# data[].attributes.headline
Finding Bright Spots in the Global Coral Reef Catastrophe

# data[].attributes.subject
Finding Bright Spots in the Global Coral Reef Catastrophe

# data[].attributes.body
News piece on current efforts & our determination to look up, not down!

# data[].attributes.attachment_link.readable_text
Alexis Rosenfeld / Getty Images

The first-ever report on the world's coral reefs presents a grim picture, as losses mount due to global warming. But there are signs of hope - some regions are having coral growth, and researchers found that corals can recover if given a decade of reprieve from hot water.

When ecological genomicist Christian Voolstra started work on corals in Saudi Arabia in 2009, one of the biggest bonuses to his job was scuba diving on the gorgeous reefs. Things have changed. "I was just back in September and I was shocked," says Voolstra, now at the University of Konstanz in Germany. "There's a lot of rubble. The fish are missing. The colors are missing."

It's a sad but now familiar story. Earlier this month, the Global Coral Reef Monitoring Network released the first-ever report collating global statistics on corals, documenting the status of reefs across 12,000 sites in 73 countries over 40 years. Overall, they report, the world has lost 14 percent of its corals from 2009 to 2018 - that's about 11,700 square kilometers of coral wiped out.

"If this had happened to the Amazon, if overnight it had turned white or black, it would be in the news everywhere," says Voolstra. "Because it's underwater, no one notices."

Corals are facing tough times from global warming: Prolonged marine heat waves, which are on the rise, cause corals to expel their symbiotic algae (called zooxanthellae), leaving the bleached corals weak and vulnerable. Local pollution continues to be a problem for corals, but global warming is emerging as the predominant threat. In 2018, the International Panel on Climate Change reported that 1.5 degrees Celsius of global warming would cause global coral reefs to decline by 70-90 percent (warming currently stands at 1.2 degrees C). A 2-degree C warmer world would lose more than 99 percent of its corals.

"The point is you save some reefs so they can go through the dark ages of climate change," says a coral researcher.

There are some hints of hope. The Global Coral Reef Monitoring Network report shows that corals can recover globally if given about a decade of reprieve from hot waters. Some spots - particularly the Coral Triangle in East Asia, which hosts nearly a third of global corals - have bucked the trend and seen coral growth. There are hints that corals might be adapting to warmer conditions. And research is burgeoning on creative ways to improve coral restoration, from selectively breeding super corals to spreading probiotics on stressed reefs.

"I'm hopeful," says Voolstra. But it's going to take a lot of quick action, he says, and even then we won't be able to save all reefs. "That's impossible. The point is you save some reefs so they can go through the dark ages of climate change."

From 1978, when the Global Coral Reef Monitoring Network's data collection began, hard coral on the world's reefs held relatively steady for decades. That changed dramatically in 1998 with the first global mass bleaching event. Warm waters around the world caused in large part by a powerful El Niño wiped out about 8 percent of living coral globally, equivalent to a grand total of 6,500 square kilometers. "All the drama started in 1998," says David Souter, coordinator of the Global Coral Reef Monitoring Network and a researcher at the Australian Institute of Marine Science in Townsville. "Corals are actually pretty good at sustaining short, sharp temperature increases, but when it starts to last months, we see real issues."

Astonishingly, however, by 2010 global coral coverage was roughly back to pre-1998 levels. "That's good news," says Souter. "Even though reefs got knocked down, they got back up again." When "old growth" corals are wiped out, the new ones that move in are often faster-growing, weedier species (just as with trees after a forest fire), says Souter. It's great to have this growth, he says, but these opportunistic corals are often more vulnerable to disease, heat, and storms.

A global decline has largely been the trend since 2010, plunging corals back below 1998 levels. That's due in large part to two more global bleaching events, in 2010 and 2015-2017, from which corals haven't been given enough reprieve. There has been a tiny, 2 percent uptick in live coral since 2019, though it's too soon to say if that might continue. "If you were a really optimistic person you might say that this occurred even while temperatures are high, so maybe we're seeing adaptation," says Souter.

During the long, relatively stable and healthy period for corals in the 1990s and early 2000s, the average reef was about 30 percent live hard coral and 15 percent macroalgae like seaweeds and turf. That's twice as much coral as algae. Since 2009, that ratio has slipped to about 1.5 as reef macroalgae has boomed by 20 percent. While seaweed also makes for a productive ecosystem, it's not the same as the complex architecture made by reefs, and it supports different fish.

Encouragingly, the so-called Coral Triangle of the East Asian Seas stands out as a bold exception. This region holds almost a third of the world's coral reefs - and it anomalously hosts more live hard coral and less macroalgae today than in the early 1980s, despite rising water temperatures. That's thought to be thanks to genetic diversity among the region's 600 species of coral, which is allowing corals to adapt to warm waters. "Perhaps diversity has provided some protection," says Souter, while a healthy population of herbivorous fish and urchins are keeping seaweeds down.

"The fact that the reefs retain the ability to bounce back, that's amazing," says the scientist who coordinated the report.

The other three main global regions for coral - the Pacific, holding more than a quarter of the global total; Australia, with 16 percent; and the Caribbean, with 10 percent - all host less coral today than when measurements started. "The Caribbean is a really tragic and desperate case," says Voolstra, with only 50 or so species of coral and a new disease wiping them out.

It could all be worse, notes Souter. "Reefs are probably, on average, better off than I thought," he says. "The fact that the reefs retain the ability to bounce back, that's amazing."

In the face of punishing conditions, coral conservationists globally are working to protect corals from pollution and actively restore them. One recent study, led by Lisa Boström-Einarsson of James Cook University in Australia, trawled through the literature and found more than 360 coral restoration projects across 56 countries. Most are focused on transplanting bits of coral from a flourishing spot to a struggling one, or "gardening" baby corals in nurseries and planting them out. They also include innovative efforts like using electricity to prompt calcification on artificial reefs (an old but still-controversial idea), and using a diamond blade saw to slice tiny, fast-growing microfragments off slow-growing corals.

Other researchers are piloting projects to spray coral larvae onto reefs that need it most - this should be faster and easier than hand-planting corals, but it's unclear yet how many of the larvae survive. "If it works, it will produce much greater gains more rapidly," says Souter.

Boström-Einarsson and colleagues found an encouragingly high average survival rate of 66 percent for the restored corals in these 362 projects. But these happy numbers mask more sobering facts. Almost half of the projects were in just a handful of countries; most lasted less than 18 months; and the median size was a tiny 100 square meters. Worse, the coral gains were often temporary. In one case in Indonesia, a three-year project dramatically increased coral cover and fish - which were then decimated by a heat wave six months after the project ended.

Such efforts are still worthwhile and raise awareness about corals, says Voolstra. But there are some techniques that could make them far more effective and far bigger in scale.

One bold strategy is to selectively breed corals to create super-strains best adapted to a warmer world - but this work is still very preliminary. "Corals take longer to breed and raise up than cows, so we have been betting more on finding heat-resistant individuals that are already out there than on making new ones in the lab," says Stephen Palumbi at Stanford University, a marine biologist who focuses on corals around the Pacific Island nation of Palau. Palumbi has developed a tank that runs small samples of coral through a heat test on site, and is now working to make it cheaper - in part, he says, by borrowing components from the home brewing industry. Voolstra, too, has developed a tool for on-site stress testing; he was this summer granted $4 million from the Paul Allen Foundation to take his effort global.

Researchers are building an emergency 'Noah's Ark' for corals, keeping their genetic diversity alive in tanks on land.

Heat tolerance, though, isn't the only thing that corals need. Selecting the ones that can survive the heat might also inadvertently select ones that are less resistant to disease, for example, or slower growing. "We need to understand this better," says Voolstra.

A different strategy is to tweak the organisms that live in and around corals and help them to grow, including the symbiotic zooxanthellae and bacteria. Getting corals to adopt heat-tolerant zooxanthellae is a great idea that could theoretically have a huge impact, says Voolstra, but it's hard to do. The union is like an intimate marriage, and it's difficult to shift. Changing corals' bacteria, which tend to live on a mucous layer on the outside of the corals, is easier, and seems to boost overall coral health. "They bleach the same way but recover better," says Voolstra. One recent study led by microbiologist Raquel Peixoto from King Abdulla University showed that lathering corals in probiotics could improve coral survival after a heat wave by 40 percent. "It's still experimental and proof of concept," says Peixoto, who is experimenting with robotic submarines that could drop slow-release probiotic pills onto reefs to release bacteria slowly over weeks.

A further-flung option being toyed with in Australia is the idea of brightening clouds over a reef in an attempt to shield them from extreme heat. "It's totally left field," laughs Souter, but should work the same way as cloud seeding for agriculture: A sprayed mist of seawater encourages clouds to form and shields the ground from direct light. This year researchers trialed the idea; they haven't yet published their results. If it works, scaling up would be a massive project: they anticipate they would need a thousand stations with hundreds of sprayers each to lower solar radiation by about 6.5 percent over the Great Barrier Reef during a heat wave. Questions remain about whether the effort would be worth the energy cost, and what the net effects would be on ecosystems throughout the region.

A lot more work needs to be done on the real-world utility of these strategies, says Voolstra, to see what actually works. "Then you put truckloads of money into whatever really makes a difference," he says. Different reefs will require different solutions, making all these strategies important says Peixoto. "It's all hands on deck.:

In the meantime, Voolstra supports the idea of investing heavily in sanctuaries: spots, like the Northern Red Sea, where corals are already adapted to handling hot waters but are threatened by other factors, like sewage, pollution, construction, and fish farms. Local efforts to tackle non-climate-related hazards for corals can be very effective. The Belize Barrier Reef Reserve System was taken off the list of World Heritage sites in danger in 2018, for example, after a push to protect that ecosystem and ban oil development.

If protecting a handful of refugia from humans doesn't seem like a big enough effort, last year researchers also launched a project to build an emergency "Noah's Ark" for corals across global aquaria, keeping their genetic diversity alive in tanks on land.

When the IPCC declared in 2018 that 99 percent of corals would be lost in a 2-degree C warmer world, says Voolstra, that was really shocking. The goal now is to whittle that 99 percent down to 90 percent or less, he says, so that reefs have at least a chance of bouncing back. "Whatever we do, it gets much worse before it gets better."

Nicola Jones is a freelance journalist based in Pemberton, British Columbia, just outside of Vancouver. With a background in chemistry and oceanography, she writes about the physical sciences, most often for the journal Nature. She has also contributed to Scientific American, Globe and Mail, and New Scientist and serves as the science journalist in residence at the University of British Columbia. More →

# data[].attributes.attachment_link.title
Finding Bright Spots in the Global Coral Reef Catastrophe

# data[].attributes.attachment_link.description
The first-ever report on the world's coral reefs presents a grim picture, as losses mount due to global warming. But there are signs of hope - some regions are having coral growth, and researchers found that corals can recover if given a decade of reprieve from hot water.

# data[].attributes.body
What nature-based adaptive mechanisms are available to spur novel intervention strategies to save coral reefs? Have a read and let us know what you think!

# data[].attributes.attachment_link.readable_text
Subscribe to Journal

Get full journal access for 1 year

All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.

Rent or Buy article

Get time limited or full article access on ReadCube.

All prices are NET prices.

Additional access options:

References

1. 17.
Gattuso, J.-P. et al. Contrasting futures for ocean and society from different anthropogenic CO ₂ emissions scenarios. Science 349, aac4722 (2015).

Article Google Scholar

2. 21.
Muller, E. M., Sartor, C., Alcaraz, N. I. & van Woesik, R. Spatial Epidemiology of the Stony-Coral-Tissue-Loss Disease in Florida. Front. Mar. Sci. 7, 163 (2020).

Article Google Scholar

3. 28.
Climate change widespread, rapid, and intensifying. IPCC (9 August 2021); https://www.ipcc.ch/2021/08/09/ar6-wg1-20210809-pr.

4. 32.
Knowlton, N. et al. Rebuilding Coral Reefs: A Decadal Grand Challenge (International Coral Reef Society and Future Earth Coasts, 2021) https://doi.org/10.53642/NRKY9386.

5. 56.
Howells, E. J. et al. Enhancing the heat tolerance of reef-building corals to future warming. Sci. Adv. 7 (2021).

6. 67.
Rodríguez-Casariego, J. A. et al. Genome-Wide DNA Methylation Analysis Reveals a Conserved Epigenetic Response to Seasonal Environmental Variation in the Staghorn Coral Acropora cervicornis. Front. Mar. Sci. 7, 822 https://doi.org/10.3389/fmars.2020.560424 (2020).

7. 105.
Muscatine, L. The role of symbiotic algae in carbon and energy flux in reef corals. Coral Reefs 25, 75-87 (1990).

Google Scholar

8. 106.
Trench, R. K. Microalgal-invertebrate symbiosis, a review. Endocytobiosis Cell Res. 9, 135-175 (1993).

Google Scholar

9. 107.
Pogoreutz, C. et al. in Cellular Dialogues in the Holobiont (eds Bosch, T. C. G. & Hadfield, M. G.) 91-118 (CRC, 2020). https://doi.org/10.1201/9780429277375-7.

10. 136.
Mies, M., Sumida, P. Y. G., Rädecker, N. & Voolstra, C. R. Marine Invertebrate Larvae Associated with Symbiodinium: A Mutualism from the Start? Front. Ecol. Evol. 5, 56 https://www.frontiersin.org/article/10.3389/fevo.2017.00056 (2017).

11. 139.
National Academies of Sciences, Engineering, and Medicine. A Research Review of Interventions to Increase the Persistence and Resilience of Coral Reefs (National Academies Press, 2019). This book reviews restoration interventions, detailing latest emerging technologies and approaches.

12. 234.
Sangsawang, L. et al. ¹³C and ¹⁵N assimilation and organic matter translocation by the endolithic community in the massive coral Porites lutea. R. Soc. Open Sci. 4, 171201 (2017).

Article Google Scholar

13. 243.
Hagedorn, M. et al. Successful demonstration of assisted gene flow in the threatened coral Acropora palmata across genetically-isolated caribbean populations using cryopreserved sperm. Cold Spring Harb. Lab. https://doi.org/10.1101/492447 (2018).

Article Google Scholar

Download references

Acknowledgements

C.R.V. acknowledges funding from the German Research Foundation (DFG) (grants 433042944 and 458901010). R.S.P. acknowledges funding from King Abdullah University of Science and Technology (grant FCC/1/1973-51-01). J.E.P. acknowledges funding from the University of South Florida Research & Innovation Internal Awards Program (grant 0142687). K.M.Q. acknowledges funding from the Australian Institute of Marine Science (AIMS). E.M.M. was supported by the Mote Eminent Scholarship and the National Science Foundation (NSF) (OCE-1452538). M.A. acknowledges funding from King Abdullah University of Science and Technology (grant FCC/1/1973-36-01).

