Qm and SVN (Table 3.1), which both displayed a trend to be greater at higher light levels, supported in part by post hoc tests (Figures 3.5 b, and 3.6 b). However, the difference in SVN between lower and higher light levels in A. muricata was much less than seen in the other two species (and likewise for Qm). A. muricata saw a difference of 0.04 in SVN between 226 and 91 µmol quanta·m-2·s-1 and of 0.15 between 371 and 226 µmol quanta·m-2·s-1. Meanwhile, mounding Porites spp. saw differences of 0.23 and 0.5 respectively, whilst M. monasteriata saw differences of

0.43 and 0.54 respectively. A. muricata saw a difference of 0.03 in Qm between 226 and 91 µmol quanta·m-2·s-1 and of 0.09 between 371 and 226 µmol quanta·m-2·s-1. Meanwhile, mounding Porites spp. saw differences of 0.11 and 0.11 respectively, whilst M. monasteriata saw differences of 0.16 and 0.15. Thus, the same quantities of additional incident light had a greater impact on Qm and SVN in mounding Porites spp. and M. monasteriata than in A. muricata.

3.4.5 Physiological dysfunction following bleaching.

In mounding Porites spp. and M. monasteriata, an interactive effect of light and temperature on

Fv/Fm was present. In both species, Fv/Fm declined with increasing light at every temperature, but the slope of the decline became slightly steeper with each increment of temperature increase (Figures 7.4, 7.5).

I proposed the hypothesis that under (a) thermally induced bleaching and (b) light change-induced

2 bleaching, physiological dysfunction (declines in Fv/Fm, decreased net photosynthesis per cm ) will be more severe in species that bleach at a higher thermal threshold or light change threshold. To address part (a) of my hypothesis, I first established the thresholds for thermally induced bleaching 84 in the three species. In A. muricata, symbiont loss of 26% happened at 29°C (compared to 27°C) and 83 % at 31°C (compared to 29°C) (Figure 3.2 a). In mounding Porites spp. symbiont loss was only observed at 31°C, where symbionts declined by 43 % (compared to 29°C) (Figure 3.2 c). In M. monasteriata, statstically significant symbiont loss was only observed at 29°C, where symbionts declined by 33 % compared to 27°C (Figure 3.2 e).

Thus, part (a) of my hypothesis was that under thermally induced bleaching, physiological dysfunction will be more severe in mounding Porites spp. than in A. muricata or M. monasteriata. In order to remove the confounding effect of temperature, I first compare physiological dysfunction in all three species at 31°C compared to a baseline of 27°C. A. muricata experienced a statistically

net 2 significant decline in Fv/Fm of 18% (Figure 3.7 a) and in Pmax per cm of 96% (Figure 3.8 a).

Mounding Porites spp. experienced a statistically significant decline in Fv/Fm of 22% (Figure 3.7 c)

net 2 and in Pmax per cm of 84% (Figure 3.8 c). Compared to these species, M. monasteriata experienced a much larger statistically significant decline in Fv/Fm (36 %) (Figure 3.7 e) but a

net 2 smaller decline in Pmax per cm (68%) (Figure 3.8 e). Thus, part (a) of this hypothesis was not upheld.

To address part (b) of my hypothesis, I first established the thresholds for light change-induced bleaching in the three species, following acclimation of corals to light at 238 µmol quanta·m-2·s-1 (accumulating an average daily dose of 11 mol quanta·m-2·24hr-1). In A. muricata, symbiont density loss was not induced by light levels up to 371 µmol quanta·m-2·s-1 (Figure 3.2 b) (there were no significant differences in cell numbers amongst light levels in post hoc tests, in spite of a significant main effect of light in Table 3.1). In mounding Porites spp., areal symbiont densities decreased between 91 and 371 µmol quanta·m-2·s-1 (Figure 3.2 d) by 43 % but did not significantly change between 226 and 371 µmol quanta·m-2·s-1. In M. monasteriata, symbiont densities at 371 µmol quanta·m-2·s-1 were significantly lower than at the two dimmest light levels, 91 and 165 µmol quanta·m-2·s-1, by 70% and 54% respectively (Figure 3.2 f). Bleaching in A. muricata was not induced under intense light (371 µmol quanta·m-2·s-1), and there was no difference in the light threshold for bleaching between M. monasteriata and mounding Porites. Therefore I was not able to test the hypothesis that physiological dysfunction (declines in Fv/Fm, decreased net photosynthesis per cm2) will be more severe in species that bleach at a greater light change threshold.

Declines in Fv/Fm occurred with increasing light level in all three species. Between 91 and 371 µmol

-2 -1 quanta·m ·s , Fv/Fm declined by 10% in A. muricata, 19% in mounding Porites spp. and 23% in M. monasteriata (Figure 3.7 b, d, f). There were no statistically significant differences among light

85 net 2 levels for Pmax per cm in any of the three species (Table 3.1, Figure 3.8 b, d, f) (expressed as a significant main effect but no significant differences in the post hoc tests in M. monasteriata).

3.5 Discussion

To better understand how coral mortality following bleaching arises and may be predicted, this study investigated whether mortality correlated with Symbiodinium loss and/or host protein loss. Loss of host cells is mechanistically linked to loss of symbiont cells during coral bleaching. Mechanisms of symbiont loss that involve loss of host cells include the detachment of symbiont- containing cells from the host endoderm (Gates et al. 1992) or necrosis of symbiont-containing host cells (Dunn et al. 2002). Other mechanisms for symbiont loss during coral bleaching may not involve host cell loss, such as the programmed cell death of symbiont cells (Dunn et al. 2002) or the autophagy of symbiont cells (Downs et al. 2009). As well as a structural component of tissue, protein is a form of stored energy and may thus be lost from coral tissue through metabolism (Rodrigues and Grottoli 2007). It may also be lost through the loss of entire host cells as a result of a breakdown in cell adhesion, apoptosis or modes of bleaching that involve shedding or destruction of host cells containing symbionts (Gates et al. 1992; Dunn et al. 2002, 2004). The use, in this study, of protein loss as a proxy of cell loss assumes that catabolic metabolism of stored protein is minimal.

Low host tissue may lead to a greater risk of host mortality (Thornhill et al. 2011), therefore, whether mechanisms involving host cell loss predominate during symbiont cell loss may be of great import to the host’s mortality risk following bleaching. A stable level of host protein can, erroneously, be inferred to decline, when measured per cm2 of coral surface, if substantial skeletal linear extension has occurred. However, this was unlikely to be a confounding factor in this study as coral skeletal linear extension is usually depressed at thermal anomalies equivalent to the temperatures (29-31°C) at which host protein decline occurred (Tanzil et al. 2009; Cantin et al. 2010). Indeed, skeletal growth rate declined or remained unchanged at 29°C and 31°C in A. muricata and mounding Porites spp. (Appendix 7.5). In A. muricata, the proportional losses of symbiont cells at 29°C and 31°C were not in ratio to the losses of host protein at these temperatures (compared to baseline levels in non-bleached corals). Therefore, the loss of symbiont cells may primarily occur through pathways that do not involve the loss of host cells, or alternatively, the loss of host protein may not be a good proxy for the loss of host cells in A. muricata. In mounding Porites spp., symbiont cell loss was closer to host protein loss (both occurred only at 31°C), and so

86 the loss of symbiont cells may occur through a mechanism related to the loss of host cells. In M. monasteriata, host protein actually increased at the temperature at which symbiont cell loss occurred (29°C), before decreasing again at 31°C, suggesting that loss of symbionts did not occur through a pathway that involved the loss of host cells.

Loss of symbiont cells involves an increase in the internal tissue light field (Enríquez et al. 2005), and the rise in host protein in M. monasteriata may be a mechanism for photoacclimation to the higher light level. This principle has been demonstrated in sea anemones, where symbionts in a species possessing thicker tissues are less photoinhibited, possibly due to greater attenuation of light by host tissue, than those in a species with thinner tissues (Dimond et al. 2012; Fisher et al. 2012). Similarly, among morphs of Montipora digitata, symbiont density and chlorophyll density were positively correlated with host fluorescent protein content (Klueter et al. 2006). The tissue of M. monasteriata contains large quantities (2-6% of the total protein mass) of photoprotective host proteins (Dove et al. 2008). As host protein gain at 29°C was probably photoacclimative, the mild symbiont density decline at 29°C in M. monasteriata may also have been a photoacclimative change (in the sense that elevated temperature can increase the photoinhibitive capacity of the incident light level: Hoegh-Guldberg 1999). The loss of host protein at 31°C, apparently independent of a loss of symbiont cells, probably conferred no photo-acclimative advantage, so could be categorised as a detrimental response. The loss of host endodermal tissue under heat stress, possibly due to the effects of heat on host cell adhesion, has been documented in a number of other Cnidaria (Schmid et al. 1981; Gates et al. 1992; Sawyer and Muscatine 2001).

Some correlations were found between the percent of mortality (fragments containing no tissue or entirely necrotic tissue) and either the areal host protein or symbiont densities of the remaining living fragments. As significant mortality only occurred at 31°C, mortality in M. monasteriata appeared to be correlated with a loss of host protein but not with the loss of symbiont cells (though symbiont cells showed a trend to decline in some light treatments at 31°C: Figure 7.3). In A. muricata, mortality (which was high at 31°C) correlated with a large decline in symbiont cell densities in all but the middle light treatment (Figure 7.2) but only with a mild loss of host protein (Figure 3.3 a). In mounding Porites spp., no mortality was observed. Among colour morphs of Acropora aspera, the level of coral mortality following a combined heating and recovery episode was correlated with areal symbiont and host protein loss of approximately equal levels (Dove 2004). These results suggest that impending mortality is probably indicated by different magnitudes of host protein loss or symbiont cell loss in different coral species.

87 The mean SVN and Qm values in some temperature treatments (27°C and 29°C for A. muricata) and light treatments (≤ 226 µmol quanta·m-2·s-1 in A. muricata; 91 µmol quanta·m-2·s-1 in mounding Porites spp. and M. monasteriata) were below zero, their theoretical minimum (Figures 3.5 a, b, d, f and 3.6 a, b, d, f) (Bilger and Björkman 1990; Iglesias-Prieto et al. 2004). After inspecting the equations for both variables (section 3.3.3), it is clear that the presence of negative SVN and Qm values is due to Fmʹ (maximal fluorescence measured at midday) exceeding Fm (maximal fluorescence measured in the evening). As maximal fluorescence (Fm or Fmʹ) is a measure of the chlorophyll a fluorescence signal in the area beneath the probe when the yield of photochemical quenching is zero (Heinz Walz GmbH 1998), it is dependent on the areal chlorophyll a density in addition to the relative amount of fluorescence that is quenched via NPQ. Thus, a negative SVN and

Qm (i.e. Fmʹ>Fm) could indicate that chlorophyll a density per unit area has decreased between midday and evening. This could occur via a reduction in the pigment bed of each PSU, a reduction in the number of PSU per symbiont (Prézelin 1987), or a reduction in symbiont cell areal density (Hoegh-Guldberg and Smith 1989). The former phenomenon would also explain the trend for a decrease in Qm values in all three species with increasing temperature (Figure 3.5 a, c, e) as reductions in the pigment bed per PSU reduce the rate of photon capture, and therefore the maximum excitation pressure over PSII (Mauzerall and Greenbaum 1989). Alternatively, Fm could have been driven lower than Fm’ by the redox reduction of the plastoquinone pool during the evening, through chlororespiratory activity (Middlebrook et al. 2010).

By comparing values of α (from an ETR vs irradiance curve) with other experimental results, I was able to investigate some mechanisms of photoadaptation that may be related to different susceptibility to light change-induced bleaching in A. muricata, mounding Porites spp., and M. monasteriata. In this study, α represents the efficiency with which downwelling light generates electron turnover at PSII. Thus it indicates the degree to which downwelling photons are absorbed and sent to the water splitting complex, versus reflection or non-photochemical quenching. In contrast, α derived from a P vs E curve – measured via optodes or carbon isotopes – represents the efficiency with which incident light is utilised to evolve oxygen or fix carbon, respectively. The standardisation of the P vs E curve (e.g. per chlorophyll a, per symbiont cell, per coral tissue mass or per unit surface area) may affect the direction and magnitude of change in α upon movement from higher to lower light (and vice versa) (Falkowski and Dubinsky 1981; Harland and Davies 1994). Similarly, particular standardisations of α derived from an ETR vs irradiance curve will affect the pattern of response: e.g. α normalised to symbiont cell density trends upwards under light limitation and downwards under high light, whilst α normalised to chlorophyll content displays the opposite pattern (Frade et al. 2008) The standardisation of α in this thesis was electrons turned over

88 at PSII per photon of downwelling PAR. In Chapter 2, the α of A. muricata increased through time when A. muricata (originally acclimated to 171 µmol quanta·m-2·s-1) was exposed to the highest light level used (282 µmol quanta·m-2·s-1). Furthermore, A. muricata did not display statistically significant loss of symbionts at 282 or 218 µmol quanta·m-2·s-1. The α of mounding Porites spp. decreased when it was exposed to moderate and high light levels, and mounding Porites spp. displayed mild to moderate bleaching at these levels (Chapter 2). By analysing data from Anthony and Hoegh-Guldberg (2003b), I identified that M. monasteriata is similar to mounding Porites spp. in that exposure of a colony to increased light causes a decrease in α. Furthermore, colonies of M. monasteriata transplanted from moderate to high light habitats display evidence of bleaching (a decrease in chlorophyll per cm2, data for symbionts per cm2 not available: Anthony and Hoegh- Guldberg 2003b).

As α is related to light limitation, there may be a link between robustness to light change-induced bleaching and susceptibility to light-limitation in the study species. Colonies exposed to light- limitation may increase the optical cross section of their PSU and increase the proportion of PSU that are functional, leading to a greater electron transfer rate at PSII which increases the slope of α (Chalker et al. 1983; Falkowski and Raven 2007). Areal symbiont density responses indeed suggest that A. muricata is photoadapted (meaning an evolutionary adaptation to a particular range of light) to very high light, whereas mounding Porites spp. and M. monasteriata are photoadapted to less extreme light levels. Areal densities in A. muricata did not change with light level, whilst those in mounding Porites spp. and M. monasteriata decreased between the lowest and highest light levels.

Qm (averaged over all treatments) was very low in A. muricata compared to the other two species and to values from other corals (Iglesias-Prieto et al. 2004). Whilst the confounding factor of potential loss of pigment bed per unit area between midday and evening was present, very low values of Qm in A. muricata were still present at 25°C when no loss of pigment would be expected.

Furthermore, increases in light caused increases in Qm that were of far smaller magnitude in A. muricata than in mounding Porites spp. or M. monasteriata.

These patterns of Qm among species suggest that A. muricata possesses regulatory mechanisms that enable it to buffer against increases in light to maintain a high ratio of open to closed reaction centres. Whilst its average NPQ rate is probably lower, A. muricata has a higher average Fv/Fm (suggesting a greater ratio of photochemical vs fluorescence de-excitation of light captured by the pigment bed ) than in the other two species (section 3.4.2). At least some Symbiodinium types possess an enhanced capacity for photoprotection via photochemical quenching through alternate electron transport pathways (Reynolds et al. 2008), an ability to change the number of light-

89 harvesting pigments associated with each PSU, or to alter the enzymatic activity of the dark reactions (Chang et al. 1983). Photoprotective host pigments exist in A. muricata, though the internal light level in A. muricata host tissues was found to be greater than that in another coral species (Seriatopora caliendrum) (Hennige et al. 2008). An enhanced capacity for alternative electron transport, a greater average rate of carbon fixation, a smaller average PSU size, or photoprotective host pigmentation are possibilities that may explain the lower Qm in A. muricata.

The Qm values reported here for A. muricata (~ zero) are much lower than those reported for A. muricata (~ 0.3) by Crawley et al. (2010). There may have been methodological differences in coral handling or Qm measurement between these two studies, however, the Qm anomaly is probably too large to be caused by these factors alone. In that study, A. muricata were sampled at Orpheus Island in the central Great Barrier Reef. A. muricata may host a different symbiont type at that location (A. muricata at the nearby Trunk Reef host subclade C2: van Oppen et al. 2001), leading to potential photophysiological differences between those corals and the C3-associating A. muricata used here.

I hypothesised that in species that bleached at lower thresholds of light or temperature, the physiological consequences of bleaching would be less severe; this was not upheld for thermally induced bleaching (and my results were not conclusive for light change-induced bleaching). This

net 2 study measured the physiological consequences of bleaching using Fv/Fm and Pmax per cm . Amongst the three species, severity of symbiont density decline (respectively, 83%, 43% and 33% in A. muricata, mounding Porites spp. and M. monasteriata) appeared to correlate with the severity

net 2 of decline in Pmax per cm (96%, 84% and 69%), but not the severity of Fv/Fm decline (18%, 22%

net and 36%). As Pmax is indicative of the capacity for autotrophy and therefore has a direct link to holobiont health (Yentsch et al. 2002), my results indicate a direct link between the severity of symbiont population size reduction and the severity of impacts to the coral. The fact that Fv/Fm does

net not reflect the severity of impacts to Pmax could reflect the fact that Fv/Fm can be depressed by some photoprotective mechanisms (such as PSII “islands” that diffuse light energy as heat; Matsubara and Chow 2004; Takahashi and Badger 2011). The effect of bleaching at a lower light change or thermal threshold on holobiont functioning (e.g. growth), over longer time-frames, may

net 2 or may not not be mirrored by Pmax per cm , so additional investigations of this study’s third hypothesis could be worthwhile.

This study identified that decreases in light at high temperature may be a significant stressor, causing bleaching or loss of host protein in some species. In A. muricata, symbiont cell density loss occurred in all light treatments at 31°C, but the loss at 226 µmol quanta·m-2·s-1, the “no light

90 change” treatment, was moderate (33% compared to the average over all light levels at 29°C). In contrast, cell densities declined to close to zero at 91 and 165 µmol quanta·m-2·s-1, the “light decrease” treatments, and 307 and 371 µmol quanta·m-2·s-1, the “light increase” treatments (Figure 7.2). In mounding Porites spp., host protein declined under light decrease (91 µmol quanta·m-2·s-1) and light increase (371 µmol quanta·m-2·s-1) at 31°C, but not under no light change (Figure 3.3 d). Foster (2005), found that low light (simulating turbid conditions) did not have an interactive effect with stressful temperature to influence symbiont cell densities in Montastrea carvernosa. However, the ability of extended periods of low light to induce bleaching (in the absence of heat stress) is well documented (Bessell-Browne et al. 2017). Photoacclimation to decreased light involves the up- regulation of protein synthesis for the manufacture of additional photosynthetic pigments (Falkowski and Raven 2007) and is thus energetically costly (Raven 1984). I contend that the energetic costs of low light photoacclimation, combined with the reductions in capacity for photoautotrophy at high temperature (Figure 3.8 a, c) may be responsible for the declines in protein in mounding Porites spp. and symbiont cell densities in A. muricata at reduced light at 31°C.

To predict coral bleaching using combined light and temperature data, and to select reef areas for conservation, knowledge of what conditions of combined light and temperature anomalies will cause corals to bleach is needed (Skirving et al. 2018). Understanding the magnitude of the differences among species in their bleaching response to light and temperature stress is key. If coral taxa differ markedly in the levels of light stress, temperature stress, and both combined, at which bleaching is triggered, then separate bleaching prediction algorithms will be required for different species or groups of species (Skirving et al. 2018). Prediction of bleaching at the whole-of-reef scale may require the particular coral community composition, and the different susceptibilities of the corals within the community to light and temperature stress, to be known (McClanahan et al. 2007c). Knowledge of the susceptibility of whole reefs to bleaching is important to making decisions on the allocation of conservation resources among coral reefs (Cvitanovic et al. 2013).

My characterisation of species differences in stress thresholds under high light, temperature, and the combination of the two has implications for remote sensing bleaching prediction. The Light Stress Damage (LSD) algorithm has recently been developed as a method to incorporate remotely-sensed light and sea-surface temperature (SST) into one product for the prediction of coral bleaching. This algorithm is designed to be calibrated to the particular climatology (defined here as the light and temperature conditions experienced over the past decades) of individual reef locations (Skirving et al. 2018). Similarly, the most widely used algorithm for bleaching prediction, Degree Heating Weeks, is calibrated to the particular temperature-only climatology of individual reef locations (Liu

91 et al. 2006). In addition to the climatological differences among reef locations that are taken into account in these algorithms, my results indicate that it is necessary to take into account species differences in responses within individual reef locations. However, the results also suggest that this task is achievable: groups of species with similar responses can be found, saving the arduous task of making calibrations for each individual species at a location. For instance, high light level, independently of any stressful temperature, (e.g conditions that might be experienced during a sudden decrease in turbidity) causes decreases in cell densities in mounding Porites spp. and M. monasteriata, but not in A. muricata (Figure 3.2 b, d, e).

This approach can be illustrated by applying my results for A. muricata, M. monasteriata and mounding Porites spp. to near-inshore reefs in Halifax Bay on the Great Barrier Reef as a case study (Morgan et al. 2016) (and dismissing, temporarily, the possibility of differences in responses among congeners). Paluma Shoals in Halifax Bay supports 6 major reef types, including sand substrate with many massive Porites rus colonies, reef ridges dominated by Montipora (55% of coral cover) and branching Acropora (16%), and rubble areas containing Montipora (13% of coral cover) and Acropora (19%) (Morgan et al. 2016). Under the scenario (for illustrative purposes) of a heat anomaly of +4°C (equivalent to 31°C at Heron Island), and no change in light (plausible as the region experiences naturally high turbidity), based on this study’s results’ the sand reef type could expect moderate to high bleaching, whilst the reef ridges and rubble areas may experience less bleaching. Under an alternate scenario of a sudden decrease in turbidity but no change in temperature, we could expect paling in the sand reef and in half or one-eighth of the coral benthos in the reef ridges and rubble areas, respectively.

3.5.1 Conclusions

The prediction of coral bleaching severity, mortality at high water temperature, and bleaching of entire reef communities is informed by the results of this study. The severity of the decline in net photosynthesis following bleaching was not impacted by the temperature threshold at which bleaching occurred but was positively correlated with the number of symbionts lost. Mortality at high water temperature may have been instigated by host cell loss in M. monasteriata and by severe symbiont cell loss in A. muricata, but did not occur in mounding Porites. The factors that cause bleaching differed among the study species (e.g. light elevations alone caused bleaching in two species, whilst, in a third species, light elevations caused bleaching only at high temperatures), and understanding these differences will facilitate the development of bleaching prediction of

92 taxonomically heterogeneous coral communities. The dichotomy between the occurrence of less severe bleaching in mounding Porites spp. (compared to A. muricata) at high temperature but a lower light change threshold for bleaching in the same taxon requires further investigation.

My results also illuminated aspects of the ecophysiology of the study species. Moderately high symbiont densities in the “no-change” light treatment at 31°C underscore the importance of light change in determining the response of A. muricata to heat stress. Additionally, A. muricata could be distinguished by low rates of Qm compared to the other species and smaller increases in Qm at higher light levels, demonstrating, along with observations on the robustness of α to increased light, that A. muricata is light-limited even at experimentally-administered high light levels. This physiological trait manifests in an absence of any main effect of light on symbiont cell densities in A. muricata, helping to explain the observed species differences in bleaching susceptibility under high light.

93 Table 3.1: Probability values for statistical tests.

Statistically significant probability values are highlighted in bold. “Temperature” is abbreviated as “Temp.”. Probability values that are less than 0.05 are highlighted in bold.

net -2 net -1 -2 -2 Coral Fv/Fm Qm SVN Pmax ·cm Pmax ·cell Protein·cm Cells·cm

A. muricata Temp. < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 0.06 < 0.01

F3,60 = 31.8 F2,44 = 21.1 F2,44 = 5.95 F2,18 = 109 F2,17 = 49.8 F2,16 = 3.38 F2,17 = 842 Light < 0.01 < 0.01 < 0.01 < 0.01 0.08 0.29 < 0.01

F4,60 = 8.44 F4,44 = 21.3 F4,44 = 23.8 F4,18 = 5.26 F4,17 = 2.51 F4,16 = 1.37 F4,17 = 11.4 Temp.×light 0.11 0.14 0.66 0.44 0.3 0.91 < 0.01

F12,60 = 1.62 F8,44 = 1.64 F8,44 = 0.73 F8,18 = 1.05 F8,17 = 1.31 F8,16 = 0.39 F8,17 = 5.31 Mounding Porites spp. Temp. < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01

F3,36 = 56.8 F2,27 = 104 F2,27 = 10.8 F2,27 = 71.8 F2,23 = 39.6 F2,23 = 24.6 F2,23 = 36 Light < 0.01 < 0.01 < 0.01 0.06 0.85 0.95 < 0.01

F2,36 = 109 F2,27 = 151 F2,27 = 76.4 F2,27 = 3.23 F2,23 = 0.16 F2,23 = 0.05 F2,23 = 10.7 Temp.×light 0.01 0.12 0.64 0.41 0.22 < 0.01 0.18

F6,36 = 3.15 F4,27 = 1.99 F4,27 = 0.64 F4,27 = 1.04 F4,23 = 1.56 F4,23 = 5.17 F4,23 = 1.71 M. monasteriata

Temp. < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 0.01 < 0.01

F3,60 = 204 F2,44 = 46.6 F2,44 = 132 F2,45 = 74.1 F2,40 = 16.5 F2,41 = 4.68 F2,41 = 827 Light < 0.01 < 0.01 < 0.01 0.02 0.04 < 0.01 < 0.01

F4,60 = 175 F4,44 = 185 F4,44 = 159 F4,45 = 3.38 F4,40 = 2.69 F4,41 = 4.44 F4,41 = 19.8 Temp.×light < 0.01 0.04 0.47 0.95 0.52 0.42 < 0.01

F12,60 = 7.3 F8,44 = 2.24 F8,44 = 0.98 F8,45 = 0.34 F8,40 = 0.9 F8,41 = 1.05 F8,41 = 13.2

94 A group of coral specimens were slowly raised in a temperature ramp from setpoints of 22°C to ~31°C whilst a control group of corals was maintained at 21.5°C (the “control temperature”). When the heated group reached 25°C, 27°C, 29°C and 31°C (the “treatment temperatures”), portions of both the heated specimens and the control specimens were moved into five light treatments (illustrated as five grey bars), and exposed, for a further nine days, to the temperature they’d been at, at the time of their movement.

95 (a) 2 (b) 2 1.8 1.8 1.6 * 1.6 1.4 1.4 1.2 * 1.2 1 1 0.8 0.8 0.6 0.6 0.4 * 0.4 0.2 0.2 0 0 Symbiontdensity 10ᶝcells·cmˉ²)(× 22 27 29 31 Symbiontdensity10ᶝ cells·cmˉ²)(× 91 165 226 307 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹)

(c) 2 (d) 2 1.8 1.8 1.6 1.6 1.4 1.4 * 1.2 * 1.2 1 1 * 0.8 * 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 Symbiontdensity 10ᶝcells·cmˉ²) (× Symbiontdensity10ᶝ cells·cmˉ²)(× 22 27 29 31 91 226 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹)

(e) 2 (f) 2 1.8 1.8 1.6 1.6 1.4 1.4 1.2 1.2 * ^ 1 1 0.8 * 0.8 * 0.6 0.6 0.4 0.4 *^ 0.2 0.2 0 0 Symbiontdensity 10ᶝcells·cmˉ²) (× Symbiontdensity10ᶝ cells·cmˉ²) (× 22 27 29 31 91 165 226 307 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹)

Figure 3.2: Symbiont cells per cm² among the temperature levels and light levels. The panels are (a, b) A. muricata, (c, d) mounding Porites spp., and (e, f) M. monasteriata. The values for each temperature level were averaged across all light levels, and vice versa. Symbols (*, ^) indicate post hoc significant differences (p < 0.05). The light blue column, 22°C, was not included in the statistical comparison among temperature levels. Error bars are standard error of the mean.

96 (a) 5 (b) 5 4.5 4.5 4 4 3.5 3.5 3 3 2.5 2.5 2 2 1.5 1.5 1 1 0.5 0.5 0 0 22 27 29 31 91 165 226 307 371 Water Soluble Host Protein (mg·cmˉ²) Protein Host Water Soluble temperature (°C) (mg·cmˉ²) Protein Host Water Soluble light level (µmol photons·mˉ²·sˉ¹) (c) 5 (d) 5 91 µmol photons·mˉ²·sˉ¹ 4.5 4.5 226 µmol photons·mˉ²·sˉ¹ 4 4 371 µmol photons·mˉ²·sˉ¹ 3.5 3.5 3 3 2.5 2.5 aefg 2 2 bcd 1.5 * 1.5 a be dg 1 * 1 cf 0.5 0.5 0 0 22 27 29 31 27 29 31 27 29 31 27 29 31 Water Soluble Host Protein (mg·cmˉ²) Protein Host Water Soluble Water Soluble Host Protein (mg·cmˉ²) Protein Host Water Soluble temperature (°C) temperature (°C) (e) 5 (f) 5 4.5 4.5 *^ 4 4 3.5 3.5 3 * ^ 3 2.5 2.5 2 2 1.5 1.5 1 1 0.5 0.5 0 0 22 27 29 31 91 165 226 307 371 Water Soluble Host Protein (mg·cmˉ²) Protein Host Water Soluble Water Soluble Host Protein (mg·cmˉ²) Protein Host Water Soluble temperature (°C) light level (µmol photons·mˉ²·sˉ¹)

Figure 3.3: Water-soluble protein per cm² of coral surface. The panels are (a, b) A. muricata, (c, d) mounding Porites spp. and (e, f) M. monasteriata. The values for each temperature level were averaged across all light levels, and vice versa (except for panel (d)). Symbols (*, a, etc.) indicate significant post hoc differences (p < 0.05). Error bars are standard error of the mean. 22°C was not included in the statistical comparison of temperatures.

