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Ecosystems are intrinsically linked, such that management actions in one ecosystem can influence adjacent ecosystems. However, adequate data, and even protocols, for monitoring cross-ecosystem responses to conservation initiatives are lacking. Here, we evaluate potential indicators, operating on different spatial, temporal, and biological scales, for measuring the effects of island-based restoration on coral reef ecosystems. We show that island restoration status had consistent effects on populations of tropical seabirds across spatial scales from 100 m to entire islands. Seabirds, in turn, provided nutrient subsidies that were incorporated by marine algae and coral-reef fishes, with the most pronounced effects closer to shore, at leeward sites, and at low trophic levels. Microbes and macroalgae exhibited assemblage-level responses to seabird-derived nutrients entering the marine environment, but there were few differences in coral reef benthic and fish assemblages. By identifying and focusing on specific indicators such as macroalgal nutrients, managers can better monitor cross-ecosystem responses to conservation interventions with limited resources. 

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. 

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. 

Abstract Many ecosystems around the world are rapidly deteriorating due to both local and global pressures, and perhaps none so precipitously as coral reefs. Management of coral reefs through maintenance (e.g., marine‐protected areas, catchment management to improve water quality), restoration, as well as global and national governmental agreements to reduce greenhouse gas emissions (e.g., the 2015 Paris Agreement) is critical for the persistence of coral reefs. Despite these initiatives, the health and abundance of corals reefs are rapidly declining and other solutions will soon be required. We have recently discussed options for using assisted evolution (i.e., selective breeding, assisted gene flow, conditioning or epigenetic programming, and the manipulation of the coral microbiome) as a means to enhance environmental stress tolerance of corals and the success of coral reef restoration efforts. The 2014–2016 global coral bleaching event has sharpened the focus on such interventionist approaches. We highlight the necessity for consideration of alternative (e.g., hybrid) ecosystem states, discuss traits of resilient corals and coral reef ecosystems, and propose a decision tree for incorporating assisted evolution into restoration initiatives to enhance climate resilience of coral reefs.