Author information

Affiliations

1. Department of Biology, University of Konstanz, Konstanz, Germany

Christian R. Voolstra

2. Climate Change Cluster, Faculty of Science, University of Technology Sydney, Ultimo, New South Wales, Australia

David J. Suggett

3. Red Sea Research Center (RSRC), Division of Biological and Environmental Science and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

Raquel S. Peixoto & Manuel Aranda

4. Institute of Microbiology, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil

Raquel S. Peixoto

5. Department of Integrative Biology, University of South Florida, Tampa, FL, USA

John E. Parkinson

6. Australian Institute of Marine Science, Townsville, Queensland, Australia

Kate M. Quigley & David G. Bourne

7. Department of Biology, University of Miami, Coral Gables, FL, USA

Cynthia B. Silveira

8. Aquatic Research Facility, Environmental Sustainability Research Centre, University of Derby, Derby, UK

Michael Sweet

9. Mote Marine Laboratory, Sarasota, FL, USA

Erinn M. Muller

10. Department of Biological Sciences, Old Dominion University, Norfolk, VA, USA

Daniel J. Barshis

11. College of Science and Engineering, James Cook University, Townsville, Queensland, Australia

David G. Bourne

Contributions

Researching data for article: C.R.V., R.P., J.E.P., K.M.Q., C.B.S., M.S., M.A.; substantial contribution to discussion of content: C.R.V., D.J.S., R.P., J.E.P., K.M.Q., C.B.S., D.G.B., M.A.; writing: C.R.V., D.J.S., R.P., J.E.P., K.M.Q., C.B.S., D.G.B., M.A.; review/editing manuscript before submission: C.R.V., D.J.S., R.P., J.E.P., K.M.Q., C.B.S., M.S., E.M.M., D.J.B., D.G.B., M.A.

Corresponding author

Correspondence to Christian R. Voolstra.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Earth & Environment thanks D. Huang, who co-reviewed with E. Bollati; J. Wiedenmann; S.-L. Tang; and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Glossary

Coral bleaching

Discolouration of coral tissue due to the loss of microalgal symbionts triggered by climate change-induced ocean warming and thermal stress anomalies.

Assisted evolution

Human interventions aimed at speeding up natural evolutionary processes to increase the rate of adaptation of threatened species.

Adaptive capacity

The ability of coral holobionts to respond and adjust to environmental stress.

Acclimation

The physiological process of becoming accustomed to a new condition.

Environmental adaptation

The process of (meta-)organismal change used more broadly to denote adjustment to prevailing environmental conditions, for example in the context of host microbiome changes

Evolutionary adaptation

The process of genetic change through which populations become better attuned to their environment over generations.

Restoration

The action of returning something to a former condition, for instance through reinstatement of the original functional or genetic diversity.

Environmental hardening

The preconditioning of coral colonies to elevated temperatures as a means to increase tolerance to future heat stress events (can also apply to other stressors).

Microhabitats

A small area that differs from the surrounding habitat, with unique conditions that could select for unique genotypes that might not be found in the remainder of the area.

Beneficial Microorganisms for Corals

(BMCs). Umbrella term to define (microbial) symbionts that promote coral health and can be used as probiotics.

Coral probiotics

Live microorganisms to benefit coral host health.

Coral prebiotics

Molecules that modulate bacterial (microbial) association to benefit coral host health.

Lysis

A common outcome of viral infections, whereby cells are actively induced by viruses to release newly assembled viruses that can then infect other cells.

Genetically modified organisms

(GMOs). Organisms whose genomes are engineered to produce specific traits of interest.

Environmental rehabilitation

The action of restoring to an improved condition to allow species and ecosystems to thrive under altered environmental conditions.

About this article

Cite this article

Voolstra, C.R., Suggett, D.J., Peixoto, R.S. et al. Extending the natural adaptive capacity of coral holobionts. Nat Rev Earth Environ (2021). https://doi.org/10.1038/s43017-021-00214-3
Download citation

Get shareable link

# data[].attributes.attachment_link.title
Extending the natural adaptive capacity of coral holobionts - Nature Reviews Earth & Environment

# data[].attributes.attachment_link.description
Anthropogenic climate change and environmental degradation destroy coral reefs, the ecosystem services they provide, and the livelihoods of close to a billion people who depend on these services. Restoration approaches to increase the resilience of corals are therefore necessary to counter environmental pressures relevant to climate change projections.

# data[].attributes.body
Dr. Nils Rädecker won the best paper award by the Uni Konstanz Department of Biology for his recent paper "Heat stress destabilizes nutrient cycling" in PNAS. Congratulations!

# data[].attributes.body
The research interest of our group builds around the concept of 'metaorganisms' or 'holobionts' and how microbial associates support host physiology and adaptation to a changing environment. In particular, we study coral holobionts, the keystone species of the critically threatened reef ecosystems.

# data[].attributes.body
We are always seeking enthusiastic students to join our group. If you like corals, diving, computers, and want to make a difference: shoot us an email and come by!

# data[].attributes.headline
SequAna - Sequencing Analysis Core Facility

# data[].attributes.subject
SequAna - Sequencing Analysis Core Facility

# data[].attributes.body
University of Konstanz Core Facility providing support to researchers to maximize research impact through access to analytical expertise and resources.

# data[].attributes.attachment_link.title
SequAna - Sequencing Analysis Core Facility

# data[].attributes.body
CBASS 84

# data[].attributes.attachment_link.readable_text
Christian R. Voolstra , Nitin S. Baliga , Daniel J. Barshis ,

Corresponding Author

Department of Biological Sciences, Old Dominion University, Norfolk, USA
Correspondence
Christian R. Voolstra, Department of Biology, University of Konstanz, Konstanz 78457, Germany.

Email: christian.voolstra@uni-konstanz.de

Daniel J. Barshis, Department of Biological Sciences, Old Dominion University, Mills Godwin Building 302J, Norfolk, VA 23529, USA.

Email: dbarshis@odu.edu
Search for more papers by this author

Institute for Systems Biology, Seattle, USA

Departments of Biology and Microbiology, University of Washington, Seattle, USA

Molecular and Cellular Biology Program, University of Washington, Seattle, USA

Lawrence Berkeley National Laboratory, Berkeley, USA
Search for more papers by this author

Corresponding Author

Department of Biology, University of Konstanz, Konstanz, Germany
Correspondence
Christian R. Voolstra, Department of Biology, University of Konstanz, Konstanz 78457, Germany.

Email: christian.voolstra@uni-konstanz.de

Daniel J. Barshis, Department of Biological Sciences, Old Dominion University, Mills Godwin Building 302J, Norfolk, VA 23529, USA.

Email: dbarshis@odu.edu
Search for more papers by this author

First published:

Voolstra, Valenzuela, Barshis are contributed equally authors.

Abstract

Corals from the northern Red Sea, in particular the Gulf of Aqaba (GoA), have exceptionally high bleaching thresholds approaching >5℃ above their maximum monthly mean (MMM) temperatures. These elevated thresholds are thought to be due to historical selection, as corals passed through the warmer Southern Red Sea during recolonization from the Arabian Sea. To test this hypothesis, we determined thermal tolerance thresholds of GoA versus central Red Sea (CRS) Stylophora pistillata corals using multi-temperature acute thermal stress assays to determine thermal thresholds. Relative thermal thresholds of GoA and CRS corals were indeed similar and exceptionally high (~7℃ above MMM). However, absolute thermal thresholds of CRS corals were on average 3℃ above those of GoA corals. To explore the molecular underpinnings, we determined gene expression and microbiome response of the coral holobiont. Transcriptomic responses differed markedly, with a strong response to the thermal stress in GoA corals and their symbiotic algae versus a remarkably muted response in CRS colonies. Concomitant to this, coral and algal genes showed temperature-induced expression in GoA corals, while exhibiting fixed high expression (front-loading) in CRS corals. Bacterial community composition of GoA corals changed dramatically under heat stress, whereas CRS corals displayed stable assemblages. We interpret the response of GoA corals as that of a resilient population approaching a tipping point in contrast to a pattern of consistently elevated thermal resistance in CRS corals that cannot further attune. Such response differences suggest distinct thermal tolerance mechanisms that may affect the response of coral populations to ocean warming.

1 INTRODUCTION

Coral reefs have undergone drastic declines in recent years due to increasing anthropogenic pressure. In particular, coral bleaching, that is, the loss of Symbiodiniaceae, due to ocean warming is now among the main drivers of reef degradation (Hughes et al., 2018). Without their algal symbionts, corals lose their primary source of nutrition, often leading to widespread coral mortality. Hence, a better understanding of coral bleaching and the mechanistic underpinnings that influence susceptibility or resilience to thermal stress are of major importance.

The common notion is that corals can bleach and suffer mortality at just 1-2℃ above their maximum monthly mean (MMM) temperatures (Glynn and D'croz L, 1990; Jokiel & Coles, 1977, 1990), but we actually know little about empirical temperature thresholds, as most of our knowledge comes from predictive modelling, observational studies, or long-term thermal exposure experiments. Similarly, susceptibility of corals to bleaching has been attributed to genomic differences of corals and algal symbionts (Aranda et al., 2016; Barshis et al., 2013; Bhattacharya et al., 2016; DeSalvo et al., 2008, 2010; Voolstra et al., 2017), association with different microalgal symbiont species (Hume et al., 2016, 2020; Ziegler et al., 2017), or changes in the associated bacteria (Osman et al., 2020; Ziegler et al., 2017). However, it is unknown whether the same mechanisms or factors contribute to thermal tolerance of corals at large, that is, across species and sites. Corals from the Northern Red Sea, for instance, harbor exceptionally high thermal tolerance limits approaching >5℃ above their summer maxima, which are lethal for corals elsewhere (Evensen et al., 2021; Fine et al., 2013; Krueger et al., 2017; Osman et al., 2018; Savary et al., 2021). The Red Sea at large is one of the warmest ocean basins where corals thrive despite water surface temperatures commonly exceeding 30℃ in the summer (Roik et al., 2016, 2018; Voolstra et al., 2020; Ziegler et al., 2019). Nevertheless, we are uncertain about the consistency of this thermal tolerance across the gradient of the Red Sea, and whether the same holobiont assemblages and molecular mechanisms are at play across different spatial scales.

Here we sought to investigate the consistency of thermal tolerance limits across spatial scales using the Coral Bleaching Automated Stress System (CBASS) (Voolstra et al., 2020) to run a series of identical short-term thermal stress assays on colonies of the coral Stylophora pistillata from three warmer central Red Sea (CRS) sites (0.3-10 km overwater distance) and a cooler Northern Red Sea site in the Gulf of Aqaba (GoA) (~1,000 km overwater distance). Subsequent molecular examination allowed for a direct comparison of the response patterns underlying the determined temperature thresholds and revealed differences in the gene expression response and holobiont configurations that suggest distinct thermal tolerance mechanisms are at play across spatial scales. Our work demonstrates the utility of running standardized short-term heat stress assays for determination of thermal thresholds with subsequent molecular interrogation, which provide a powerful framework to identify differences in thermal tolerance response patterns and associated holobiont signatures (e.g., alleles, genes, microbes).

2 MATERIALS AND METHODS

2.1 Study sites and sample collection

In August 2018, fragments of Stylophora pistillata coral colonies were collected from one site in the northern Red Sea in the Gulf of Aqaba (GoA), the Interuniversity Institute for Marine Science (IUI) coral nursery (ICN) (N 29.50000° E 34.93333°), and three sites in the central Red Sea (CRS), Al Fahal reef (AF) (N 22.29634° E38.95914°), the ocean-facing exposed site of Tahala reef (ExT) (N 22.26189°, E 39.04878°), and the land-facing protected site of Tahala reef (PrT) (N 22.26302° E 39.05165°). At each of the four sites, seven coral colonies were sampled, with 4 ramets collected per colony, using SCUBA at depths of 2-8 m. Colonies were sampled at least 5 m apart from each other to minimize the potential of sampling clonal genotypes. Sampled specimens were stored in Ziploc plastic bags upon underwater collection and transported to shore in a cooler filled with seawater where they were subjected to short-term thermal stress assays (see below). Sample collection and short-term heat stress assays for all sites were conducted within two weeks of each other (7 August for AF, 15 August for ExT and PrT, and 20 August for ICN) in parallel at the GoA ICN site, by a team of three researchers, and at the three CRS sites (AF, ExT, and PrT), by a separate team of six researchers. Every effort was made to standardize the sampling and experimental procedures, with corals at all four sites being collected the morning of the experiment day, transported to shore in seawater-filled coolers, and immediately placed in the experimental aquaria/tanks prior to starting the thermal exposures.

2.2 Short-term thermal stress assays

Ramets of seven colonies (i.e., genets) of Stylophora pistillata from each of the four sites were subjected to short-term thermal stress assays. For the GoA site, experiments were run using a set of small containers with manual heating and chilling controls. For the central Red Sea sites, we employed the Coral Bleaching Automated Stress System (CBASS) (Voolstra et al., 2020). For each site, coral ramets from all seven colonies were distributed across four tanks, so that ramets from each colony (i.e., genet) were exposed to each one of four distinct temperature treatment conditions (baseline = 30℃, medium = 33℃, high = 36℃, extreme = 39℃), totalling 112 fragments (7 colonies × 4 temperatures × 4 sites) (Table S1). The experiments started at noon (12:00h). The four temperature treatment conditions were as follows. The baseline (control) tank was maintained at 30℃ for the entire duration of the experiment. The other three tanks were heated to 33℃, 36℃, and 39℃ over a period of 3 h. The respective temperatures were held for 3 h, and then decreased to 30℃ over the course of 1 h (19:00h). All corals were kept at 30℃ overnight until sampling the following morning, completing the 18 h short-term thermal stress assay (Voolstra et al., 2020). All tanks were continuously supplied with Red Sea seawater and light intensity was set to photosynthetic active radiation of 600 µmol photons m −2s −1 to match in situ light fields using a Li-Cor Li-193, spherical quantum sensor (Grottoli et al., 2021).

2.3 Photosynthetic efficiency and visual coral bleaching assessment

Experimental tanks were covered with a tarp at 19:00h (following the heat stress and subsequent ramping down to the control temperature) to ensure complete darkness. After 1 h (20:00h), we measured dark-adapted maximum quantum yield (F v/F m) of photosystem II of all coral fragments in all temperature treatments using a pulse amplitude modulated (PAM) fluorometer (WALZ). Temperature tolerance thresholds were determined for each site as the mean (across all genets) temperature at which photosynthetic efficiency dropped to 50% of the value at baseline temperatures, defined as the Effective Dose 50 or ED50 (Evensen et al., 2021) using the DRC package in R (Ritz et al., 2015). Statistical differences among site-specific ED50s were assessed via a one-way ANOVA (aov function) with individual genet ED50s as the response variable and site as the factor, with subsequent Tukey's post hoc testing via the TukeyHSD function in R. R script and data are available at GitHub ( https://github.com/reefgenomics/CBASS84/tree/master/CBASS84_PAM). For visual bleaching assessment, coral fragments were photographed before being snap-frozen in liquid nitrogen and stored at -80℃ until further processing (Data S1). All coral fragments showed increased paling/whiteness at higher heat stress temperatures.