97 Percentage of dead corals (light blue) and living corals (dark blue) are indicated by the portions of each pie chart.

98 (a) 0.3 (b) 0.3 0.25 0.25 0.2 0.2 0.15 0.15 0.1 0.1 *^ m *^ m Q Q ~ 0.05 0.05 ^ ^ *~ * 0 * 0 -0.05 -0.05 -0.1 -0.1 22 25 27 29 91 165 226 307 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹)

(c) 0.35 (d) 0.4 0.3 0.3 0.25 0.3 * 0.2 0.2 0.15 * ^ 0.2 m m

Q Q * 0.1 *^ 0.1 0.05 0.1 0 0.0 * -0.05 -0.1 22 25 27 29 91 226 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹)

(e) 0.35 (f) 0.35 ^ 0.3 0.3 * 0.25 *^ 0.25 *^ 0.2 * ^ 0.2 0.15 0.15 m m Q Q *^ 0.1 0.1 0.05 0.05 0 0 *^ -0.05 -0.05 22 25 27 29 91 165 226 307 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹)

Figure 3.5: Maximum excitation pressure over photosystem II among the levels of each factor in (a, b) A. muricata, (c, d) mounding Porites spp., and (e, f) M. monasteriata. The values for each temperature level were averaged across all light levels, and vice versa. Symbols (*,^,~) indicate significant post hoc differences (p < 0.05). Qm was not measured at 31°C. Error bars are standard error of the mean. 22°C was not included in the statistical comparison of temperature levels.

99 (a) 0.8 (b) 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 *^ 0.1 *^ 0.1 ~ Stern-Volmer Stern-Volmer ^ Stern-Volmer ^ 0 * 0 *~ * -0.1 -0.1 -0.2 -0.2 Non-Photochemical Quenching Non-Photochemical Non-Photochemical Quenching Non-Photochemical 22 25 27 29 91 165 226 307 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹) (c) 0.8 (d) 0.8 0.7 0.7 * 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 * 0.1 0.1 Stern-Volmer Stern-Volmer Stern-Volmer Stern-Volmer 0 0 * -0.1 -0.1

Non-Photochemical Quenching Non-Photochemical -0.2 -0.2 Quenching Non-Photochemical 22 25 27 29 91 226 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹)

(e) 0.9 0.9 (f) ^ 0.8 0.8 * 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 *^ 0.3 0.3 0.2 0.2

Stern-Volmer Stern-Volmer *^ Stern-Volmer Stern-Volmer 0.1 0.1 *^ 0 0 -0.1 -0.1 Quenching Non-Photochemical Non-Photochemical Quenching Non-Photochemical 22 25 27 29 91 165 226 307 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹) Figure 3.6: Stern-Volmer quenching parameter for non-photochemical quenching. Panels are (a, b) A. muricata, (c, d) mounding Porites spp., and (e, f) M. monasteriata. The values for each temperature level were averaged across all light levels, and vice versa. Symbols (*, ^, ~) indicate treatments that are significantly different from one another (p < 0.05) in post hoc testing.

Error bars are standard error of the mean. SVN was not measured at 31°C. The light blue column, 22°C, was not included in the statistical comparison of temperature levels.

100 (a) 0.7 (b) 0.7 * ^ ~ *^ ~ 0.6 0.6 ^ *~ *^~ 0.5 0.5 0.4 0.4 m m /F /F

v 0.3 v 0.3 F F 0.2 0.2 0.1 0.1 0 0 22 25 27 29 31 91 165 226 307 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹)

(c) 0.7 (d) 0.7 0.6 * 0.6 * ^ * * 0.5 *^ 0.5 * 0.4 0.4 m m /F /F

v 0.3 v 0.3 F F 0.2 0.2 0.1 0.1 0 0 22 25 27 29 31 91 226 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹) (e) 0.7 (f) 0.7 *^~ 0.6 * ^ 0.6 *^ ^~ * 0.5 0.5 ^ *~ 0.4 *^ 0.4 m m /F /F v v 0.3 0.3 F F 0.2 0.2 0.1 0.1 0 0 22 25 27 29 31 91 165 226 307 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹)

Figure 3.7: Dark-adapted quantum yield of photosystem II among temperature and light levels. The panels are (a, b) A. muricata, (c, d) mounding Porites spp., and (e, f) M. monasteriata. The values for each temperature level were averaged across all light levels, and vice versa. Symbols (*, ^, ~) indicate significant differences between treatments (p < 0.05) in post hoc testing. Error bars are standard error of the mean. The light blue column, 22°C, was not included in the statistical comparison of temperature levels.

101 (a) 1.8 (b) 1.8 1.6 1.6 1.4 1.4 1.2 * 1.2 1 1

₂ 0.8 0.8 * ₂ 0.6 0.6 0.4 0.4 µo ·cmˉ²·hrˉ¹) O (µmol µo ·cmˉ²·hrˉ¹) O (µmol

Max net photosynthesis Max photosynthesis net 0.2 * Max photosynthesis net 0.2 0 0 22 27 29 31 91 165 226 307 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹)

(c) 1.8 (d) 1.8 1.6 1.6 1.4 1.4 1.2 1.2 1 * 1 0.8

0.8 ₂ ₂ 0.6 * 0.6 0.4 0.4 µo ·cmˉ²·hrˉ¹) O (µmol µo ·cmˉ²·hrˉ¹) O (µmol

* Max photosynthesis net 0.2 Max net photosynthesis Max photosynthesis net 0.2 0 0 22 27 29 31 91 226 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹) (e) 1.8 * (f) 1.8 1.6 1.6 1.4 1.4 1.2 * 1.2 1 1 0.8 0.8 ₂ ₂ 0.6 * 0.6 0.4 0.4 µo ·cmˉ²·hrˉ¹) O (µmol (µmol O ·cmˉ²·hrˉ¹) ·cmˉ²·hrˉ¹) O (µmol

Max net photosynthesis Max photosynthesis net 0.2 Max net photosynthesis Max photosynthesis net 0.2 0 0 22 27 29 31 91 165 226 307 371 temperature (°C) light level (µmol photons·mˉ²·sˉ¹)

Figure 3.8: Maximum net photosynthesis per cm² of coral surface. The panels are (a, b) A. muricata, (c, d) mounding Porites spp., and (e, f) M. monasteriata. The values for each temperature level were averaged across all light levels, and vice versa. Symbols (*, ^, ~) indicate treatments that are significantly different (p < 0.05) in post hoc testing. Error bars are

net 2 standard error of the mean. Pmax per cm was not measured at 25°C. The light blue column, 22°C, was not included in the statistical comparison of temperature levels.

102 4 The impacts of ocean acidification on photosynthesis and population size of the dinoflagellate symbiont in scleractinian corals.

4.1 Abstract

1. Ocean acidification will change numerous seawater chemical parameters that drive coral physiological processes. Symbiodinium photosynthesis and its relationship to the coral host rely on proton concentrations and CO2, chemical species whose concentrations are altered by ocean acidification. Whilst the impacts of ocean acidification on coral calcification are well studied, its impacts on Symbiodinium in intact associations with corals are less well characterised.

2. An experiment was performed to expose specimens of the scleractinian coral Pocillopora acuta from Kāne‘ohe Bay, Oahu, to eight combinations of light, temperature, and acidification. Symbiodinium population size and photosynthetic responses were measured. A compilation of data on these responses from the coral literature was also made.

3. Ocean acidification (slightly in excess of an RCP8.5 scenario for Kāne‘ohe Bay) did not affect Symbiodinium areal densities or areal Pnet in P. acuta and did not affect the severity of thermal bleaching or light change-induced cell loss in this species. Ocean acidification increased the areal rate of light-enhanced dark respiration, and enhanced the increase in areal dark respiration at elevated temperature, departing from the theoretical expectation that respiration should decrease under acidification due to its expected negative impact on proton efflux from coral tissue. Measurements also suggested an increase in areal Pgross under ocean acidification, which benefited the coral through an increased P/R ratio. However, elevated temperature in conjunction with acidified conditions reduced the P/R ratio to that seen at elevated temperature under non-acidified conditions, removing any benefit of the increase in photosynthesis.

4. I found that ocean acidification caused statistically-significant reductions in Symbiodinium areal densities in 14 (about one fourth of all) published exposures and no cases where symbionts increased under ocean acidification. Among the instances of symbiont reduction, the level of ocean acidification used was very high (mean of +1803 µatm above controls). As symbiont populations are known to undergo diel fluctuations in population size, this finding warrants investigation of the

103 cumulative impact on symbiont populations of very high acidification during night time low tides, a regular phenomenon in many shallow-water coral reefs.

4.2 Introduction

Through their position as intracellular symbionts of scleractinian corals, Symbiodinium dinoflagellates play a key role in coral reef ecosystems. In addition to supplying their hosts with organic carbon (generated during photosynthesis) and amino acids, Symbiodinium supply corals with glycerol and oxygen, both thought to drive calcification (Colombo-Pallotta et al. 2010; Wijgerde et al. 2012). Much of the autotrophic energy supplied to corals is later released to seawater as mucus (Wild et al. 2004), and dissolved organic matter (Lewis and Smith 1971), becoming food for heterotrophic or mixotrophic reef organisms including other corals (Tremblay et al. 2012; Levas et al. 2016), sponges (Rix et al. 2017), and animals obligately associating with corals (Wallace 1999). Thus, any human impact that affects the symbionts within corals could subsequently affect corals’ capacities for photoautotrophy, rates of calcification, and energy flow through the reef ecosystem.

The symbiosis between corals and Symbiodinium is intimately associated with the chemical species, and properties, of dissolved inorganic carbon (DIC) in seawater. Carbon dioxide, when dissolved in seawater, reacts with water to form (as % of total DIC at pH 8.05) aqueous carbon dioxide plus carbonic acid (~0.6%), carbonate ion (~11.7%), and bicarbonate ion (~87.7%) (Kleypas et al. 1999;

Barker et al. 2003). Corals supply their symbionts with CO2, the substrate of photosynthesis, through respiration (Muscatine et al. 1989), seawater-to-tissue passive diffusion of aqueous CO2

(Wooldridge 2014b), or active conversion of seawater bicarbonate into CO2 (Weis et al. 1989).

Corals utilise DIC from the seawater along with respired CO2 to build, respectively, 25-40% and 60-75% of the calcium carbonate in their skeletons (Goreau 1977; Erez 1978; Furla et al. 2000). Coral calcification and respiration also generate protons that must be exported from the internal sites of these reactions (Jokiel 2011a).

Increased atmospheric carbon dioxide will alter the DIC system in seawater (Kleypas et al. 1999), thereby potentially impacting key processes of the coral-dinoflagellate symbiosis. The oceanic absorption of anthropogenic carbon dioxide increases the carbonic acid content, which reversibly dissociates into (elevating the concentration of) protons and bicarbonate ions (Miller and Wheeler 2012). Protons and free carbonate react to form bicarbonate, so the elevated proton concentration

104 decreases the pool of free carbonate in the ocean (Miller and Wheeler 2012). Changes in these chemical species could have a number of physiological effects. Increased bicarbonate and increased aqueous CO2 could favour photosynthesis (Mackey et al. 2015). The increased seawater proton concentration could interfere with the export of protons (generated as a metabolic byproduct) from organism tissues (Jokiel 2011b), interfering with respiration and calcification. Meanwhile, reduced oceanic carbonate could reduce the formation of calcium carbonate (in animal structures) or favour its dissolution under the right physical conditions (Miller and Wheeler 2012). The majority of research on corals under ocean acidification has focused on calcification, which declines dramatically in some species, but less so in many others (McCulloch et al. 2012; Zunino et al. 2017). Because of the tight linkages between seawater DIC and key biochemical processes in the symbiosis, the impacts of ocean acidification beyond calcification need to be explored.

Early experiments on the Cnidaria-dinoflagellate symbiosis under elevated pCO2 observed declines in rate of photosynthesis (Goiran et al. 1996; Reynaud et al. 2003), increased cell-specific density (the average number of symbionts per cell among the host cells that contain at least one symbiont:

Muscatine et al. 1998; Reynaud et al. 2003), and changes in Fv/Fm (Iguchi et al. 2012; Noonan and Fabricius 2016). Associated research has detected changes in gene expression in the host (Kaniewska et al. 2012) and symbiont (Crawley et al. 2010), and changes in Symbiodinium concentrations in some coral species, but not others (Krief et al. 2010; Wall et al. 2014).

Change in Symbiodinium densities in corals under ocean acidification is an area for concern. Observed declines in Symbiodinium concentration cannot be categorised as severe bleaching per se. For instance, under ocean acidification, Hii et al. (2009) found declines in areal symbiont densities of 31% – 70%, and Kaniewska et al. (2012) found declines of 55%. These levels are akin to the levels seen during seasonal symbiont loss (“physiological bleaching”) in corals (Fitt et al. 2000), and coral mortality following acidification-induced symbiont loss has not yet been reported, to my knowledge. However, could a decline in Symbiodinium population due to ocean acidification exacerbate bleaching during thermal stress? Anlauf et al. (2011) documented a synergistic impact of temperature and pCO2 on symbiont densities in new coral recruits, wherein elevated pCO2 ameliorated the decrease in symbionts per polyp caused by temperature alone. In contrast, Anthony et al. (2008) documented a combined impact of heat stress and ocean acidification, which synergistically lowered the thermal threshold for luminance loss, a proxy of bleaching, in two coral species.

105 Due to its central role in coral photobiology, it is reasonable to suspect that light level may be involved in determining the impact of ocean acidification on Symbiodinium density and photobiology (Suggett et al. 2013). During an experiment conducted at a subsaturating light level, ocean acidification caused an increase in chlorophyll content per Symbiodinium cell and no cell loss (Crawley et al. 2010). In contrast, the apparent bleaching under ocean acidification in Anthony et al. (2008) occurred at a high light level (>1200 µmol.m-2.s-1 at noon). Beyond this experimental evidence, light intensity plays a key mechanistic role in hypotheses for how the loss of symbiont cells due to ocean acidification might exacerbate thermal bleaching.

One mechanistic hypothesis is that, where ocean acidification causes Symbiodinium loss, that loss could alter the physical properties of the coral tissue in a way that increases the risk of thermal bleaching. The microstructure of the coral calcium carbonate skeleton scatters light, increasing and homogenising the light levels within the coral tissue (Terán et al. 2010). When symbionts are lost, due to any cause, so are points of light absorption and the amount of scattered light within coral tissue per remaining symbiont cell increases (Enríquez et al. 2005). This increases the photonic stress on each remaining symbiont, an effect that may act as a positive feedback to exacerbate the symbiont cell loss that occurs under elevated temperatures (Enríquez et al. 2005; Wangpraseurt et al. 2017).

A second mechanistic hypothesis for how ocean acidification may affect thermal bleaching involves a possible fertilisation effect of ocean acidification on Symbiodinium, paradoxically leading to resource limitation at high temperature. Under ocean acidification, the higher availability of CO2 as a substrate for photosynthesis could increase the symbiont cell population in coral tissues

(Wooldridge 2009). According to the “CO2 limitation” hypothesis, such a symbiont population increase could increase the photosynthetic demand for CO2 at high light or temperature beyond that which the host was capable of supplying. This could lead to a collapse in the quantity of photosynthate produced and consequent host-mediated expulsion of symbionts (Wooldridge 2009), or the signal of low pCO2 could directly trigger programmed cell death in symbiont cells (Vardi et al. 1999; Dunn et al. 2002). Increases in cell-specific density (closely related to increases in symbiont-host cell ratio) under ocean acidification have been observed (Reynaud et al. 2003), providing evidence for the first step of the two involved in “CO2 limitation” bleaching.

The genus Pocillopora consists of branching species possessing thin to relatively thick, tightly packed branches; colonies can be flattened or hemispherical depending on the microhabitat (Veron and Pichon 1976). In reef surveys of bleaching episodes, the genus usually appears to be sensitive to

106 thermal bleaching (Marshall and Baird 2000; Stimson et al. 2002; McClanahan et al. 2004), however, thermal sensitivity of individual species does vary (Rowan 2004). Furthermore, Pocillopora has traits (a low respiratory rate and thin tissues) that suggest reliance on the active uptake of CO2 from seawater to supply the carbon fixation pathway, a trait that is linked to thermal bleaching-sensitivity (Loya et al. 2001; Wooldridge 2014b). During experimental acidification, P. damicornis displayed resistance to decreases in calcification (Schoepf et al. 2013; Huang et al. 2014; Comeau et al. 2014b, 2014a) and P. verrucosa calcification suffered only mild declines (Comeau et al. 2014b). Bahr (2016) did find an acidification-induced decline in P. damicornis calcification, however, high pCO2 remedied thermally induced declines in calcification. Thus, in general, the genus Pocillopora is thermal bleaching-sensitive but skeletogenically-robust under acidification.

In this study, I examined the following questions: (1) (published data) What is the evidence for changes in symbiont densities and symbiont-

related physiological parameters in corals exposed to experimentally elevated pCO2?

(2) (experiment) Does exposure to experimentally elevated pCO2 influence symbiont densities, photosynthesis and holobiont respiration in Pocillopora acuta? After a long period

of adjustment to experimentally elevated pCO2, is thermal bleaching more likely? Does light

moderate the effect of experimentally elevated pCO2 on thermal bleaching?

To examine these questions, I used the hypotheses:

a) (published data and experiment) Experimentally elevated pCO2 by itself will cause a decline in symbiont areal densities;

b) (experiment) Light level will interact with experimentally elevated pCO2 to influence the magnitude of change in symbiont densities;

c) (experiment) Prolonged exposure to high CO2 will result in greater sensitivity to thermal bleaching;

d) (experiment) Experimentally elevated pCO2 will cause a decrease in respiration;

e) (experiment) Experimentally elevated pCO2 will cause an increase in photosynthesis.

107 4.3 Methods

4.3.1 Published data exploration

I compiled data from peer-reviewed studies (Table 4.2) that had exposed corals to both elevated pCO2 and a control pCO2 condition, and that had recorded one or more measures of coral photobiology. I recorded commonly reported variables relating to photosynthesis: symbiont cells

2 net 2 net 2 gross 2 net gross per cm , Fv/Fm, P per cm , Pmax per cm , P per cm , Pmax per symbiont cell, Pmax per symbiont cell, respiration per cm2, symbiont to host cell ratio, chlorophyll a per symbiont cell,

2 2 chlorophyll a per cm , chlorophyll c2 per symbiont cell, and chlorophyll c2 per cm . In each publication, I defined one “exposure” to be a replicated experiment on a coral species that was exposed to a control pCO2 condition and an elevated pCO2 condition, under identical light, temperature and nutrient conditions. Thus, studies of multiple species, and/or factors (e.g. different temperature levels) that were factorially crossed with multiple pCO2 conditions, contained several or more exposures. For each exposure in each study, I noted whether a statistically significant change in each reported variable between control and elevated pCO2 conditions had been found. Where a statistically significant change was found, I recorded the percentage change in that variable in the elevated pCO2 treatment compared to the control, and then standardised the percent change to the change in pCO2 (the elevated pCO2 level minus control pCO2 level) of that exposure. pCO2 partial pressure (in µatm) was not reported in Hii et al. (2009), so consequently, this value had to be

-1 calculated from the reported CO2 content of seawater in µmol∙kg . The experimental light level was reported in most studies but not included in the literature compilation because the information required to make contrasts – either the use of multiple light levels within an experiment or measurements of the light level of exposure prior to experiments (e.g. in the field) – were not reported.

4.3.2 Site description

Experimental work was conducted at Hawaiʻi Institute of Marine Biology (HIMB) at Moku o Loʻe

Island, Kāne‘ohe Bay, Oahu. Kāne‘ohe Bay experiences pCO2 levels above the atmospheric average, varying between 400-500 µatm during normal conditions (Fagan and Mackenzie 2007). Meteorologically, the Hawaiian Islands experience only two seasons, summer (May to October) and winter (November to April). Sea surface temperature in the region of Kāne‘ohe Bay has a maximum monthly mean of 27°C (NOAA Coral Reef Watch 2017b). Water temperature at Moku o Loʻe

108 Island closely tracks spatially-averaged surface water temperature of Kāne‘ohe Bay (Bathen 1968). In late Summer 2014, Kāne‘ohe Bay was experiencing anomalously high water temperatures, averaging 28.19°C in October 2014 (s.e.m. = 0.01, n = 4464). Corals were taken from within a few metres of horizontal distance of the east-facing reef crest (near the position known as the 500-yard marker) of the HIMB Marine Refuge. This crest is dominated by Porites compressa, Montipora capitata, and Pocillopora acuta, which typically are of a compacted growth form. P. acuta is typically found growing on the walls of small hollows in the reef crest. Low tide usually reaches to ~30 cm above the reef crest, which experiences aerial exposure at extreme low tides. In early summer (2014), Kāne‘ohe Bay daytime peak light levels were measured (4 π integrating sphere, LI- COR, Lincoln NE, USA) as 1500 µmol.m-2.s-1 at ~2 cm below the water surface.

4.3.3 Coral collection

Eight whole colonies of Pocillopora acuta were collected on October 13 and October 29, 2014, at 0.2-1.5 metres depth. The coral specimens were collected under Special Activity Permit 2015-8 issued by the state of Hawai‘i, Department of Land and Natural Resources, Division of Aquatic Resources. Within 3 days of collection, corals were fragmented into 3-5 cm branches, hereafter referred to as nubbins, and mounted on PVC bases using Splash Zone A-788 Two-part Epoxy Compound (first collection) or a hot glue gun (second collection). Nubbins then recovered for 21-37 days in two 1300 l outdoor aquaria under ~60% shade cloth (daily peak of 200-300 µmol quanta·mˉ2·sˉ1: Putnam et al. 2016), in flow-through water pumped from Kāne‘ohe Bay and cooled to 26.05°C (s.e.m. = 0.01, n = 4869).

4.3.4 Experimental design

One nubbin of each colony was placed into each of 24 indoor aquaria (n = 8 nubbins per aquarium) the day prior to a 25 day aquarium acclimation period (21 November 2014–15 December 2014)

(during which the pCO2 level in the elevated pCO2 treatment was gradually increased: Figure 4.1 a), where they remained for the 39-day exposure to full pCO2 conditions (16 December 2014-23 January 2015). Each aquarium (44.8 l) also contained fourteen other nubbins for another study that were removed on day 32 of the experiment. Twelve aquaria received 400 µmol quanta·m-2·s-1 at the midday light maximum and twelve received 800 µmol quanta·m-2·s-1 at the midday light maximum from Aqua Illuminations Sol Light Emitting Diode lamps (C2 Development Inc., Ames, USA). The

109 photoperiod (12:12 hr, light: dark) had a 5 am sunrise, five hours of linear increase in light intensity, stable midday light from 10 am to 12 pm, five hours of linear decrease in light intensity, and sunset at 5 pm. Corals were maintained at 25.78°C (± 0.04 s.e.m., n = 168) during the acclimation period,

24.59°C (± 0.06, n = 153), for the first 32 days of the exposure to full pCO2 conditions, and for the final 7 days, each CO2 × light combination was split into 3 aquaria kept at 24.59°C (± 0.06, n = 11), and 3 aquaria raised to 29.05°C (± 0.22, n = 11), using immersion and in-line aquarium heaters.

Aquaria received flow-through seawater at ~435 µatm pCO2 (control pCO2) or ~961 µatm pCO2

-1 -1 (elevated pCO2) (Table 4.1), at a flow rate of circa 1.5 l·aquarium ·min , and each contained a pump for circulation (2630–3000 l·hr-1).

The pCO2 treatments (Figure 4.1, Table 4.1) were achieved by carbonating or aerating seawater that had been pumped directly from Kāne‘ohe Bay. For the elevated pCO2 treatment, the signal from a pH sensor in each of two header tanks was passed to an Apex Aquacontroller control system

(Neptune Systems, Morgan Hill, USA), which modulated the injection of CO2 into header tanks via solenoids (Peter Paul Electronics Co., Inc., City of New Britain, USA), thereby maintaining a preset pH level (7.6) in the header tank seawater. CO2 gas was dispersed in header tanks by pairing the outlet of the gas hose with an ECO-185 water pump (EcoPlus). For the control pCO2 treatment, present-day air (4 l·min-1) was dispersed into the water of two header tanks. Each header tank supplied 6 experimental aquaria. Twice weekly, during light hours, water from each experimental aquaria was sampled for alkalinity measurement on a T50 titrator (Mettler Toledo, Greifensee, Switzerland) beside certified reference material (Marine Physical Laboratory at University of California, San Diego, USA). Salinity, pH and temperature were recorded in each aquaria at the time of water sampling. Carbonate speciation in each treatment (Table 4.1) was calculated using the seacarb package in R. Measurements performed every 6 hours over one 24 hour period indicated diel variations in pCO2 of circa 400 µatm and 150 µatm in elevated pCO2 and control pCO2 treatments, respectively, with minima during midday (Figure 4.1 b). As experimental aquaria were downstream from the header tanks (where pCO2 was controlled) and the residence time (duration to turnover 99% of water: Escobal 1996) in each aquarium was moderate (137 minutes), diel pCO2 fluctuations were almost certainly due to net respiration by night and net photosynthesis by day within aquaria. This compares to diel fluctuations of 200-300 µatm on the Kāne‘ohe Bay barrier reef and of 274, 585 and 764 µatm on various patch reefs at Molokai Island (106 km east) (Yates and Halley 2006; Shamberger et al. 2011). To provide a context within GHG atmospheric concentration future scenarios, I modelled the expected values of daytime pCO2 in Kāne‘ohe Bay at end of century under the RCP8.5 emissions scenario for comparison with the pCO2 values achieved in this study (Appendix 7.6).

110 4.3.5 Respirometry, symbiont density, chlorophyll a and surface area measurements

On days 36-39, respirometry measurements were performed, in two 260 ml cylindrical Perspex chambers that were filled with filtered seawater (0.25 µm) at the temperature and CO2 concentration of the treatment relevant to each coral nubbin. Each chamber, containing an individual nubbin, was immersed in a water bath thermally controlled to 24.77°C (± 0.03, n = 45) or 29.68°C (± 0.02, n = 44) with an F500 Recirculating Cooler (Julabo, Seelbach, Germany). Magnetic stir bars circulated the water in each chamber. Following a period of low light acclimation (30 minutes at < 10 µmol quanta·m-2·s-1), each nubbin was incubated for ~10 min in darkness (measuring dark respiration, DR), illuminated for ~10 min at the midday light level of the relevant treatment (measuring daytime net photosynthesis, Pnet) (light source described above), and exposed to darkness again for ~10 min (measuring light-enhanced dark respiration, LEDR) and then repatriated to its original treatment aquarium. Gross photosynthesis at midday light (Pgross) was calculated as Pnet – LEDR. The ratio of photosynthesis to respiration was calculated by dividing Pnet by DR. The quantity of LEDR attributable to a post-illumination enhancement in respiration rate was calculated as LEDR – DR. Intra-chamber oxygen concentrations were determined at 1-s logging intervals with a Fospor fibre optic probe on a Neofox Oxygen Sensing System (Ocean Optics, Dunedin, USA). Whilst the chamber water may have had an altered pCO2 concentration by the time of the LEDR measurement, the ratio of the duration of the photosynthesis period to that of the respiration period prior to LEDR measurement approximately matches typical P/R ratios of pocilloporid corals (2-4: Coles and Jokiel

1977). For this reason the chamber water pCO2 concentrations at the commencement of LEDR measurement may not have been substantially different from their original levels.

On the final day of the experiment, the remaining nubbins were snap frozen in liquid nitrogen and transported on dry ice to a -80°C freezer. Tissue was airbrushed off thawed nubbins with a pressurised stream of buffer at 4°C (0.4 M NaCl, 0.05 M EDTA); blastates were processed for ~10 s with a T25 500 W variable speed homogeniser (IKA Works Inc., Wilmington NC, USA). To determine chlorophyll a, a portion of the blastate was centrifuged (4500 r.c.f., 5 min), and the pellet of dinoflagellates was extracted in 100% acetone for 48 hours at -20°C. The absorbances of the resulting extracts were read at specific wavelengths in a glass 96 well plate (path length 6 mm) with a Spectramax M2 spectrophotometer (Molecular Devices, Sunnyvale, USA). The absorbances at 663 nm and 630 nm were corrected by subtracting the absorbance at 750 nm, and then were used to calculate the concentration of chlorophyll a by the equation of Jeffrey and Humphrey (1975).

111 Symbiodinium suspensions were stained with Lugol’s solution and cell counts were determined with a Neubauer haemocytometer (Reichert Inc., Buffalo, USA). Per sample, symbiont cells in five separate squares (each 0.1µl in volume) of a haemocytometer chamber were counted, with 2-4 independent chamber fills. Chambers were filled using a cylindrical Pasteur pipette to avoid concentration of cells by gravity experienced in conical pipette tips. Coral skeletal surface areas were estimated using the double wax dipping method (Vytopil and Willis 2001).

