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Over the past two decades, researchers have searched for methods to better understand the relationship between coral hosts and their microbiomes. Data on how coral-associated bacteria are involved in their host’s responses to stressors that cause bleaching, disease, and other deleterious effects can elucidate how they may mediate, ameliorate, and exacerbate interactions between the coral and the surrounding environment. At the same time tracking coral bacteria dynamics can reveal previously undiscovered mechanisms of coral resilience, acclimatization, and evolutionary adaptation. Although modern techniques have reduced the cost of conducting high-throughput sequencing of coral microbes, to explore the composition, function, and dynamics of coral-associated bacteria, it is necessary that the entire procedure, from collection to sequencing, and subsequent analysis be carried out in an objective and effective way. Corals represent a difficult host with which to work, and unique steps in the process of microbiome assessment are necessary to avoid inaccuracies or unusable data in microbiome libraries, such as off-target amplification of host sequences. Here, we review, compare and contrast, and recommend methods for sample collection, preservation, and processing (e.g., DNA extraction) pipelines to best generate 16S amplicon libraries with the aim of tracking coral microbiome dynamics. We also discuss some basic quality assurance and general bioinformatic methods to analyze the diversity, composition, and taxonomic profiles of the microbiomes. This review aims to be a generalizable guide for researchers interested in starting and modifying the molecular biology aspects of coral microbiome research, highlighting best practices and tricks of the trade. 

16S rRNA gene profiling (amplicon sequencing) is a popular technique for understanding host-associated and environmental microbial communities. Most protocols for sequencing amplicon libraries follow a standardized pipeline that can differ slightly depending on laboratory facility and user. Given that the same variable region of the 16S gene is targeted, it is generally accepted that sequencing output from differing protocols are comparable and this assumption underlies our ability to identify universal patterns in microbial dynamics through meta-analyses. However, discrepant results from a combined 16S rRNA gene dataset prepared by two labs whose protocols differed only in DNA polymerase and sequencing platform led us to scrutinize the outputs and challenge the idea of confidently combining them for standard microbiome analysis. Using technical replicates of reef-building coral samples from two species, Montipora aequituberculata and Porites lobata , we evaluated the consistency of alpha and beta diversity metrics between data resulting from these highly similar protocols. While we found minimal variation in alpha diversity between platform, significant differences were revealed with most beta diversity metrics, dependent on host species. These inconsistencies persisted following removal of low abundance taxa and when comparing across higher taxonomic levels, suggesting that bacterial community differences associated with sequencing protocol are likely to be context dependent and difficult to correct without extensive validation work. The results of this study encourage caution in the statistical comparison and interpretation of studies that combine rRNA gene sequence data from distinct protocols and point to a need for further work identifying mechanistic causes of these observed differences. 

Microbiomes are essential features of holobionts, providing their hosts with key metabolic and functional traits like resistance to environmental disturbances and diseases. In scleractinian corals, questions remain about the microbiome's role in resistance and resilience to factors contributing to the ongoing global coral decline and whether microbes serve as a form of holobiont ecological memory. To test if and how coral microbiomes affect host health outcomes during repeated disturbances, we conducted a large‐scale (32 exclosures, 200 colonies, and 3 coral species sampled) and long‐term (28 months, 2018–2020) manipulative experiment on the forereef of Mo'orea, French Polynesia. In 2019 and 2020, this reef experienced the two most severe marine heatwaves on record for the site. Our experiment and these events afforded us the opportunity to test microbiome dynamics and roles in the context of coral bleaching and mortality resulting from these successive and severe heatwaves. We report unique microbiome responses to repeated heatwaves inAcropora retusa,Porites lobata, andPocilloporaspp., which included: microbiome acclimatization inA. retusa, and both microbiome resilience to the first marine heatwave and microbiome resistance to the second marine heatwave inPocilloporaspp. Moreover, observed microbiome dynamics significantly correlated with coral species‐specific phenotypes. For example, bleaching and mortality inA. retusaboth significantly increased with greater microbiome beta dispersion and greater Shannon Diversity, whileP. lobatacolonies had different microbiomes across mortality prevalence. Compositional microbiome changes, such as changes to proportions of differentially abundant putatively beneficial to putatively detrimental taxa to coral health outcomes during repeated heat stress, also correlated with host mortality, with higher proportions of detrimental taxa yielding higher mortality inA. retusa. This study reveals evidence for coral species‐specific microbial responses to repeated heatwaves and, importantly, suggests that host‐dependent microbiome dynamics may provide a form of holobiont ecological memory to repeated heat stress.

 

ABSTRACT A growing body of research has established that the microbiome can mediate the dynamics and functional capacities of diverse biological systems. Yet, we understand little about what governs the response of these microbial communities to host or environmental changes. Most efforts to model microbiomes focus on defining the relationships between the microbiome, host, and environmental features within a specified study system and therefore fail to capture those that may be evident across multiple systems. In parallel with these developments in microbiome research, computer scientists have developed a variety of machine learning tools that can identify subtle, but informative, patterns from complex data. Here, we recommend using deep transfer learning to resolve microbiome patterns that transcend study systems. By leveraging diverse public data sets in an unsupervised way, such models can learn contextual relationships between features and build on those patterns to perform subsequent tasks (e.g., classification) within specific biological contexts. 

Prospects for coral persistence through increasingly frequent and extended heatwaves seem bleak. Coral recovery from bleaching is only known to occur after temperatures return to normal, and mitigation of local stressors does not appear to augment coral survival. Capitalizing on a natural experiment in the equatorial Pacific, we track individual coral colonies at sites spanning a gradient of local anthropogenic disturbance through a tropical heatwave of unprecedented duration. Unexpectedly, some corals survived the event by recovering from bleaching while still at elevated temperatures. These corals initially had heat-sensitive algal symbiont communities, endured bleaching, and then recovered through proliferation of heat-tolerant symbionts. This pathway to survival only occurred in the absence of strong local stressors. In contrast, corals in highly disturbed areas were already dominated by heat-tolerant symbionts, and despite initially resisting bleaching, these corals had no survival advantage in one species and 3.3 times lower survival in the other. These unanticipated connections between disturbance, coral symbioses and heat stress resilience reveal multiple pathways to coral survival through future prolonged heatwaves.