2.4 RNA-Seq library generation

For nucleic acid extraction, coral tissue was sprayed off from frozen fragments using airflow from a sterile, 1,000 µl pipette tip connected via a rubber hose to a benchtop air pressure valve and 1 ml of ice-cold 0.22 µm filtered seawater (FSW) for a maximum of 3 min. Following this, an aliquot of 100 µl of the tissue slurry was added to each of 400 µl of buffer RLT (Qiagen) and 400 µl of buffer ATL (Qiagen) for subsequent RNA and DNA isolation, respectively. Total RNA was isolated from corals exposed to 30℃, 33℃, and 36℃ to make a total of 84 fragments (7 colonies × 3 temperatures × 4 sites). RNA was isolated using the Qiagen RNeasy 96 kit according to the manufacturer's recommendations. Total RNA was quantified using the Qubit RNA HS Assay Kit on a Qubit 2.0 Fluorometer (Thermo Fisher Scientific) and quality-checked using the Agilent RNA 6000 Nano kit on the Agilent 2100 bioanalyzer (Agilent Technologies). RNA-Seq libraries were prepared using the TruSeq Stranded Total RNA kit (Illumina) and sequenced on eight lanes of an Illumina HiSeq 4000 using 2 × 151 bp paired-end reads in groups of 8-11 samples/lane. Samples from the 39℃ heat stress were not processed, given that we observed visible tissue loss that potentially compromised sample integrity (Data S1). Samples ICN1-36, ICN3-33, ICN6-30, AF2-33, and PrT6-36 were removed from downstream analyses due to low number of sequence reads obtained (Table S2).

2.5 Gene expression analysis

Paired-end Illumina reads were processed using TrimGalore version 0.4.3 (Krueger, 2012) following Illumina default quality filtering steps. Reads were further trimmed for low-quality ends (Phred score <20) and cleaned up for adapter contamination with TrimGalore. For sequence alignment, reference genomic gene sets for Stylophora pistillata (v1.0) (Voolstra et al., 2017) and Symbiodinium microadriaticum (v1.0) (Aranda et al., 2016; Nand et al., 2021) were obtained from http://spis.reefgenomics.org ( n = 25,769 genes) and http://smic.reefgenomics.org ( n = 49,109 genes), respectively (Liew et al., 2016), and gene sets were combined to create a merged reference. Transcript abundance estimation was performed by using kallisto v0.44.0 (Bray et al., 2016). Differential gene expression analysis was performed using DESeq2 package v1.22.2 (Love et al., 2014) in R after importing kallisto transcript abundance estimates with the tximport package v1.14.2 (Soneson et al., 2015) and separating S. pistillata and S. microadriaticum reads prior to DESeq2 analysis. To account for large dispersion with low read counts and create more accurate log2 fold change (LFC) estimates, the lfcShrink function for shrinking LFC estimates was applied. Transcripts with s-values (Stephens, 2017) smaller than 0.005 were defined as significantly differentially expressed (Data S2). In addition, transcript quantifications in transcripts per million (TPM) were obtained using kallisto v0.44.0 (Bray et al., 2016) (Data S2).

The analysis workflow, implemented as custom python and R scripts, is available at GitHub ( https://github.com/reefgenomics/CBASS84/tree/master/CBASS84_RNASeq). Sequence data determined in this study are available under NCBI BioProject PRJNA681108 ( https://www.ncbi.nlm.nih.gov/bioproject/PRJNA681108). To determine front- and back-loaded gene candidates ( sensu Barshis et al., 2013), we filtered for genes that were at least four-fold differentially expressed at 30℃ between ICN and AF corals or their algal symbionts and that had expression values above an accumulative sum of 30 (TPM). This subset of genes was further selected for those that showed significant differential expression between 30℃ and 33℃ and between 30℃ and 36℃ in ICN or AF corals and their algal symbionts, respectively (Data S3). We also conducted the aforementioned analyses between corals from the ExT and PrT sites to test for regional front-/back-loading. Moreover, we identified the most variably expressed genes across all temperatures and reef sites to elucidate the cohort of genes that are most responsive to thermal stress (Data S3). Gene ontology (GO) enrichment analysis was done based on genomic gene set annotations available from http://reefgenomics.org and performed using the topGO package v2.42.0 (Alexa & Rahnenfuhrer, 2018) in R with a recommended cutoff of p <.001 (Data S3).

2.6 Coral host single nucleotide polymorphism (SNP) analysis

In order to confirm the uniqueness of sampled colonies (i.e., that they were true genets vs. clonal ramets) and to understand expression variation associated with genotypes and populations, SNPs for the different samples were determined based on the coral host RNA-Seq data. Raw RNA-Seq reads were mapped with STAR 2.5.3a (Dobin et al., 2013) with a single pass. BAM files were used for calling SNPs with the bcftools mpileup and call functions from SAMtools 1.8 (Li et al., 2009). Bcftools call was used with the arguments filter -s LowQual -e "QUAL<30 || DP>100" to create a single, merged vcf file for all SNPs and all samples excluding alignments with low mapping quality (<30 mapping quality) and higher than expected coverage (DP>100). This file was then further filtered using VCFtools 0.1.16 (Danecek et al., 2011) and the following options: --max-missing 0.5 --mac 3 --minQ 30 --minDP 10 --max-alleles 2 --maf 0.015 --remove-indels. Five low coverage individuals that were removed from the differential expression analyses were then removed from the vcf file and the remaining SNPs were further filtered using the --max-missing 0.95 parameter to include only SNPs that had quality calls in at least 95% of the remaining 79 individuals (Table S2). This final filtered vcf file was then converted into genepop format with a custom python script and imported into the adegenet package (Jombart, 2008) in the R environment for principal component analysis (PCA) and plotting. Pairwise allelic distances (Hamming's distance) were calculated using the poppr package (Kamvar et al., 2014) and subsequently plotted using the hclust function in R (Aichelman & Barshis, 2020). Documentation and scripts used to conduct the analysis are available at GitHub ( https://github.com/reefgenomics/CBASS84/tree/master/CBASS84_SNPs).

2.7 Symbiodiniaceae ITS2 and bacterial 16S marker gene library generation

DNA isolation was performed using the Qiagen DNeasy 96 Blood & Tissue kit (Qiagen) following the manufacturer's instructions with minor adjustments. Briefly, coral tissue samples aliquoted for DNA isolation (see above) were thawed and equilibrated to room temperature. The slurry was vortexed and 180 µl of each sample were transferred to a microtube with 20 µl of proteinase K for incubation at 56℃ for 1 h. DNA extractions were then continued according to the manufacturer's instructions. In addition to the coral samples selected for 16S marker gene sequencing, three negative controls testing for "contaminants" from (i) the FSW used for spraying off the coral tissue, (ii) the DNA extraction procedure, and (iii) PCR consumables were included to account for putative laboratory and kit contaminants introduced during sample preparation. DNA concentrations were quantified by Qubit dsDNA High Sensitivity Assay Kit (Invitrogen). ITS2 (Symbiodiniaceae) and 16S (bacteria) amplicon libraries were prepared for sequencing on the Illumina MiSeq platform. To amplify the ITS2 region, the primers SYM_VAR_5.8S2 [5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGAATTGCAGAACTCCGTGAACC-3'] and SYM_VAR_REV [5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGCGGGTTCWCTTGTYTGACTTCATGC-3'] (Hume et al., 2013, 2015, T 2018) were used (Illumina adaptor overhangs underlined). To amplify the variable regions 5 and 6 of the 16S rRNA gene, the primers 784F [5′-CGTCGGCAGCGTCAGATGTGTATAAGAGACAGAGGATTAGATACCCTGGTA-3′] and 1061R [5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGCRRCACGAGCTGACGAC-3′] (Andersson et al., https://www.ncbi.nlm.nih.gov/bioproject/PRJNA681108). 2008; Bayer et al., 2013) were used (Illumina adaptor overhangs underlined). Triplicate PCRs were performed (10-50 ng of DNA from each coral sample per PCR reaction) using the Qiagen Multiplex PCR kit and a final primer concentration of 0.5 μM in a reaction volume of 10 μl. Thermal cycling conditions for ITS2 PCR amplifications were: 95℃ for 15 min, 30 cycles of 95℃ for 30 s, 56℃ for 90 s, and 72℃ for 30 s, followed by a final extension step of 72℃ for 10 min. Thermal cycling conditions for 16S PCR amplifications were: 95℃ for 15 min, 27 cycles of 95℃ for 30 s, 55℃ for 90 s, and 72℃ for 30 s, followed by a final extension step of 72℃ for 10 min. After the PCRs, 5 µl of the PCR products were run on a 1% agarose gel to confirm successful amplification. Triplicate PCRs for each sample were pooled, and samples were cleaned using ExoProStar 1-step (GE Healthcare). Samples were indexed using the Nextera XT Index Kit v2. Successful addition of indexes was confirmed by comparing the length of the initial PCR product to the corresponding indexed sample on a 1% agarose gel. Samples were then cleaned and normalized using the SequalPrep Normalization Plate Kit (Invitrogen). The ITS2 and 16S amplicon libraries were pooled separately (4 μl per sample) and concentrated using a CentriVap Benchtop Vacuum Concentrator (Labconco). Quality of the libraries was assessed using the Agilent High Sensitivity DNA Kit on the Agilent 2100 Bioanalyzer (Agilent Technologies), quantification was done using the Qubit dsDNA High Sensitivity Assay Kit (Invitrogen). The ITS2 and 16S amplicon libraries were sequenced on a single MiSeq run. Libraries were pooled in a one-third ITS2 to two-thirds 16S ratio to achieve higher read numbers for 16S amplicons. Sequencing was performed at 6 pM with 20% phiX on the Illumina MiSeq platform at 2 x 301 bp paired-end V3 chemistry according to the manufacturer's specifications. Sequence data determined in this study are available under NCBI BioProject PRJNA681108 (

2.8 Symbiodiniaceae community analysis

The SymPortal ( https://symportal.org) analytical framework was used to analyze Symbiodiniaceae ITS2 sequence data (Hume et al., 2019). Briefly, demultiplexed and paired forward and reverse fastq.gz files outputted from the Illumina sequencing were submitted directly to SymPortal. Firstly, sequence quality control was conducted as part of the SymPortal pipeline using mothur 1.43.0 (Schloss et al., 2009), the BLAST+ suite of executables (Camacho et al., 2009), and minimum entropy decomposition (MED) (Eren et al., 2015). Then, ITS2 type profiles (representative of putative Symbiodiniaceae taxa or genotypes) were predicted and characterized by specific sets of defining intragenomic ITS2 sequence variants (DIVs). Finally, the ITS2 sequence and ITS2 type profile abundance count tables (Data S4), as well as the Bray Curtis-based between-sample and between-ITS2 type profile dissimilarities, as output by the SymPortal analysis, were directly used to plot data. Scripts used for data curation and plotting are available at GitHub ( https://github.com/reefgenomics/CBASS84/tree/master/CBASS84_ITS2).

2.9 Bacterial community analysis

Sequence data fastq files were analysed in mothur 1.39.5 (Schloss et al., 2009). Briefly, remaining adaptors and primers were trimmed and read pairs were concatenated. Unique sequences were removed using split.abund, followed by alignment to the 16 rRNA SILVA database, release 132 (Quast et al., 2013). Chimeras were identified using VSEARCH and removed for subsequent analysis. Clustering was done using the OptiClust algorithm and taxonomic annotation was done using the Greengenes (release May 2013) and SILVA (release 138) 16S rRNA databases. Sequences annotated as chloroplast, mitochondria, unknown, or archaea were discarded. OTU abundance table, taxonomy, and fasta files (Data S5) generated by the make.shared, classify.otu, and get.oturep commands in mothur respectively, were used as input files for statistical analyses in R. OTUs were considered putative contaminants if the ratio of their relative abundance across all negative controls to the relative abundance across all coral samples was >10%. Barplots showing the relative abundances of the most abundant 20 families were plotted using ggplot2 v2.3.1.0 (Wickham, 2011). Ordination plots and Bray-Curtis dissimilarities were calculated in Phyloseq v1.26.1 (McMurdie & Holmes, 2013). The same analyses were carried out in ASV space with resembling results; however, due to backward compatibility to reference OTU sequences from a previous study (in particular from the genus Endozoicomonas: Neave et al., 2017a; Neave et al., 2017b), we opted for OTU-based analyses. Scripts used for data analysis and plotting are available at GitHub ( https://github.com/reefgenomics/CBASS84/tree/master/CBASS84_16S).

3 RESULTS

3.1 Differential thermal tolerance thresholds between corals from the northern and central Red Sea

To assess the consistency of the exceptional thermal tolerance of corals from the Red Sea and the underlying holobiont patterns, we investigated the response to heat stress in Red Sea Stylophora pistillata corals. Using a set of small aquaria and the coral bleaching automated stress system (CBASS) (Voolstra et al., 2020), we determined thermal tolerance thresholds by running a series of identical short-term acute heat stress assays (Voolstra et al., 2020). Thermal thresholds were calculated as the temperature point at which a loss of 50% photosynthetic efficiency was inferred, henceforth referred to as the effective dose 50 (ED50) (Evensen et al., 2021). ED50s provide an empirical, comparable, and standardized proxy for coral thermotolerance (similar to the LD50 used in toxicological studies), based on the notion that increased/decreased loss of photosynthetic efficiency with temperature reflects differences in coral bleaching susceptibility (Voolstra et al., 2020). We determined thermal thresholds at two spatial scales: large-scale (~1,000 km overwater distance) between corals from the northern Red Sea at the IUI Coral Nursery (ICN) in the Gulf of Aqaba (GoA) and corals from the central Red Sea (CRS) at Al Fahal reef (AF) and the exposed (ExT) and protected (PrT) sites of Tahala reef, and small-scale (0.3-10 km overwater distance) between corals from the AF, ExT, and PrT CRS sites (Figure 1a). The thermal tolerance threshold of GoA corals was significantly different from all three CRS sites ( all p <.0001, Figure 1b). Notably, the ED50 thermal limit for GoA corals, deemed to be among the most temperature resilient corals in the world (Fine et al., 2013; Osman et al., 2018; Savary et al., 2021), was on average >3℃ below that of CRS corals (ED50 GoA =34.08℃ ± 0.30℃ vs. ED50 CRS =37.54℃ ± 0.30℃ [mean ±SE]). However, corals from the GoA and the CRS exhibited a temperature tolerance threshold of about +7℃ above their regional maximum monthly mean (MMM) temperature (MMM GoA: 27.01℃; MMM AF: 30.76℃; MMM ExT: 30.74℃; MMM PrT 30.75℃). Among the CRS reef sites, corals from the protected site of Tahala reef exhibited the highest ED50 thermal threshold (ED50 PrT 38.27℃ vs. ED50 ExT 37.56℃ vs. ED50 AF 36.80℃) and were significantly different from corals at the AF site ( p <.05, Figure 1b). Apart from the differences in ED50 thermal thresholds, and conversely, the similarities found with respect to relative thermal limits, the distinct shapes of the temperature response curves of corals from the GoA and CRS are noteworthy (Figure 1b). While GoA corals exhibit a continuous loss over increasing temperatures (Figure 1b blue curve), all CRS corals retained their F v/F m values up to a critical threshold above which they dropped drastically (Figure 1 b yellow, pink, red curves).