4.3.6 Statistical analysis

Analyses were performed with the lmer function of R (lme4 package), which enables the mixed- effects modelling of factorial designs with fully-crossed random effects. Temperature, light and pCO2 were fixed effects; aquarium and colony (fully crossed and nested within treatment) were random effects. ANOVA (type III) with Satterthwaite approximation for denominator degrees of freedom (d.f.) (or Kenward-Roger approximation of denominator d.f. for symbionts per cm2, and chlorophyll a per symbiont) were performed on the best fitting model using the lmerTest package. Normality of distribution was checked using quantile-quantile plots, and homoskedasticity was checked using Levene’s test and plots of residuals versus fitted values.

For response variables in which substantial heteroskedasticity was present (chlorophyll a per cm2, P/R ratio, LEDR per symbiont, DR per symbiont, LEDR – DR per symbiont), the mean of each coral colony across aquaria within each treatment was taken, and a mixed model with fixed effects as above, with coral colony as a random effect1, and with explicit modelling of heteroskedasticity, was performed using the function lme (in the nlme R package: Pinheiro and Bates 2000).

In a multiple-factor ANOVA on untransformed data, interaction terms refer to additive interactive effects between or among factors (Griffen et al. 2016). To check for a multiplicative interactive effect of pCO2 and temperature on symbiont cell densities, an ANOVA was performed on natural logarithm-transformed symbiont cell densities.

In the results section, all response variables are reported as “mean ± standard error of the mean”.

1 The random effect unit was also the finest unit of replication in this case, and was thus not a true random effect 112 4.4 Results

4.4.1 Published data exploration

In published exposures of coral species to ocean acidification under constant light, temperature and nutrients, five photobiological parameters were reported for many experiments, whilst a further ten parameters were reported infrequently (Table 4.2). Three variables had 6 or more exposures in which a statistically significant effect of ocean acidification was detected (symbiont cells per cm2, chlorophyll a per cell and chlorophyll a per cm2), providing a minimally sufficient sample size to analyse the direction of change in these variables under ocean acidification. Of those exposures (n= 14) that detected a statistically significant change in symbionts per cm2, all detected a decrease in this parameter under ocean acidification (Figure 4.2 a). One exposure detected a statistically significant decrease in chlorophyll a per symbiont cell, whilst five detected an increase (Figure 4.2 b). Five exposures detected a statistically significant decrease in chlorophyll a per cm2 whilst three detected an increase (Figure 4.2 c). For most variables, there were high numbers of null effects (no effect of an ocean acidification exposure on a reported variable) (Table 4.2). This could be evidence that publication bias is a minimal problem in this analysis of published data.

4.4.2 Experimental results

Modelling (Appendix 7.6) suggested that the experimental pCO2 treatment (daytime: 961 µatm) was approximately 200 µatm higher than the mean level predicted for Kāne‘ohe Bay under the RCP8.5 scenario at midday (785.97 µatm [Nightingale et al. (2000) transfer velocity], 776.51 µatm [Komori and Shimada (1995) transfer velocity], n = 7 days) at the inner-bay edge of the barrier reef. To our knowledge, no pCO2 transfer velocity parameterisation currently incorporates the effect of air bubble formation in the skin layer of seawater on CO2 transfer velocity, which could be significant at shallow coral reefs (Nightingale et al. 2000; Kitada et al. 2006). Thus the transfer velocity used in the model may be lower than in reality, and hence our daytime predictions of future seawater pCO2 may be underestimates. The pCO2 control (daytime: 435 µatm) was also slightly higher than the present day pCO2 values recorded for Kāne‘ohe Bay (midday: 406 µatm) (Shamberger et al. 2011). The measured and predicted values for Kāne‘ohe Bay were made at the inner-bay edge of the barrier reef located between inner Kāne‘ohe Bay and the open ocean. Whilst this location is spatially separated from the collection site (Moku o Loʻe island), the water at the collection site reflects the spatially averaged water temperature over the entirety of Kāne‘ohe Bay (Bathen 1968),

113 and open-ocean water flowing over the barrier reef is a major source of water input to the bay (Hearn 1999). As temperature-transfer is closely related to mass-transfer, this is a reason to predict that the pCO2 conditions in the water at the collection site will closely match that averaged over the entirety of the bay, and therefore, probably that at the inner-bay edge of the barrier reef.

2 In P. acuta, CO2 did not influence the density of symbiont cells per cm , chlorophyll a per symbiont cell, or chlorophyll a per cm2, either alone or in combination with temperature and/or light (probability values in Table 4.3). For symbiont cells per cm2, this applied to both untransformed data (where the null hypothesis for interaction terms is “no additive interaction”) (Table 4.3), and log (base e) transformed data (where the null hypothesis for interaction terms is “no multiplicative interaction”) (Griffen et al. 2016). Statistical results for the latter were very similar to those of the untransformed data and so are not reported in Table 4.3.

gross 2 2 CO2 had a significant main effect on P per cm , LEDR per cm and interactive effects with temperature on some other parameters. Elevated CO2 caused a 15% increase in the rate of LEDR

2 -2· -1 per cm (µmol O2 consumed·cm hr , control: 0.48 ± 0.02 s.e.m., elevated: 0.56 ± 0.03 s.e.m.). The

gross 2 net impacts of CO2 on LEDR may have had flow-on effects to P per cm , calculated as P – LEDR.

gross 2 CO2 had a main effect on the rate of P per cm , with elevated pCO2 resulting in a 12% increase

-2· -1 (µmol O2·cm hr , control: 1.18 ± 0.05, elevated: 1.32 ± 0.06). Whilst there was not a significant main effect of CO2 on LEDR per symbiont cell (p = 0.1030) or LEDR – DR per symbiont cell (p =

0.0586), LEDR – DR per symbiont cell trended downwards under elevated pCO2 (pmol O2

-1 -1 consumed·symbiont ·hr , control: 0.024 ± 0.024, elevated: 0.021 ± 0.031). CO2 did not have a significant main effect, but did have a significant interaction with temperature, for the P/R ratio, DR per symbiont and DR per cm2 (discussed below).

Temperature had a statistically significant main effect on most parameters except LEDR – DR symbiont-1 (Table 4.3). Elevated temperature caused a statistically significant decline in symbionts cm-2, chlorophyll a cm-2, host cell density, Pnet cm-2, and Pgross cm-2 (Table 4.4). Elevated temperature caused a statistically significant increase in chlorophyll a symbiont-1, S/H cell ratio, DR symbiont-1, LEDR cm-2, and LEDR symbiont-1 (Table 4.4). There was a statistically significant interaction of temperature with light for chlorophyll a cm-2 (Tukey’s test: low light 25°C > low light 29°C >high light 25°C = high light 29°C) (Figure 4.3). There was no interactive effect of light and temperature for symbiont cells cm-2 (Table 4.3), however the difference between the two temperatures at low light was much greater than the difference between the two temperatures at high light.

114 The light level had a significant main effect on symbiont cells cm-2, chlorophyll a symbiont-1, chlorophyll a cm-2, DR symbiont-1, LEDR symbiont-1, and LEDR – DR symbiont-1. Both light intensities (400 and 800 µmol photons·m-2·s-1 at midday, which were 100-200 and 500-600 µmol photons·m-2·s-1 above the pre-experiment level, respectively) appeared to be above photosynthetic saturation for P. acuta, as there was no statistically significant difference in Pnet cm-2 or Pgross cm-2 between the two light treatments (Table 4.3). Elevated light caused a statistically significant decrease in symbiont cells cm-2, chlorophyll a symbiont-1, chlorophyll a cm-2, and LEDR – DR symbiont-1 (Table 4.5). Elevated light caused a statistically significant increase in DR symbiont-1, and LEDR symbiont-1 (Table 4.5).

gross 2 In addition, light had an interactive effect with CO2 and temperature to affect P per cm (Figure 4.5 a, and Table 4.3 [three-way interaction]) (a close to significant interactive effect was also found

net 2 gross 2 for P per cm ). At low light, there was a 52% decrease in P per cm at 29°C elevated pCO2 versus 25°C elevated pCO2 (Tukey's test p = 0.0446), but no such significant difference between any two other combinations of light, temperature and CO2. Thus, at low light combined with

gross 2 elevated temperature, elevated pCO2 did not appear to stimulate an increase in P per cm .

2 CO2 interacted with temperature to affect DR per cm , DR per symbiont, and P/R ratio. For both standardisations of DR, the increase in DR with elevated temperature was exacerbated by elevated pCO2, but the effect was strongest for DR per symbiont (Figure 4.4 a, b). At 25°C, the P/R ratio at elevated pCO2 became much greater than that at control pCO2, whilst at 29°C, the P/R ratio did not change with pCO2 (Figure 4.5 b). Thus, elevated pCO2 increased the slope (in the negative direction) of the P/R ratio to temperature relationship.

Tissue sloughing over large areas of the coral fragment was seen in one fragment in each of the high light 25°C elevated pCO2 treatment and the low light 29°C elevated pCO2 treatment.

4.5 Discussion

Acidification has the potential to impact cellular homeostasis (Jokiel 2011b), the supply of substrate to the photosynthetic dark reactions (Wooldridge 2009), and the total protein content per unit area of coral surface (Horwitz and Fine 2014), factors that directly affect the symbionts of corals. Scleractinian corals and coral reefs benefit from carbon autotrophically fixed by dinoflagellate symbionts, with carbon in excess of symbiont requirements flowing on to the host (Muscatine et al.

115 1981) and the surrounding reef (Lewis and Smith 1971). Acidification will occur within the context of variable temperature and light environments on reefs, factors that also have a direct impact on endosymbiont photosynthesis. The goal of the present study was, therefore, to examine how the interplay of acidification, light and temperature affect photosynthesis and population size of the dinoflagellate symbionts within coral tissue.

The first hypothesis, that symbiont cell densities per unit of coral surface area will decline under ocean acidification, was strongly supported by literature evidence taken at high acidification levels. In this study, areal symbiont densities did not change with ocean acidification (pH 7.7) over 4 combinations of light and temperature. In 40 published exposures, areal symbiont densities showed no statistically significant increase nor decrease under ocean acidification. In a further 14 published exposures, areal symbiont densities displayed a statistically significant decline under ocean acidification. The level of ocean acidification used in these exposures may have played a role in determining which studies identified a change in symbiont densities, and which did not. The mean difference in partial pressure of CO2 between acidified and control treatments in those exposures that identified a decline (1803 µatm) was significantly greater than that in those exposures that detected no change (768 µatm) (Welch’s t-test, t(d.f. = 18) = 2.99, p = 0.0078). Mild changes in symbiont numbers may not register as statistically significant, and therefore my literature review design would not have detected trends for smaller decreases or increases in symbiont numbers at the less extreme levels of ocean acidification. However, in two cases, statistically significant declines in symbiont numbers were seen at less extreme acidification levels (relevant to end-of-century projections): Montipora monasteriata at a CO2 partial pressure of ~600 µatm and A. millepora at 1180 µatm (Kaniewska et al. 2012; Schoepf et al. 2013). In several other cases, no significant changes in symbiont densities were detected at very high acidification of ~2000 µatm (Stylophora pistillata: Godinot et al. 2011; Porites australiensis: Iguchi et al. 2012) and ~4000 µatm (Stylophora pistillata: Krief et al. 2010; Porites sp. and Acropora eurystoma: Horwitz and Fine

2014). Nevertheless, very high pCO2 levels induced symbiont density reductions in most cases.

There was some literature evidence that demonstrated that ocean acidification can alter the quantity of chlorophyll a per symbiont. Chlorophyll a per symbiont cell did not change under ocean acidification in the experimental study of P. acuta. However, in six out of seventeen exposures in the literature, ocean acidification caused statistically significant alterations in chlorophyll a per symbiont cell (Hii et al. 2009; Crawley et al. 2010; Krief et al. 2010; Ogawa et al. 2013; Horwitz and Fine 2014). In five out of six of these cases, chlorophyll a per symbiont cell increased (Figure 4.2b). In P. acuta, the (expected) decrease in symbiont densities with an increase in temperature

116 (from 25°C to 29°C) was, unusually, accompanied by an increase in chlorophyll a per symbiont cell. Whilst this has been observed in some coral species (Seriatopora hystrix: Hoegh-Guldberg and Smith 1989; temperate Balanophyllia europaea: Caroselli et al. 2015; Seriatopora caliendrum: Baghdasarian et al. 2017), increased temperature typically causes decreases in either chlorophyll a per symbiont cell (Kleppel et al. 1989), or areal symbiont density, or both (Hoegh-Guldberg 1999).

In at least some cases, symbiont loss under ocean acidification may be related to an increased capacity for photochemical quenching due to greater availability of CO2. Of those five exposures that experienced increases in chlorophyll a per symbiont cell, two also experienced statistically significant declines in symbionts per cm2 (Hii et al. 2009; Krief et al. 2010), a further two experienced a declining trend in symbionts per cm2 (Crawley et al. 2010; Krief et al. 2010), and one experienced no significant decline nor declining trend in symbionts per cm2 (Crawley et al. 2010 - the results differed at the two elevated pCO2 levels used in that paper). Within a cell, a decrease in the areal density of particles containing chlorophyll (e.g. chloroplasts) coupled with an increase in the number of chlorophyll molecules per particle decreases the quantity of light absorbed per chlorophyll molecule, a phenomenon known as the packaging effect (Duyens 1956; Baird et al. 2007). I hypothesise that this concept could be generalised to suspensions of algal cells, where a decrease in the density of algal cells in suspension coupled with an increase in the chlorophyll content per algal cell decreases the average amount of light absorbed per chlorophyll molecule, because an increase in the amount of chlorophyll per cell would require an increase in the degree of packaging of chlorophyll molecules. This could be a strategy to reduce the amount of light received per chlorophyll molecule, whilst conserving the investment (of the symbiont population as a whole) that has been made in chlorophyll synthesis. However, increased chlorophyll per cell may increase the cell’s capability for photosysthesis (Prézelin 1987), whilst decreased cell density may increase coral skeletal scattering and therefore increase the light available per cell (Enríquez et al. 2005). Thus, these responses may be directed towards increasing the rate of photosynthesis per symbiont cell due to the increased quantity of CO2 available as the substrate in the Calvin-Benson cycle.

I found no evidence to support my experimentally-tested second and third hypotheses, that light will interact with coral bleaching to influence the degree of change in areal symbiont densities, and that medium-term exposure to ocean acidification will enhance thermally induced bleaching. In P. acuta, areal symbiont cell densities declined after exposure to elevated temperature or to higher light, but a lack of both an additive and a multiplicative interactive effect of pCO2 and temperature

(Table 4.3) demonstrates that pCO2 did not alter the amount of bleaching (either loss of symbiont density or chlorophyll a) under heat stress.

117 Elevated pCO2 impacted respiration rate in P. acuta, providing evidence that countered my fourth hypothesis, that dark respiration will be reduced by ocean acidification. Overall, light-enhanced

2 dark respiration per cm increased with elevated pCO2 by 15%. There were no differences in respiration rate (normalised to surface area) between present-day and acidified conditions within each temperature. However, the increase in respiration rate with increased temperature, an expected biotic response (Gillooly et al. 2001), was enhanced in P. acuta by elevated pCO2 (Figure 4.4 a, b).

Dark respiration has been hypothesised to decline under ocean acidification, due to the fact that reduced ocean pH increases the energy required by a coral to translocate protons (generated during respiration) from its tissues to the seawater (Jokiel 2011b; Kaniewska et al. 2012; Mackey et al. 2015). In 17 out of 21 exposures in the published literature, ocean acidification had no impact on holobiont respiration normalised to coral surface area (Table 4.2). In those cases where it did have an impact, respiration under acidification at the control temperature decreased in A. millepora (Kaniewska et al. 2012), and Porites cylindrica (Hii et al. 2009) but increased in Galaxea fascicularis (Hii et al. 2009). Like this chapter, Comeau et al. (2017) found that ocean acidification alone caused no change in respiration of P. damicornis normalised to surface area, but found an increase in respiration of P. damicornis normalised to biomass. Though there may be an effect of acidification on proton disposal (tipping the favour towards reduced respiration), this may be countered in Pocillopora by increased energetic requirements for maintaining physiological functions under acidification (such as calcification, already an energy intensive process: Allemand et al. 2004).

P. damicornis and P. acuta maintain their rate of calcification, normalised to both biomass and skeletal area, under elevated pCO2; at the same conditions, however, both species experience declines in biomass or energy reserves (Comeau et al. 2013, 2014b, 2014b; Wall et al. 2017). This could be a signature of increased metabolism of stored energy to maintain the rate of calcification under ocean acidification (Comeau et al. 2013) (and possibly, also to offset the increased energetic costs of respiration itself: Mackey et al. 2015). In free-swimming larvae of Pocillopora, respiration rate displays a trend to decrease under elevated pCO2 (Putnam et al. 2013), supporting the idea that maintenance of calcification under acidification is a factor contributing to the prevention of a decline in the respiration rate in adults. Enhanced expression of genes involved in heterotrophy and autotrophy has been observed in Pocillopora under moderate to very elevated pCO2 (857-2181 µatm), suggesting that normal physiological functioning becomes energetically more costly (Vidal-

Dupiol et al. 2013). The use of an acidification level in this chapter (961 µatm) that is at the moderate end of the aforementioned range may explain why this effect manifested as increased

118 respiration only when elevated pCO2 was combined with elevated temperature. The fate of coral respiration under ocean acidification may hang on the balance between the increased energy required by corals to transfer protons from their tissues to seawater versus whether or not a coral species chooses to allocate additional energy to calcification.

The increased light-enhanced dark respiration under acidification that was observed in P. acuta does not necessarily oppose the hypothesis (Kaniewska et al. 2012) that respiration will decrease due to the effects of lower seawater pH on the export of respired protons. Light-enhanced dark respiration indicates either increases in the availability of substrates created by photosynthesis in the light or increases in alternative electron transport pathways. Increased rates of respiration can occur due to increased availability of photosynthates following Calvin-Benson cycle activity under illumination. The reestablishment of the chloroplasts’ capacity for induction in the 10 min post- illumination by oxidative depletion of stromal metabolites produced during the light, and a small contribution from re-oxidation of spent electron transport carriers, may also cause LEDR (Stokes et al. 1990). Alternatively, the light-driven production of ATP and NADPH by electron transport can drive reactions external to the Calvin-Benson cycle that produce substrates capable of fuelling mitochondrial respiration (Parys and Jastrzębski 2006). The alternate photochemical quenching mechanisms of chlororespiration and the Mehler Ascorbate-Peroxidase cycle may also contribute (Stokes et al. 1990). Also, increased photorespiration may contribute to LEDR, though photorespiration would be expected to decrease under acidification because of the increased abundance of CO2 (Rowan et al. 1996). Thus, LEDR acts as a bellwether of one or more forms of photochemical quenching, and so it is likely that at least one photochemical quenching form increased under ocean acidification in P. acuta. Alternatively, the increased LEDR per cm2 under ocean acidification may simply be due to changes in DR due to ocean acidification, as LEDR (as measured in this study) integrates over both the underlying DR rate and the additional changes in the oxygen signal immediately post-illumination. However, the difference between LEDR and DR per symbiont cell is the component of LEDR that is light-enhanced, and the fact that this component trended higher (close to significant: p = 0.0586) under elevated pCO2 lends weight to the hypothesis of increased photochemical quenching under acidification.

Light level may play an important role in delineating what the impacts of ocean acidification upon the rate of LEDR actually mean for the holobiont. In A. muricata, elevated pCO2 (1160–1500 µatm) was found to both increase LEDR (normalised to symbiont cell) and decrease the expression of the gene that encodes the enzyme PGPase that eliminates phosphoglycolate, a by-product of photorespiration that inhibits carbon fixation (Crawley et al. 2010). In addition, whilst LEDR

119 net increased, Pmax remained the same per cell, suggesting that gross photosynthesis may have increased under the elevated pCO2 experimental conditions (Crawley et al. 2010). However, the decrease in PGPase expression either suggests (i) a lesser need for phosphoglycolate removal and therefore signifies reduced photorespiration (expected when CO2 availability increases: Rowan et al. 1996). Or, it suggests (ii) that PGPase expression was inhibited by high pCO2, in which case phosphoglycolate may have built up and potentially inhibited carbon fixation. As it is a photoprotective mechanism, photorespiration would be less likely to be operating under a low light intensity (Park et al. 1996). Therefore, the use of a sub-saturating experimental light level in Crawley et al. (2010) lends credence to suggestion (ii), which would mean that the probable increase in Pgross is not due to an increase in carbon fixation, but to another photochemical quenching mechanism in operation (and likewise for LEDR). However, this may be less of a concern for the interpretation of the elevated Pgross under acidification that was measured in this chapter, due to my use of experimental light intensities that were above saturation.

Elevated pCO2 impacted net photosynthesis per unit area with statistical significance in 4 of 15 exposures identified in the literature search, and further analysis provided mixed evidence to support my fifth hypothesis, that photosynthesis rate will increase under ocean acidification. In two published cases (but both in one species, Seriatopora hysterix: Noonan and Fabricius 2016),

net elevated pCO2 caused P per unit area to increase, whilst in Porites cylindrica and Porites irregularis, Pnet per area decreased under ocean acidification (Hii et al. 2009; Comeau et al. 2017).

net In the experiment on P. acuta, P per unit area did not change under elevated pCO2, a finding shared with Comeau et al. (2017) in the congener P. damicornis. However, as elevated pCO2 affected respiration, this suggested that gross photosynthesis would have to have increased in order to maintain the unchanging rate of Pnet that I observed. Indeed, daytime Pgross per cm2 did increase under increased pCO2 (except at the low-light/high-temperature combination).

The impact of changes in photosynthesis under ocean acidification will depend on whether it contributes to an improved energy balance, or whether increased energy demands will outstrip increased photosynthesis. In P. acuta at 25°C, the P/R ratio was higher under ocean acidification compared to the control condition (Figure 4.5 b). This pattern is clear evidence of a “CO2 fertilisation” effect. However, an increase in temperature to 29°C eliminated the difference in P/R ratio between control and elevated pCO2. Thus, elevated temperature removes the energetic advantage conferred by “CO2 fertilisation” on P. acuta.

120 Recently, the thermal stress-tolerant Symbiodinium glynii (previously known as subclade D1) has been described as a major symbiont of Pocillopora spp. in the eastern and western Pacific (Wham et al. 2017). Symbiodinium types found in P. damicornis near this study’s collecting locality (Pochon et al. 2010; Stat et al. 2008; M. Stat pers. comm.) and in Pocillopora spp. at the Northwestern Hawaiian Islands (Stat et al. 2015) appear to exclusively belong to clade C (e.g. subclades C1, C1c, C1d, C3, C42; Pochon et al. 2010). Whilst Symbiodinium glynii has been found in Montipora spp. in Oahu (Wham et al. 2017), and Pocillopora spp. are known to flexibly associate with both clade C and clade D (Cunning et al. 2013), clade D (and by elimination, S. glynii) has not yet been reported to occur in Pocillopora spp. in Kāne‘ohe Bay. Associations of Pocillopora with clade C are known to be more susceptible to thermal stress than those involving clade D (though there is variability in susceptibility within clade C) (Rowan 2004; Fitt et al. 2009). The fact that the P. acuta used in my study were likely hosting clade C hints that susceptibility of photosynthesis and symbiont population size to thermal stress does not imply susceptibility of the same to ocean acidification.

The prediction of bleaching by utilising ocean acidification in addition to temperature is relevant to those species that experience symbiont loss at ocean acidification levels possible in the near future. Experimental exposures of corals to ocean acidification have utilised ocean acidification levels that did not fluctuate through a diel cycle (with notable exceptions: Dufault et al. 2012; Comeau et al. 2014c; Putnam et al. 2016), and whilst symbiont loss has been observed with such profiles at near future levels (~600µatm) in Montipora monasteriata (Schoepf et al. 2013), all other observations of symbiont loss under diel-stable carbonate chemistry have occurred only at distant-future acidification levels. However, pCO2 fluctuates over a diel cycle on reefs that experience high biological forcing of the carbonate system (i.e. shallow reefs with moderate or high benthic cover and low rates of water turnover) (Hofmann et al. 2011). Furthermore, on such reefs, increases in ocean acidification will greatly exceed oceanic means at low tides during the night-time (Shaw et al.

2013). Multiplication of a changed DIC concentration by the Revelle factor (=(δpCO2/δDIC)/

(pCO2/DIC)), an indicator of buffering capacity (proportional to the DIC:TA ratio) gives the resulting partial pressure of CO2. The Revelle factor is predicted to increase with increased acidification in the ocean (Sabine et al. 2004). During darkness at low tides, respiration can add large amounts of DIC to the reef water. During this spike, an elevated Revelle factor due to anthropogenic acidification will amplify the effect on pCO2 of the DIC added to seawater by respiration (Shaw et al. 2013). My experiment probably demonstrates this Revelle-enhancing effect of acidification, as the absolute difference in pCO2 between night and day was much greater in the acidified aquaria compared to the control aquaria (Figure 4.1 b).

121 Due to the effect of acidification on the Revelle factor, accurate experimental assessment of which shallow-water coral species are susceptible to symbiont loss under near-future ocean acidification levels could require the inclusion of short exposures during darkness to very high levels of ocean acidification. Symbiont populations in corals are known to undergo diel fluctuations in population size (Stimson and Kinzie 1991; Jones and Yellowlees 1997). High acidification during symbiont density changes at night, but not during symbiont density changes in the opposing direction during the day, may alter the balance between population growth and contraction. The physiological evidence that ocean acidification causes symbiont loss in some corals provides an imperative to research the impact of this phenomenon, with a long-term view towards the possible inclusion of ocean acidification in bleaching prediction methods.

4.5.1 Conclusions

The accurate prediction of coral bleaching using remotely-sensed temperature and light data may be impacted by declines in symbiont areal densities under ocean acidification. I identified that symbiont cell loss occurred in 14 published instances of coral species exposed (in the main) to very high levels of ocean acidification. Such cell loss may additively contribute to bleaching from other stressors, or may synergistically enhance (or possibly suppress) bleaching from other stressors. Further research is needed to investigate such an effect for common forms of bleaching (i.e. thermal and hyposaline bleaching). I found no synergy between ocean acidification and temperature during thermally induced bleaching in a species that did not experience cell loss under ocean acidification alone (P. acuta). Observations of the loss of symbionts under ocean acidification are practically relevant to those coral reef bleaching prediction methods (Wellington et al. 2001; Maynard et al. 2008b; Spillman and Alves 2009) that use satellite remotely-sensed temperature data. Ocean acidification can now be measured using satellite-detected sea surface temperature and salinity (Gledhill et al. 2008, 2009; Sun et al. 2012; Land et al. 2015; Sabia et al. 2015). Thus, it could be possible to include ocean acidification as a predictive parameter in such algorithms, where its inclusion is supported by physiological data.

122 Table 4.1: Seawater physical and chemical parameters.

Values are expressed as: mean, ± standard error of the mean, (sample size). The pH was measured as mV with a DG 115-SC glass pH electrode (Mettler Toledo) connected to an Orion 3 Star pH Portable meter (Thermo Scientific, Waltham, USA). The pH (total scale) was calculated from millivolts and water temperature, using a temperature gradient calibration of the glass pH electrode whilst it was immersed in a standardised Tris buffer (Marine Physical Laboratory at University of California, San Diego, USA). The temperature was measured with a TraceableTM Digital Thermometer (Control Company, City of Webster, USA) and salinity with a 63 pH Salinity Conductivity Temperature meter (YSI, Yellow Springs, USA). All other parameters were calculated from pH, salinity and temperature using the R package, seacarb.

- 2- Treatment midday Temperature Temperature pHtotal TA pCO2 HCO3 CO3 Ωarag light (°C), mean (°C), mean (µmol∙kg-1) (µatm) (µmol∙kg-1) (µmol∙kg-1) (µmol quanta over days over days ∙m-2∙s-1) 1-32 33-39 high light 800 24.6±0.2(21) 28.83±0.21(3) 8.01±0.01(24) 2174±4(24) 420±17(24) 1702±16(24) 190±6(24) 3.03±0.1(24)

low CO2 temp stress high light 800 24.6±0.2(21) 24.63±0.1(3) 8.02±0.01(24) 2177±4(24) 410±16(24) 1708±13(24) 189±6(24) 3.01±0.09(24)

low CO2 low temp low light 400 24.8±0.2(21) 29.79±0.21(3) 7.99±0.01(24) 2173±4(24) 444±16(24) 1716±13(24) 184±5(24) 2.94±0.08(24)

low CO2 temp stress low light 400 24.5±0.2(21) 24.55±0.08(3) 7.99±0.01(24) 2176±4(24) 449±13(24) 1735±10(24) 178±4(24) 2.82±0.06(24)

low CO2 low temp high light 800 24.5±0.2(14) 29.39±0.26(2) 7.71±0.03(16) 2179±6(16) 974±72(16) 1912±19(16) 108±7(16) 1.72±0.11(16)

high CO2 temp stress high light 800 24.4±0.2(14) 24.7±0.33(2) 7.72±0.02(16) 2181±6(16) 919±44(16) 1916±12(16) 107±5(16) 1.7±0.08(16)

high CO2 low temp low light 400 24.9±0.2(21) 28.3±0.41(3) 7.7±0.02(24) 2179±5(24) 984±47(24) 1916±13(24) 106±5(24) 1.7±0.08(24)

high CO2 temp stress low light 400 24.4±0.2(20) 24.51±0.05(3) 7.71±0.02(23) 2181±5(23) 960±53(23) 1917±16(23) 107±6(23) 1.69±0.1(23)

high CO2 low temp

123 Table 4.2: Summary of the numbers of published instances of thirteen photobiological variables measured in experimental exposures of coral species to acidification.