3.2 RNA-Seq yields and genotype analysis

We subjected coral samples from seven colonies exposed to three temperatures (30℃, 33℃, 36℃) from four sites (ICN, AF, ExT, PrT) to RNA-Seq. The average number of sequenced read pairs across all samples used for the analyses was 34,512,756 (Table S2). Following adapter trimming and quality filtering, on average 9,666,810 read pairs (i.e., 19.3 mio. sequence reads) mapped to the genes of the coral S. pistillata and 6,451,749 read pairs (i.e., 12.9 mio. sequence reads) mapped to the genes of the algal symbiont S. microadriaticum. While mapped read coverage for S. pistillata was rather even across samples and sites, we found a lower mapping efficiency for S. microadriaticum samples from the ICN site (Figure S1), further corroborating the notion of fine-scale genetic differentiation of Symbiodinium symbionts between sites, as evidenced by the ITS2 analysis (see below).

To assess the presence of clonal coral genotypes and expression variation associated with genotypes and populations, we conducted a single nucleotide polymorphism (SNP) analysis based on SNPs called from the coral host RNA-Seq data. We determined 1,807,439 raw, unfiltered SNPs, of which 36,498 were called in 95% of the 79 samples used in the RNA-Seq analysis and that also met our specific filtering criteria (see Methods). Based on a hierarchical clustering dendrogram of pairwise Hamming's distances (Aichelman & Barshis, 2020), each individual colony appeared to be a unique genotype/genet (i.e., no cross-colony clones), as indicated by the consistent clustering of within-colony samples (i.e., ramets) across temperature treatments (Figure S2). Based on a principal component analysis of these data using one sample/genotype, coral populations clustered by site with some overlap across sites (Figure S3). Interestingly, corals from the central Red Sea PrT site partially overlapped with the Gulf of Aqaba ICN site, whereas corals from thecentral Red Sea ExT and AF sites overlapped completely (Figure S3).

3.3 Disparate patterns of gene expression in coral hosts and algal symbionts from the Gulf of Aqaba and central Red Sea under heat stress

To elucidate the molecular response patterns underlying the differences in temperature tolerance thresholds, we analyzed gene expression of the coral hosts and algal symbionts at the control/baseline (30℃) and at the 33℃ and 36℃ heat stress temperatures (i.e., seven colonies/site across four sites across three temperatures = 84 samples) (Data S2, Data S3). Coral samples from the GoA and CRS sites were separated by region. Closer inspection revealed that corals from both regions differed with regard to their response to the temperature treatments: corals from the GoA displayed discrete expression responses over temperatures, as shown by the distinct colony clustering (Figure 2a) and the high number of differentially expressed genes (DEGs) between temperature comparisons (GoA site ICN, 30℃ vs. 33℃: 105 DEGs; 30℃ vs. 36℃: 2,766 DEGs; 33℃ vs. 36℃: 1,903 DEGs; Figure 2c; Data S3). In contrast, corals from the CRS did not exhibit a clear temperature response, as indicated by the clustering of all CRS corals across temperatures (Figure 2a) and the almost complete absence of DEGs between temperatures for any of the CRS sites (CRS sites AF/ExT/PrT, 30℃ vs. 33℃: 0/0/0 DEGs; 30℃ vs. 36℃: 14/16/4 DEGs; 33℃ vs. 36℃: 22/12/4 DEGs; Figure 2c; Data S3). Similarly, algal symbiont gene expression clustered foremost by region with some differentiation across temperatures for algal symbionts from GoA corals and an absence thereof in CRS corals (Figure 2b). As observed for the coral host gene expression, we found a higher number of DEGs across temperatures for GoA algal symbionts versus a largely mute response in CRS Symbiodiniaceae (Figure 2d; Data S3).

To further explore the pattern of gene expression responsiveness in GoA corals vs. the transcriptional stasis of CRS corals, we looked for genes that exhibited front-/back-loading, suggested as a mechanism that conveys increased thermal tolerance (Barshis et al., 2013). Characterization of DEGs that had at least four-fold higher/lower expression at the control/baseline temperature (30℃) in corals from the AF site in comparison to the ICN site and that also exhibited differential expression between 30℃ and 33℃ and between 30℃ and 36℃ within the ICN site were considered front-/back-loaded in CRS corals and inducible in GoA corals. We determined 26 genes exhibiting front-loading and 1 gene showing back-loading in AF corals, as well as a further 33 and 88 front- and back-loaded genes, respectively, in AF coral-associated Symbiodiniaceae (Figure 2e, Data S3). Conversely, and importantly, we could not detect any gene in GoA corals or their algal symbionts that exhibited front-loading (and only 1 gene exhibited back-loading); further, no front-/back-loaded genes were identified between PrT and ExT sites for corals and their algal symbionts. This supports the notion that only CRS corals exhibit fixed high/low expression of genes. By contrast, these genes show consecutive induction/suppression across temperatures in GoA corals. This, in turn, suggests distinct mechanisms of thermal tolerance in S. pistillata across Red Sea regions, that is, even for the same ocean basin and species. Conversely, mechanisms appear similar across sites within a region.

Closer examination of the front-loaded genes revealed three S. pistillata homologs of matrix metalloproteinases (Spis5851, Spis5854, Spis5860), further supported by GO functional enrichment analysis (GO:0004222 metalloendopeptidase activity, p-value=.00099), to be of functional significance (Data S3). Metalloproteinases were also previously identified among the set of front-loaded genes between temperature-tolerant and -sensitive Acropora hyacinthus in American Samoa (Barshis et al., 2013). Additionally, two Pax3 transcription factors (Spis374 and Spis375) and two homologues of TNF receptor-associated factor 3 (Spis12948 and Spis12951) putatively involved in apoptosis and activation of the immune response were identified (Häcker et al., 2011; Margue et al., 2000). The latter was confirmed by GO functional enrichment (GO:0033209 tumor necrosis factor-mediated signalling, p-value =.00075). Notably, we could not detect any heat shock proteins (HSPs) among the front-loaded genes, converse to what was previously found (Barshis et al., 2013). Rather, heat shock proteins (Spis4493, Spis4494, Spis4499, Spis19922, Spis19923, Spis19925) were among a list of genes that were most temperature-responsive across all sites, in line with their ubiquitous induction upon exposure to stressful conditions found across animal and plant species (Whitley et al., 1999) (Figure S4, Data S3). Among the front-loaded genes in algal symbionts, we found three genes with homology to high-affinity nitrate transporters (Smic4635, Smic4659, Smic19959) and two oxidoreductases (Smic8517, Smic40364) implicated to play a role in thermal stress susceptibility (Baumgarten et al., 2013), in addition to a suite of metabolic genes (Smic191, Smic2041, Smic32540, Smic36419) (Data S3). GO analysis could further corroborate these results, as highlighted by significant enrichments of genes associated with nitrate assimilation (GO:0042128, p-value =4.2E-05) and the glycolytic process (GO:0006096, p-value =.00089).

3.4 Thermally stable site-specific algal symbiont communities

We elucidated the fine-scale structure of the algal symbiont community of coral hosts from the GoA and CRS sites using the analytical framework SymPortal (Hume et al., 2019). Our analysis revealed unanimous association with microalgae of the genus Symbiodinium (LaJeunesse et al., 2018). Despite this broadscale similarity, we found fine-scale genetic differentiation of associated Symbiodinium symbionts between sites that was stable across temperatures, as evidenced by the ITS2 type profiles (Figure 3a, Data S4). Clustering by sample similarity further revealed that algal symbionts from all CRS sites were close to each other (with some outliers) and distinct from GoA algal symbionts (Figure 3b). The observed pattern of symbiont association indicates high host fidelity of algal symbionts across sites, as reported previously (Howells et al., 2020; Hume et al., 2020; Terraneo et al., 2019). Further to that, our results support fine-scale differentiation even between geographically close-by sites, as the central Red Sea sites (AF, ExT, ExP) all differed with regard to symbiont identities (Figure 3). Notably, sample dissimilarity increased with geographic distance, although the distinct clustering of some microalgal symbionts from ExT corals may suggest that divergence aligns with prevailing environmental differences, rather than geographical distance (Figure 3b).

3.5 Bacterial community dynamics resemble gene expression patterns of corals from the northern and central Red Sea

Bacterial community composition ( n = 2,264 OTUs, Data S5) profoundly changed in GoA corals in contrast to stable bacterial community association of coral colonies across temperatures for CRS sites (all P adj >0.05, PERMANOVA, Figure 4a and c). This resembles the coral host and algal symbiont gene expression patterns (Figure 2c and d), in particular with regard to a ramping response, as evidenced by the increasing number of differentially abundant bacterial taxa (OTUs) between temperature comparisons in GoA corals and absence thereof in CRS corals (Figure 4b). This notion was further confirmed statistically, in that all CRS sites were significantly different from GoA corals at all treatment temperatures with regard to coral and algal gene expression as well as bacterial community composition (all P adj <0.05, PERMANOVA, Data S2, Data S5). Among CRS sites, bacterial community composition of AF corals was significantly different from ExT and PrT sites across all temperatures (P adj <0.05, PERMANOVA), whereas the latter two resembled each other (P adj >0.05, PERMANOVA).

Changes in the bacterial community structure in GoA corals followed a distinct pattern over the temperature increase: only 4 OTUs (Figure 4b) were differentially abundant between 30℃ and 33℃ in GoA corals, whereas 80 and 42 OTUs were differentially abundant in the 30℃ versus 36℃ and the 33℃ versus 36℃ comparisons, respectively (Figure 4b). Despite the high number of differentially abundant bacterial taxa, the overall pattern was driven by a relative loss of Endozoicimonaceae (2 OTUs, mean log2 fold change = -7.31) and a relative increase of Vibrionaceae (15 OTUs, mean log2 fold change = +9.77) and Rhodobacteraceae (21 OTUs, mean log2 fold change = +6.46), suggesting dysbiosis and loss of microbiome structure (Figure 4c) (Voolstra & Ziegler, 2020). S. pistillata colonies from CRS sites by comparison largely retained association with Endozoicimonaceae . In this context, bacteria of the genus Endozoicomonas are typically ascribed to be abundantly associated with S. pistillata in the Red Sea and globally (Bayer et al., 2013; Neave et al., 2017a). However, Endozoicomonas were only dominantly associated with corals from the ICN and the PrT sites, but comprised a rather small proportion of the bacterial community in corals from the AF and ExT sites (Data S5). This suggests fine-scale structuring of bacterial community composition across spatially adjacent reefs (Roder et al., 2015; Ziegler et al., 2019; Ziegler, Seneca, et al., 2017). In light of the known thermotolerance differences between the ExT and PrT reef sites (Pineda et al., 2013; Voolstra et al., 2020), it is tempting to speculate that abundance of Endozoicomonas may contribute to the observed differences in bleaching susceptibility, although prior studies found that their relative abundance was not correlated with bleaching (Pogoreutz et al., 2018; Shiu et al., 2020). Corals from the PrT site were further characterized by a relative prevalence of Simkaniaceaea, which comprise obligate intracellular bacteria of eukaryotes (Everett, 2014) and were recently shown to be a hallmark of corals from pristine environments (Ziegler et al., 2019), though their function is at present unknown. Notably, the nature of the data does not allow us to draw conclusions on bacterial biomass, that is, increase/decrease of bacterial cell densities. It is desirable to understand bacterial biomass dynamics in future experiments to better assert whether bacteria are lost from the holobiont with increasing temperature stress (similar to algal symbiont loss in bleaching) or whether we indeed observe bacterial community restructuring (Costa et al., 2021).

4 DISCUSSION

4.1 The need for a standardized experimental framework to further our understanding of the factors underlying coral thermal tolerance

Despite global coral reef loss driven by ocean warming that triggers coral bleaching (Hughes et al., 2018), coral reef regions (Fine et al., 2013; Guest et al., 2012; Osman et al., 2018), populations (Barshis et al., 2013; Palumbi et al., 2014; Voolstra et al., 2020), and individual coral genotypes (Dixon et al., 2015; Palumbi et al., 2014) with enhanced bleaching resilience exist. However, a detailed understanding of the underlying factors and molecular underpinnings that drive increased thermal tolerance remains elusive. Here, we conducted identical acute thermal stress assays across four reef sites (totaling ~1,000 km overwater distance) to assess thermal limits of corals from the northernmost Red Sea in the Gulf of Aqaba and the central Red Sea, employing a coordinated, standardized experimental framework (CBASS asssays) and subsequent molecular characterization of the coral holobiont heat stress response. Counter to previous work suggesting that the extraordinary thermal tolerance from corals of the Gulf of Aqaba might equal that of southern/central Red Sea populations (Fine et al., 2013; Osman et al., 2018), we could demonstrate that while corals throughout the Red Sea harbor consistent thermal tolerance thresholds exceeding +7℃ above their regional maximum monthly mean (MMM) temperature, absolute thermal limits differed according to the prevailing MMM temperature. Although it is unclear at present how ED50-derived thermal thresholds relate to natural bleaching thresholds, our determined thermal limits correspond very well with the exceptional thermal tolerance of S. pistillata from the Gulf of Aqaba that was found to sustain temperatures of +5℃ above MMM for a period of two weeks, equivalent to 9.4 DHW (Bellworthy & Fine, 2017; Krueger et al., 2017; Savary et al., 2021). Importantly, the experimentally determined ED50 of 34.08℃ for the ICN population is 3.28℃ higher than the maximum temperature ever recorded for this site over the last decade (based on data from The Israel National Monitoring Program at the Gulf of Eilat). Irrespective of how exactly ED50s relate to DHWs (or other means of determining bleaching thresholds), our work and previous work supports the notion that higher thermal tolerance thresholds in short-term heat stress assays are representative of increased thermal stress resilience in natural settings; with ED50s serving a standardized proxy for bleaching susceptibility (Ziegler et al., 2017; Morikawa & Palumbi, 2019; Voolstra et al., 2020; Evensen et al., 2021). Importantly, we found different thermal thresholds across different sites at both large and small scales, which were reflected by holobiont molecular response differences. This highlights the utility and potential of pairing mobile short-term heat stress assays for standardized phenotyping to detect thermal tolerance differences with subsequent investigation of the molecular response to obtain a better understanding of the factors shaping and the mechanisms underlying thermal tolerance.