Data from the current study were not included. Photosynthesis measurements are reported in the

net gross categories of light-saturated photosynthesis (Pmax or Pmax ), or photosynthesis that was not stated as being at light-saturation (Pnet and Pgross). “Chlorophyll” is abbreviated as “Chl.”. Data sources were: (Reynaud et al. 2003; Anthony et al. 2008; Marubini et al. 2008; Hii et al. 2009; Crawley et al. 2010; Krief et al. 2010; Godinot et al. 2011; Houlbrèque et al. 2012; Iguchi et al. 2012; Kaniewska et al. 2012; Ogawa et al. 2013; Schoepf et al. 2013; Tremblay et al. 2013; Enochs et al. 2014; Horwitz and Fine 2014; Wall et al. 2014; Biscéré et al. 2015; Towle et al. 2015; Vogel et al. 2015; Hoadley et al. 2016; Noonan and Fabricius 2016; Baghdasarian et al. 2017).

Numbers of acidification exposures with significant changes (compared to control conditions) in the response variable Response significant no significant Variable change change not stated Symbiont cells per cm2 14 40 1 Chl. a per cm2 8 25 0 Chl. a per symbiont cell 6 14 0 respiration per cm2 3 17 1 Pnet per cm2 4 11 0

Fv/Fm 4 10 0 Pgross per cm2 4 3 1 Pnet per unit of Chl. a 1 5 0 2 Chl. c2 per cm 2 4 0 Chl. c2 per symbiont cell 3 2 0

net 2 Pmax per cm 0 2 0 gross Pmax per symbiont cell 2 0 1 symbiont to host cell ratio 2 0 0 net Pmax per symbiont cell 1 0 1

124 Table 4.3: Probability values given by ANOVAs performed on mixed effects models.

Abbreviations used for factors are T (temperature), L (light), C (CO2) and “×” refers to an interaction between the relevant factors. “Chlorophyll” is abbreviated as “chl.”.

Factors Symbionts µg chl. a pg chl. a DR per Pnet per Pnet/DR LEDR per Pgross per LEDR per DR per LEDR per cm2 per cm2 per cm2 cm2 ratio cm2 cm2 symbiont symbiont minus DR symbiont per symbiont Temp. <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.99

F1,14=19.8 F1,49=9.69 F1,14=25.7 F1,85=11.6 F1,19=27.7 F1,21=38 F1,88=11.2 F1,84=11.6 F1,21=72.1 F1,21= 55.8 F1,21=.0001 Light <0.01 <0.01 <0.01 0.33 0.19 0.8 0.23 0.27 <0.01 <0.01 <0.01

F1,14=11.9 F1,49=123 F1,14=18.5 F1,86=0.95 F1,19=1.81 F1,21=0.07 F1,88=1.44 F1,86=1.22 F1,21=16.89 F1,21=32.6 F1,21=14.7

CO2 0.62 0.93 0.93 0.11 0.68 0.84 0.02 0.03 0.1 0.08 0.06

F1,14=0.26 F1,49=0.01 F1,14=0.01 F1,85=2.59 F1,19=0.17 F1,21=0.04 F1,88=5.52 F1,85=4.79 F1,21=2.91 F1,21=3.46 F1,21=4 T×L 0.14 <0.01 0.80 0.77 0.31 0.07 0.63 0.08 0.26 0.57 0.21

F1,14=2.44 F1,49=11.9 F1,14=0.07 F1,85=0.08 F1,19=1.11 F1,21=3.73 F1,88=0.23 F1,84=3.06 F1,21=1.31 F1,21=0.33 F1,21=1.67 T×C 0.63 0.84 0.85 <0.05 0.94 0.03 0.17 0.94 0.06 0.02 0.16

F1,14=0.24 F1,49=0.04 F1,14=0.04 F1,84=3.98 F1,19=0.01 F1,21=5.52 F1,88=1.92 F1,84=0.01 F1,21=3.84 F1,21=6.58 F1,21=2.1 L×C 0.93 0.91 0.44 0.17 0.20 0.28 0.28 0.31 0.66 0.59 0.91

F1,14=0.01 F1,49=0.01 F1,14=0.63 F1,85=1.88 F1,19=1.76 F1,21=1.25 F1,88=1.18 F1,85=1.04 F1,21=0.2 F1,21=0.29 F1,21=0.01 T×L×C 0.55 0.42 0.40 0.89 0.06 0.22 0.79 0.02 0.94 0.9 0.74

F1,14=0.38 F1,49=0.65 F1,14=0.75 F1,84=0.02 F1,19=3.85 F1,21=1.6 F1,88=0.07 F1,84=5.85 F1,21=0.01 F1,21=0.02 F1,21=0.11

125 Table 4.4: Statistically significant main effects of temperature.

Parameter Units Value at 25°C Value at 29°C % change at elevated temp. symbionts cm-2 105·cells·cm-2 8.26 ± 0.26 5.99 ± 0.22 -27% chlorophyll a pg·cell-1 3.85 ± 0.07 4.74 ± 0.09 +23% symbiont-1 chlorophyll a cm-2 µg·cm-2 3.17 ± 0.12 2.85 ± 0.12 -10% S/H cell ratio 4.40 ± 0.58 6.81 ± 0.93 +55% host cell density 105·cells·cm-2 6.10 ± 0.81 2.37 ± 0.34 -61% net -2 -2 -1 P cm µmol O2·cm ·h 0.90 ± 0.06 0.54 ± 0.04 -57% gross -2 -2 -1 P cm µmol O2·cm ·h 1.37 ± 0.06 1.12 ± 0.05 -18% -1 DR symbiont pmol O2 0.58 ± 0.04 1.05 ± 0.08 +81% consumed·symbiont-1·h-1 -2 -2 -1 LEDR cm µmol O2 consumed·cm ·h 0.46 ± 0.02 0.57 ± 0.03 +24% -1 LEDR symbiont pmol O2 0.57 ± 0.03 1.02 ± 0.08 +79% consumed·symbiont-1·h-1

126 Table 4.5: Statistically significant main effects of light.

Parameter Units Value at 400 Value at 800 % change at µmol µmol elevated light. photons·m-2·s-1 photons·m-2·s-1 symbiont cells cm-2 105·cells·cm-2 7.9 ± 0.28 6.17 ± 0.2 -22% chlorophyll a pg·symbiont-1 4.63 ± 0.08 3.86 ± 0.09 -17% symbiont-1 chlorophyll a cm-2 µg·cm-2 3.6 ± 0.11 2.32 ± 0.07 -36% -1 DR symbiont pmol O2 0.72 ± 0.06 0.93 ± 0.08 +29% consumed·symbiont-1·h-1 -1 LEDR symbiont pmol O2 0.69 ± 0.06 0.91 ± 0.08 +32% consumed·symbiont-1·h-1

LEDR – DR pmol O2 0.028 ± 0.026 0.016 ± 0.028 -43% symbiont-1 consumed·symbiont-1·h-1

127 (a) Graph of ocean acidification (as the daytime partial pressure of pCO2) during the course of the experiment, averaged over all aquaria within the low acidification treatments (solid line) and high acidification treatments (dashed line). Days -24 to 0 are the aquaria acclimation period, and the exposure to full experimental acidification conditions occurred over days 1-39. Error bars are standard error of the mean.

(b) The cycle of seawater pCO2 over a 24 hour period in each experimental combination of light and pCO2, colour-coded as Blue: low light, control pCO2; Purple: high light, control pCO2; Orange: low light, elevated pCO2; Red: high light, elevated pCO2. Measurements were taken during the time intervals of 2200-2300 hr, 0400-0500 hr, 1000-1100 hr, and 1600-1700 hr. A heightened difference between day and night may be due to an increased Revelle factor under acidified conditions. An increased dark respiration rate under acidified conditions may have also contributed, though this was observed only at 29°C whereas the diel cycle of seawater pCO2 was measured at 24.6°C. Each point represents measurements from n = 3 aquaria; error bars are standard error of the mean.

128 Figure 4.2: Histograms summarising published data of acidification impacts on coral symbionts. (a) Histogram of percent changes in symbiont cells per cm2 under ocean acidification compared to control seawater conditions, standardised to the partial pressure of pCO2 increase (the pCO2 difference between the elevated pCO2 treatment and the control treatment), with units of milli- atmospheres (matm). Each count is an exposure of a coral species to ocean acidification (with light, temperature, and nutrients held constant) from a peer-reviewed paper in which a statistically significant change in symbiont cells per cm2 was detected. Data are from: (Hii et al. 2009; Krief et al. 2010; Kaniewska et al. 2012; Schoepf et al. 2013; Tremblay et al. 2013; Horwitz and Fine 2014). (b) Histogram of percent changes in chlorophyll a per symbiont cell under ocean acidification compared to control seawater conditions, standardised to the partial pressure of pCO2 increase (the

129 pCO2 difference between the ocean acidification treatment and the control treatment). Each count is an exposure of a coral species to ocean acidification (with light, temperature, and nutrients held constant) in a peer-reviewed paper in which a statistically significant change in chlorophyll a per symbiont cell was detected. Data are from: (Hii et al. 2009; Crawley et al. 2010; Krief et al. 2010; Horwitz and Fine 2014). (c) Histogram of percent changes in chlorophyll a per cm2 under ocean acidification compared to control seawater conditions, standardised to the partial pressure of pCO2 increase (the pCO2 difference between the ocean acidification treatment and the control treatment). Each count is an exposure of a coral species to ocean acidification (with light, temperature, and nutrients held constant) in a peer-reviewed paper in which a statistically significant change in chlorophyll a per cm2 was detected. Data are from: (Schoepf et al. 2013; Tremblay et al. 2013; Biscéré et al. 2015; Noonan and Fabricius 2016).

130 4.5 25°C xy 4 29°C

3.5 zw

3

2.5 yw xz

2

Chlorophylla (µg·cmˉ²) 1.5

1

0.5

0 400 µmol quanta·mˉ²·sˉ¹ 800 µmol quanta·mˉ²·sˉ¹

Figure 4.3: Areal densities of chlorophyll a. Depicted are the areal chlorophyll a densities in the four combinations of light and temperature pooled across both levels of pCO2 (as acidification level had no significant effect on this parameter). The light levels are the daily maxima (the light intensity between 10 am and 12 noon). Treatments labelled with the same letter are significantly different from one another (p < 0.05) by Tukey’s post hoc test of the interactive effect of light and temperature. The two leftmost treatments (25°C and 29°C, at low light) were marginally not significantly different from one another (Tukey's test, p = 0.056). Error bars are standard error of the mean.

131 (a) 0.8 25°C xy 0.7 29°C

0.6 x y 0.5 ₂ 0.4

0.3

0.2

0.1 Darkrespiration (µmol O consumed·cmˉ²·hrˉ¹) 0 control pCO₂ elevated pCO₂

(b) 1.4 25°C xw 29°C 1.2

1.0 zy

0.8 xy

₂ zw 0.6

0.4

0.2

0.0 Darkrespiration (pmol O consumed·symbiontˉ¹·hrˉ¹) control pCO₂ elevated pCO₂

Figure 4.4: Dark respiration.

2 (a) DR per cm per hour in P. acuta exposed to four combinations of pCO2 and temperature, averaged across both levels of light. Treatments labelled with the same letter are significantly different (p < 0.05) in the post hoc test of the interactive effect of temperature and pCO2. The difference between elevated and control acidification at 29°C was marginally non-significant (Tukey's test, p = 0.052). Error bars (in both panel (a) and (b)) are standard error of the mean.

(b) DR per symbiont cell per hour in P. acuta in the four combinations of pCO2 and temperature, averaged across both levels of light. Treatments labelled with the same letter are significantly different (p < 0.05) in the post hoc test of the interactive effect of temperature and pCO2.

132 (a) 25°C z 1.6 29°C 1.4

₂ 1.2 z 1

0.8

0.6

0.4

0.2 Gross photosynthesis (µmol O (µmol photosynthesis Gross ·cmˉ²·hrˉ¹) 0 400 µmol quanta·mˉ²·sˉ¹ 800 µmol quanta·mˉ²·sˉ¹ 400 µmol quanta·mˉ²·sˉ¹ 800 µmol quanta·mˉ²·sˉ¹

control pCO₂ elevated pCO₂

(b) 25°C w z 3 29°C

2.5 x y

2

1.5 w y

P/R P/R ratio x z 1

0.5

0 control pCO₂ elevated pCO₂ Figure 4.5: Gross photosynthesis and P/R ratio. gross 2 (a) P per cm per hour in P. acuta exposed to eight combinations of pCO2, light intensity, and temperature. The light levels are the daily maxima in the light treatments (the light intensity between 10am and 12 noon). Treatments labelled with the same letter are significantly different

133 from one another (p < 0.05) in the post hoc test of the interactive effect of light, temperature and pCO2. Error bars are standard error of the mean.

-2 -1 (b) P/R ratio in P. acuta, the ratio of net photosynthesis (in µmol O2·cm ·hr , under the light levels

-2 -1 used in the experiment) to dark respiration (in µmol O2 consumed·cm ·hr ). In this graph the P/R ratio is given for the four combinations of temperature and acidification, averaged over both levels of light, because light level had no significant effect. Treatments labelled with the same letter are significantly different from one another (p < 0.05) in the post hoc test of the interactive effect of light, temperature and pCO2.

134 5 Conclusions

5.1 Overview

The overall aim of this thesis was to address gaps in physiological knowledge of coral responses to abiotic forcing (high light, heat and acidification), guided by information needs in the field of coral bleaching prediction. In the introductory chapter, I reviewed the literature to identify these gaps, and in discussing each outcome of this thesis, I will, where possible, highlight the knowledge gap it addresses. I first discuss how the coral responses to light change identified in Chapters 2 and 3 suggest modifications to the design of the Light Stress Damage (LSD) bleaching prediction algorithm. I then identify how changes in host protein and mortality at high temperature, identified in Chapter 3, have implications for the prediction of the impact of coral bleaching on the host. I discuss the possible mechanisms underlying the biological basis of observed species differences in responses to thermal and light stress. I use this as a platform to discuss prediction of bleaching and the likelihood of mortality at the level of entire coral reef communities. I discuss how the identification of bleaching at very high ocean acidification thresholds (Chapter 4) informs long-term and near-future prediction of coral bleaching. The implications of physiological responses under ocean acidification that do not manifest as coral bleaching are discussed. I penultimately discuss several methodological advances in experimental coral physiology that have been established in this thesis. The new conceptual aspects of bleaching prediction identified by this thesis, along with recent bleaching survey data of the Great Barrier Reef 2016 bleaching event, are likely to be sufficient to guide the development of a new bleaching prediction method.

5.2 Refinement of the Light Stress Damage algorithm.

The LSD algorithm for the prediction of coral bleaching represents a major advance on earlier methods, by utilising inputs of both temperature and light. The LSD algorithm uses the following equation to calculate a quantity representing the amount of light change-induced stress experienced by corals (excess excitation energy: EEE) due to day-to-day changes in light intensity of all days leading up to, and including, the present day (Skirving et al. 2018):

EEEi = PARi – acclim PARi-1 (5.1)

where acclim PARi = acclim PARi-1 + ɛ(PARi – acclim PARi-1) (5.2)

135 where EEEi is the stress on the present day i, PARi is the photosynthetically active radiation measured on day i, and ɛ is the photoacclimation rate. This duo of equations is recursive: EEEi is dependent on the acclim PAR of the previous day (which is in turn dependent on the acclim PAR of the day before that) and so by extrapolation, the equation for EEEi relies on measurements of PAR on every day back to day zero. Day zero is the winter solstice and so the equation for EEEi is reset every year. In the current LSD algorithm, the photoacclimation rate ɛ is set to one value (Anthony and Hoegh-Guldberg 2003a), a rate (0.13) derived from the average over conditions of both increase and decrease in light level, of different magnitudes (Skirving et al. 2018).

This equation of the LSD algorithm is informed by the results of this study in two ways: determination of whether ɛ should vary, and determination of whether decreases in light (not just increases) are also detrimental. In the literature review (Chapter 1), I identified that the rate of photoacclimation might be affected by the direction of light change (either up or down), the magnitude of the light change (the difference between new and original light level) and the temperature of the water. I designed an experiment (Chapter 2) to explore if the rate of photoacclimation is influenced by one or more of these three factors by exposing A. muricata and mounding Porites spp. to three light change conditions (one decrease, one small increase, and one large increase), at two temperatures (one below and one above the maximum monthly mean).

This experiment identified (1) that photoacclimation rates were often different when measured with

net P vs Fv/Fm; (2) that photoacclimation of Fv/Fm proceeded most rapidly at 24°C vs 29°C in mounding Porites spp., (3) that photoacclimation of Pnet was more rapid under light decrease vs light increases in mounding Porites. Overall, the experiment demonstrated that photoacclimation rate, ɛ, should vary with either the water temperature or the direction of light change, depending on the physiological parameter in question. Therefore, the choice of physiological parameter used to measure ɛ for use in the LSD algorithm will have a great bearing on how that photoacclimation rate is implemented in the algorithm. The thresholds of modelled Fv/Fm that trigger bleaching in the algorithm may need to be adjusted, depending on the source of the photoacclimation rate. This finding is of immediate relevance to the LSD algorithm, given that in its current operational form (NOAA Coral Reef Watch 2017c), a photoacclimation rate that was measured using Pnet is used (Skirving et al. 2018).

This study will inform this algorithm through identifying that decreases in light level may be as detrimental as increases in light level, for some species. If this is the case, then the form of the equation (comprised of (5.1) and (5.2)) would change fundamentally. Evidence from Chapter 3

136 suggested that at 31°C (an anomaly of 3.88°C above MMM), symbiont densities in A. muricata declined substantially when there was an alteration in light level (both an increase or a decrease) but that decline was far more muted when there was no alteration in light level. Whilst this effect was not observed for symbiont densities in mounding Porites spp., a related effect (decreased host protein per cm2 when light increased or decreased at elevated temperature) was observed in this taxon. The current form of the equation, by tracking the polarity of light change (i.e. + versus –), forces EEE to decrease when light decreases and to increase when light increases. Whilst this approach is concurrent with the definition of EEE, my results suggest that EEE, as defined in this manner, is not an appropriate proxy for light stress in corals. In other words, light decrease, in addition to light increase, experimentally exacerbates thermal bleaching in A. muricata and possibly M. monasteriata, and thermally induced host protein loss in mounding Porites spp., whereas, in the EEE concept, a light decrease should (at least partly) alleviate these forms of thermally induced stress. A new equation (replacing (5.1) and (5.2)) to specify how light change affects thermally induced stress may be required based on this experimental evidence.

5.3 Predicting coral mortality following coral bleaching

Coral bleaching onset is well predicted by temperature-only bleaching prediction algorithms, but other impacts of coral bleaching are not, particularly bleaching severity or mortality (McClanahan et al. 2007c; Eakin et al. 2010a). In Chapter 3 of this study, I investigated whether coral mortality following bleaching could be predicted by the amount of host tissue (measured as water-soluble protein) and/or the amount of symbiont cells that were lost during bleaching. The three species studied displayed a variety of patterns of how host tissue and symbiont cell loss did or did not reflect mortality patterns. In A. muricata, symbiont density was not affected by light level (with statistical significance), nor was host tissue quantity or mortality. Symbiont levels declined by 83% (between 29°C and 31°C) whilst host tissue levels declined by a smaller fraction (25%, not statistically significant), and substantial mortality (67%) was seen at 31°C. Thus, the severity of symbiont loss was matched by a high degree of mortality but was accompanied by a less-than- commensurate loss of host tissue.

In mounding Porites spp., bleaching and host tissue loss were almost commensurate: symbiont loss at 31°C (compared to 29°C) was 43%, with 30% loss of protein, but no mortality was seen. In M. monasteriata, symbiont densities decreased (by 33%) between 27°C and 29°C, whilst host protein increased (by 38%) between these temperatures, and there was no substantial mortality at 29°C.

137 This suggests that bleaching at 29°C did not have long-term consequences for the coral host and that subsequent mortality at this temperature may have been low. Protein levels decreased from 29°C to 31°C (by 27%) at the same time that mortality of 23% occurred, whilst no significant changes in cell densities occurred at 31°C, and thus mortality and protein loss were correlated.

These patterns may illustrate different mechanisms of symbiont cell loss among the three species. In M. monasteriata, the mechanisms of symbiont loss probably do not involve the destruction of the host tissue holding the symbionts, whilst in mounding Porites spp., loss of symbionts via the destruction or loss of host tissue may occur, and in A. muricata both modes may occur with the former mode dominating. Mortality was seen following severe symbiont loss at 31°C in A. muricata. Some mortality accompanied moderate tissue loss at 31°C in M. monasteriata, with symbiont loss at 29°C but no additional symbiont loss at 31°C. Therefore, my results suggest that there is no single, universally common, pattern of symbiont loss or host cell loss that can be used to indicate near future coral mortality following coral bleaching. However, these results are potentially useful for identifying groups of species with shared responses that may be of use in predicting mortality patterns over whole coral communities (further discussed in section 5.5).

5.4 Biological differences among holobionts

Synthesising the results of Chapters 2 and 3, I am able to identify another suite of differences in species responses for which the biological causes can be identified. These differences may best be thought of as differences among holobionts, as whilst most of the data to follow deals with responses of Symbiodinium, several studies have identified that the coral host plays a role in determining symbiont responses (Fitt et al. 2009; Fisher et al. 2012). The A. muricata holobiont displayed evidence of light limitation, including increases in both α (the slope of the light-limited part of an ETR vs irradiance curve) and areal net photosynthesis through time at all light levels (Chapter 2), and no bleaching occurred in response to increases in light level alone (Chapter 3). These traits contrast to mounding Porites spp. and M. monasteriata, which displayed evidence of photoinhibition and bleaching as light level increased. Both holobionts displayed relatively large reductions in α with increases in light level compared to A. muricata (Chapter 2 and Chapter 3). Mounding Porites spp. displayed photoinhibition of areal net photosynthesis at high light and temperature (Chapter 2), bleaching in response to increases in light level (Chapter 3), and increases in photon pressure per symbiont cell in response to increases in light that were an order of

138 magnitude larger than that seen in A. muricata (Chapter 2). M. monasteriata displayed bleaching in response to high light level alone (Chapter 3).

The comparative levels of maximum excitation pressure over PSII (Qm) in the three species help explain the biological basis behind this suite of responses in A. muricata versus mounding Porites spp. and M. monasteriata. Very low levels of Qm in A. muricata under a range of light and temperature combinations suggest the ability for either NPQ or photochemical quenching or both to be induced rapidly in response to antennae excitation in the A. muricata holobiont, resulting in an open electron transport chain under most light and temperature levels. In contrast the higher Qm levels in the mounding Porites spp. and M. monasteriata holobionts suggest that quenching through NPQ, photochemical quenching or both is less rapidly induced in response to antennae excitation, resulting in saturation of the electron transport chain under higher light levels, and leading to photoinhibition and possibly bleaching at high light. This mechanism could illustrate why the overall level of NPQ, measured through SVN, appeared to be much higher in M. monasteriata and mounding Porites spp. compared to A. muricata (Chapter 3): in the former species, it was required as a protective mechanism due to the higher risk of photoinhibition (though the rapidity with which additional NPQ is induced when needed could be lower in these species compared to A. muricata). However, these results contrast with the relatively rapid onset of mortality in A. muricata at high temperatures that was observed in this study (Chapter 3) and others (Baird and Marshall 2002), in comparison to other species. Theoretically, the lower risk of electron transport chain over-reduction signified by the low Qm in A. muricata would result in a reduced risk of photoinhibition and subsequent bleaching (Hoegh-Guldberg 1999; Iglesias-Prieto et al. 2004). This result corroborates other evidence (from Chapter 2) that thermally induced bleaching in A. muricata may not primarily originate via photoinhibition, and highlights that there is still much to learn about species differences in bleaching mechanisms.

5.5 Predicting bleaching of whole coral communities

Temperature-only bleaching indices provide a univariate prediction of the onset of bleaching at one entire location (usually a “pixel” that corresponds to a square in 5 × 5 km2 grid, containing a whole or part of a reef). However, prediction of the range of bleaching responses that may be seen in the coral communities at a location is valuable, enabling more accurate prediction of the percent of coral cover bleached. Chapter 3 indicated that this might be achievable through grouping coral species together into groups that share similar responses to particular conditions of light change,

139 temperature rise, or both. For instance, mounding Porites spp., M. monasteriata, and P. acuta all bleached in response to increasing light (at a range of temperatures), whilst A. muricata did not bleach in response to increasing light (except at very high temperature). A different set of responses was identified in the analysis of impacts of thermal stress on coral mortality (discussed in section 5.3).

A broad physiological survey of responses (bleaching onset and mortality) to different light and temperature combinations may enable most coral species to be assigned to one of a smaller number of groups, within which responses may be near-identical (section 5.9). The use of these groups would then enable the proportions of coral communities that bleach or die in response to particular light and temperature conditions to be predicted. In Chapter 3, I demonstrated how knowledge of responses of mounding Porites, Montipora and Acropora could be applied to predict the percent of bleaching in Paluma Shoals, Far North Queensland.

5.6 Bleaching under acidification

The question of whether acidification causes coral bleaching has been debated for some time, due to conflicting results of experiments designed to address this question (Anthony et al. 2008; Wall et al. 2014). In Chapter 4, a literature compilation identified that ocean acidification causes a statistically significant change in symbiont populations in 26% of experimental exposures in which symbiont densities were measured. In every case, the change was a decline in symbiont population size. Further analysis of the literature data helped to resolve the issue of why a quarter of published exposures found a decrease and three-quarters found no change. Exposures that induced a decline had used a pCO2 level (specifically, the difference between the ocean acidification treatment and control) that was, on average, 135% higher than in the studies that found no change.

This finding has clear ramifications for the use of DHW and DHM for the long-term prediction of coral bleaching many decades into the future. Some modelling studies have anticipated that ocean acidification will be proven to reduce the thermal thresholds for coral bleaching, and have incorporated such reductions into bleaching predictions under future acidification and warming scenarios at the end of this century (Kwiatkowski et al. 2015). My literature compilation suggests that, for most species, such reductions in bleaching thresholds will not be statistically detectable under the range of pCO2 increases expected this century. However, there are exceptions – M. monasteriata was found to bleach at ~600 μatm of pCO2, a level anticipated for this century

140 (Schoepf et al. 2015). Furthermore, the restricted sample sizes involved in aquaria studies may not detect mild effects of ocean acidification on symbiont densities, and hence the presence or absence of such an effect during this century is not completely settled. The literature compilation does suggest that thermal bleaching thresholds could be lowered by high levels of ocean acidification (circa 1800 μatm above present day, a level that could be neared by the year 2300 under RCP 8.5: Hartin et al. 2016). Thus, studies that perform bleaching predictions for following centuries or at extreme ocean acidification scenarios may need to adjust the thermal thresholds used in DHM and DHW to account for the effect of acidification on bleaching.

The interactive effect of ocean acidification and light on coral calcification has been debated for some time and demonstrated in some circumstances (Dufault et al. 2013; Vogel et al. 2015). Acidification-induced declines in calcification under reduced light have been demonstrated, and may occur due to reduced photosynthesis and therefore due to the reduced photosynthetic enhancement of calcification (Suggett et al. 2013). Due to the link between photosynthesis and coral bleaching, it is reasonable to hypothesise that bleaching under ocean acidification may also be modulated by light level.

In my factorial experiment on P. acuta involving two levels each of light, temperature and acidification, I observed no interactive effect of light and acidification or temperature and acidification on symbiont density loss. The literature compilation findings suggest that the level of acidification I used was too low to induce statistically significant symbiont loss by itself. Nevertheless, interactions between two factors, in the absence of a main effect of one of the factors, are possible in experimental studies (Pitz 2008). Therefore, I am unable to say whether interactive effects between temperature and acidification or light and acidification on areal symbiont densities were not observed because the ocean acidification level was too low, or because such interactions simply do not exist for P. acuta. The impacts of light on coral responses to acidification were not investigated in my literature compilation, as light level was rarely used as an independent variable in the studies I reviewed. However, a range of photoacclimation strategies are known amongst Symbiodinium strains (Chang et al. 1983; Iglesias-Prieto and Trench 1994) and among coral holobionts (Chapter 2). The impact of a holobiont’s response to acidification on its photoacclimation strategy, and vice versa, could be a fertile ground for exploration.

In addition to any effective lowering of the thermal bleaching threshold, ocean acidification could impact on coral bleaching by reducing the capacity for coral reefs to recover following a bleaching event. Mortality is followed by macroalgal-fouling of the skeletal framework of dead corals, and so

141 reef recovery involves the formation of a new layer of skeleton over this structure by surviving coral tissue (Diaz-Pulido et al. 2009) or new recruits. Clearly, the impairment of coral growth due to acidification will reduce the rate of regrowth of a reef. By extension, any enhancement or suppression of the impact of acidification on coral skeletogenesis by extremes of light or temperature will be of importance to reef recovery following coral bleaching. Fortunately, some species, including the P. acuta genotypes studied in this thesis, have a rate of skeletogenesis that is apparently resistant to decline under moderate ocean acidification (Wall et al. 2017).