4.2 Signatures of thermal tolerance across regions and coral holobiont compartments

The different thermal thresholds seen in the F v/F m data were reflected in the gene expression responses of corals and their algal symbionts as well as in the composition of the algal symbiont and bacterial communities. Transcriptional response patterns differed substantially between GoA and CRS corals with a strong response to the thermal stress in GoA corals vs. a muted/static response in CRS corals (up to 2,766 vs. up to 22 differentially expressed genes between temperature treatments in GoA and CRS corals, respectively). Notably, transcriptional differences in GoA corals were minor between the 30℃ control and the 33℃ heat stress temperature (105 DEGs), corroborating the notion that 33℃ is below the critical thermal threshold of GoA corals (Bellworthy & Fine, 2017; Evensen et al., 2021; Krueger et al., 2017). Conversely, differences were most pronounced between 30℃ and 36℃, but we also found substantial gene expression differences between 33℃ and 36℃. Overall, our data indicate the ramping up of a gene expression response with increasing heat stress in GoA corals in stark contrast to the largely muted response in CRS corals. The ramping up versus mute response of GoA vs. CRS corals is further highlighted by the presence of front- and back-loaded genes in CRS corals and the rather complete absence in GoA corals. Notably, a subset of the front-loaded genes in CRS corals were previously identified in Acropora hyacinthus corals from American Samoa (Barshis et al., 2013). On the one hand, this suggests the conservation of thermal tolerance genes across large geographic distances and coral species, and consequently, that universal molecular signatures of thermal tolerance exist. On the other hand, corals can show strikingly disparate heat stress response patterns, even for coral colonies/populations of the same species of the same ocean basin, indicating that distinct mechanisms of thermal tolerance exist. To further understand the dynamics of such response patterns and their utility as indicators of differential thermal tolerance, determination of expression patterns over the course of the heat stress (e.g., before, during, after heat stress, and after the recovery period) may be critical ( sensu Savary et al., 2021). Similarly, reciprocal transplant or common garden follow-up experiments can further evidence whether the differences observed are genetically fixed or a consequence of acclimation (Palumbi et al., 2014; Thomas et al., 2018).

It is also striking that coral host gene expression patterns were mirrored in the algal symbiont. At large, the level of responsiveness in algal symbionts from GoA corals was unexpected, given the commonly ascribed paucity of regulation at the transcriptional level for Symbiodiniaceae (Baumgarten et al., 2013; Bayer et al., 2012), signified by fixed expression differences between symbiont genera and species (Barshis et al., 2014; Davies et al., 2018; Parkinson et al., 2016). However, the notion that algal symbionts in hospite respond transcriptionally to heat stress is now corroborated by several studies (Bellantuono et al., 2019; Fifer et al., 2021; Studivan et al., 2021) and suggest that differential gene expression analysis may provide further insight with regard to relevant cellular processes. Although we did not find direct overlap between algal symbiont front-loaded genes in comparison to another heat stress study (Fifer et al., 2021), a common denominator was that of a stress response being observed based on the functional annotation of identified genes. Hence, and unsurprisingly, differences between algal symbiont species and genotypes may exist, with putative relevance for the stress tolerance of the coral holobiont. Notably, corals from all sites associated with distinct and (largely) consistent microalgal symbiont genotypes, suggesting host fidelity and local adaptation (Howells et al., 2020; Hume et al., 2020). Despite the notion that corals from all sites harbor specific symbiont species, the consistent gene expression stasis of algal symbionts across CRS corals and the disparate responsiveness in Symbiodiniaceae from GoA corals corroborates that the geographically further apart symbionts are also genetically more differentiated.

Bacterial community composition of GoA corals changed dramatically under heat stress in contrast to CRS corals that displayed rather consistent assemblages, in support of previous work that thermally more susceptible corals have less stable microbiomes (Ziegler et al., 2017). At present, however, any presumptive functional consequences of these differences for coral holobiont physiology are largely unknown, despite it becoming broadly accepted that bacteria contribute to metaorganism biology and fitness (Bang et al., 2018; McFall-Ngai et al., 2013; Pogoreutz et al., 2020; Rosado et al., 2019; Santoro et al., 2021; Voolstra & Ziegler, 2020; Peixoto et al., 2021). In cases of rapid environmental change, the role of the microbiome in supporting adaptation of the metaorganism may become even more pronounced, given that microbial-mediated change can be achieved through association with different microbes or exchange/incorporation of new genetic material (e.g., via horizontal gene transfer) and does not rely on adaptive evolution per se (Cárdenas et al., 2020; Voolstra & Ziegler, 2020).

4.3 Contrasting heat stress response patterns of coral holobionts suggest distinct mechanisms of thermal tolerance

Our analyses indicate distinct patterns in the response to heat stress of corals from geographically distant sites in the Red Sea: corals from the GoA show dramatic changes in response to temperature across holobiont compartments versus coral-algal transcriptional and bacterial community stasis at the CRS sites. To ascertain that the observed differences between GoA and CRS corals do not align with the presence of putative cryptic lineages that would need to be considered (Burgess et al., 2021; Dziedzic et al., 2019), we conducted a preliminary population genetics analysis based on SNPs obtained from the coral RNA-Seq data. While a full-fledged population genomics analysis is beyond the scope of this study, there did appear to be greater population differentiation between corals from the ICN/PrT sites on the one hand and corals from the ExT/AF sites on the other hand based on our SNP analysis (Figure S3). This indicates that corals from the CRS sites are not uniformly differentiated from the ICN site, contrary to what would be expected if we were looking at two diverged and distinct lineages. While our analysis cannot definitely determine whether corals from the CRS and GoA represent different populations of the same lineage or multiple cryptic lineages, the overlap between some samples within the PrT population and the ICN population, suggest long-distance dispersal is putatively possible. Our results are also in line with the notion that only Stylophora pistillata clade 4 occurs in the Red Sea ( sensu Keshavmurthy et al., 2013; Malik et al., 2021).

We posit that the observed response differences may be reflective of the nature of the underlying differences in thermal tolerance: we interpret the GoA coral response as that of a resilient population responding to the strength of a stressor. In contrast, the static pattern of consistently elevated but fixed expression states is suggestive of thermal resistance in CRS corals. It may be that both mechanisms evolved in their respective environments, but it is not clear whether they present different solutions to the same problem, or one may prove more successful than the other in countering future ocean warming. Intuitively, a response-resilience mechanism seems more adaptable to changing environments than a static-resistance mechanism that lets coral holobionts operate at maximum capacity irrespective of the external input, as any additional stress cannot lead to a change in response. Further insight may come from the photosynthetic efficiency response curves, which reflect the molecular patterns observed: CRS corals exhibit stable F v/F m values over the experimental temperatures assessed with RNA-Seq (i.e., 30℃, 33℃, 36℃). In contrast, GoA corals exhibit a slow and increasing F v/F m loss with temperature. Pending whether such differences in mechanisms of thermal tolerance are predictive of susceptibility to future ocean warming, they may help in targeting coral colonies, species, and reefs for conservation and restoration efforts. Importantly, our work supports the existence of common gene 'signatures' (e.g., high expression of metalloproteinases under heat stress) irrespective of the underlying thermal tolerance mechanism. Such putatively universal signatures may become important in the screening for and engineering of corals with superior thermal resilience/resistance.

ACKNOWLEDGEMENTS

Research reported in this publication was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project numbers 433042944 and 458901010 to CRV. We further acknowledge funding by the Paul G. Allen Family Foundation ("Global Search") and a BiNational Science Foundation grant (no. 2016403) to DJB and MF.

Funding Statement: Open Access funding enabled and organized by Projekt DEAL. WOA Institution: UNIVERSITAET KONSTANZ. Blended DEAL: Projekt DEAL.

RNA-Seq, ITS2, 16S fastq sequencing data files have been made available at NCBI under BioProject PRJNA681108 https://www.ncbi.nlm.nih.gov/bioproject/PRJNA681108. Scripts for PAM data analysis and plotting, coral host and microalgal gene expression analyses, Symbiodiniaceae and bacterial community analyses and plotting, and coral host SNP analyses can be accessed at https://github.com/reefgenomics/CBASS84. Pictures of colony fragments after short-term thermal stress are available as Supporting Information (Data S1). Gene expression estimates (TPM) for 49,109 Symbiodinium microadriaticum and 25,769 Stylophora pistillata genes, PERMANOVA statistics on pairwise comparisons between temperatures & sites, and differentially expressed genes (DESeq) for S. pistillata and S. microadriaticum are available as Supporting Information (Data S2). Overview of differentially expressed genes, front- and back-loaded genes, GO enrichments, and heat stress response genes for S. pistillata and S. microadriaticum are available as supplementary data (Data S3). Symbiodiniaceae ITS2 type profiles, ITS2 sequences abundance, and ITS2 sequences are available as Supporting Information (Data S4). Bacterial 16S sequence statistics, taxa (OTUs) abundance table, and statistical analyses are available as Supporting Information (Data S5).

Abstract

Corals from the northern Red Sea, in particular the Gulf of Aqaba (GoA), have exceptionally high bleaching thresholds approaching >5℃ above their maximum monthly mean (MMM) temperatures. These elevated thresholds are thought to be due to historical selection, as corals passed through the warmer Southern Red Sea during recolonization from the Arabian Sea. To test this hypothesis, we determined thermal tolerance thresholds of GoA versus central Red Sea (CRS) Stylophora pistillata corals using multi-temperature acute thermal stress assays to determine thermal thresholds. Relative thermal thresholds of GoA and CRS corals were indeed similar and exceptionally high (~7℃ above MMM). However, absolute thermal thresholds of CRS corals were on average 3℃ above those of GoA corals. To explore the molecular underpinnings, we determined gene expression and microbiome response of the coral holobiont. Transcriptomic responses differed markedly, with a strong response to the thermal stress in GoA corals and their symbiotic algae versus a remarkably muted response in CRS colonies. Concomitant to this, coral and algal genes showed temperature-induced expression in GoA corals, while exhibiting fixed high expression (front-loading) in CRS corals. Bacterial community composition of GoA corals changed dramatically under heat stress, whereas CRS corals displayed stable assemblages. We interpret the response of GoA corals as that of a resilient population approaching a tipping point in contrast to a pattern of consistently elevated thermal resistance in CRS corals that cannot further attune. Such response differences suggest distinct thermal tolerance mechanisms that may affect the response of coral populations to ocean warming.

1 INTRODUCTION

Coral reefs have undergone drastic declines in recent years due to increasing anthropogenic pressure. In particular, coral bleaching, that is, the loss of Symbiodiniaceae, due to ocean warming is now among the main drivers of reef degradation (Hughes et al., 2018). Without their algal symbionts, corals lose their primary source of nutrition, often leading to widespread coral mortality. Hence, a better understanding of coral bleaching and the mechanistic underpinnings that influence susceptibility or resilience to thermal stress are of major importance.

The common notion is that corals can bleach and suffer mortality at just 1-2℃ above their maximum monthly mean (MMM) temperatures (Glynn and D'croz L, 1990; Jokiel & Coles, 1977, 1990), but we actually know little about empirical temperature thresholds, as most of our knowledge comes from predictive modelling, observational studies, or long-term thermal exposure experiments. Similarly, susceptibility of corals to bleaching has been attributed to genomic differences of corals and algal symbionts (Aranda et al., 2016; Barshis et al., 2013; Bhattacharya et al., 2016; DeSalvo et al., 2008, 2010; Voolstra et al., 2017), association with different microalgal symbiont species (Hume et al., 2016, 2020; Ziegler et al., 2017), or changes in the associated bacteria (Osman et al., 2020; Ziegler et al., 2017). However, it is unknown whether the same mechanisms or factors contribute to thermal tolerance of corals at large, that is, across species and sites. Corals from the Northern Red Sea, for instance, harbor exceptionally high thermal tolerance limits approaching >5℃ above their summer maxima, which are lethal for corals elsewhere (Evensen et al., 2021; Fine et al., 2013; Krueger et al., 2017; Osman et al., 2018; Savary et al., 2021). The Red Sea at large is one of the warmest ocean basins where corals thrive despite water surface temperatures commonly exceeding 30℃ in the summer (Roik et al., 2016, 2018; Voolstra et al., 2020; Ziegler et al., 2019). Nevertheless, we are uncertain about the consistency of this thermal tolerance across the gradient of the Red Sea, and whether the same holobiont assemblages and molecular mechanisms are at play across different spatial scales.

Here we sought to investigate the consistency of thermal tolerance limits across spatial scales using the Coral Bleaching Automated Stress System (CBASS) (Voolstra et al., 2020) to run a series of identical short-term thermal stress assays on colonies of the coral Stylophora pistillata from three warmer central Red Sea (CRS) sites (0.3-10 km overwater distance) and a cooler Northern Red Sea site in the Gulf of Aqaba (GoA) (~1,000 km overwater distance). Subsequent molecular examination allowed for a direct comparison of the response patterns underlying the determined temperature thresholds and revealed differences in the gene expression response and holobiont configurations that suggest distinct thermal tolerance mechanisms are at play across spatial scales. Our work demonstrates the utility of running standardized short-term heat stress assays for determination of thermal thresholds with subsequent molecular interrogation, which provide a powerful framework to identify differences in thermal tolerance response patterns and associated holobiont signatures (e.g., alleles, genes, microbes).

2 MATERIALS AND METHODS

2.1 Study sites and sample collection

In August 2018, fragments of Stylophora pistillata coral colonies were collected from one site in the northern Red Sea in the Gulf of Aqaba (GoA), the Interuniversity Institute for Marine Science (IUI) coral nursery (ICN) (N 29.50000° E 34.93333°), and three sites in the central Red Sea (CRS), Al Fahal reef (AF) (N 22.29634° E38.95914°), the ocean-facing exposed site of Tahala reef (ExT) (N 22.26189°, E 39.04878°), and the land-facing protected site of Tahala reef (PrT) (N 22.26302° E 39.05165°). At each of the four sites, seven coral colonies were sampled, with 4 ramets collected per colony, using SCUBA at depths of 2-8 m. Colonies were sampled at least 5 m apart from each other to minimize the potential of sampling clonal genotypes. Sampled specimens were stored in Ziploc plastic bags upon underwater collection and transported to shore in a cooler filled with seawater where they were subjected to short-term thermal stress assays (see below). Sample collection and short-term heat stress assays for all sites were conducted within two weeks of each other (7 August for AF, 15 August for ExT and PrT, and 20 August for ICN) in parallel at the GoA ICN site, by a team of three researchers, and at the three CRS sites (AF, ExT, and PrT), by a separate team of six researchers. Every effort was made to standardize the sampling and experimental procedures, with corals at all four sites being collected the morning of the experiment day, transported to shore in seawater-filled coolers, and immediately placed in the experimental aquaria/tanks prior to starting the thermal exposures.