5.7 Other acidification stress

This study also identified effects of ocean acidification on holobiont respiration. In Chapter 4, respiration in P. acuta increased with an increase in temperature from 24.5°C to 29°C, as expected due to the tight coupling between temperature and metabolic rate observed throughout the kingdoms of life (Gillooly et al. 2001). However, ocean acidification exacerbated the increase in respiration rate seen with increased temperature. This could be because, under ocean acidification, there were either (a) more photosynthetic products to burn, or (b) greater costs involved in cellular homeostasis (e.g. in maintaining rates of calcification).

An increased respiration rate may have multiple consequences for coral health. The combined respiration rate of the host and symbiont cells wields a prominent influence on the O2 saturation state of the tissues. During the night-time hours, an initially high rate of respiration may deplete the oxygen content of the coral tissue layer near the calicoblastic epithelium (Kühl et al. 1995), causing metabolism in this layer to switch to the anaerobic mode that has a low yield of ATP (Livingstone 1991; Murphy and Richmond 2016). This could have unforeseen effects on coral physiology, as many coral physiological processes that depend on ATP have a diel, “circadian”-like cycle (e.g. nighttime linear extension) (Barnes and Crossland 1980; Gladfelter 1983; Levy et al. 2011).

By this century’s close, ocean acidification will be a chronic phenomenon (constantly there), as will a year-round elevation in temperature by several degrees (Hoegh-Guldberg et al. 2007). Therefore, physiological changes such as an increased respiration rate in P. acuta may become chronic stressors with long-term detrimental effects (which may not be observed within a typical aquaria- experiment timeframe). Several observations of tissue sloughing (without bleaching) in P. acuta were made at high temperature and high acidification, perhaps hinting at a higher long-term rate of mortality under these conditions.

142 5.8 Methodological advances in experimental coral physiology

In Chapter 3, I introduced an approach that allows two-factor experiments with large numbers of treatments to be performed, where the water temperature is one of the two factors. Experimental corals were slowly raised from the control temperature to higher temperature levels, and as four preselected temperatures were reached, one quarter of the experimental corals were moved to a new set of aquaria, maintained at the temperature they had just reached, and split among five light treatments. At the same time, control corals, continually maintained at 22°C, were also exposed to the same five light treatments. This experimental approach enabled 20 treatments (each a combination of one light and one temperature level) to be administered. Heating rate appears to influence the physiological response of corals to a given temperature anomaly (Middlebrook et al. 2010). The approach just described has the advantage of replicating a close-to-natural rate of heating, which is otherwise hard to replicate in aquaria experiments due time constraints.

In Chapter 4, I utilised an experimental design for the analysis of the combined impacts of light, ocean acidification and temperature on corals that facilitates medium-term changes to ocean acidification stress to occur before heat stress is applied. Corals were exposed to full ocean acidification and light conditions for a period of 32 days, before heat stress was applied in the final seven days. I used this experimental design due to the rationale that, under the “CO2 fertilisation” hypothesis of acidification-enhancement of thermal bleaching, an increase in the size of the symbiont populations under acidification is theorised to lead to resource limitation and exacerbated levels of bleaching when high temperatures occur (Wooldridge 2009). As the fertilisation effect of acidification may take some time to manifest, the application of heat stress for the entire time that corals are exposed to acidification could obscure such an effect, by continually exerting a strong downward pressure on symbiont population size. To my knowledge, Chapter 4 is the first study of the combined impacts of acidification and heat stress on symbiont populations in corals that has staged the introduction of the two factors with the above rationale in mind (Reynaud et al. 2003; Anthony et al. 2008; Anlauf et al. 2011; Ogawa et al. 2013; Schoepf et al. 2013; Wall et al. 2014; Hoadley et al. 2016; Baghdasarian et al. 2017), although one investigation of bleaching in corals via chlorophyll loss has used a staged introduction of acidification (21 days) then heat stress (Noonan and Fabricius 2016). Whilst the “CO2 fertilisation” hypothesis was not supported by my experimental results, the finding that symbiont loss is induced by high acidification levels suggests that this experimental design will be relevant for the mechanistic investigation of symbiont loss under such conditions.

143 In Chapter 3, a caveat relating to the measurement of Qm (the maximum excitation pressure over

PSII) and SVN (the Stern-Volmer quenching parameter for NPQ) was identified. Negative values of

Qm were recorded, which in theory should not occur because Qm is an indication of the proportion of reaction centres that remain open (ie. those for which the electron transport chain is not fully reduced) under daily maximum light (Iglesias-Prieto et al. 2004). Negative values of SVN were also recorded, which also should not occur as it would indicate that daytime NPQ has a negative rate, or that NPQ at night is greater than NPQ during the day, both physiological impossibilities. For both

Qm and SVN, negative values were caused by maximal fluorescence at midday (Fmʹ) exceeding that in the evening (Fm). This could come about through the redox reduction of the plastoquinone pool during the evening by chlororespiration (Middlebrook et al. 2010). Alternatively, Fm could have been driven lower than Fm’ through a reduction in chlorophyll a density per unit area between early afternoon and evening of the same day. This is possible in situations where a coral is going through a physiological challenge (Dove et al. 2006), such as in most of the treatments used in Chapter 3. Therefore, when measured under conditions in which corals are not fully acclimated to their environment, values of Qm and SVN may require careful interpretation.

5.9 Concluding remarks

Advances in the design of bleaching prediction methods may be achievable by combining the results of this study with survey data of the 2016 Great Barrier Reef bleaching event. During this event, detailed, taxonomically-resolved surveys of bleaching and mortality were made (Hughes et al. 2017b). Corresponding time-series of the sea-surface temperatures and sea-surface light levels along the GBR before and during bleaching were acquired through NOAA satellites (Hedley et al. 2016). This study has identified new aspects of the form of the algorithm required to predict bleaching using both change in light level and water temperature (section 5.2). Furthermore, this study has identified specific modes of physiological response (e.g. no bleaching under elevated light alone in A. muricata) among the three coral species studied at Heron Island, suggesting that several different but widely-applicable responses to light change alone, temperature change alone, and light and temperature change together exist among scleractinian corals at Heron Island. These insights can now be applied to analyse the Great Barrier Reef bleaching survey data, together with the corresponding temperature and light satellite time-series, to develop a bleaching and mortality prediction algorithm (based on a modified Light Stress Damage algorithm) for groups of scleractinian species that share similar bleaching and mortality responses on the Great Barrier Reef. The development of a new bleaching prediction algorithm informed by the outcomes of this thesis may thus be achievable using data and insights that are already available.

144 6 References

Achituv Y, Dubinsky Z (1990) Evolution and zoogeography of coral reefs. In: Dubinsky Z. (eds) Ecosystems of the World 25: Coral Reefs. Elsevier, pp 1–9

Ainsworth TD, Heron SF, Ortiz JC, Mumby PJ, Grech A, Ogawa D, Eakin CM, Leggat W (2016) Climate change disables coral bleaching protection on the Great Barrier Reef. Science 352:338–342

Ainsworth TD, Hoegh-Guldberg O, Heron SF, Skirving WJ, Leggat W (2008) Early cellular changes are indicators of pre-bleaching thermal stress in the coral host. J Exp Mar Biol Ecol 364:63–71

Allemand D, Ferrier-Pagès C, Furla P, Houlbrèque F, Puverel S, Reynaud S, Tambutté É, Tambutté S, Zoccola D (2004) Biomineralisation in reef-building corals: from molecular mechanisms to environmental control. Comptes Rendus Palevol 3:453–467

Andersson AJ, Yeakel KL, Bates NR, de Putron SJ (2014) Partial offsets in ocean acidification from changing coral reef biogeochemistry. Nat Clim Change 4:56–61

Anlauf H, D’Croz L, O’Dea A (2011) A corrosive concoction: The combined effects of ocean warming and acidification on the early growth of a stony coral are multiplicative. J Exp Mar Biol Ecol 397:13–20

Anthony KRN, Hoegh-Guldberg O (2003a) Kinetics of photoacclimation in corals. Oecologia 134:23–31

Anthony KRN, Hoegh-Guldberg O (2003b) Variation in Coral Photosynthesis, Respiration and Growth Characteristics in Contrasting Light Microhabitats: An Analogue to Plants in Forest Gaps and Understoreys? Funct Ecol 17:246–259

Anthony KRN, Kline DI, Diaz-Pulido G, Dove S, Hoegh-Guldberg O (2008) Ocean acidification causes bleaching and productivity loss in coral reef builders. Proc Natl Acad Sci U S A 105:17442–17446

Australian Institute of Marine Science (2017) Water Temperature at 1.7 metre Heron Island Relay Pole. Accessed 9 August 2017. http://data.aims.gov.au/aimsrtds/yearlytrends.xhtml

Baghdasarian G, Osberg A, Mihora D, Putnam H, Gates RD, Edmunds PJ (2017) Effects of Temperature and pCO2 on Population Regulation of Symbiodinium spp. in a Tropical Reef Coral. Biol Bull 232:123–139

Bahr KD, Jokiel PL, Rodgers KS (2016) Relative sensitivity of five Hawaiian coral species to high temperature under high-pCO2 conditions. Coral Reefs 35:729–738

Baird AH, Marshall PA (2002) Mortality, growth and reproduction in scleractinian corals following bleaching on the Great Barrier Reef. Mar Ecol Prog Ser 237:133–141

Baird ME, Timko PG, Wu L (2007) The effect of packaging of chlorophyll within phytoplankton and light scattering in a coupled physical–biological ocean model. Mar Freshw Res 58:966– 981

145 Baker AC (2001) Reef corals bleach to survive change. Nature 411:765–766

Baker AC, Glynn PW, Riegl B (2008) Climate change and coral reef bleaching: An ecological assessment of long-term impacts, recovery trends and future outlook. Estuar Coast Shelf Sci 80:435–471

Baker DM, Freeman CJ, Wong JCY, Fogel ML, Knowlton N (2018) Climate change promotes parasitism in a coral symbiosis. ISME J 12:921–930

Banaszak AT, García Ramos M, Goulet TL (2013) The symbiosis between the gastropod Strombus gigas and the dinoflagellate Symbiodinium: An ontogenic journey from mutualism to parasitism. J Exp Mar Biol Ecol 449:358–365

Banks C (2015) Response of corals to changes in warming or acidification under different light conditions. Honours Thesis, The University of Queensland

Barker S, Higgins JA, Elderfield H (2003) The future of the carbon cycle: review, calcification response, ballast and feedback on atmospheric CO2. Philos Trans R Soc Math Phys Eng Sci 361:1977–1999

Barnes DJ, Crossland CJ (1980) Diurnal and seasonal variations in the growth of a staghorn coral measured by time-lapse photography. Limnol Oceanogr 25:1113–1117

Bathen KH (1968) A Descriptive Study of the Physical Oceanography of Kaneohe Bay, Oahu, Hawai‘i. Hawai‘i Institute of Marine Biology (formerly Hawai‘i Marine Laboratory), Honolulu, HI, USA

Bellantuono AJ, Granados-Cifuentes C, Miller DJ, Hoegh-Guldberg O, Rodriguez-Lanetty M (2012) Coral Thermal Tolerance: Tuning Gene Expression to Resist Thermal Stress. PLoS ONE 7:e50685

Bellwood DR, Hughes TP, Folke C, Nystrom M (2004) Confronting the coral reef crisis. Nature 429:827–833

Berkelmans R (2002) Time-integrated thermal bleaching thresholds of reefs and their variation on the Great Barrier Reef. Mar Ecol Prog Ser 229:73–82

Berkelmans R, Willis BL (1999) Seasonal and local spatial patterns in the upper thermal limits of corals on the inshore central Great Barrier Reef. Coral Reefs 18:

Bessell-Browne P, Negri AP, Fisher R, Clode PL, Duckworth A, Jones R (2017) Impacts of turbidity on corals: The relative importance of light limitation and suspended sediments. Mar Pollut Bull 117:161–170

Bhagooli R (2013) Inhibition of Calvin–Benson cycle suppresses the repair of photosystem II in Symbiodinium: implications for coral bleaching. Hydrobiologia 714:183–190

Bilger W, Björkman O (1990) Role of the xanthophyll cycle in photoprotection elucidated by measurements of light-induced absorbance changes, fluorescence and photosynthesis in leaves of Hedera canariensis. Photosynth Res 25:173–185

Biscéré T, Rodolfo-Metalpa R, Lorrain A, Chauvaud L, Thébault J, Clavier J, Houlbrèque F (2015) Responses of Two Scleractinian Corals to Cobalt Pollution and Ocean Acidification. PLoS ONE 10:e0122898

146 Borell EM, Bischof K (2008) Feeding sustains photosynthetic quantum yield of a scleractinian coral during thermal stress. Oecologia 157:593

Brown BE (2011) Mining/Quarrying of Coral Reefs. In: Hopley D. (eds) Encylopedia of Modern Coral Reefs: Structure, Form and Process. Springer, Netherlands, pp 707–711

Buddemeier RW, Fautin DG (1993) Coral bleaching as an adaptive mechanism: A testable hypothesis. Bioscience 43:320–326

Buxton L, Gutowska MA, Sawney S, Owen R, Trapido-Rosenthal H (2002) Increases in the activity of zooxanthellae nitric acid oxide synthase are associated with the temperature-induced bleaching of the coral Madracis mirabilis (Abstract). Integr Comp Biol 42:1205

Buxton L, Takahashi S, Hill R, Ralph PJ (2012) Variability in the Primary Site of Photosynthetic Damage in Symbiodinium sp. (Dinophyceae) Exposed to Thermal Stress. J Phycol 48:117– 126

Cantin NE, Cohen AL, Karnauskas KB, Tarrant AM, McCorkle DC (2010) Ocean Warming Slows Coral Growth in the Central Red Sea. Science 329:322–325

Caroselli E, Falini G, Goffredo S, Dubinsky Z, Levy O (2015) Negative response of photosynthesis to natural and projected high seawater temperatures estimated by pulse amplitude modulation fluorometry in a temperate coral. Front Physiol 6:317

Carpenter KE, Abrar M, Aeby G, Aronson RB, Banks S, Bruckner A, Chiriboga A, Cortés J, Delbeek JC, DeVantier L, Edgar GJ, Edwards AJ, Fenner D, Guzmán HM, Hoeksema BW, Hodgson G, Johan O, Licuanan WY, Livingstone SR, Lovell ER, Moore JA, Obura DO, Ochavillo D, Polidoro BA, Precht WF, Quibilan MC, Reboton C, Richards ZT, Rogers AD, Sanciangco J, Sheppard A, Sheppard C, Smith J, Stuart S, Turak E, Veron JEN, Wallace C, Weil E, Wood E (2008) One-Third of Reef-Building Corals Face Elevated Extinction Risk from Climate Change and Local Impacts. Science 321:560–563

Cesar HSJ (2000) Coral Reefs: Their Functions, Threats and Economic Value. In: Cesar H.S.J. (eds) Collected essays on the economics of coral reefs. Coastal Oceans Research and Development in the Indian Ocean, Kenya, pp 14–39

Chalker B (1981) Simulating light-saturation curves for photosynthesis and calcification by reef- building corals. Mar Biol 63:135–141

Chalker BE, Dunlap WC, Oliver JK (1983) Bathymetric adaptations of reef-building corals at Davies Reef, Great Barrier Reef, Australia. II. Light saturation curves for photosynthesis and respiration. J Exp Mar Biol Ecol 73:37–56

Chalker BE, Taylor DL (1978) Rhythmic variations in calcification and photosynthesis associated with the coral Acropora cervicornis Lamarck. Proc R Soc B Biol Sci 201:179–189

Chang SS, Prézelin BB, Trench RK (1983) Mechanisms of photoadaptation in three strains of the symbiotic dinoflagellate Symbiodinium microadriaticum. Mar Biol 76:219–229

Chisholm JRM, Gattuso J-P (1991) Validation of the alkalinity anomaly technique for investigating calcification and photosynthesis in coral reef communities. Limnol Oceanogr 36:1232–1239

Coles SL, Jokiel PL (1977) Effects of temperature on photosynthesis and respiration in hermatypic corals. Mar Biol 43:209–216

147 Coles SL, Jokiel PL (1978) Synergistic effects of temperature, salinity and light on the hermatypic coral Montipora verrucosa. Mar Biol 49:187–195

Coles SL, Jokiel PL, Lewis CR (1976) Thermal Tolerance in Tropical versus Subtropical Pacific Reef Corals. Pac Sci 30:159–166

Colombo-Pallotta MF, Rodríguez-Román A, Iglesias-Prieto R (2010) Calcification in bleached and unbleached Montastraea faveolata: evaluating the role of oxygen and glycerol. Coral Reefs 29:899–907

Comeau S, Carpenter RC, Edmunds PJ (2017) Effects of pCO2 on photosynthesis and respiration of tropical scleractinian corals and calcified algae. ICES J Mar Sci 74:1092–1102

Comeau S, Carpenter RC, Nojiri Y, Putnam HM, Sakai K, Edmunds PJ (2014a) Pacific-wide contrast highlights resistance of reef calcifiers to ocean acidification. Proc R Soc B Biol Sci 281:20141339–20141339

Comeau S, Edmunds PJ, Spindel NB, Carpenter RC (2013) The responses of eight coral reef calcifiers to increasing partial pressure of CO2 do not exhibit a tipping point. Limnol Oceanogr 58:388–398

Comeau S, Edmunds PJ, Spindel NB, Carpenter RC (2014b) Fast coral reef calcifiers are more sensitive to ocean acidification in short-term laboratory incubations. Limnol Oceanogr 59:1081–1091

Comeau S, Edmunds PJ, Spindel NB, Carpenter RC (2014c) Diel pCO2 oscillations modulate the response of the coral Acropora hyacinthus to ocean acidification. Mar Ecol Prog Ser 501:99–111

Crawley A, Kline DI, Dunn S, Anthony K, Dove S (2010) The effect of ocean acidification on symbiont photorespiration and productivity in Acropora formosa. Glob Change Biol 16:851–863

Csaszar NBM, Ralph PJ, Frankham R, Berkelmans R, van Oppen MJH (2010) Estimating the Potential for Adaptation of Corals to Climate Warming. PLoS ONE 5:e9751

Cunning R, Glynn PW, Baker AC (2013) Flexible associations between Pocillopora corals and Symbiodinium limit utility of symbiosis ecology in defining species. Coral Reefs 32:795– 801

Cunning R, Ritson-Williams R, Gates R (2016) Patterns of bleaching and recovery of Montipora capitata in Kāne‘ohe Bay, Hawai‘i, USA. Mar Ecol Prog Ser 551:131–139

Cvitanovic C, Wilson SK, Fulton CJ, Almany GR, Anderson P, Babcock RC, Ban NC, Beeden RJ, Beger M, Cinner J, Dobbs K, Evans LS, Farnham A, Friedman KJ, Gale K, Gladstone W, Grafton Q, Graham NAJ, Gudge S, Harrison PL, Holmes TH, Johnstone N, Jones GP, Jordan A, Kendrick AJ, Klein CJ, Little LR, Malcolm HA, Morris D, Possingham HP, Prescott J, Pressey RL, Skilleter GA, Simpson C, Waples K, Wilson D, Williamson DH (2013) Critical research needs for managing coral reef marine protected areas: Perspectives of academics and managers. J Environ Manage 114:84–91

Davy SK, Allemand D, Weis VM (2012) Cell Biology of Cnidarian-Dinoflagellate Symbiosis. Microbiol Mol Biol Rev MMBR 76:229–261

148 DeSalvo MK, Sunagawa S, Fisher PL, Voolstra CR, Iglesias-Prieto R, Medina M (2010) Coral host transcriptomic states are correlated with Symbiodinium genotypes. Mol Ecol 19:1174–1186

Desilets DJ, Kissinger PT, Lytle FE (1987) Improved method for determination of Stern-Volmer quenching constants. Anal Chem 59:1244–1246

Diaz-Pulido G, McCook LJ, Dove S, Berkelmans R, Roff G, Kline DI, Weeks S, Evans RD, Williamson DH, Hoegh-Guldberg O (2009) Doom and Boom on a Resilient Reef: Climate Change, Algal Overgrowth and Coral Recovery. PLoS ONE 4:e5239

Dimond J, Carrington E (2008) Symbiosis regulation in a facultatively symbiotic temperate coral: zooxanthellae division and expulsion. Coral Reefs 27:601–604

Dimond JL, Bingham BL, Muller-Parker G, Oakley CA (2013) Symbiont physiology and population dynamics before and during symbiont shifts in a flexible algal-cnidarian symbiosis. J Phycol 49:1074–1083

Dimond JL, Holzman BJ, Bingham BL (2012) Thicker host tissues moderate light stress in a cnidarian endosymbiont. J Exp Biol 215:2247–2254

Donner SD, Knutson TR, Oppenheimer M (2007) Model-based assessment of the role of human- induced climate change in the 2005 Caribbean coral bleaching event. Proc Natl Acad Sci U S A 104:5483–5488

Donner SD, Skirving WJ, Little CM, Oppenheimer M, Hoegh-Guldberg O (2005) Global assessment of coral bleaching and required rates of adaptation under climate change. Glob Change Biol 11:2251–2265

Dove S (2004) Scleractinian corals with photoprotective host pigments are hypersensitive to thermal bleaching. Mar Ecol Prog Ser 272:99–116

Dove S, Ortiz JC, Enríquez S, Fine M, Fisher P, Iglesias-Prieto R, Thornhill D, Hoegh-Guldberg O (2006) Response of holosymbiont pigments from the scleractinian coral Montipora monasteriata to short-term heat stress. Limnol Oceanogr 51:1149–1158

Dove SG, Hoegh-Guldberg O (2006) The Cell Physiology of Coral Bleaching. In: Phinney J.T., Hoegh-Guldberg O., Kleypas J., Skirving W., Strong A. (eds) Coral Reefs and Climate Change: Science and Management. American Geophysical Union, pp 55–71

Dove SG, Lovell C, Fine M, Deckenback J, Hoegh-Guldberg O, Iglesias-Prieto R, Anthony KRN (2008) Host pigments: potential facilitators of photosynthesis in coral symbioses. Plant Cell Environ 31:1523–1533

Downs CA, Kramarsky-Winter E, Martinez J, Kushmaro A, Woodley CM, Loya Y, Ostrander GK (2009) Symbiophagy as a cellular mechanism for coral bleaching. Autophagy 5:211–216

Dufault AM, Cumbo VR, Fan T-Y, Edmunds PJ (2012) Effects of diurnally oscillating pCO2 on the calcification and survival of coral recruits. Proc R Soc B Biol Sci 279:2951–2958

Dufault AM, Ninokawa A, Bramanti L, Cumbo VR, Fan T-Y, Edmunds PJ (2013) The role of light in mediating the effects of ocean acidification on coral calcification. J Exp Biol 216:1570– 1577

149 Dunn SR, Bythell JC, Le Tissier MDA, Burnett WJ, Thomason JC (2002) Programmed cell death and cell necrosis activity during hyperthermic stress-induced bleaching of the symbiotic sea anemone Aiptasia sp. J Exp Mar Biol Ecol 272:29–53

Dunn SR, Pernice M, Green K, Hoegh-Guldberg O, Dove SG (2012) Thermal Stress Promotes Host Mitochondrial Degradation in Symbiotic Cnidarians: Are the Batteries of the Reef Going to Run Out? PLoS ONE 7:e39024

Dunn SR, Thomason JC, Le Tissier MDA, Bythell JC (2004) Heat stress induces different forms of cell death in sea anemones and their endosymbiotic algae depending on temperature and duration. Cell Death Differ 11:1213–1222

Dunne RP, Brown BE (2001) The influence of solar radiation on bleaching of shallow water reef corals in the Andaman Sea, 1993-1998. Coral Reefs 20:201–210

Duyens LNM (1956) The flattering of the absorption spectrum of suspensions, as compared to that of solutions. Biochim Biophys Acta 19:1–12

Eakin CM, Morgan JA, Heron SF, Smith TB, Liu G, Alvarez-Filip L, Baca B, Bartels E, Bastidas C, Bouchon C, Brandt M, Bruckner AW, Bunkley-Williams L, Cameron A, Causey BD, Chiappone M, Christensen TRL, Crabbe MJC, Day O, Guardia E de la, Díaz-Pulido G, DiResta D, Gil-Agudelo DL, Gilliam DS, Ginsburg RN, Gore S, Guzmán HM, Hendee JC, Hernández-Delgado EA, Husain E, Jeffrey CFG, Jones RJ, Jordán-Dahlgren E, Kaufman LS, Kline DI, Kramer PA, Lang JC, Lirman D, Mallela J, Manfrino C, Maréchal J-P, Marks K, Mihaly J, Miller WJ, Mueller EM, Muller EM, Toro CAO, Oxenford HA, Ponce-Taylor D, Quinn N, Ritchie KB, Rodríguez S, Ramírez AR, Romano S, Samhouri JF, Sánchez JA, Schmahl GP, Shank BV, Skirving WJ, Steiner SCC, Villamizar E, Walsh SM, Walter C, Weil E, Williams EH, Roberson KW, Yusuf Y (2010a) Caribbean Corals in Crisis: Record Thermal Stress, Bleaching, and Mortality in 2005. PLoS ONE 5:e13969

Eakin CM, Nim CJ, Brainard RE, Aubrecht C, Elvidge C, Gledhill DK, Muller-Karger F, Mumby PJ, Skirving WJ, Strong AE, Wang MH, Weeks S, Wentz F, Ziskin D (2010b) Monitoring Coral Reefs from Space. Oceanography 23:118–133

Edmunds P (2012) Effect of pCO2 on the growth, respiration, and photophysiology of massive Porites spp. in Moorea, French Polynesia. Mar Biol 159:2149–2160

Edmunds PJ (1994) Evidence that reef-wide patterns of coral bleaching may be the result of the distribution of bleaching-susceptible clones. Mar Biol 121:137–142

Edmunds PJ, Davies PS (1988) Post-illumination stimulation of respiration rate in the coral Porites porites. Coral Reefs 7:7–9

Enochs IC, Manzello DP, Carlton R, Schopmeyer S, Hooidonk R, Lirman D (2014) Effects of light and elevated pCO2 on the growth and photochemical efficiency of Acropora cervicornis. Coral Reefs 33:477–485

Enríquez S, Méndez ER, Iglesias-Prieto R (2005) Multiple scattering on coral skeletons enhances light absorption by symbiotic algae. Limnol Oceanogr 50:1025–1032

Erez J (1978) Vital effect on stable-isotope composition seen in foraminifera and coral skeletons. Nature 273:199–202

150 Escobal PR (1996) Aquatic Systems Engineering: Devices and How they Function. Dimension Engineering Press, Oxnard, California

Escoubas JM, Lomas M, LaRoche J, Falkowski PG (1995) Light intensity regulation of cab gene transcription is signaled by the redox state of the plastoquinone pool. Proc Natl Acad Sci 92:10237–10241

Fabricius KE, De’ath G (2004) Identifying ecological change and its causes: a case study on coral reefs. Ecol Appl 14:1448–1465

Fabricius KE, Langdon C, Uthicke S, Humphrey C, Noonan S, De’ath G, Okazaki R, Muehllehner N, Glas MS, Lough JM (2011) Losers and winners in coral reefs acclimatized to elevated carbon dioxide concentrations. Nat Clim Change 1:165–169

Fabricius KE, Wolanski E (2000) Rapid Smothering of Coral Reef Organisms by Muddy Marine Snow. Estuar Coast Shelf Sci 50:115–120

Fagan KE, Mackenzie FT (2007) Air–sea CO2 exchange in a subtropical estuarine-coral reef system, Kaneohe Bay, Oahu, Hawaii. Mar Chem 106:174–191

Fagoonee I, Wilson HB, Hassell MP, Turner JR (1999) The Dynamics of Zooxanthellae Populations: A Long-Term Study in the Field. Science 283:843–845

Falkowski PG, Dubinsky Z (1981) Light-shade adaptation of Stylophora pistillata, a hermatypic coral from the Gulf of Eilat. Nature 289:172–174

Falkowski PG, Owens TG (1980) Light—Shade Adaptation: Two Strategies in Marine Phytoplankton. Plant Physiol 66:592–595

Falkowski PG, Raven JA (2007) Aquatic photosynthesis. Princeton University Press, Princeton, USA, and Oxford, UK

Falter JL, Lowe RJ, Atkinson MJ, Monismith SG, Schar DW (2008) Continuous measurements of net production over a shallow reef community using a modified Eulerian approach. J Geophys Res 113:

Falter JL, Zhang Z, Lowe RJ, McGregor F, Keesing J, McCulloch MT (2014) Assessing the drivers of spatial variation in thermal forcing across a nearshore reef system and implications for coral bleaching. Limnol Oceanogr 59:1241–1255

Ferrier-Pages C, Richard C, Forcioli D, Allemand D, Pichon M, Shick JM (2007) Effects of temperature and UV radiation increases on the photosynthetic efficiency in four scleractinian coral species. Biol Bull 213:76–87

Fischer E, Jones G (2012) Atmospheric dimethylsulphide production from corals in the Great Barrier Reef and links to solar radiation, climate and coral bleaching. Biogeochemistry 110:31–46

Fisher PL, Malme MK, Dove S (2012) The effect of temperature stress on coral–Symbiodinium associations containing distinct symbiont types. Coral Reefs 31:473–485

Fitt WK, Gates RD, Hoegh-Guldberg O, Bythell JC, Jatkar A, Grottoli AG, Gomez M, Fisher P, Lajuenesse TC, Pantos O, Iglesias-Prieto R, Franklin DJ, Rodrigues LJ, Torregiani JM, van Woesik R, Lesser MP (2009) Response of two species of Indo-Pacific corals, Porites

151 cylindrica and Stylophora pistillata, to short-term thermal stress: The host does matter in determining the tolerance of corals to bleaching. J Exp Mar Biol Ecol 373:102–110

Fitt WK, McFarland FK, Warner ME, Chilcoat GC (2000) Seasonal patterns of tissue biomass and densities of symbiotic dinoflagellates in reef corals and relation to coral bleaching. Limnol Oceanogr 45:677–685

Forsman ZH, Barshis DJ, Hunter CL, Toonen RJ (2009) Shape-shifting corals: molecular markers show morphology is evolutionarily plastic in Porites. BMC Evol Biol 9:45

Foster K (2005) Effects of Reduced Light and Elevated Temperature on the Zooxanthellae Concentrations and Diameters, Pigment Concentrations, and Colony Color of Montastrea cavernosa. MSc thesis, Nova Southeastern University

Frade PR, Englebert N, Faria J, Visser PM, Bak RPM (2008) Distribution and photobiology of Symbiodinium types in different light environments for three colour morphs of the coral Madracis pharensis: is there more to it than total irradiance? Coral Reefs 27:913–925

Furla P, Galgani I, Durand I, Allemand D (2000) Sources and mechanisms of inorganic carbon transport for coral calcification and photosynthesis. J Exp Biol 203:3445–3457

Garde LA, Spillman CM, Heron SF, Beeden RJ (2014) Reef Temp Next Generation: A new operational system for monitoring reef thermal stress. J Oper Oceanogr 7:21–33

Gates RD, Baghdasarian G, Muscatine L (1992) Temperature Stress Causes Host Cell Detachment in Symbiotic Cnidarians: Implications for Coral Bleaching. Biol Bull 182:324–332

Gattuso J-P, Jaubert J (1984) Premières données concernant l’action de la lumière sur le métabolisme, la croissance et la calcification in situ du Sclèractinaire hermatypique Stylophora pistillata. Comptes Rendus Académie Sci Paris 299:585–590

Gattuso JP, Jaubert J (1990) Effect of Light on Oxygen and Carbon Dioxide Fluxes and on Metabolic Quotients Measured in-Situ in a Zooxanthellate Coral. Limnol Oceanogr 35:1796–1804

Geel C, Versluis W, Snel JFH (1997) Estimation of oxygen evolution by marine phytoplankton from measurement of the efficiency of Photosystem II electron flow. Photosynth Res 51:61– 70

Gierz SL, Gordon BR, Leggat W (2016) Integral Light-Harvesting Complex Expression In Symbiodinium Within The Coral Acropora aspera Under Thermal Stress. Sci Rep 6:25081

Gillooly JF, Brown JH, West GB, Savage VM, Charnov EL (2001) Effects of Size and Temperature on Metabolic Rate. Science 293:2248–2251

Gladfelter EH (1983) Skeletal development in Acropora cervicornis II. Diel patterns of calcium carbonate accretion. Coral Reefs 2:91–100

Gledhill DK, Wanninkhof R, Eakin CM (2009) Observing ocean acidification from space. Oceanography 22:48–59

Gledhill DK, Wanninkhof R, Millero FJ, Eakin M (2008) Ocean acidification of the Greater Caribbean Region 1996–2006. J Geophys Res Oceans 113:C10031

152 Gleeson MW, Strong AE (1995) Applying MCSST to coral reef bleaching. Adv Space Res 16:151– 154

Glynn PW (1984) Widespread Coral Mortality and the 1982–83 El Niño Warming Event. Environ Conserv 11:133

Glynn PW, D’Croz L (1990) Experimental evidence for high temperature stress as the cause of El Niño-coincident coral mortality. Coral Reefs 8:181–191

Godinot C, Houlbrèque F, Grover R, Ferrier-Pagès C (2011) Coral Uptake of Inorganic Phosphorus and Nitrogen Negatively Affected by Simultaneous Changes in Temperature and pH. PLoS ONE 6:e25024

Goiran C, Al-Moghrabi S, Allemand D, Jaubert J (1996) Inorganic carbon uptake for photosynthesis by the symbiotic coral/dinoflagellate association I. Photosynthetic performances of symbionts and dependence on sea water bicarbonate. J Exp Mar Biol Ecol 199:207–225

Gorbunov MY, Kolber ZS, Lesser MP, Falkowski PG (2001) Photosynthesis and photoprotection in symbiotic corals. Limnol Oceanogr 46:75–85

Goreau TF (1964) Mass Expulsion of Zooxanthellae from Jamaican Reef Communities after Hurricane Flora. Science 145:383–386

Goreau TF (1977) Coral skeletal chemistry: physiological and environmental regulation of stable isotopes and trace metals in Montastrea annularis. Proc R Soc B Biol Sci 196:291–315

Goreau TJ, Hayes RL (1994) Coral Bleaching and Ocean “Hot Spots.” Ambio 23:176–180

Goreau TJ, Macfarlane AH (1990) Reduced growth rate of Montastrea annularis following the 1987–1988 coral-bleaching event. Coral Reefs 8:211–215

Goss R, Jakob T (2010) Regulation and function of xanthophyll cycle-dependent photoprotection in algae. Photosynth Res 106:103–122

Goulet TL (2006) Most corals may not change their symbionts. Mar Ecol Prog Ser 321:1–7

Graham NAJ, McClanahan TR, MacNeil MA, Wilson SK, Polunin NVC, Jennings S, Chabanet P, Clark S, Spalding MD, Letourneur Y, Bigot L, Galzin R, Öhman MC, Garpe KC, Edwards AJ, Sheppard CRC (2008) Climate Warming, Marine Protected Areas and the Ocean-Scale Integrity of Coral Reef Ecosystems. PLoS ONE 3:e3039

Griffen B, Belgrad B, Cannizzo Z, Knotts E, Hancock E (2016) Rethinking our approach to multiple stressor studies in marine environments. Mar Ecol Prog Ser 543:273–281

Grottoli AG, Rodrigues LJ, Juarez C (2004) Lipids and stable carbon isotopes in two species of Hawaiian corals, Porites compressa and Montipora verrucosa, following a bleaching event. Mar Biol 145:621–631

Grottoli AG, Warner ME, Levas SJ, Aschaffenburg MD, Schoepf V, McGinley M, Baumann J, Matsui Y (2014) The cumulative impact of annual coral bleaching can turn some coral species winners into losers. Glob Change Biol 20:3823–3833

153 Hansen LB, Kamstrup N, Hansen BU (2002) Estimation of net short-wave radiation by the use of remote sensing and a digital elevation model-a case study of a high arctic mountainous area. Int J Remote Sens 23:4699–4718

Harland AD, Davies PS (1994) Time-course of photoadaptation in the symbiotic sea anemone Anemonia viridis. Mar Biol 119:45–51

Harland AD, Davies PS (1995) Symbiont photosynthesis increases both respiration and photosynthesis in the symbiotic sea anemone Anemonia viridis. Mar Biol 123:715–722

Hartin CA, Bond-Lamberty B, Patel P, Mundra A (2016) Ocean acidification over the next three centuries using a simple global climate carbon-cycle model: projections and sensitivities. Biogeosciences 13:4329–4342

Hawkins TD, Warner ME (2017) Warm preconditioning protects against acute heat-induced respiratory dysfunction and delays bleaching in a symbiotic sea anemone. J Exp Biol 220:969–983

Hearn CJ (1999) Wave-breaking hydrodynamics within coral reef systems and the effect of changing relative sea level. J Geophys Reasearch 104:30007–30019

Hedley JD, Roelfsema CM, Chollett I, Harborne AR, Heron SF, Weeks S, Skirving WJ, Strong AE, Eakin CM, Christensen TRL, Ticzon V, Bejarano S, Mumby PJ (2016) Remote Sensing of Coral Reefs for Monitoring and Management: A Review. Remote Sens 8:118

Heinz Walz GmbH (1998) Underwater Fluorometer Diving-PAM Submersible Photosynthesis Yield Analyzer Handbook of Operation. Heinz Walz GmbH, Effeltrich, Germany

Hennige SJ, Smith DJ, Walsh S-J, McGinley MP, Warner ME, Suggett DJ (2010) Acclimation and adaptation of scleractinian coral communities along environmental gradients within an Indonesian reef system. J Exp Mar Biol Ecol 391:143–152

Hennige SJ, Suggett DJ, Warner ME, McDougall K, Smith DJ (2008) Unravelling coral photoacclimation: Symbiodinium strategy and host modification. 128–132

Heron ML, Heron SF, Skirving WJ (2008) Mitigation of coral bleaching on the reef front by wave mixing. Eos Trans Am Geophys Union 89:Western Pacific Geophysics Meeting Supplement, Abstract OS51B-02

Herron HA, Mauzerall D (1972) The development of photosynthesis in a greening mutant of Chlorella and an analysis of the light saturation curve. Plant Physiol 50:141–148

Hii Y-S, Bolong AMA, Yang T-T, Liew H-C (2009) Effect of elevated carbon dioxide on two scleractinian corals: Porites cylindrica (Dana, 1846) and Galaxea fascicularis (Linnaeus, 1767). J Mar Biol 2009:1–7

Hill R, Ralph PJ (2006) Photosystem II Heterogeneity of in hospite Zooxanthellae in Scleractinian Corals Exposed to Bleaching Conditions. Photochem Photobiol 82:1577

Hitchcock GL (1980a) Influence of temperature on the growth rate of Skeletonema costatum in response to variations in daily light intensity. Mar Biol 57:261–269

Hitchcock GL (1980b) Diel variation in chlorophyll a, carbohydrate and protein content of the marine diatom Skeletonema costatum. Mar Biol 57:271–278

154 Ho DT, DeCarlo EH, Schlosser P (2017) Air-sea gas exchange in a subtropical coral reef ecosystem (poster).

Hoadley KD, Pettay DT, Grottoli AG, Cai W-J, Melman TF, Levas S, Schoepf V, Ding Q, Yuan X, Wang Y, Matsui Y, Baumann JH, Warner ME (2016) High-temperature acclimation strategies within the thermally tolerant endosymbiont Symbiodinium trenchii and its coral host, Turbinaria reniformis, differ with changing pCO2 and nutrients. Mar Biol 163:134

Hoegh-Guldberg O (1999) Climate change, coral bleaching and the future of the world’s coral reefs. Mar Freshw Res 50:839–866

Hoegh-Guldberg O, Cai R, Poloczanska ES, Brewer PG, Sundby S, Hilmi K, Fabry VJ, Jung S (2014) The Ocean. In: Barros V.R., Field C.B., Dokken D.J., Mastrandrea M.D., Mach K.J., Bilir T.E., Chatterjee M., Ebi K.L., Estrada Y.O., Genova R.C., Girma B., Kissel E.S., Levy A.N., MacCracken S., Mastrandrea P.R., White L.L. (eds) Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel of Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp 1655–1731

Hoegh-Guldberg O, Mumby PJ, Hooten AJ, Steneck RS, Greenfield P, Gomez E, Harvell CD, Sale PF, Edwards AJ, Caldeira K, Knowlton N, Eakin CM, Iglesias-Prieto R, Muthiga N, Bradbury RH, Dubi A, Hatziolos ME (2007) Coral reefs under rapid climate change and ocean acidification. Science 318:1737–1742

Hoegh-Guldberg O, Smith JG (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 hysterix Dana. J Exp Mar Biol Ecol 129:279–303

Hofmann GE, Smith JE, Johnson KS, Send U, Levin LA, Micheli F, Paytan A, Price NN, Peterson B, Takeshita Y, Matson PG, Crook ED, Kroeker KJ, Gambi MC, Rivest EB, Frieder CA, Yu PC, Martz TR (2011) High-Frequency Dynamics of Ocean pH: A Multi-Ecosystem Comparison. PLOS ONE 6:e28983

Hohmann-Marriott MF, Takizawa K, Eaton-Rye JJ, Mets L, Minagawa J (2010) The redox state of the plastoquinone pool directly modulates minimum chlorophyll fluorescence yield in Chlamydomonas reinhardtii. FEBS Lett 584:1021–1026

Hoogenboom M, Beraud E, Ferrier-Pagès C (2010) Relationship between symbiont density and photosynthetic carbon acquisition in the temperate coral Cladocora caespitosa. Coral Reefs 29:21–29

Hoogenboom MO, Campbell DA, Beraud E, DeZeeuw K, Ferrier-Pagès C (2012) Effects of Light, Food Availability and Temperature Stress on the Function of Photosystem II and Photosystem I of Coral Symbionts. PLoS ONE 7:e30167 van Hooidonk R, Maynard J, Tamelander J, Gove J, Ahmadia G, Raymundo L, Williams G, Heron SF, Planes S (2016) Local-scale projections of coral reef futures and implications of the Paris Agreement. Sci Rep 6:srep39666 van Hooidonk R, Maynard JA, Liu Y, Lee S-K (2015) Downscaled projections of Caribbean coral bleaching that can inform conservation planning. Glob Change Biol 21:3389–3401

155 Horwitz R, Fine M (2014) High CO2 detrimentally affects tissue regeneration of Red Sea corals. Coral Reefs 33:819–829

Houlbrèque F, Rodolfo-Metalpa R, Jeffree R, Oberhänsli F, Teyssié JL, Boisson F, Al-Trabeen K, Ferrier-Pagès C (2012) Effects of increased pCO2 on zinc uptake and calcification in the tropical coral Stylophora pistillata. Coral Reefs 31:101–109

Huang H, Yuan X, Cai W, Zhang C, Li X, Liu S (2014) Positive and negative responses of coral calcification to elevated pCO2: case studies of two coral species and the implications of their responses. Mar Ecol Prog Ser 502:145–156

Hughes SW (2005) Archimedes revisited: a faster, better, cheaper method of accurately measuring the volume of small objects. Phys Educ 40:468

Hughes TP, Baird AH, Bellwood DR, Card M, Connolly SR, Folke C, Grosberg R, Hoegh- Guldberg O, Jackson JBC, Kleypas J, Lough JM, Marshall P, Nystrom M, Palumbi SR, Pandolfi JM, Rosen B, Roughgarden J (2003) Climate change, human impacts, and the resilience of coral reefs. Science 301:929–933

Hughes TP, Barnes ML, Bellwood DR, Cinner JE, Cumming GS, Jackson JBC, Kleypas J, van de Leemput IA, Lough JM, Morrison TH, Palumbi SR, van Nes EH, Scheffer M (2017a) Coral reefs in the Anthropocene. Nature 546:82–90

Hughes TP, Kerry JT, Álvarez-Noriega M, Álvarez-Romero JG, Anderson KD, Baird AH, Babcock RC, Beger M, Bellwood DR, Berkelmans R, Bridge TC, Butler IR, Byrne M, Cantin NE, Comeau S, Connolly SR, Cumming GS, Dalton SJ, Diaz-Pulido G, Eakin CM, Figueira WF, Gilmour JP, Harrison HB, Heron SF, Hoey AS, Hobbs J-PA, Hoogenboom MO, Kennedy EV, Kuo C, Lough JM, Lowe RJ, Liu G, McCulloch MT, Malcolm HA, McWilliam MJ, Pandolfi JM, Pears RJ, Pratchett MS, Schoepf V, Simpson T, Skirving WJ, Sommer B, Torda G, Wachenfeld DR, Willis BL, Wilson SK (2017b) Global warming and recurrent mass bleaching of corals. Nature 543:373–377

Hutchings PA, Hoegh-Guldberg O (2008) Calcification, Erosion and the Establishment of the Framework of Coral Reefs. In: Hutchings P.A., Kingsford M.J., Hoegh-Guldberg O. (eds) The Great Barrier Reef: Biology, Environment and Management. CSIRO Publishing, pp 74– 84

Iglesias Prieto R, Reyes Bonilla H, Riosmena Rodríguez R (2003) Effects of 1997-1998 ENSO on coral reef communities in the Gulf of California, Mexico. Geofísica Int 42:467–471

Iglesias-Prieto R (1995) The Effects of Elevated Temperature on the Photosynthetic Responses of Symbiotic Dinoflagellates. In: Mathis P. (eds) Photosynthesis: from Light to Biosphere. Kluwer Academic Publishers, Netherlands, pp 793–796

Iglesias-Prieto R, Beltran VH, LaJeunesse TC, Reyes-Bonilla H, Thome PE (2004) Different algal symbionts explain the vertical distribution of dominant reef corals in the eastern Pacific. Proc R Soc Lond B Biol Sci 271:1757–63

Iglesias-Prieto R, Matta JL, Robins WA, Trench RK (1992) Photosynthetic response to elevated temperature in the symbiotic dinoflagellate Symbiodinium microadriaticum in culture. Proc Natl Acad Sci U S A 89:10302–10305

156 Iglesias-Prieto R, Trench R (1994) Acclimation and adaptation to irradiance in symbiotic dinoflagellates. I. Responses of the photosynthetic unit to changes in photon flux density. Mar Ecol Prog Ser 113:163–175

Iglesias-Prieto R, Trench RK (1997) Acclimation and adaptation to irradiance in symbiotic dinoflagellates. II. Response of chlorophyll–protein complexes to different photon-flux densities. Mar Biol 130:23–33

Iguchi A, Ozaki S, Nakamura T, Inoue M, Tanaka Y, Suzuki A, Kawahata H, Sakai K (2012) Effects of acidified seawater on coral calcification and symbiotic algae on the massive coral Porites australiensis. Mar Environ Res 73:32–36

Iluz D, Dubinsky Z (2015) Coral photobiology: new light on old views. Zoology 118:71–78

IPCC (2013) Summary for Policymakers. In: Stocker T.F., Qin D., Plattner G.-K., Tignor M., Allen S.K., Boschung J., Nauels A., Xia Y., Bex V., Midgley (eds) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,

Jeans J, Campbell DA, Hoogenboom MO (2013) Increased reliance upon photosystem II repair following acclimation to high-light by coral-dinoflagellate symbioses. Photosynth Res 118:219–229

Jeffrey SW, Humphrey GF (1975) New Spectrophotometric Equations for Determining Chlorophylls a, b, c1, and c2 in Higher Plants, Algae, and Natural Phytoplankton. Biochem Physiol Pflanz 167:191–194

John, Huber (2005) Instruction Manual Oxy-4 Four Channel Fibre-Optic Oxygen Meter. PreSens Precision Sensing Gmbh, Regensburg

Johnson CE, Goulet TL (2007) A comparison of photographic analyses used to quantify zooxanthella density and pigment concentrations in Cnidarians. J Exp Mar Biol Ecol 353:287–295

Jokiel PL (2004) Temperature Stress and Coral Bleaching. In: Rosenberg E., Loya Y. (eds) Coral Health and Disease. Springer-Verlag, Berlin Heidelberg, pp 401–425

Jokiel PL (2011a) The reef coral two compartment proton flux model: A new approach relating tissue-level physiological processes to gross corallum morphology. J Exp Mar Biol Ecol 409:1–12

Jokiel PL (2011b) Ocean Acidification and Control of Reef Coral Calcification by Boundary Layer Limitation of Proton Flux. Bull Mar Sci 87:639–657

Jokiel PL, Brown EK (2004) Global warming, regional trends and inshore environmental conditions influence coral bleaching in Hawaii. Glob Change Biol 10:1627–1641

Jones RJ, Hoegh-Guldberg O, Larkum AWD, Schreiber U (1998) Temperature-induced bleaching of corals begins with impairment of the CO2 fixation mechanism in zooxanthellae. Plant Cell Environ 21:1219–1230

Jones RJ, Yellowlees D (1997) Regulation and control of intracellular algae (= zooxanthellae) in hard corals. Philos Trans R Soc B Biol Sci 352:457–468

157 Kaniewska P, Campbell PR, Kline DI, Rodriguez-Lanetty M, Miller DJ, Dove S, Hoegh-Guldberg O (2012) Major Cellular and Physiological Impacts of Ocean Acidification on a Reef Building Coral. PLoS ONE 7:e34659

Kitada Y, Fujimura H, Tokeshi R, Oomori T (2006) Air-sea CO2 flux and gas exchange coefficient at the Sesoko coral reefs, Okinawa, Japan. J Jpn Coral Reef Soc 8:51–60

Kleppel GS, Dodge RE, Reese CJ (1989) Changes in Pigmentation Associated with the Bleaching of Stony Corals. Limnol Oceanogr 34:1331–1335

Kleypas JA, Buddemeier RW, Archer D, Gattuso J-P, Langdon C, Opdyke BN (1999) Geochemical Consequences of Increased Atmospheric Carbon Dioxide on Coral Reefs. Science 284:118– 120

Klueter A, Loh W, Hoegh-Guldberg O, Dove S (2006) Physiological and genetic properties of two fluorescent colour morphs of the coral Montipora digitata. Symbiosis 42:123–134

Knowlton N (2001) The future of coral reefs. Proc Natl Acad Sci 98:5419–5425

Knowlton N, Rohwer F (2003) Multispecies microbial mutualisms on coral reefs: the host as a habitat. Am Nat 162:S51-62

Komori S, Nagaosa R, Murakami Y (1993) Turbulence structure and mass transfer across a sheared air–water interface in wind-driven turbulence. J Fluid Mech 249:161–183

Komori S, Shimada T (1995) Gas transfer across a wind-driven air-water interface and the effects of sea water on CO2 transfer. In: Jähne B., Monahan E.C. (eds) Air-water gas transfer: selected papers from the third international symposium on air-water gas transfer. AEON Verlag and Studio, Hanau, pp 553–569

Krief S, Hendy EJ, Fine M, Yam R, Meibom A, Foster GL, Shemesh A (2010) Physiological and isotopic responses of scleractinian corals to ocean acidification. Geochim Cosmochim Acta 74:4988–5001

Kuguru B, Winters G, Beer S, Santos SR, Chadwick NE (2007) Adaptation strategies of the corallimorpharian Rhodactis rhodostoma to irradiance and temperature. Mar Biol 151:1287– 1298

Kühl M, Cohen Y, Dalsgaard T, Jørgensen BB, Revsbech NP (1995) Microenvironment and photosynthesis of zooxanthellae in scleractinian corals studied with microsensors for O2, pH and light. Mar Ecol Prog Ser 117:159–172

Kühlmann DHH (1988) The Sensitivity of Coral Reefs to Environmental Pollution. Ambio 17:13– 21

Kwiatkowski L, Cox P, Halloran PR, Mumby PJ, Wiltshire AJ (2015) Coral bleaching under unconventional scenarios of climate warming and ocean acidification. Nat Clim Change 5:777–781

Land PE, Shutler JD, Findlay HS, Girard-Ardhuin F, Sabia R, Reul N, Piolle J-F, Chapron B, Quilfen Y, Salisbury J, Vandemark D, Bellerby R, Bhadury P (2015) Salinity from Space Unlocks Satellite-Based Assessment of Ocean Acidification. Environ Sci Technol 49:1987– 1994

158 Langlois L, Hoogenboom M (2014) Capacity for short-term physiological acclimation to light does not control the lower depth distributions of branching corals. Mar Ecol Prog Ser 508:149– 162

Laszlo I, Ciren P, Liu H, Kondragunta S, Tarpley JD, Goldberg MD (2008) Remote sensing of aerosol and radiation from geostationary satellites. Adv Space Res 41:1882–1893

Leggat W, Whitney S, Yellowlees D (2004) Is coral bleaching due to the instability of the zooxanthellae dark reactions? Symbiosis 37:137–153

Lenth RV (2016) Least-squares means: The R Package lsmeans. J Stat Softw 69:1–33

Lesser M (1997) Oxidative stress causes coral bleaching during exposure to elevated temperatures. Coral Reefs 16:187–192

Lesser MP, Farrell JH (2004) Exposure to solar radiation increases damage to both host tissues and algal symbionts of corals during thermal stress. Coral Reefs 23:367–377

Lesser MP, Gorbunov MY (2001) Diurnal and bathymetric changes in chlorophyll fluorescence yields of reef corals measured in situ with a fast repetition rate fluorometer. Mar Ecol Prog Ser 212:69–77

Lesser MP, Stochaj WR, Tapley DW, Shick JM (1990) Bleaching in coral reef anthozoans: effects of irradiance, ultraviolet radiation, and temperature on the activities of protective enzymes against active oxygen. Coral Reefs 8:225–232

Levas S, Grottoli AG, Schoepf V, Aschaffenburg M, Baumann J, Bauer JE, Warner ME (2016) Can heterotrophic uptake of dissolved organic carbon and zooplankton mitigate carbon budget deficits in annually bleached corals? Coral Reefs 35:495–506

Levy O, Kaniewska P, Alon S, Eisenberg E, Karako-Lampert S, Bay L, Reef R, Rodriguez-Lanetty M, Miller D, Hoegh-Guldberg O (2011) Complex diel cycles of gene expression in coral- algal symbiosis. Science 331:175

Lewis DH, Smith DC (1971) The Autotrophic Nutrition of Symbiotic Marine Coelenterates with Special Reference to Hermatypic Corals. I. Movement of Photosynthetic Products between the Symbionts. Proc R Soc Lond B Biol Sci 178:111–129

Liu G, Skirving WJ, Geiger EF, De La Cour JL, Marsh BL, Heron SF, Tirak KV, Strong AE, Eakin CM (2017) NOAA Coral Reef Watch’s 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3 and Four-Month Outlook Version 4. Reef Encount 32:39–45

Liu G, Strong AE, Skirving W (2003) Remote sensing of sea surface temperatures during 2002 Barrier Reef coral bleaching. Eos Trans Am Geophys Union 84:137–141

Liu G, Strong AE, Skirving W, Arzayus LF (2006) Overview of NOAA Coral Reef Watch Program’s Near-Real-Time Satellite Global Coral Bleaching Monitoring Activities. 10th International Coral Reef Symposium 1783–1793

Livingstone DR (1991) Origins and Evolution of Pathways of Anaerobic Metabolism in the Animal Kingdom. Am Zool 31:522–534

159 Logan CA, Dunne JP, Eakin CM, Donner SD (2014) Incorporating adaptive responses into future projections of coral bleaching. Glob Change Biol 20:125–139

Loya Y, Sakai K, Yamazato K, Nakano Y, Sambali H, van Woesik R (2001) Coral bleaching: the winners and the losers. Ecol Lett 4:122–131

MacKellar MC, McGowan HA (2010) Air-sea energy exchanges measured by eddy covariance during a localised coral bleaching event, Heron Reef, Great Barrier Reef, Australia. Geophys Res Lett 37:L24703

Mackey K, Morris JJ, Morel F, Kranz S (2015) Response of Photosynthesis to Ocean Acidification. Oceanography 25:74–91

Manzello DP, Berkelmans R, Hendee JC (2007) Coral bleaching indices and thresholds for the Florida Reef Tract, Bahamas, and St. Croix, US Virgin Islands. Mar Pollut Bull 54:1923– 1931

Marsh JA Jr (1970) Primary productivity of reef-building calcareous red algae. Ecology 51:255– 263

Marshall P, Schuttenberg H (2006) A Reef Manager’s Guide to Coral Bleaching. Great Barrier Reef Marine Park Authority, Townsville, Australia

Marshall PA, Baird AH (2000) Bleaching of corals on the Great Barrier Reef: differential susceptibilities among taxa. Coral Reefs 19:155–163

Marubini F, Ferrier-Pagès C, Furla P, Allemand D (2008) Coral calcification responds to seawater acidification: a working hypothesis towards a physiological mechanism. Coral Reefs 27:491–499

Masiri I, Nunez M, Weller E (2008) A 10-year climatology of solar radiation for the Great Barrier Reef: implications for recent mass coral bleaching events. Int J Remote Sens 29:4443–4462

Matsubara S, Chow WS (2004) Populations of photoinactivated photosystem II reaction centers characterized by chlorophyll a fluorescence lifetime in vivo. Proc Natl Acad Sci U S A 101:18234–18239

Mauzerall D, Greenbaum NL (1989) The absolute size of a photosynthetic unit. Biochim Biophys Acta BBA - Bioenerg 974:119–140

Maxwell K, Johnson GN (2000) Chlorophyll fluorescence—a practical guide. J Exp Bot 51:659– 668

Maynard JA, Anthony KRN, Marshall PA, Masiri I (2008a) Major bleaching events can lead to increased thermal tolerance in corals. Mar Biol 155:173–182

Maynard JA, Johnson JE, Marshall PA, Eakin CM, Goby G, Schuttenberg H, Spillman CM (2009) A Strategic Framework for Responding to Coral Bleaching Events in a Changing Climate. Environ Manage 44:1–11

Maynard JA, Turner PJ, Anthony KRN, Baird AH, Berkelmans R, Eakin CM, Johnson J, Marshall PA, Packer GR, Rea A, Willis BL (2008b) ReefTemp: An interactive monitoring system for coral bleaching using high-resolution SST and improved stress predictors. Geophys Res Lett 35:L05603