2.2 Short-term thermal stress assays

Ramets of seven colonies (i.e., genets) of Stylophora pistillata from each of the four sites were subjected to short-term thermal stress assays. For the GoA site, experiments were run using a set of small containers with manual heating and chilling controls. For the central Red Sea sites, we employed the Coral Bleaching Automated Stress System (CBASS) (Voolstra et al., 2020). For each site, coral ramets from all seven colonies were distributed across four tanks, so that ramets from each colony (i.e., genet) were exposed to each one of four distinct temperature treatment conditions (baseline = 30℃, medium = 33℃, high = 36℃, extreme = 39℃), totalling 112 fragments (7 colonies × 4 temperatures × 4 sites) (Table S1). The experiments started at noon (12:00h). The four temperature treatment conditions were as follows. The baseline (control) tank was maintained at 30℃ for the entire duration of the experiment. The other three tanks were heated to 33℃, 36℃, and 39℃ over a period of 3 h. The respective temperatures were held for 3 h, and then decreased to 30℃ over the course of 1 h (19:00h). All corals were kept at 30℃ overnight until sampling the following morning, completing the 18 h short-term thermal stress assay (Voolstra et al., 2020). All tanks were continuously supplied with Red Sea seawater and light intensity was set to photosynthetic active radiation of 600 µmol photons m −2s −1 to match in situ light fields using a Li-Cor Li-193, spherical quantum sensor (Grottoli et al., 2021).

2.3 Photosynthetic efficiency and visual coral bleaching assessment

Experimental tanks were covered with a tarp at 19:00h (following the heat stress and subsequent ramping down to the control temperature) to ensure complete darkness. After 1 h (20:00h), we measured dark-adapted maximum quantum yield (F v/F m) of photosystem II of all coral fragments in all temperature treatments using a pulse amplitude modulated (PAM) fluorometer (WALZ). Temperature tolerance thresholds were determined for each site as the mean (across all genets) temperature at which photosynthetic efficiency dropped to 50% of the value at baseline temperatures, defined as the Effective Dose 50 or ED50 (Evensen et al., 2021) using the DRC package in R (Ritz et al., 2015). Statistical differences among site-specific ED50s were assessed via a one-way ANOVA (aov function) with individual genet ED50s as the response variable and site as the factor, with subsequent Tukey's post hoc testing via the TukeyHSD function in R. R script and data are available at GitHub ( https://github.com/reefgenomics/CBASS84/tree/master/CBASS84_PAM). For visual bleaching assessment, coral fragments were photographed before being snap-frozen in liquid nitrogen and stored at -80℃ until further processing (Data S1). All coral fragments showed increased paling/whiteness at higher heat stress temperatures.

2.4 RNA-Seq library generation

For nucleic acid extraction, coral tissue was sprayed off from frozen fragments using airflow from a sterile, 1,000 µl pipette tip connected via a rubber hose to a benchtop air pressure valve and 1 ml of ice-cold 0.22 µm filtered seawater (FSW) for a maximum of 3 min. Following this, an aliquot of 100 µl of the tissue slurry was added to each of 400 µl of buffer RLT (Qiagen) and 400 µl of buffer ATL (Qiagen) for subsequent RNA and DNA isolation, respectively. Total RNA was isolated from corals exposed to 30℃, 33℃, and 36℃ to make a total of 84 fragments (7 colonies × 3 temperatures × 4 sites). RNA was isolated using the Qiagen RNeasy 96 kit according to the manufacturer's recommendations. Total RNA was quantified using the Qubit RNA HS Assay Kit on a Qubit 2.0 Fluorometer (Thermo Fisher Scientific) and quality-checked using the Agilent RNA 6000 Nano kit on the Agilent 2100 bioanalyzer (Agilent Technologies). RNA-Seq libraries were prepared using the TruSeq Stranded Total RNA kit (Illumina) and sequenced on eight lanes of an Illumina HiSeq 4000 using 2 × 151 bp paired-end reads in groups of 8-11 samples/lane. Samples from the 39℃ heat stress were not processed, given that we observed visible tissue loss that potentially compromised sample integrity (Data S1). Samples ICN1-36, ICN3-33, ICN6-30, AF2-33, and PrT6-36 were removed from downstream analyses due to low number of sequence reads obtained (Table S2).

2.5 Gene expression analysis

Paired-end Illumina reads were processed using TrimGalore version 0.4.3 (Krueger, 2012) following Illumina default quality filtering steps. Reads were further trimmed for low-quality ends (Phred score <20) and cleaned up for adapter contamination with TrimGalore. For sequence alignment, reference genomic gene sets for Stylophora pistillata (v1.0) (Voolstra et al., 2017) and Symbiodinium microadriaticum (v1.0) (Aranda et al., 2016; Nand et al., 2021) were obtained from http://spis.reefgenomics.org ( n = 25,769 genes) and http://smic.reefgenomics.org ( n = 49,109 genes), respectively (Liew et al., 2016), and gene sets were combined to create a merged reference. Transcript abundance estimation was performed by using kallisto v0.44.0 (Bray et al., 2016). Differential gene expression analysis was performed using DESeq2 package v1.22.2 (Love et al., 2014) in R after importing kallisto transcript abundance estimates with the tximport package v1.14.2 (Soneson et al., 2015) and separating S. pistillata and S. microadriaticum reads prior to DESeq2 analysis. To account for large dispersion with low read counts and create more accurate log2 fold change (LFC) estimates, the lfcShrink function for shrinking LFC estimates was applied. Transcripts with s-values (Stephens, 2017) smaller than 0.005 were defined as significantly differentially expressed (Data S2). In addition, transcript quantifications in transcripts per million (TPM) were obtained using kallisto v0.44.0 (Bray et al., 2016) (Data S2).

The analysis workflow, implemented as custom python and R scripts, is available at GitHub ( https://github.com/reefgenomics/CBASS84/tree/master/CBASS84_RNASeq). Sequence data determined in this study are available under NCBI BioProject PRJNA681108 ( https://www.ncbi.nlm.nih.gov/bioproject/PRJNA681108). To determine front- and back-loaded gene candidates ( sensu Barshis et al., 2013), we filtered for genes that were at least four-fold differentially expressed at 30℃ between ICN and AF corals or their algal symbionts and that had expression values above an accumulative sum of 30 (TPM). This subset of genes was further selected for those that showed significant differential expression between 30℃ and 33℃ and between 30℃ and 36℃ in ICN or AF corals and their algal symbionts, respectively (Data S3). We also conducted the aforementioned analyses between corals from the ExT and PrT sites to test for regional front-/back-loading. Moreover, we identified the most variably expressed genes across all temperatures and reef sites to elucidate the cohort of genes that are most responsive to thermal stress (Data S3). Gene ontology (GO) enrichment analysis was done based on genomic gene set annotations available from http://reefgenomics.org and performed using the topGO package v2.42.0 (Alexa & Rahnenfuhrer, 2018) in R with a recommended cutoff of p <.001 (Data S3).

2.6 Coral host single nucleotide polymorphism (SNP) analysis

In order to confirm the uniqueness of sampled colonies (i.e., that they were true genets vs. clonal ramets) and to understand expression variation associated with genotypes and populations, SNPs for the different samples were determined based on the coral host RNA-Seq data. Raw RNA-Seq reads were mapped with STAR 2.5.3a (Dobin et al., 2013) with a single pass. BAM files were used for calling SNPs with the bcftools mpileup and call functions from SAMtools 1.8 (Li et al., 2009). Bcftools call was used with the arguments filter -s LowQual -e "QUAL<30 || DP>100" to create a single, merged vcf file for all SNPs and all samples excluding alignments with low mapping quality (<30 mapping quality) and higher than expected coverage (DP>100). This file was then further filtered using VCFtools 0.1.16 (Danecek et al., 2011) and the following options: --max-missing 0.5 --mac 3 --minQ 30 --minDP 10 --max-alleles 2 --maf 0.015 --remove-indels. Five low coverage individuals that were removed from the differential expression analyses were then removed from the vcf file and the remaining SNPs were further filtered using the --max-missing 0.95 parameter to include only SNPs that had quality calls in at least 95% of the remaining 79 individuals (Table S2). This final filtered vcf file was then converted into genepop format with a custom python script and imported into the adegenet package (Jombart, 2008) in the R environment for principal component analysis (PCA) and plotting. Pairwise allelic distances (Hamming's distance) were calculated using the poppr package (Kamvar et al., 2014) and subsequently plotted using the hclust function in R (Aichelman & Barshis, 2020). Documentation and scripts used to conduct the analysis are available at GitHub ( https://github.com/reefgenomics/CBASS84/tree/master/CBASS84_SNPs).

2.7 Symbiodiniaceae ITS2 and bacterial 16S marker gene library generation

DNA isolation was performed using the Qiagen DNeasy 96 Blood & Tissue kit (Qiagen) following the manufacturer's instructions with minor adjustments. Briefly, coral tissue samples aliquoted for DNA isolation (see above) were thawed and equilibrated to room temperature. The slurry was vortexed and 180 µl of each sample were transferred to a microtube with 20 µl of proteinase K for incubation at 56℃ for 1 h. DNA extractions were then continued according to the manufacturer's instructions. In addition to the coral samples selected for 16S marker gene sequencing, three negative controls testing for "contaminants" from (i) the FSW used for spraying off the coral tissue, (ii) the DNA extraction procedure, and (iii) PCR consumables were included to account for putative laboratory and kit contaminants introduced during sample preparation. DNA concentrations were quantified by Qubit dsDNA High Sensitivity Assay Kit (Invitrogen). ITS2 (Symbiodiniaceae) and 16S (bacteria) amplicon libraries were prepared for sequencing on the Illumina MiSeq platform. To amplify the ITS2 region, the primers SYM_VAR_5.8S2 [5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGAATTGCAGAACTCCGTGAACC-3'] and SYM_VAR_REV [5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGCGGGTTCWCTTGTYTGACTTCATGC-3'] (Hume et al., 2013, 2015, T 2018) were used (Illumina adaptor overhangs underlined). To amplify the variable regions 5 and 6 of the 16S rRNA gene, the primers 784F [5′-CGTCGGCAGCGTCAGATGTGTATAAGAGACAGAGGATTAGATACCCTGGTA-3′] and 1061R [5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGCRRCACGAGCTGACGAC-3′] (Andersson et al., https://www.ncbi.nlm.nih.gov/bioproject/PRJNA681108). 2008; Bayer et al., 2013) were used (Illumina adaptor overhangs underlined). Triplicate PCRs were performed (10-50 ng of DNA from each coral sample per PCR reaction) using the Qiagen Multiplex PCR kit and a final primer concentration of 0.5 μM in a reaction volume of 10 μl. Thermal cycling conditions for ITS2 PCR amplifications were: 95℃ for 15 min, 30 cycles of 95℃ for 30 s, 56℃ for 90 s, and 72℃ for 30 s, followed by a final extension step of 72℃ for 10 min. Thermal cycling conditions for 16S PCR amplifications were: 95℃ for 15 min, 27 cycles of 95℃ for 30 s, 55℃ for 90 s, and 72℃ for 30 s, followed by a final extension step of 72℃ for 10 min. After the PCRs, 5 µl of the PCR products were run on a 1% agarose gel to confirm successful amplification. Triplicate PCRs for each sample were pooled, and samples were cleaned using ExoProStar 1-step (GE Healthcare). Samples were indexed using the Nextera XT Index Kit v2. Successful addition of indexes was confirmed by comparing the length of the initial PCR product to the corresponding indexed sample on a 1% agarose gel. Samples were then cleaned and normalized using the SequalPrep Normalization Plate Kit (Invitrogen). The ITS2 and 16S amplicon libraries were pooled separately (4 μl per sample) and concentrated using a CentriVap Benchtop Vacuum Concentrator (Labconco). Quality of the libraries was assessed using the Agilent High Sensitivity DNA Kit on the Agilent 2100 Bioanalyzer (Agilent Technologies), quantification was done using the Qubit dsDNA High Sensitivity Assay Kit (Invitrogen). The ITS2 and 16S amplicon libraries were sequenced on a single MiSeq run. Libraries were pooled in a one-third ITS2 to two-thirds 16S ratio to achieve higher read numbers for 16S amplicons. Sequencing was performed at 6 pM with 20% phiX on the Illumina MiSeq platform at 2 x 301 bp paired-end V3 chemistry according to the manufacturer's specifications. Sequence data determined in this study are available under NCBI BioProject PRJNA681108 (

2.8 Symbiodiniaceae community analysis

The SymPortal ( https://symportal.org) analytical framework was used to analyze Symbiodiniaceae ITS2 sequence data (Hume et al., 2019). Briefly, demultiplexed and paired forward and reverse fastq.gz files outputted from the Illumina sequencing were submitted directly to SymPortal. Firstly, sequence quality control was conducted as part of the SymPortal pipeline using mothur 1.43.0 (Schloss et al., 2009), the BLAST+ suite of executables (Camacho et al., 2009), and minimum entropy decomposition (MED) (Eren et al., 2015). Then, ITS2 type profiles (representative of putative Symbiodiniaceae taxa or genotypes) were predicted and characterized by specific sets of defining intragenomic ITS2 sequence variants (DIVs). Finally, the ITS2 sequence and ITS2 type profile abundance count tables (Data S4), as well as the Bray Curtis-based between-sample and between-ITS2 type profile dissimilarities, as output by the SymPortal analysis, were directly used to plot data. Scripts used for data curation and plotting are available at GitHub ( https://github.com/reefgenomics/CBASS84/tree/master/CBASS84_ITS2).

2.9 Bacterial community analysis

Sequence data fastq files were analysed in mothur 1.39.5 (Schloss et al., 2009). Briefly, remaining adaptors and primers were trimmed and read pairs were concatenated. Unique sequences were removed using split.abund, followed by alignment to the 16 rRNA SILVA database, release 132 (Quast et al., 2013). Chimeras were identified using VSEARCH and removed for subsequent analysis. Clustering was done using the OptiClust algorithm and taxonomic annotation was done using the Greengenes (release May 2013) and SILVA (release 138) 16S rRNA databases. Sequences annotated as chloroplast, mitochondria, unknown, or archaea were discarded. OTU abundance table, taxonomy, and fasta files (Data S5) generated by the make.shared, classify.otu, and get.oturep commands in mothur respectively, were used as input files for statistical analyses in R. OTUs were considered putative contaminants if the ratio of their relative abundance across all negative controls to the relative abundance across all coral samples was >10%. Barplots showing the relative abundances of the most abundant 20 families were plotted using ggplot2 v2.3.1.0 (Wickham, 2011). Ordination plots and Bray-Curtis dissimilarities were calculated in Phyloseq v1.26.1 (McMurdie & Holmes, 2013). The same analyses were carried out in ASV space with resembling results; however, due to backward compatibility to reference OTU sequences from a previous study (in particular from the genus Endozoicomonas: Neave et al., 2017a; Neave et al., 2017b), we opted for OTU-based analyses. Scripts used for data analysis and plotting are available at GitHub ( https://github.com/reefgenomics/CBASS84/tree/master/CBASS84_16S).

3 RESULTS

3.1 Differential thermal tolerance thresholds between corals from the northern and central Red Sea

To assess the consistency of the exceptional thermal tolerance of corals from the Red Sea and the underlying holobiont patterns, we investigated the response to heat stress in Red Sea Stylophora pistillata corals. Using a set of small aquaria and the coral bleaching automated stress system (CBASS) (Voolstra et al., 2020), we determined thermal tolerance thresholds by running a series of identical short-term acute heat stress assays (Voolstra et al., 2020). Thermal thresholds were calculated as the temperature point at which a loss of 50% photosynthetic efficiency was inferred, henceforth referred to as the effective dose 50 (ED50) (Evensen et al., 2021). ED50s provide an empirical, comparable, and standardized proxy for coral thermotolerance (similar to the LD50 used in toxicological studies), based on the notion that increased/decreased loss of photosynthetic efficiency with temperature reflects differences in coral bleaching susceptibility (Voolstra et al., 2020). We determined thermal thresholds at two spatial scales: large-scale (~1,000 km overwater distance) between corals from the northern Red Sea at the IUI Coral Nursery (ICN) in the Gulf of Aqaba (GoA) and corals from the central Red Sea (CRS) at Al Fahal reef (AF) and the exposed (ExT) and protected (PrT) sites of Tahala reef, and small-scale (0.3-10 km overwater distance) between corals from the AF, ExT, and PrT CRS sites (Figure 1a). The thermal tolerance threshold of GoA corals was significantly different from all three CRS sites ( all p <.0001, Figure 1b). Notably, the ED50 thermal limit for GoA corals, deemed to be among the most temperature resilient corals in the world (Fine et al., 2013; Osman et al., 2018; Savary et al., 2021), was on average >3℃ below that of CRS corals (ED50 GoA =34.08℃ ± 0.30℃ vs. ED50 CRS =37.54℃ ± 0.30℃ [mean ±SE]). However, corals from the GoA and the CRS exhibited a temperature tolerance threshold of about +7℃ above their regional maximum monthly mean (MMM) temperature (MMM GoA: 27.01℃; MMM AF: 30.76℃; MMM ExT: 30.74℃; MMM PrT 30.75℃). Among the CRS reef sites, corals from the protected site of Tahala reef exhibited the highest ED50 thermal threshold (ED50 PrT 38.27℃ vs. ED50 ExT 37.56℃ vs. ED50 AF 36.80℃) and were significantly different from corals at the AF site ( p <.05, Figure 1b). Apart from the differences in ED50 thermal thresholds, and conversely, the similarities found with respect to relative thermal limits, the distinct shapes of the temperature response curves of corals from the GoA and CRS are noteworthy (Figure 1b). While GoA corals exhibit a continuous loss over increasing temperatures (Figure 1b blue curve), all CRS corals retained their F v/F m values up to a critical threshold above which they dropped drastically (Figure 1 b yellow, pink, red curves).

3.2 RNA-Seq yields and genotype analysis

We subjected coral samples from seven colonies exposed to three temperatures (30℃, 33℃, 36℃) from four sites (ICN, AF, ExT, PrT) to RNA-Seq. The average number of sequenced read pairs across all samples used for the analyses was 34,512,756 (Table S2). Following adapter trimming and quality filtering, on average 9,666,810 read pairs (i.e., 19.3 mio. sequence reads) mapped to the genes of the coral S. pistillata and 6,451,749 read pairs (i.e., 12.9 mio. sequence reads) mapped to the genes of the algal symbiont S. microadriaticum. While mapped read coverage for S. pistillata was rather even across samples and sites, we found a lower mapping efficiency for S. microadriaticum samples from the ICN site (Figure S1), further corroborating the notion of fine-scale genetic differentiation of Symbiodinium symbionts between sites, as evidenced by the ITS2 analysis (see below).

To assess the presence of clonal coral genotypes and expression variation associated with genotypes and populations, we conducted a single nucleotide polymorphism (SNP) analysis based on SNPs called from the coral host RNA-Seq data. We determined 1,807,439 raw, unfiltered SNPs, of which 36,498 were called in 95% of the 79 samples used in the RNA-Seq analysis and that also met our specific filtering criteria (see Methods). Based on a hierarchical clustering dendrogram of pairwise Hamming's distances (Aichelman & Barshis, 2020), each individual colony appeared to be a unique genotype/genet (i.e., no cross-colony clones), as indicated by the consistent clustering of within-colony samples (i.e., ramets) across temperature treatments (Figure S2). Based on a principal component analysis of these data using one sample/genotype, coral populations clustered by site with some overlap across sites (Figure S3). Interestingly, corals from the central Red Sea PrT site partially overlapped with the Gulf of Aqaba ICN site, whereas corals from thecentral Red Sea ExT and AF sites overlapped completely (Figure S3).

3.3 Disparate patterns of gene expression in coral hosts and algal symbionts from the Gulf of Aqaba and central Red Sea under heat stress

To elucidate the molecular response patterns underlying the differences in temperature tolerance thresholds, we analyzed gene expression of the coral hosts and algal symbionts at the control/baseline (30℃) and at the 33℃ and 36℃ heat stress temperatures (i.e., seven colonies/site across four sites across three temperatures = 84 samples) (Data S2, Data S3). Coral samples from the GoA and CRS sites were separated by region. Closer inspection revealed that corals from both regions differed with regard to their response to the temperature treatments: corals from the GoA displayed discrete expression responses over temperatures, as shown by the distinct colony clustering (Figure 2a) and the high number of differentially expressed genes (DEGs) between temperature comparisons (GoA site ICN, 30℃ vs. 33℃: 105 DEGs; 30℃ vs. 36℃: 2,766 DEGs; 33℃ vs. 36℃: 1,903 DEGs; Figure 2c; Data S3). In contrast, corals from the CRS did not exhibit a clear temperature response, as indicated by the clustering of all CRS corals across temperatures (Figure 2a) and the almost complete absence of DEGs between temperatures for any of the CRS sites (CRS sites AF/ExT/PrT, 30℃ vs. 33℃: 0/0/0 DEGs; 30℃ vs. 36℃: 14/16/4 DEGs; 33℃ vs. 36℃: 22/12/4 DEGs; Figure 2c; Data S3). Similarly, algal symbiont gene expression clustered foremost by region with some differentiation across temperatures for algal symbionts from GoA corals and an absence thereof in CRS corals (Figure 2b). As observed for the coral host gene expression, we found a higher number of DEGs across temperatures for GoA algal symbionts versus a largely mute response in CRS Symbiodiniaceae (Figure 2d; Data S3).

To further explore the pattern of gene expression responsiveness in GoA corals vs. the transcriptional stasis of CRS corals, we looked for genes that exhibited front-/back-loading, suggested as a mechanism that conveys increased thermal tolerance (Barshis et al., 2013). Characterization of DEGs that had at least four-fold higher/lower expression at the control/baseline temperature (30℃) in corals from the AF site in comparison to the ICN site and that also exhibited differential expression between 30℃ and 33℃ and between 30℃ and 36℃ within the ICN site were considered front-/back-loaded in CRS corals and inducible in GoA corals. We determined 26 genes exhibiting front-loading and 1 gene showing back-loading in AF corals, as well as a further 33 and 88 front- and back-loaded genes, respectively, in AF coral-associated Symbiodiniaceae (Figure 2e, Data S3). Conversely, and importantly, we could not detect any gene in GoA corals or their algal symbionts that exhibited front-loading (and only 1 gene exhibited back-loading); further, no front-/back-loaded genes were identified between PrT and ExT sites for corals and their algal symbionts. This supports the notion that only CRS corals exhibit fixed high/low expression of genes. By contrast, these genes show consecutive induction/suppression across temperatures in GoA corals. This, in turn, suggests distinct mechanisms of thermal tolerance in S. pistillata across Red Sea regions, that is, even for the same ocean basin and species. Conversely, mechanisms appear similar across sites within a region.

Closer examination of the front-loaded genes revealed three S. pistillata homologs of matrix metalloproteinases (Spis5851, Spis5854, Spis5860), further supported by GO functional enrichment analysis (GO:0004222 metalloendopeptidase activity, p-value=.00099), to be of functional significance (Data S3). Metalloproteinases were also previously identified among the set of front-loaded genes between temperature-tolerant and -sensitive Acropora hyacinthus in American Samoa (Barshis et al., 2013). Additionally, two Pax3 transcription factors (Spis374 and Spis375) and two homologues of TNF receptor-associated factor 3 (Spis12948 and Spis12951) putatively involved in apoptosis and activation of the immune response were identified (Häcker et al., 2011; Margue et al., 2000). The latter was confirmed by GO functional enrichment (GO:0033209 tumor necrosis factor-mediated signalling, p-value =.00075). Notably, we could not detect any heat shock proteins (HSPs) among the front-loaded genes, converse to what was previously found (Barshis et al., 2013). Rather, heat shock proteins (Spis4493, Spis4494, Spis4499, Spis19922, Spis19923, Spis19925) were among a list of genes that were most temperature-responsive across all sites, in line with their ubiquitous induction upon exposure to stressful conditions found across animal and plant species (Whitley et al., 1999) (Figure S4, Data S3). Among the front-loaded genes in algal symbionts, we found three genes with homology to high-affinity nitrate transporters (Smic4635, Smic4659, Smic19959) and two oxidoreductases (Smic8517, Smic40364) implicated to play a role in thermal stress susceptibility (Baumgarten et al., 2013), in addition to a suite of metabolic genes (Smic191, Smic2041, Smic32540, Smic36419) (Data S3). GO analysis could further corroborate these results, as highlighted by significant enrichments of genes associated with nitrate assimilation (GO:0042128, p-value =4.2E-05) and the glycolytic process (GO:0006096, p-value =.00089).

3.4 Thermally stable site-specific algal symbiont communities

We elucidated the fine-scale structure of the algal symbiont community of coral hosts from the GoA and CRS sites using the analytical framework SymPortal (Hume et al., 2019). Our analysis revealed unanimous association with microalgae of the genus Symbiodinium (LaJeunesse et al., 2018). Despite this broadscale similarity, we found fine-scale genetic differentiation of associated Symbiodinium symbionts between sites that was stable across temperatures, as evidenced by the ITS2 type profiles (Figure 3a, Data S4). Clustering by sample similarity further revealed that algal symbionts from all CRS sites were close to each other (with some outliers) and distinct from GoA algal symbionts (Figure 3b). The observed pattern of symbiont association indicates high host fidelity of algal symbionts across sites, as reported previously (Howells et al., 2020; Hume et al., 2020; Terraneo et al., 2019). Further to that, our results support fine-scale differentiation even between geographically close-by sites, as the central Red Sea sites (AF, ExT, ExP) all differed with regard to symbiont identities (Figure 3). Notably, sample dissimilarity increased with geographic distance, although the distinct clustering of some microalgal symbionts from ExT corals may suggest that divergence aligns with prevailing environmental differences, rather than geographical distance (Figure 3b).

3.5 Bacterial community dynamics resemble gene expression patterns of corals from the northern and central Red Sea

Bacterial community composition ( n = 2,264 OTUs, Data S5) profoundly changed in GoA corals in contrast to stable bacterial community association of coral colonies across temperatures for CRS sites (all P adj >0.05, PERMANOVA, Figure 4a and c). This resembles the coral host and algal symbiont gene expression patterns (Figure 2c and d), in particular with regard to a ramping response, as evidenced by the increasing number of differentially abundant bacterial taxa (OTUs) between temperature comparisons in GoA corals and absence thereof in CRS corals (Figure 4b). This notion was further confirmed statistically, in that all CRS sites were significantly different from GoA corals at all treatment temperatures with regard to coral and algal gene expression as well as bacterial community composition (all P adj <0.05, PERMANOVA, Data S2, Data S5). Among CRS sites, bacterial community composition of AF corals was significantly different from ExT and PrT sites across all temperatures (P adj <0.05, PERMANOVA), whereas the latter two resembled each other (P adj >0.05, PERMANOVA).

Changes in the bacterial community structure in GoA corals followed a distinct pattern over the temperature increase: only 4 OTUs (Figure 4b) were differentially abundant between 30℃ and 33℃ in GoA corals, whereas 80 and 42 OTUs were differentially abundant in the 30℃ versus 36℃ and the 33℃ versus 36℃ comparisons, respectively (Figure 4b). Despite the high number of differentially abundant bacterial taxa, the overall pattern was driven by a relative loss of Endozoicimonaceae (2 OTUs, mean log2 fold change = -7.31) and a relative increase of Vibrionaceae (15 OTUs, mean log2 fold change = +9.77) and Rhodobacteraceae (21 OTUs, mean log2 fold change = +6.46), suggesting dysbiosis and loss of microbiome structure (Figure 4c) (Voolstra & Ziegler, 2020). S. pistillata colonies from CRS sites by comparison largely retained association with Endozoicimonaceae . In this context, bacteria of the genus Endozoicomonas are typically ascribed to be abundantly associated with S. pistillata in the Red Sea and globally (Bayer et al., 2013; Neave et al., 2017a). However, Endozoicomonas were only dominantly associated with corals from the ICN and the PrT sites, but comprised a rather small proportion of the bacterial community in corals from the AF and ExT sites (Data S5). This suggests fine-scale structuring of bacterial community composition across spatially adjacent reefs (Roder et al., 2015; Ziegler et al., 2019; Ziegler, Seneca, et al., 2017). In light of the known thermotolerance differences between the ExT and PrT reef sites (Pineda et al., 2013; Voolstra et al., 2020), it is tempting to speculate that abundance of Endozoicomonas may contribute to the observed differences in bleaching susceptibility, although prior studies found that their relative abundance was not correlated with bleaching (Pogoreutz et al., 2018; Shiu et al., 2020). Corals from the PrT site were further characterized by a relative prevalence of Simkaniaceaea, which comprise obligate intracellular bacteria of eukaryotes (Everett, 2014) and were recently shown to be a hallmark of corals from pristine environments (Ziegler et al., 2019), though their function is at present unknown. Notably, the nature of the data does not allow us to draw conclusions on bacterial biomass, that is, increase/decrease of bacterial cell densities. It is desirable to understand bacterial biomass dynamics in future experiments to better assert whether bacteria are lost from the holobiont with increasing temperature stress (similar to algal symbiont loss in bleaching) or whether we indeed observe bacterial community restructuring (Costa et al., 2021).