160 McClanahan TR, Ateweberhan M, Graham NAJ, Wilson SK, Sebastian CR, Guillaume MMM, Bruggemann JH (2007a) Western Indian Ocean coral communities: bleaching responses and susceptibility to extinction. Mar Ecol Prog Ser 337:1–13

McClanahan TR, Ateweberhan M, Muhando CA, Maina J, Mohammed MS (2007b) Effects of climate and seawater temperature variation on coral bleaching and mortality. Ecol Monogr 77:503–525

McClanahan TR, Ateweberhan M, Sebastián CR, Graham N a. J, Wilson SK, Bruggemann JH, Guillaume MMM (2007c) Predictability of coral bleaching from synoptic satellite and in situ temperature observations. Coral Reefs 26:695–701

McClanahan TR, Baird AH, Marshall PA, Toscano MA (2004) Comparing bleaching and mortality responses of hard corals between southern Kenya and the Great Barrier Reef, Australia. Mar Pollut Bull 48:327–335

McCulloch M, Falter J, Trotter J, Montagna P (2012) Coral resilience to ocean acidification and global warming through pH up-regulation. Nat Clim Change 2:623–627

McGinley MP, Aschaffenburg MD, Pettay DT, Smith RT, LaJeunesse TC, Warner ME (2012) Symbiodinium spp. in colonies of eastern Pacific Pocillopora spp. are highly stable despite the prevalence of low-abundance background populations. Mar Ecol Prog Ser 462:1–7

Meesters EH, Bak RP (1993) Effects of coral bleaching on tissue regeneration potential and colony survival. Mar Ecol Prog Ser 96:189–198

Mendes JM, Woodley JD (2002) Effect of the 1995-1996 bleaching event on polyp tissue depth, growth, reproduction and skeletal band formation in Montastraea annularis. Mar Ecol Prog Ser 235:93–102

Michael KJ, Veal CJ, Nunez M (2012) Attenuation coefficients of ultraviolet and photosynthetically active wavelengths in the waters of Heron Reef, Great Barrier Reef, Australia. Mar Freshw Res 63:142–149

Middlebrook R, Anthony KRN, Hoegh-Guldberg O, Dove S (2010) Heating rate and symbiont productivity are key factors determining thermal stress in the reef-building coral Acropora formosa. J Exp Biol 213:1026–1034

Middlebrook R, Anthony KRN, Hoegh-Guldberg O, Dove S (2012) Thermal priming affects symbiont photosynthesis but does not alter bleaching susceptibility in Acropora millepora. J Exp Mar Biol Ecol 432–433:64–72

Middlebrook R, Hoegh-Guldberg O, Leggat W (2008) The effect of thermal history on the susceptibility of reef-building corals to thermal stress. J Exp Biol 211:1050–1056

Miller CB, Wheeler PA (2012) Biological Oceanography. Wiley-Blackwell, Chichester, United Kingdom

Moberg F, Folke C (1999) Ecological goods and services of coral reef ecosystems. Ecol Econ 29:215–233

Morgan KM, Perry CT, Smithers SG, Johnson JA, Daniell JJ (2016) Evidence of extensive reef development and high coral cover in nearshore environments: implications for understanding coral adaptation in turbid settings. Sci Rep 6:srep29616

161 Mumby P, Chisholm J, Edwards A, Clark C, Roark E, Andrefouet S, Jaubert J (2001a) Unprecedented bleaching-induced mortality in Porites spp. at Rangiroa Atoll, French Polynesia. Mar Biol 139:183–189

Mumby PJ, Chisholm JRM, Edwards AJ, Andrefouet S, Jaubert J (2001b) Cloudy weather may have saved Society Island reef corals during the 1998 ENSO event. Mar Ecol Prog Ser 222:209–216

Murchie EH, Harbinson J (2014) Non-Photochemical Fluorescence Quenching Across Scales: From Chloroplasts to Plants to Communities. Non-Photochemical Quenching and Energy Dissipation in Plants, Algae and Cyanobacteria. Springer, Dordrecht, pp 553–582

Murphy JWA, Richmond RH (2016) Changes to coral health and metabolic activity under oxygen deprivation. PeerJ 4:e1956

Muscatine L, Ferrier-Pages C, Blackburn A, Gates RD, Bagdasarian G, Allemand D (1998) Cell- specific density of symbiotic dinoflagellates in tropical anthozoans. Coral Reefs 17:329–337

Muscatine L, McCloskey LR, Marian RE (1981) Estimating the Daily Contribution of Carbon from Zooxanthellae to Coral Animal Respiration. Limnol Oceanogr 26:601–611

Muscatine L, Porter JW, Kaplan IR (1989) Resource partitioning by reef corals as determined from stable isotope composition. Mar Biol 100:185–193

Nightingale PD, Malin G, Law CS, Watson AJ, Liss PS, Liddicoat MI, Boutin J, Upstill-Goddard RC (2000) In situ evaluation of air-sea gas exchange parameterizations using novel conservative and volatile tracers. Glob Biogeochem Cycles 14:373–387

Nii CM, Muscatine L (1997) Oxidative stress in the symbiotic sea anemone Aiptasia pulchella (Carlgren, 1943): Contribution of the animal to superoxide ion production at elevated temperature. Biol Bull 192:444–456

Nishiyama Y, Allakhverdiev SI, Murata N (2006) A new paradigm for the action of reactive oxygen species in the photoinhibition of photosystem II. Biochim Biophys Acta BBA - Bioenerg 1757:742–749

NOAA Coral Reef Watch (2017a) NOAA Coral Reef Watch Daily Global 5-km Satellite Climatology Data for Heron Island. Accessed 16 October 2017. ftp://ftp.star.nesdis.noaa.gov/pub/sod/mecb/crw/data/5km/v3/climatology/data/

NOAA Coral Reef Watch (2017b) NOAA Coral Reef Watch Daily Global 5-km Satellite Virtual Station Time Series Data for Main Hawaiian Islands. Accessed 26 July 2017.

NOAA Coral Reef Watch (2017c) Light Stress Damage Product Suite - Caribbean and Eastern Pacific (Version 0.7, experimental product). Accessed 24 October 2017. https://coralreefwatch.noaa.gov/satellite/lsd/index.php

Noonan SHC, Fabricius KE (2016) Ocean acidification affects productivity but not the severity of thermal bleaching in some tropical corals. ICES J Mar Sci 73:715–726

Ogawa D, Bobeszko T, Ainsworth T, Leggat W (2013) The combined effects of temperature and CO2 lead to altered gene expression in Acropora aspera. Coral Reefs 32:895–907

162 Omori M, Fukami H, Kobinata H, Hatta M (2001) Significant drop of fertilization of Acropora corals in 1999: An after-effect of heavy coral bleaching? Limnol Oceanogr 46:704–706 van Oppen MJH, Palstra FP, Piquet AM-T, Miller DJ (2001) Patterns of coral–dinoflagellate associations in Acropora: significance of local availability and physiology of Symbiodinium strains and host–symbiont selectivity. Proc R Soc Lond B Biol Sci 268:1759–1767

Pandolfi JM, Bradbury RH, Sala E, Hughes TP, Bjorndal KA, Cooke RG, McArdle D, McClenachan L, Newman MJH, Paredes G, Warner RR, Jackson JBC (2003) Global Trajectories of the Long-Term Decline of Coral Reef Ecosystems. Science 301:955–958

Park Y-I, Chow WS, Osmond CB, Anderson JM (1996) Electron transport to oxygen mitigates against the photoinactivation of Photosystem II in vivo. Photosynth Res 50:23–32

Parys E, Jastrzębski H (2006) Light-enhanced dark respiration in leaves, isolated cells and protoplasts of various types of C4 plants. J Plant Physiol 163:638–647

Paton-Walsh C, Wilson S, Naylor T, Griffith D, Denmead O (2011) Transport of NOx Emissions from Sugarcane Fertilisation into the Great Barrier Reef Lagoon. Environ Model Assess 16:441–452

Piggot AM, Fouke BW, Sivaguru M, Sanford RA, Gaskins HR (2009) Change in zooxanthellae and mucocyte tissue density as an adaptive response to environmental stress by the coral, Montastraea annularis. Mar Biol 156:2379–2389

Pinheiro J, Bates D (2000) Mixed-Effects Models in S and S-PLUS. Springer Verlag, New York

Pitz G (2008) Understanding and Interpreting Interactions. https://www.unc.edu/courses/2008spring/psyc/270/001/interact.html#i9

Pochon X, Stat M, Takabayashi M, Chasqui L, Chauka LJ, Logan DDK, Gates RD (2010) Comparison of Endosymbiotic and Free-Living Symbiodinium (Dinophyceae) Diversity in a Hawaiian Reef Environment. J Phycol 46:53–65

Porter JW, Fitt WK, Spero HJ, Rogers CS, White MW (1989) Bleaching in reef corals: physiological and stable isotopic responses. Proc Natl Acad Sci 86:9342–9346

Porter JW, Muscatine L, Dubinsky Z, Falkowski PG (1984) Primary Production and Photoadaptation in Light- and Shade-Adapted Colonies of the Symbiotic Coral, Stylophora pistillata. Proc R Soc Lond B Biol Sci 222:161–180

Prada F, Caroselli E, Mengoli S, Brizi L, Fantazzini P, Capaccioni B, Pasquini L, Fabricius KE, Dubinsky Z, Falini G, Goffredo S (2017) Ocean warming and acidification synergistically increase coral mortality. Sci Rep 7:40842

Pratchett MS, McCowan D, Maynard JA, Heron SF (2013) Changes in bleaching susceptibility among corals subject to ocean warming and recurrent bleaching in Moorea, French Polynesia. PLoS ONE 8:e70443

Pratchett MS, Wilson SK, Graham NAJ, Munday PL, Jones GP, Polunin NVC (2009) Coral Bleaching and Consequences for Motile Reef Organisms: Past, Present and Uncertain Future Effects Coral Bleaching. In: Oppen M.J.H., Lough J.M., Heldmaier G., Jackson R.B., Lange O.L., Mooney H.A., Schulze E.D., Sommer U. (eds) Springer Berlin Heidelberg, pp 139– 158

163 Prézelin BB (1987) Photosynthetic physiology of dinoflagellates. In: Taylor F.J.R. (eds) The Biology of Dinoflagellates. Blackwell Scientific Publishers, Oxford, pp 174–223

Putnam HM, Davidson JM, Gates RD (2016) Ocean acidification influences host DNA methylation and phenotypic plasticity in environmentally susceptible corals. Evol Appl 9:1165–1178

Putnam HM, Mayfield AB, Fan TY, Chen CS, Gates RD (2013) The physiological and molecular responses of larvae from the reef-building coral Pocillopora damicornis exposed to near- future increases in temperature and pCO2. Mar Biol 160:2157–2173

R Core Team (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/,

Ralph PJ, Gademann R, Larkum AWD, Schreiber U (1999) In situ underwater measurements of photosynthetic activity of coral zooxanthellae and other reef-dwelling dinoflagellate endosymbionts. Mar Ecol-Prog Ser 180:139–147

Raven JA (1984) A Cost-Benefit Analysis of Photon Absorption by Photosynthetic Unicells. New Phytol 98:593–625

Raven JA, Geider RJ (2003) Adaptation, Acclimation and Regulation in Algal Photosynthesis. Photosynthesis in Algae. Springer, Dordrecht, pp 385–412

Reaka-Kudla ML (1997) Global biodiversity of coral reefs: a comparison with rainforests. In: Reaka-Kudla M.L., Wilson D.E. (eds) Biodiversity II: Understanding and Protecting Our Biological Resources. Joseph Henry Press, pp 551

Reef Resilience Network (2014) Palau – MPA Design | Reef Resilience. Accessed 15 August 2017. http://www.reefresilience.org/case-studies/palau-mpa-design/

Reynaud S, Leclercq N, Romaine-Lioud S, Ferrier-Pages C, Jaubert J, Gattuso JP (2003) Interacting effects of CO2 partial pressure and temperature on photosynthesis and calcification in a scleractinian coral. Glob Change Biol 9:1660–1668

Reynolds JM, Bruns BU, Fitt WK, Schmidt GW (2008) Enhanced photoprotection pathways in symbiotic dinoflagellates of shallow-water corals and other cnidarians. Proc Natl Acad Sci U S A 105:13674–13678

Rix L, de Goeij JM, van Oevelen D, Struck U, Al-Horani FA, Wild C, Naumann MS (2017) Differential recycling of coral and algal dissolved organic matter via the sponge loop. Funct Ecol 31:778–789

Rodolfo-Metalpa R, Huot Y, Ferrier-Pagès C (2008) Photosynthetic response of the Mediterranean zooxanthellate coral Cladocora caespitosa to the natural range of light and temperature. J Exp Biol 211:1579–1586

Rodolfo-Metalpa R, Richard C, Allemand D, Ferrier-Pagès C (2006) Growth and photosynthesis of two Mediterranean corals, Cladocora caespitosa and Oculina patagonica, under normal and elevated temperatures. J Exp Biol 209:4546–4556

Rodrigues LJ, Grottoli AG (2007) Energy reserves and metabolism as indicators of coral recovery from bleaching. Limnol Oceanogr 52:1874–1882

164 Rodríguez-Román A, Hernández-Pech X, E. Thome P, Enríquez S, Iglesias-Prieto R (2006) Photosynthesis and light utilization in the Caribbean coral Montastraea faveolata recovering from a bleaching event. Limnol Oceanogr 51:2702–2710

Rogers CS, Miller J, Muller EM, Edmunds P, Nemeth RS, Beets JP, Friedlander AM, Smith TB, Boulon R, Jeffrey CF (2008) Ecology of coral reefs in the US Virgin Islands. Coral Reefs USA 303–373

Rohwer F, Seguritan V, Azam F, Knowlton N (2002) Diversity and distribution of coral-associated bacteria. Mar Ecol Prog Ser 243:1–10

Romer M (2016) STAT 510| Applied Time Series Analysis. Accessed 16 November 2016. https://onlinecourses.science.psu.edu/stat510/node/33

Rowan R (2004) Coral bleaching: Thermal adaptation in reef coral symbionts. Nature 430:742–742

Rowan R, Whitney SM, Fowler A, Yellowlees D (1996) Rubisco in Marine Symbiotic Dinoflagellates: Form II Enzymes in Eukaryotic Oxygenic Phototrophs Encoded by a Nuclear Multigene Family. Plant Cell 8:539

Sabia R, Fernández-Prieto D, Shutler J, Donlon C, Land P, Reul N (2015) Remote sensing of surface ocean pH exploiting sea surface salinity satellite observations. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). pp 106–109

Sabine CL, Feely RA, Gruber N, Key RM, Lee K, Bullister JL, Wanninkhof R, Wong CS, Wallace DWR, Tilbrook B, Millero FJ, Peng TH, Kozyr A, Ono T, Rios AR (2004) The ocean sink for anthropogenic CO2. Science 305:367–371

Sampayo EM, Ridgway T, Bongaerts P, Hoegh-Guldberg O (2008) Bleaching susceptibility and mortality of corals are determined by fine-scale differences in symbiont type. Proc Natl Acad Sci 105:10444–10449

Sampayo EM, Ridgway T, Franceschinis L, Roff G, Hoegh-Guldberg O, Dove S (2016) Coral symbioses under prolonged environmental change: living near tolerance range limits. Sci Rep 6:36271

Sawyer SJ, Muscatine L (2001) Cellular mechanisms underlying temperature-induced bleaching in the tropical sea anemone Aiptasia pulchella. J Exp Biol 204:3443–3456

Scheufen T, Krämer WE, Iglesias-Prieto R, Enríquez S (2017) Seasonal variation modulates coral sensibility to heat-stress and explains annual changes in coral productivity. Sci Rep 7:4937

Schmid V, Stidwill R, Bally A, Marcum B, Tardent P (1981) Heat dissociation and maceration of marine Cnidaria. Wilhelm Rouxs Arch Dev Biol 190:143–149

Schoepf V, Grottoli AG, Warner ME, Cai W-J, Melman TF, Hoadley KD, Pettay DT, Hu X, Li Q, Xu H, Wang Y, Matsui Y, Baumann JH (2013) Coral Energy Reserves and Calcification in a High-CO2 World at Two Temperatures. PLoS ONE 8:e75049

Schoepf V, Stat M, Falter JL, McCulloch MT (2015) Limits to the thermal tolerance of corals adapted to a highly fluctuating, naturally extreme temperature environment. Sci Rep 5:17639

165 Schreiber U (2004) Pulse-Amplitude-Modulation (PAM) Fluorometry and Saturation Pulse Method: An Overview. In: Papageorgiou G.C., Govindjee (eds) Chlorophyll a Fluorescence: A Signature of Photosynthesis. Springer Science & Business Media, pp 279–319

Schreiber U, Gademann R, Ralph PJ, Larkum AWD (1997) Assessment of Photosynthetic Performance of Prochloron in Lissoclinum patella in hospite by Chlorophyll Fluorescence Measurements. Plant Cell Physiol 38:945–951

Secord D, Muller-Parker G (2005) Symbiont distribution along a light gradient within an intertidal cave. Limnol Oceanogr 50:272–278

Shamberger KEF, Feely RA, Sabine CL, Atkinson MJ, DeCarlo EH, Mackenzie FT, Drupp PS, Butterfield DA (2011) Calcification and organic production on a Hawaiian coral reef. Mar Chem 127:64–75

Shaw EC, McNeil BI, Tilbrook B, Matear R, Bates ML (2013) Anthropogenic changes to seawater buffer capacity combined with natural reef metabolism induce extreme future coral reef CO2 conditions. Glob Change Biol 19:1632–1641

Sheppard CRC (1999) Coral Decline and Weather Patterns over 20 Years in the Chagos Archipelago, Central Indian Ocean. Ambio 28:472–478

Sheridan C, Petersen J, Rohwer J (2012) On modifying the Arrhenius equation to compensate for temperature changes for reactions within biological systems. Water SA 38:149–151

Siebeck UE, Marshall NJ, Klüter A, Hoegh-Guldberg O (2006) Monitoring coral bleaching using a colour reference card. Coral Reefs 25:453–460

Skirving W, Enríquez S, Hedley JD, Dove S, Eakin CM, Mason RAB, De La Cour JL, Liu G, Hoegh-Guldberg O, Strong AE, Mumby PJ, Iglesias-Prieto R (2018) Remote Sensing of Coral Bleaching Using Temperature and Light: Progress towards an Operational Algorithm. Remote Sens 10:18

Skirving W, Heron M, Heron S (2006a) The Hydrodynamics of a Bleaching Event: Implications for Management and Monitoring. Coral Reefs and Climate Change: Science and Management. American Geophysical Union, pp 145–161

Skirving W, Liu G, Strong AE, Liu C, Sapper J, Arzayus F (2006b) Extreme Events and Perturbations of Coastal Ecosystems. In: Richardson L.L., LeDrew E.F. (eds) Remote Sensing of Aquatic Coastal Ecosystem Processes. pp 11–25

Skirving WJ, Heron SF, Steinberg CL, McLean C, Parker BAA, Eakin CM, Heron ML, Strong AE, Arzayus LF (2010) Determining Thermal Capacitance for Protected Area Network Design in Palau. 317

Small AM, Adey WH, Spoon D (1998) Are Current Estimates of Coral Reef Biodiversity Too Low? The View through the Window of a Microcosm. Atoll Res Bull 458:1–20

Smith S, Key G (1975) Carbon dioxide and metabolism in marine environments. Limnol Oceanogr 20:493–495

Spillman C, Alves O (2009) Dynamical seasonal prediction of summer sea surface temperatures in the Great Barrier Reef. Coral Reefs 28:197–206

166 Spillman CM, Alves O, Hudson DA (2011) Seasonal Prediction of Thermal Stress Accumulation for Coral Bleaching in the Tropical Oceans. Mon Weather Rev 139:317–331

Stat M, Morris E, Gates R (2008) Functional diversity in coral-dinoflagellate symbiosis. Proc Natl Acad Sci U S A 105:9256–9261

Stat M, Yost DM, Gates RD (2015) Geographic structure and host specificity shape the community composition of symbiotic dinoflagellates in corals from the Northwestern Hawaiian Islands. Coral Reefs 34:1075–1086

Steen RG (1986) Evidence for Heterotrophy by Zooxanthellae in Symbiosis with Aiptasia pulchella. Biol Bull 170:267–278

Steen RG (1987) Evidence for facultative heterotrophy in cultured zooxanthellae. Mar Biol 95:15– 23

Stimson J, Kinzie RA III (1991) The temporal pattern and rate of release of zooxanthellae from the reef coral Pocillopora damicornis (Linnaeus) under nitrogen-enrichment and control conditions. J Exp Mar Biol Ecol 153:63–74

Stimson J, Sakai K, Sembali H (2002) Interspecific comparison of the symbiotic relationship in corals with high and low rates of bleaching-induced mortality. Coral Reefs 21:409–421

Stokes D, Walker DA, Grof CPL, Seaton GGR (1990) Light enhanced dark respiration. In: Zelitch I. (eds) Perspectives in Biochemical and Genetic Regulation of Photosynthesis. Alan R Liss, New York,

Strong AE, Arzayus F, Skirving W, Heron SF (2006) Identifying Coral Bleaching Remotely Via Coral Reef Watch – Improved Integration and Implications for Changing Climate. In: Phinney J.T., Hoegh-Guldberg O., Kleypas J., Skirving W., Strong A. (eds) Coral Reefs and Climate Change: Science and Management. American Geophysical Union, pp 163–180

Strong AE, Barrientos CS, Duda C, Sapper J (1997) Improved satellite techniques for monitoring coral reef bleaching. In: Lessios H.A., Macintyre I.G. (eds) Proceedings of the 8th International Coral Reef Symposium, Panama City, Panama. Smithsonian Tropical Research Institute, Balboa, Panama, pp 1495–1498

Suggett DJ, Dong LF, Lawson T, Lawrenz E, Torres L, Smith DJ (2013) Light availability determines susceptibility of reef building corals to ocean acidification. Coral Reefs 32:327– 337

Suggett DJ, Smith DJ (2011) Interpreting the sign of coral bleaching as friend vs. foe. Glob Change Biol 17:45–55

Sun Q, Tang D, Wang S (2012) Remote-sensing observations relevant to ocean acidification. Int J Remote Sens 33:7542–7558

Swain TD, DuBois E, Gomes A, Stoyneva VP, Radosevich AJ, Henss J, Wagner ME, Derbas J, Grooms HW, Velazquez EM, Traub J, Kennedy BJ, Grigorescu AA, Westneat MW, Sanborn K, Levine S, Schick M, Parsons G, Biggs BC, Rogers JD, Backman V, Marcelino LA (2016) Skeletal light-scattering accelerates bleaching response in reef-building corals. BMC Ecol 16:10

167 Szmant AM, Gassman NJ (1990) The effects of prolonged “bleaching” on the tissue biomass and reproduction of the reef coral Montastrea annularis. Coral Reefs 8:217–224

Takahashi S, Badger MR (2011) Photoprotection in plants: a new light on photosystem II damage. Trends Plant Sci 16:53–60

Takahashi S, Milward SE, Yamori W, Evans JR, Hillier W, Badger MR (2010) The Solar Action Spectrum of Photosystem II Damage. Plant Physiol 153:988–993

Takahashi S, Nakamura T, Sakamizu M, van Woesik R, Yamasaki H (2004) Repair machinery of symbiotic photosynthesis as the primary target of heat stress for reef-building corals. Plant Cell Physiol 45:251–255

Tanzil JTI, Brown BE, Tudhope AW, Dunne RP (2009) Decline in skeletal growth of the coral Porites lutea from the Andaman Sea, South Thailand between 1984 and 2005. Coral Reefs 28:519–528

Tchernov D, Gorbunov MY, de Vargas C, Narayan Yadav S, Milligan AJ, Häggblom M, Falkowski PG (2004) Membrane lipids of symbiotic algae are diagnostic of sensitivity to thermal bleaching in corals. Proc Natl Acad Sci U S A 101:13531–13535

Tchernov D, Kvitt H, Haramaty L, Bibby TS, Gorbunov MY, Rosenfeld H, Falkowski PG (2011) Apoptosis and the selective survival of host animals following thermal bleaching in zooxanthellate corals. Proc Natl Acad Sci U S A 108:9905–9909

Terán E, Méndez ER, Enríquez S, Iglesias-Prieto R (2010) Multiple light scattering and absorption in reef-building corals. Appl Opt 49:5032–5042

Thornhill DJ, Rotjan RD, Todd BD, Chilcoat GC, Iglesias-Prieto R, Kemp DW, LaJeunesse TC, Reynolds JM, Schmidt GW, Shannon T, Warner ME, Fitt WK (2011) A Connection between Colony Biomass and Death in Caribbean Reef-Building Corals. PLoS ONE 6:e29535

Titlyanov EA, Titlyanova TV, Yamazato K (2001a) Formation, growth and photo-acclimation of colonies of the hermatypic coral Galaxea fascicularis under different light conditions. Symbiosis 30:257–274

Titlyanov EA, Titlyanova TV, Yamazato K, van Woesik R (2001b) Photo-acclimation dynamics of the coral Stylophora pistillata to low and extremely low light. J Exp Mar Biol Ecol 263:211–225

Tonk L, Bongaerts P, Sampayo E, Hoegh-Guldberg O (2013) SymbioGBR: a web-based database of Symbiodinium associated with cnidarian hosts on the Great Barrier Reef. BMC Ecol 13:7

Towle EK, Enochs IC, Langdon C (2015) Threatened Caribbean Coral Is Able to Mitigate the Adverse Effects of Ocean Acidification on Calcification by Increasing Feeding Rate. PLoS ONE 10:e0123394

Trapido-Rosenthal H, Zielke S, Owen R, Buxton L, Boeing B, Bhagooli R, Archer J (2005) Increased Zooxanthellae Nitric Oxide Synthase Activity Is Associated with Coral Bleaching. Biol Bull 208:3–6

168 Tremblay P, Fine M, Maguer JF, Grover R, Ferrier-Pages C (2013) Ocean acidification increases photosynthate translocation in a coral-dinoflagellates symbiosis. Biogeosciences Discuss 10:83–109

Tremblay P, Naumann M, Sikorski S, Grover R, Ferrier-Pagès C (2012) Experimental assessment of organic carbon fluxes in the scleractinian coral Stylophora pistillata during a thermal and photo stress event. Mar Ecol Prog Ser 453:63–77

Vardi A, Berman-Frank I, Rozenberg T, Hadas O, Kaplan A, Levine A (1999) Programmed cell death of the dinoflagellate Peridinium gatunense is mediated by CO2 limitation and oxidative stress. Curr Biol 9:1061–1064

Vass I, Styring S, Hundal T, Koivuniemi A, Aro E, Andersson B (1992) Reversible and irreversible intermediates during photoinhibition of photosystem II: stable reduced QA species promote chlorophyll triplet formation. Proc Natl Acad Sci U S A 89:1408–1412

Vega Thurber RL, Burkepile DE, Fuchs C, Shantz AA, McMinds R, Zaneveld JR (2014) Chronic nutrient enrichment increases prevalence and severity of coral disease and bleaching. Glob Change Biol 20:544–554

Veron JEN (1986) Corals of Australia and the Indo-Pacific. Angus & Robertson Publishers, North Ryde, Australia

Veron JEN, Hoegh-Guldberg O, Lenton TM, Lough JM, Obura DO, Pearce-Kelly P, Sheppard CRC, Spalding M, Stafford-Smith MG, Rogers AD (2009) The coral reef crisis: The critical importance of <350ppm CO2. Mar Pollut Bull 58:1428–1436

Veron JEN, Pichon M (1976) Scleractinia of Eastern Australia, Part 1, Families Thamnasteriidae, Astrocoeniidae, Pocilloporidae. Australian Government Publishing Service, Canberra

Veron JEN, Wallace CC (1984) Scleractinia of Eastern Australia. Part V. Family Acroporidae. Australian Institute of Marine Science in association with Australian National University Press, Canberra

Vidal-Dupiol J, Zoccola D, Tambutté E, Grunau C, Cosseau C, Smith KM, Freitag M, Dheilly NM, Allemand D, Tambutté S (2013) Genes Related to Ion-Transport and Energy Production Are Upregulated in Response to CO2-Driven pH Decrease in Corals: New Insights from Transcriptome Analysis. PLoS ONE 8:e58652

Vogel N, Meyer F, Wild C, Uthicke S (2015) Decreased light availability can amplify negative impacts of ocean acidification on calcifying coral reef organisms. Mar Ecol Prog Ser 521:49–61

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

Wall CB, Fan TY, Edmunds PJ (2014) Ocean acidification has no effect on thermal bleaching in the coral Seriatopora caliendrum. Coral Reefs 33:119–130

Wall CB, Mason RAB, Ellis WR, Cunning R, Gates RD (2017) Elevated pCO2 affects tissue biomass composition, but not calcification, in a reef coral under two light regimes. R Soc Open Sci 4:170683

169 Wallace CC (1999) Staghorn corals of the world: A revision of the coral genus Acropora. CSIRO Publishing, Collingwood, Victoria

Wangpraseurt D, Holm JB, Larkum AWD, Pernice M, Ralph PJ, Suggett DJ, Kühl M (2017) In vivo Microscale Measurements of Light and Photosynthesis during Coral Bleaching: Evidence for the Optical Feedback Loop? Front Microbiol 8:

Wangpraseurt D, Larkum AWD, Ralph PJ, Kühl M (2012) Light gradients and optical microniches in coral tissues. Front Microbiol 3:316

Wanninkhof R (2014) Relationship between wind speed and gas exchange over the ocean revisited: Gas exchange and wind speed over the ocean. Limnol Oceanogr Methods 12:351–362

Ward S, Harrison P, Hoegh-Guldberg O (2000) Coral bleaching reduces reproduction of scleractinian corals and increases susceptibility to future stress. 2:1123–1128

Warner M, Chilcoat G, McFarland F, Fitt W (2002) Seasonal fluctuations in the photosynthetic capacity of photosystem II in symbiotic dinoflagellates in the Caribbean reef-building coral Montastraea. Mar Biol 141:31–38

Warner ME (2005) An overview of the interpretation and current use of chlorophyll fluorescence to understand coral bleaching. Understanding the stress response of corals and Symbiodinium in a rapidly changing environment. Proceedings of the Bleaching Working Group Inaugural Workshop, Puerto Morelos. The Coral Reef Targeted Research & Capacity Building for Management (CRTR) Program, Brisbane, Australia and Washington DC, USA, pp 8–9

Warner ME, Fitt WK, Schmidt GW (1996) The effects of elevated temperature on the photosynthetic efficiency of zooxanthellae in hospite from four different species of reef coral: a novel approach. Plant Cell Environ 19:291–299

Warner ME, Fitt WK, Schmidt GW (1999) Damage to photosystem II in symbiotic dinoflagellates: A determinant of coral bleaching. Proc Natl Acad Sci U S A 96:8007–8012

Warner ME, LaJeunesse TC, Robison JD, Thur RM (2006) The ecological distribution and comparative photobiology of symbiotic dinoflagellates from reef corals in Belize: Potential implications for coral bleaching. Limnol Oceanogr 51:1887–1897

Weeks SJ, Anthony KRN, Bakun A, Feldman GC, Hoegh-Guldberg O (2008) Improved predictions of coral bleaching using seasonal baselines and higher spatial resolution. Limnol Oceanogr 53:1369–1375

Weis VM, Smith GJ, Muscatine L (1989) A “CO2 supply” mechanism in zooxanthellate cnidarians: role of carbonic anhydrase. Mar Biol 100:195–202

Wellington GM, Glynn PW, Strong AE, Navarrete SA, Wieters E, Hubbard D (2001) Crisis on coral reefs linked to climate change. Eos Trans Am Geophys Union 82:1–5

Weston AJ, Dunlap WC, Beltran VH, Starcevic A, Hranueli D, Ward M, Long PF (2015) Proteomics Links the Redox State to Calcium Signaling During Bleaching of the Scleractinian Coral Acropora microphthalma on Exposure to High Solar Irradiance and Thermal Stress. Mol Cell Proteomics 14:585–595

Wethey DS, Porter JW (1976) Sun and shade differences in productivity of reef corals. Nature 262:281–282

170 Wham DC, Ning G, LaJeunesse TC (2017) Symbiodinium glynnii sp. nov., a species of stress- tolerant symbiotic dinoflagellates from pocilloporid and montiporid corals in the Pacific Ocean. Phycologia 56:396–409

Whitaker JR, Granum PE (1980) An absolute method for protein determination based on difference in absorbance at 235 and 280 nm. Anal Biochem 109:156–159

Wijgerde T, Jurriaans S, Hoofd M, Verreth JAJ, Osinga R (2012) Oxygen and Heterotrophy Affect Calcification of the Scleractinian Coral Galaxea fascicularis. PLoS ONE 7:e52702

Wild C, Huettel M, Kleuter A, Kremb SG, Rasheed MYM, Jørgensen BB (2004) Coral mucus functions as an energy carrier and particle trap in the reef ecosystem. Nature 428:66–70

Williams E, Bunkley-Williams L (1990) The World-Wide Coral Reef Bleaching Cycle and Related Sources of Coral Mortality. Atoll Res Bull 335:1–71

Winters G, Holzman R, Blekhman A, Beer S, Loya Y (2009) Photographic assessment of coral chlorophyll contents: Implications for ecophysiological studies and coral monitoring. J Exp Mar Biol Ecol 380:25–35 van Woesik R, Sakai K, Ganase A, Loya Y (2011) Revisiting the winners and the losers a decade after coral bleaching. Mar Ecol Prog Ser 434:67–76

Wooldridge S, Done T (2004) Learning to predict large-scale coral bleaching from past events: A Bayesian approach using remotely sensed data, in-situ data, and environmental proxies. Coral Reefs 23:96–108

Wooldridge SA (2009) A new conceptual model for the warm-water breakdown of the coral-algae endosymbiosis. Mar Freshw Res 60:483–496

Wooldridge SA (2014a) Formalising a mechanistic linkage between heterotrophic feeding and thermal bleaching resistance. Coral Reefs 33:1131–1136

Wooldridge SA (2014b) Differential thermal bleaching susceptibilities amongst coral taxa: re- posing the role of the host. Coral Reefs 33:15–27

2− Yates KK, Halley RB (2006) CO3 concentration and pCO2 thresholds for calcification and dissolution on the Molokai reef flat, Hawaii. Biogeosciences 3:357–369

Yentsch CS, Yentsch CM, Cullen JJ, Lapointe B, Phinney DA, Yentsch SW (2002) Sunlight and water transparency: cornerstones in coral research. J Exp Mar Biol Ecol 268:171–183

Yonge CM, Nicholls AG (1931) Studies on the Physiology of Corals. IV. The structure, distribution and physiology of the zooxanthellae. Great Barrier Reef Expedition 1928-29 Scientific Reports Volume 1. British Museum (Natural History), London, pp 135–176

Zunino S, Canu DM, Bandelj V, Solidoro C (2017) Effects of ocean acidification on benthic organisms in the Mediterranean Sea under realistic climatic scenarios: A meta-analysis. Reg Stud Mar Sci 10:86–96

171 7 Appendices

7.1 Light spectrum of the metal halide lamps used in Chapter 2.

2.5

2

1.5

1

µmol quanta·mˉ²·nmˉ¹·sˉ¹ µmol 0.5

0 5 5 0 5 0 0 0 5 0 5 0 5 5 0 5 0 0 0 5 0 2 5 7 8 0 3 6 7 9 0 2 3 6 8 9 1 4 7 8 0 3 340 3 3 3 4 415 4 445 4 4 4 5 5 5 550 5 5 5 6 625 6 655 6 6 7

Wavelength (nm)

Figure 7.1: Light spectrum of the 10,000 K metal halide lamps from 325-700 nm. Note the presence of substantial longer-wavelength UVA. The spectrum was measured in 1 nm increments with a FieldSpec® HandHeld 2™ Spectroradiometer (ASD Inc., Boulder, Colorado).

172 7.2 Determination of correlation type for the chlorophyll fluorescence time-series.

Time-series analysis was performed on Fv/Fm time-series of (a) the median of the aquarium median per treatment and (b) the mean of the aquarium median per treatment. To identify the types of patterns present in these time-series, I used time-series diagnostics that included visual inspection of the time-series plots, plots of the autocorrelation function (ACF) and plots of the partial autocorrelation function (PACF). Interpretation of these time-series diagnostics was informed by Romer (2016). The ACF and PACF plots were created using the astsa package in R version 3.3.1 (R

Core Team 2016). Missing Fv/Fm values on days 15 and 16 were filled in by taking, respectively, the median or mean (as appropriate) Fv/Fm of days 11-19 (excluding 15-16), and the median or mean Fv/Fm of days 12-20 (excluding 15-16). A repeated measures ANOVA for days 1-14 was performed by modelling the data with a Generalised Least Squares model, under the following four correlation types: a compound symmetry model, an autoregressive model of order 1, a moving average model of order 1, and a combined autoregressive and moving average model (of order 1). I allowed all models to permit heteroskedastic variances. The model with the most efficacious correlation type was identified by comparing the Akaike Information Criterion and Bayesian Information Criterion of each model.

Two potential models were identified in the time-series analysis of Fv/Fm, an autoregressive model of order one (AR(1)), and a moving average model of order one (MA(1)). An autoregressive model of order one has the form that xt = μ + Φt·xt -1+ wt, where xt is the Fv/Fm value at day t, μ is the average Fv/Fm over all days, wt is the error at day t, and Φt is a coefficient specific to day t (Romer

2016). A moving average model of order one has the form that that xt = μ + wt + θt·wt – 1, where wt – 1 is the error at the previous day, and θt is a coefficient specific to day t (Romer 2016). In both cases the wt are assumed to be independently identically distributed, and normally distributed, with mean zero and with the same variance, at each day. An autoregressive and moving average model with both components of order one (ARMA(1)) has the form xt = μ + Φt·xt -1+ wt + θt·wt – 1. The ARMA model can be expanded to include higher order terms for either the autoregressive component or the moving average component, or for both (Romer 2016).

Based on an interpretation of the ACF and PACF plots, and the AIC values of four candidate models, the best model for the Fv/Fm time-series for many of the treatments is an ARMA model (a model with both AR and MA terms). Identification of an AR model is often best accomplished

173 using the PACF, which will have significant values for the first x lags, where x is the order of the model, and non-significant values after that (Romer 2016). In contrast, identification of an MA model is often best accomplished with the ACF, which will have the same pattern as the PACF for an AR model that is described above (Romer 2016). Where both PACF and ACF have significant values at the first x and y lags, respectively, and non-significant values after that, an ARMA model may be indicated. This is the pattern that I found for many of the treatments in both species (Table 7.1). In addition, a repeated-measures GLS model with ARMA(1) correlation type had the best AIC values out of all correlation types tested (Table 7.2).

174 Table 7.1: ACF results and PACF results for the chlorophyll fluorescence time-series.

“Autoregressive” is abbreviated as “AR”, “moving average” is abbreviated as “MA”, “number” is abbreviated as “#” and “significant” is abbreviated as “sig.”. A. muricata A. Porites mounding spp. quanta·m Light (µmol Light 282 218 117 282 218 117 282 218 117 282 218 117 -2 ·s -1 ) Temperature (°C) 29 29 29 24 24 24 29 29 29 24 24 24 # of sig. lags in sig. # of ACF 1 4 1 0 0 0 1 2 1 0 1 0 Median of aquarium median aquarium of Median # of sig. lags in sig. # of 1st, 3rd values 3rd 1st, PACF sig. 1 1 1 0 0 1 1 1 0 1 0 terms to terms order 2, AR to Possible model Possible order1 of 3,MA or ARMA with ARof with ARMA AR of order 1 of order AR ARMA with ARMA MA with terms to terms order 1. to ARMA of ARMA of ARMA of ARMA of ARMA of ARMA order order 1 1 order 1 order order 1 1 order None None None None order4 # of sig. lags in sig. # of ACF 0 1 0 0 0 0 1 1 1 0 0 0 Mean of aquarium median aquarium of Mean # of sig. lags in sig. # of PACF 0 1 0 0 0 0 1 1 1 0 0 0 Possible model Possible ARMA of ARMA of ARMA ofARMA of ARMA order order 1 1 order 1 order order 1 None None None None None None None None

175 Table 7.2: AIC and BIC values for the GLS models fitted to the chlorophyll fluorescence time- series data for days 1-14.

AIC and BIC values are listed separately for A. muricata and mounding Porites. All models permitted heteroskedasticity.

A. muricata mounding Porites spp.

Model Type d.f. AIC BIC AIC BIC

Compound Symmetry 169 -1246.17 -649.69 -964.10 -367.63

AR(1) 169 -1277.30 -680.83 -1000.51 -404.04

ARMA(1) 170 -1279.19 -679.19 -1009.4 -409.4

MA(1) 169 -1265.67 -669.2 -896.55 -300.07

176 7.3 Probability values for temporal control coral samples used in Chapter 3

Table 7.3: ANOVA table of physiological parameters compared among the control (22°C) treatments of each of the four sub-experiments. Probability values less than 0.05 are highlighted in bold.

net -2 net -1 -2 -2 Coral Fv/Fm Qm NPQ Pmax ·cm Pmax ·cell Protein·cm Cells·cm

A. muricata Light <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 F4,50=5.05 F4,50=15.9 F4,50=255 F4,18=4.94 F4,13=9.31 F4,15=25.6 F4,18=17.8 Sub- 0.02 <0.01 0.03 0.6 0.63 0.1 0.23 Experiment F3,50=3.46 F3,50=5.99 F3,50=3.33 F1,18=0.28 F1,13=0.25 F1,15=3.09 F1,18=1.54 Light×Sub- 0.79 0.14 <0.01 0.35 0.6 0.21 0.51 Experiment F12,50=0.65 F12,50=1.53 F12,50=3.15 F4,18=1.18 F4,13=0.7 F4,15=1.67 F4,18=0.86 Mounding Porites sppׅ Light <0.01 <0.01 <0.01 0.02 0.04 0.1 0.04 F2,30=73.2 F2,30=29.9 F2,30=28.2 F2,18=4.74 F2,13=4.05 F2,14=2.79 F2,13=4.25 Sub- 0.03 0.15 0.29 0.11 0.11 0.26 0.04 Experiment F3,30=3.56 F3,30=1.91 F3,30=1.3 F2,18=2.45 F2,13=2.69 F2,14=1.47 F2,13=4.02 Light×Sub- 0.91 0.92 0.72 0.49 0.79 0.81 0.95 Experiment F6,30=0.33 F6,30=0.32 F6,30=0.61 F4,18=0.89 F4,13=0.42 F4,14=0.4 F4,13=0.17 M. monasteriata

Light <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

F4,50=54.4 F4,50=81.4 F4,50=49.1 F4,27=4.22 F4,17=16 F4,25=6.06 F4,19=177 Sub- <0.01 0.85 0.65 <0.01 0.8 <0.01 <0.01

Experiment F3,50=18.9 F3,50=0.26 F3,50=0.55 F2,27=47.7 F2,17=0 F2,25=11.1 F2,19=51.2 Light×Sub- 0.07 0.66 0.91 <0.01 <0.01 <0.01 <0.01 Experiment F12,50=1.80 F12,50=0.79 F12,50=0.5 F8,27=5.33 F8,17=8 F8,25=5.53 F8,19=5.06

177 7.4 Graphs of parameters for which statistically significant interactive effects exist

2 27°C 29°C 31°C 1.8 1.6 1.4 1.2

1 0.8

0.6 0.4

Symbiont density (× cells·cmˉ²) 10ᶝ density Symbiont 0.2 0 91 165 226 307 371 91 165 226 307 371 91 165 226 307 371

light level (µmol photons·mˉ²·sˉ¹)

1 1 2 2 3 3 3 4 4 5 5 6 6 6 7 7 8 8 9 9 9 10 10 10 10 11 11 11 11 12 12 13 13 14 14 14 15 15 15 16 16 16 16 17 17 17 18 18 18 19 19 19 19 20 20 20 21 21 21 22 22 22 22 23 23 23 24 24 24 25 25 25 25 26 26 26 27 27 27 27 28 28 28 28 29 29 29 29 30 30 30 31 31 31 32 32 32 33 33 33 34 34 34 35 35 35 Figure 7.2: Symbionts·cm⁻² in Acropora muricata, with light × temperature interactions shown. In the table below the graph, treatments labelled with the same number (each column corresponds to the treatment directly above the column) are significantly different from one another (p < 0.05) in the post hoc test of the interactive effect of light and temperature. Error bars are standard error of the mean.

178 3.5 27°C 29°C 31°C 3

2.5

2

1.5

1

0.5 Symbiont density (× cells·cmˉ²) 10ᶝ density Symbiont

0 91 165 226 307 371 91 165 226 307 371 91 165 226 307 371

light level (µmol photons·mˉ²·sˉ¹)

1 1 2 2 3 3 3 4 44 5 5 6 6 7 7 88 8 99 9 10 10 10 11 11 11 12 12 12 13 13 13 14 14 14 15 15 15 16 16 16 17 17 17 18 18 18 19 19 19 20 20 20 21 21 21 22 22 22 23 23 23

Figure 7.3: Symbionts·cm⁻² in Montipora monasteriata, with light × temperature interactions shown. In the table below the graph, treatments labelled with the same number (each column corresponds to the treatment directly above the column) are significantly different from one another (p < 0.05) in the post hoc test of the interactive effect of light and temperature. Error bars are standard error of the mean.

179 0.7 25°C 27°C 29°C 31°C 0.6

0.5

0.4 m /F v

F 0.3

0.2

0.1

0 91 226 371 91 226 371 91 226 371 91 226 371

light level (µmol photons·mˉ²·sˉ¹)

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

Figure 7.4: Fv/Fm in mounding Porites spp., with light × temperature interactions shown. In the table below the graph, treatments labelled with the same number (each column corresponds to the treatment directly above the column) are significantly different from one another (p < 0.05) in the post hoc test of the interactive effect of light and temperature. Error bars are standard error of the mean.

180 0.7 25°C 27°C 29°C 31°C 0.6

0.5

0.4 m /F

v 0.3 F 0.2

0.1

0 91 165 226 307 371 91 165 226 307 371 91 165 226 307 371 91 165 226 307 371

light level (µmol photons·mˉ²·sˉ¹)

1 1 2 2 3 3 4 4 4 5 5 5 5 6 6 6 7 7 7 7 8 8 8 8 9 9 9 10 10 10 11 11 11 12 12 12 13 13 13 14 14 14 15 15 16 16 17 17 17 18 18 18 19 19 19 19 19 20 20 20 20 20 20 21 21 21 21 21 21 22 22 22 22 22 23 23 23 23 23 24 24 24 24 24 25 25 25 25 25 26 26 26 26 26 27 27 27 27 27 27 28 28 28 28 28 28 29 29 29 29 29 30 30 30 30 30 31 31 31 31 31 32 32 32 32 32 33 33 33 33 34 34 34 34 34 35 35 35 35 35 36 36 36 36 37 37 37 37 38 38 38 38 39 39 39 39 40 40 40 40 40 41 41 41 41 41 41 42 42 42 42 42 42 43 43 43 43 43 44 44 44 44 44 45 45 45 45 45 46 46 46 46 46 47 47 47 47 47 48 48 48 48 48 48 49 49 49 49 49 49 50 50 50 50 50 51 51 51 51 51 52 52 52 52 52 53 53 53 53 53 54 54 54 54 55 55 55 55 55 56 56 56 56 56 57 57 57 57 58 58 58 58 59 59 59 59 60 60 60 60 61 61 61 61 62 62 62 62 62 63 63 63 63 63 64 64 64 64 64 65 65 65 65 65 65 66 66 66 66 66 66 67 67 67 67 67 68 68 68 68 68 68 69 69 69 69 69 69 70 70 70 70 71 71 71 71 71 72 72 72 72 72 73 73 73 73 73 73 74 74 74 74 74 75 75 75 75 75 75 76 76 76 76 77 77 77 77 78 78 78 78 78 79 79 79 79 79 80 80 80 80 80 81 81 81 81 81 82 82 82 82 83 83 83 83 84 84 84 84 84 85 85 85 85 85 86 86 86 86 86 87 87 87 87 87 88 88 88 88 89 89 89 89 90 90 90 90 90 91 91 91 91 91 92 92 92 92 92 93 93 93 93 93

Figure 7.5: Fv/Fm in Montipora monasteriata, with light × temperature interactions shown. In the table below the graph, treatments labelled with the same number (each column corresponds to the treatment directly above the column) are significantly different from one another (p < 0.05) in the post hoc test of the interactive effect of light and temperature. Error bars are standard error of the mean.

181 0.4 25°C 27°C 29°C 0.35

0.3

0.25

0.2 m Q 0.15

0.1

0.05

0 91 165 226 307 371 91 165 226 307 371 91 165 226 307 371 -0.05

light level (µmol photons·mˉ²·sˉ¹)

1 1 2 2 3 3 4 4 5 5 6 6 7 7 7 8 8 8 9 9 9 10 10 10 11 11 11 12 12 12 13 13 13 14 14 14 15 15 15 16 16 16 17 17 17 18 18 18 19 19 19 20 20 20 21 21 21 22 22 22 23 23 23 24 24 24 25 25 25 26 26 26 27 27 27 28 28 28 28 29 29 29 29 30 30 30 30 31 31 31 31 31 32 32 32 32 32 33 33 33 33 33 34 34 34 34 35 35 35 35 36 36 36 36 37 37 37 37 38 38 38 38 39 39 39 39 40 40 40 40 41 41 41 41 42 42 42 42 43 43 43 43 44 44 44 44 45 45 45 45 46 46 46 46 47 47 47 47 48 48 48 48

Figure 7.6: Qm in Montipora monasteriata, with light × temperature interactions shown. In the table below the graph, treatments labelled with the same number (each column corresponds to the treatment directly above the column) are significantly different from one another (p < 0.05) in the post hoc test of the interactive effect of light and temperature. Error bars are standard error of the mean.

182 7.5 Effects of temperature on calcification rate

7.5.1 A. muricata linear extension

In the control corals, sub-experiment (SE) did not have a significant main effect on daytime calcification (F2,17 = 2.13534, p = 0.1488). However, there was a significant interaction of light level and SE (F4,17 = 3.50543, p = 0.0292, Figure 7.7), and light level also had a significant main effect

(F2,17 = 26.3614, p < 0.0001). In the main effect of light, 91 µmol < 226 µmol = 371 µmol.

In the experimental corals, both temperature level (F2,21 = 44.26753, p < 0.0001) and light level

(F2,21 = 50.14564, p < 0.0001) had a significant main effect on daytime calcification, and there was also an interactive effect of the two (F4,21 = 11.30185, p < 0.0001, Figure 7.8). In the main effect of light, 91 µmol = 226 µmol, 226 µmol = 371 µmol, and 91 µmol < 371 µmol. In the main effect of temperature, 27°C > 29 °C > 31°C.

150 *^~! 91 µmol 226 µmol 371 µmol $# 100

₃ ⁻ ⁻ ! 50 ^ *# ~$ 0

-50 Calcificationrate(nmol CaCO ·cm ²·hr ¹)

-100 2 3 4 2 3 4 2 3 4

sub-experiment

Figure 7.7: calcification rates per unit of coral surface area of the control specimens of A. muricata. The symbols indicate treatments with pairwise differences (p < 0.05) in the post hoc test of the interaction of sub-experiment and light level. Error bars are standard error of the mean. 183 300 a^c?f+ 91 µmol 250 226 µmol 200 371 µmol *b~d!e

₃150 ⁻ ⁻

100 df 50 e? c~ !+ d!e?f+ ^b a* 0

-50

-100 Calcificationrate(nmol CaCO ·cm ²·hr ¹) -150 27 29 31 27 29 31 27 29 31

temperature (°C)

Figure 7.8: calcification rates per unit of coral surface area of the experimental specimens of A. muricata. The letters and symbols indicate statistically significant differences (p < 0.05) between or among the treatments in the post hoc test of the interactive effect of temperature and light. Error bars are standard error of the mean.

7.5.2 Mounding Porites spp. linear extension

In the temperature control corals (Figure 7.9), surface area was not found to differ between the three sub-experiments (F2,18 = 0.1184, p = 0.889), suggesting (counter-intuitively) that surface area did not increase through time. There was no main effect of light (F2,18 = 0.8147, p = 0.4585) nor any interactive effect of light and sub-experiment (F4,18 = 0.2520, p = 0.9047). In the experimental corals

(Figure 7.10), there was a statistically significant main effect of temperature on surface area (F2,27 = 4.3079, p = 0.0238). However, no statistically significant differences in surface area among temperatures were revealed in the post hoc tests, though there was a slight trend for surface areas to increase with elevations in temperature. There was no main effect of light (F2,27 = 0.6082, p =

0.5516) or interactive effect of light and temperature (F4,27 = 0.3131, p = 0.8667).

184 2500

2000

1500

1000 Surface area (mm²) area Surface 500

0 2 3 4

sub-experiment

Figure 7.9: coral surface areas at each sub-experiment’s end of the control specimens of mounding Porites. There were no significant differences among treatments in post hoc test of the main effect of temperature. Error bars are standard error of the mean.

2500

2000

1500

1000 Surface area (mm²) area Surface 500

0 27 29 31

temperature (°C)

Figure 7.10: coral surface areas at each sub-experiment’s end of the experimental specimens of mounding Porites. There were no significant differences among treatments in post hoc test of the main effect of temperature. Error bars are standard error of the mean.

185 7.6 Modelling of future levels of ocean acidification in Kāne‘ohe Bay

Levels of pCO2 under future greenhouse gas emissions scenarios are well modelled for the open ocean, however, the influence of biology, bathymetry and other local effects may steer future pCO2 conditions away from equilibrium with the ocean above coastal coral reefs. To examine this effect in Kāne‘ohe Bay, I focused on the barrier reef that separates much of Kāne‘ohe Bay from the open ocean. The barrier reef is ~10 km long, ~2 km wide and ~2 m deep, and is well characterised biophysically (Shamberger et al. 2011). Predominant easterly winds drive waves that break on the eastern (open ocean) side of the barrier reef, creating wave setup that drives current from east to west across the barrier reef, pulling oceanic water into Kāne‘ohe Bay (Hearn 1999). As oceanic water travels over the barrier reef, its pCO2 content is modified through the interacting effects of benthic and microbial pelagic metabolism, changes in temperature and salinity and air-sea CO2 flux.

I constructed an algorithm in R to model the changes in carbonate chemistry in a seawater parcel as it moves from the seaward to the inner-bay side of Kāne‘ohe Bay barrier reef. To validate the model, the results were compared to measurements of carbonate chemistry made at the inner-bay side of Kāne‘ohe Bay barrier reef (Shamberger et al. 2011). The model was then executed under future predicted conditions of oceanic CO2 and atmospheric CO2 to estimate the carbonate chemistry of seawater on the inner-bay side of the barrier reef under future scenarios. In this modelling exercise, I used data from several sources. Shamberger et al. (2011) measured biologically-induced changes in the water column above the barrier reef due to photosynthesis, metabolism and calcification. Bathen (1968) measured the rate of change in the temperature and salinity of water parcels as they passed over the barrier reef.

CO2 solubility in seawater was calculated with equation F in Table 2 of Wanninkhof (2014). The density of seawater and the Schmidt number for CO2 were calculated with R packages oce (function swRho) and marelac (function gas_schmidt), respectively. CO2 transfer velocity was calculated via two methods. Firstly, with a wind speed parameterisation (Nightingale et al. 2000) that provides good agreement with measured CO2 transfer velocity values at an inner section of Kāne‘ohe Bay (a section that has a bimodal depth distribution with modes at 3 metres and 10-14 metres) (Ho et al. 2017). Secondly, with a friction velocity parameterisation (Komori and Shimada 1995) that has provided results close to measured transfer velocities on a coral reef flat (Kitada et al. 2006).

Model results for both parameterisations of the CO2 transfer velocity are reported. The friction velocity was calculated from wind speed using the results of Komori et al. (1993, equation 1). Air-

186 sea CO2 flux was calculated with the standard equation for this parameter (Wanninkhof 2014). The model structure is specified in the following figure (Figure 7.11).

I used a current speed of 10 cm·s-1 (Falter et al. 2008) and a timestep of 10 s so that the parcel of water travelled 1 m during every iteration of the model, and a present day atmospheric pCO2 concentration of 400 µatm. I performed model runs at 12 noon and midnight, with water temperature at 25°C at both times. I assumed that the rates of change in temperature and salinity during the day (Bathen 1968) were due to solar heating and evaporation, and so assumed no change in temperature and salinity at midnight.

The model predicted a mean seawater pCO2 at midday of 375.39 µatm and 375.31 µatm (n = 7 days) (using the Nightingale et al. (2000) and Komori and Shimada (1995) CO2 transfer velocity parameterisations, respectively) at the inner-bay edge of the barrier reef, slightly lower than the mean, 406 µatm, of midday measurements made at this location over the same 7 days (Shamberger et al. 2011). At midnight the model predicted a mean seawater pCO2 of 323.37 µatm and 324.39

µatm (n = 5 days) (using the Nightingale et al. (2000) and Komori and Shimada (1995) CO2 transfer velocity parameterisations, respectively), whilst the mean of midnight measurements over the same five days was 400 µatm (Shamberger et al. 2011). The larger discrepancy between the measured and predicted values at night may be because rates of change in temperature and salinity at night are greater than zero, so without data on this, I did not proceed with further modelling of nighttime values.

I then updated the model to reflect the RCP8.5 greenhouse gas concentration pathway at the end of the twenty-first century. This scenario has 3.7°C of warming (IPCC 2013), and an atmospheric CO2 concentration of 900 ppm (Shaw et al. 2013). Assuming equilibrium between the open ocean and atmosphere, and that salinity and alkalinity remained unchanged from present day values (as per Andersson et al. 2014), I calculated oceanic DIC under RCP8.5 using seacarb in R. I assumed that the rates of change in seawater temperature and salinity over the barrier reef during the day were the same as in the present day model. Rates of net productivity and net ecosystem calcification were also kept the same as the present day values, following the approach of Shaw et al. (2013). The mean level of pCO2 predicted by this model for Kāne‘ohe Bay under the RCP8.5 scenario at midday (n = 7 days) were 785.97 µatm (using the Nightingale et al. (2000) transfer velocity) and 776.51 µatm (using the Komori and Shimada (1995) transfer velocity).

187 188