4 DISCUSSION

4.1 The need for a standardized experimental framework to further our understanding of the factors underlying coral thermal tolerance

Despite global coral reef loss driven by ocean warming that triggers coral bleaching (Hughes et al., 2018), coral reef regions (Fine et al., 2013; Guest et al., 2012; Osman et al., 2018), populations (Barshis et al., 2013; Palumbi et al., 2014; Voolstra et al., 2020), and individual coral genotypes (Dixon et al., 2015; Palumbi et al., 2014) with enhanced bleaching resilience exist. However, a detailed understanding of the underlying factors and molecular underpinnings that drive increased thermal tolerance remains elusive. Here, we conducted identical acute thermal stress assays across four reef sites (totaling ~1,000 km overwater distance) to assess thermal limits of corals from the northernmost Red Sea in the Gulf of Aqaba and the central Red Sea, employing a coordinated, standardized experimental framework (CBASS asssays) and subsequent molecular characterization of the coral holobiont heat stress response. Counter to previous work suggesting that the extraordinary thermal tolerance from corals of the Gulf of Aqaba might equal that of southern/central Red Sea populations (Fine et al., 2013; Osman et al., 2018), we could demonstrate that while corals throughout the Red Sea harbor consistent thermal tolerance thresholds exceeding +7℃ above their regional maximum monthly mean (MMM) temperature, absolute thermal limits differed according to the prevailing MMM temperature. Although it is unclear at present how ED50-derived thermal thresholds relate to natural bleaching thresholds, our determined thermal limits correspond very well with the exceptional thermal tolerance of S. pistillata from the Gulf of Aqaba that was found to sustain temperatures of +5℃ above MMM for a period of two weeks, equivalent to 9.4 DHW (Bellworthy & Fine, 2017; Krueger et al., 2017; Savary et al., 2021). Importantly, the experimentally determined ED50 of 34.08℃ for the ICN population is 3.28℃ higher than the maximum temperature ever recorded for this site over the last decade (based on data from The Israel National Monitoring Program at the Gulf of Eilat). Irrespective of how exactly ED50s relate to DHWs (or other means of determining bleaching thresholds), our work and previous work supports the notion that higher thermal tolerance thresholds in short-term heat stress assays are representative of increased thermal stress resilience in natural settings; with ED50s serving a standardized proxy for bleaching susceptibility (Ziegler et al., 2017; Morikawa & Palumbi, 2019; Voolstra et al., 2020; Evensen et al., 2021). Importantly, we found different thermal thresholds across different sites at both large and small scales, which were reflected by holobiont molecular response differences. This highlights the utility and potential of pairing mobile short-term heat stress assays for standardized phenotyping to detect thermal tolerance differences with subsequent investigation of the molecular response to obtain a better understanding of the factors shaping and the mechanisms underlying thermal tolerance.

4.2 Signatures of thermal tolerance across regions and coral holobiont compartments

The different thermal thresholds seen in the F v/F m data were reflected in the gene expression responses of corals and their algal symbionts as well as in the composition of the algal symbiont and bacterial communities. Transcriptional response patterns differed substantially between GoA and CRS corals with a strong response to the thermal stress in GoA corals vs. a muted/static response in CRS corals (up to 2,766 vs. up to 22 differentially expressed genes between temperature treatments in GoA and CRS corals, respectively). Notably, transcriptional differences in GoA corals were minor between the 30℃ control and the 33℃ heat stress temperature (105 DEGs), corroborating the notion that 33℃ is below the critical thermal threshold of GoA corals (Bellworthy & Fine, 2017; Evensen et al., 2021; Krueger et al., 2017). Conversely, differences were most pronounced between 30℃ and 36℃, but we also found substantial gene expression differences between 33℃ and 36℃. Overall, our data indicate the ramping up of a gene expression response with increasing heat stress in GoA corals in stark contrast to the largely muted response in CRS corals. The ramping up versus mute response of GoA vs. CRS corals is further highlighted by the presence of front- and back-loaded genes in CRS corals and the rather complete absence in GoA corals. Notably, a subset of the front-loaded genes in CRS corals were previously identified in Acropora hyacinthus corals from American Samoa (Barshis et al., 2013). On the one hand, this suggests the conservation of thermal tolerance genes across large geographic distances and coral species, and consequently, that universal molecular signatures of thermal tolerance exist. On the other hand, corals can show strikingly disparate heat stress response patterns, even for coral colonies/populations of the same species of the same ocean basin, indicating that distinct mechanisms of thermal tolerance exist. To further understand the dynamics of such response patterns and their utility as indicators of differential thermal tolerance, determination of expression patterns over the course of the heat stress (e.g., before, during, after heat stress, and after the recovery period) may be critical ( sensu Savary et al., 2021). Similarly, reciprocal transplant or common garden follow-up experiments can further evidence whether the differences observed are genetically fixed or a consequence of acclimation (Palumbi et al., 2014; Thomas et al., 2018).

It is also striking that coral host gene expression patterns were mirrored in the algal symbiont. At large, the level of responsiveness in algal symbionts from GoA corals was unexpected, given the commonly ascribed paucity of regulation at the transcriptional level for Symbiodiniaceae (Baumgarten et al., 2013; Bayer et al., 2012), signified by fixed expression differences between symbiont genera and species (Barshis et al., 2014; Davies et al., 2018; Parkinson et al., 2016). However, the notion that algal symbionts in hospite respond transcriptionally to heat stress is now corroborated by several studies (Bellantuono et al., 2019; Fifer et al., 2021; Studivan et al., 2021) and suggest that differential gene expression analysis may provide further insight with regard to relevant cellular processes. Although we did not find direct overlap between algal symbiont front-loaded genes in comparison to another heat stress study (Fifer et al., 2021), a common denominator was that of a stress response being observed based on the functional annotation of identified genes. Hence, and unsurprisingly, differences between algal symbiont species and genotypes may exist, with putative relevance for the stress tolerance of the coral holobiont. Notably, corals from all sites associated with distinct and (largely) consistent microalgal symbiont genotypes, suggesting host fidelity and local adaptation (Howells et al., 2020; Hume et al., 2020). Despite the notion that corals from all sites harbor specific symbiont species, the consistent gene expression stasis of algal symbionts across CRS corals and the disparate responsiveness in Symbiodiniaceae from GoA corals corroborates that the geographically further apart symbionts are also genetically more differentiated.

Bacterial community composition of GoA corals changed dramatically under heat stress in contrast to CRS corals that displayed rather consistent assemblages, in support of previous work that thermally more susceptible corals have less stable microbiomes (Ziegler et al., 2017). At present, however, any presumptive functional consequences of these differences for coral holobiont physiology are largely unknown, despite it becoming broadly accepted that bacteria contribute to metaorganism biology and fitness (Bang et al., 2018; McFall-Ngai et al., 2013; Pogoreutz et al., 2020; Rosado et al., 2019; Santoro et al., 2021; Voolstra & Ziegler, 2020; Peixoto et al., 2021). In cases of rapid environmental change, the role of the microbiome in supporting adaptation of the metaorganism may become even more pronounced, given that microbial-mediated change can be achieved through association with different microbes or exchange/incorporation of new genetic material (e.g., via horizontal gene transfer) and does not rely on adaptive evolution per se (Cárdenas et al., 2020; Voolstra & Ziegler, 2020).

4.3 Contrasting heat stress response patterns of coral holobionts suggest distinct mechanisms of thermal tolerance

Our analyses indicate distinct patterns in the response to heat stress of corals from geographically distant sites in the Red Sea: corals from the GoA show dramatic changes in response to temperature across holobiont compartments versus coral-algal transcriptional and bacterial community stasis at the CRS sites. To ascertain that the observed differences between GoA and CRS corals do not align with the presence of putative cryptic lineages that would need to be considered (Burgess et al., 2021; Dziedzic et al., 2019), we conducted a preliminary population genetics analysis based on SNPs obtained from the coral RNA-Seq data. While a full-fledged population genomics analysis is beyond the scope of this study, there did appear to be greater population differentiation between corals from the ICN/PrT sites on the one hand and corals from the ExT/AF sites on the other hand based on our SNP analysis (Figure S3). This indicates that corals from the CRS sites are not uniformly differentiated from the ICN site, contrary to what would be expected if we were looking at two diverged and distinct lineages. While our analysis cannot definitely determine whether corals from the CRS and GoA represent different populations of the same lineage or multiple cryptic lineages, the overlap between some samples within the PrT population and the ICN population, suggest long-distance dispersal is putatively possible. Our results are also in line with the notion that only Stylophora pistillata clade 4 occurs in the Red Sea ( sensu Keshavmurthy et al., 2013; Malik et al., 2021).

We posit that the observed response differences may be reflective of the nature of the underlying differences in thermal tolerance: we interpret the GoA coral response as that of a resilient population responding to the strength of a stressor. In contrast, the static pattern of consistently elevated but fixed expression states is suggestive of thermal resistance in CRS corals. It may be that both mechanisms evolved in their respective environments, but it is not clear whether they present different solutions to the same problem, or one may prove more successful than the other in countering future ocean warming. Intuitively, a response-resilience mechanism seems more adaptable to changing environments than a static-resistance mechanism that lets coral holobionts operate at maximum capacity irrespective of the external input, as any additional stress cannot lead to a change in response. Further insight may come from the photosynthetic efficiency response curves, which reflect the molecular patterns observed: CRS corals exhibit stable F v/F m values over the experimental temperatures assessed with RNA-Seq (i.e., 30℃, 33℃, 36℃). In contrast, GoA corals exhibit a slow and increasing F v/F m loss with temperature. Pending whether such differences in mechanisms of thermal tolerance are predictive of susceptibility to future ocean warming, they may help in targeting coral colonies, species, and reefs for conservation and restoration efforts. Importantly, our work supports the existence of common gene 'signatures' (e.g., high expression of metalloproteinases under heat stress) irrespective of the underlying thermal tolerance mechanism. Such putatively universal signatures may become important in the screening for and engineering of corals with superior thermal resilience/resistance.

ACKNOWLEDGEMENTS

Research reported in this publication was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project numbers 433042944 and 458901010 to CRV. We further acknowledge funding by the Paul G. Allen Family Foundation ("Global Search") and a BiNational Science Foundation grant (no. 2016403) to DJB and MF.

Funding Statement: Open Access funding enabled and organized by Projekt DEAL. WOA Institution: UNIVERSITAET KONSTANZ. Blended DEAL: Projekt DEAL.

RNA-Seq, ITS2, 16S fastq sequencing data files have been made available at NCBI under BioProject PRJNA681108 https://www.ncbi.nlm.nih.gov/bioproject/PRJNA681108. Scripts for PAM data analysis and plotting, coral host and microalgal gene expression analyses, Symbiodiniaceae and bacterial community analyses and plotting, and coral host SNP analyses can be accessed at https://github.com/reefgenomics/CBASS84. Pictures of colony fragments after short-term thermal stress are available as Supporting Information (Data S1). Gene expression estimates (TPM) for 49,109 Symbiodinium microadriaticum and 25,769 Stylophora pistillata genes, PERMANOVA statistics on pairwise comparisons between temperatures & sites, and differentially expressed genes (DESeq) for S. pistillata and S. microadriaticum are available as Supporting Information (Data S2). Overview of differentially expressed genes, front- and back-loaded genes, GO enrichments, and heat stress response genes for S. pistillata and S. microadriaticum are available as supplementary data (Data S3). Symbiodiniaceae ITS2 type profiles, ITS2 sequences abundance, and ITS2 sequences are available as Supporting Information (Data S4). Bacterial 16S sequence statistics, taxa (OTUs) abundance table, and statistical analyses are available as Supporting Information (Data S5).

# data[].attributes.attachment_link.title
Contrasting heat stress response patterns of coral holobionts across the Red Sea suggest distinct mechanisms of thermal tolerance

# data[].attributes.attachment_link.description
Christian R. Voolstra , Nitin S. Baliga , Daniel J. Barshis , Corresponding Author Department of Biological Sciences, Old Dominion University, Norfolk, USA Correspondence Christian R. Voolstra, Department of Biology, University of Konstanz, Konstanz 78457, Germany. Email: christian.voolstra@uni-konstanz.de Daniel J. Barshis, Department of Biological Sciences, Old Dominion University, Mills Godwin Building 302J, Norfolk, VA 23529, USA.

# data[].attributes.body
Development of novel methods, concepts, and frameworks to delineate  function and identity of aquatic metaorganism member species
SymPortal (symportal.org) - Analytical framework and platform for genetically resolving the algal symbionts of reef corals using next-generation sequencing (NGS) data of the ITS2 rDNA (paper)
ReefGenomics.org (reefgenomics.org) - online open access repository to centralize genomic and transcriptomic data from marine organisms (paper)
Microbiome flexibility - The potential/capacity for dynamic restructuring of the host microbiome in face of environmental change (paper)

# data[].attributes.body
Employment/establishment of the seawater anemone Aiptasia as a  model system to study regulatory mechanisms of the cnidarian-algal symbioses.
Aiptasia genome (paper)
Aiptasia microbiome manipulation (paper)
Aiptasia NanoSIMS (paper)

# data[].attributes.body
Development of analytical frameworks and diagnostics that support climate change resilience using field-based physiological and genomic coral reef surveys
CBASS (Coral Bleaching Automated Stress System) - newly developed portable experimental system to run standardized short-term acute heat stress assays to identify resilient genotypes/reefs for downstream experimental examination and prioritization for conservation/restoration (paper, paper, paper). CBASS assays allow determination of the ED50, the standardized temperature threshold at which 50% of initial photosystem efficiency is lost (paper).

# data[].attributes.body
Save the corals: The Paul G. Allen Family Foundation funds the rapid stress test “CBASS” to assess the climate resilience of corals with a total of 4 million dollars. (link)

# data[].attributes.headline
New Research seeks to crack the code of Coral Reef Heat Resilience

# data[].attributes.subject
New Research seeks to crack the code of Coral Reef Heat Resilience

# data[].attributes.body
Paul G. Allen Family Foundation grants $7.2 million to support researchers working to help coral reefs survive the impacts of climate change. A global team of researchers from ODU, ISB, AIMS, and PSU led by Professor Christian Voolstra at Konstanz University of Germany are searching for corals with natural resilience to climate change, or “super corals”, so they can be prioritized for conservation and restoration efforts and studied to understand what makes a coral reef more heat tolerant. (link)

# data[].attributes.body
Cracking the coral thermal tolerance code - Global Search for Genetic Regulators of Coral Resilience to Thermal Stress (link)

# data[].attributes.headline
German Research Foundation - Deutsche Forschungsgemeinschaft (DFG)

# data[].attributes.subject
German Research Foundation - Deutsche Forschungsgemeinschaft (DFG)

# data[].attributes.body
Genomics basis of coral resilience in a warming world (link)

A global view of the biocomplexity underlying coral holobiont health and climate change resilience (link)

# data[].attributes.body
LinkedIn (link)
Twitter @reefgenomics (link)

# data[].attributes.headline
Carol Buitrago-Lopez, PhD student at KAUST

# data[].attributes.subject
Carol Buitrago-Lopez, PhD student at KAUST

# data[].attributes.body
link

# data[].attributes.body
link