Responses of Coral-Associated Bacterial Communities to Local and Global Stressors
Bibliographic record
Abstract
The microbial contribution to ecological resilience is still largely overlooked in coral reef ecology. Coral-associated bacteria serve a wide variety of functional roles with reference to the coral host, and thus, the composition of the overall microbiome community can strongly influence coral health and survival. Here, we synthesize the findings of recent studies (n=45) that evaluated the impacts of the top three stressors facing coral reefs, climate change, water pollution and overfishing, on coral microbiome community structure and diversity. Contrary to the species losses that are typical of many ecological communities under stress, here we show that microbial richness tends to be higher rather than lower for stressed corals (i.e. in ~60% of cases), regardless of the stressor. Microbial responses to stress were taxonomically consistent across stressors, with specific taxa typically increasing in abundance (e.g. Vibrionales, Flavobacteriales, Rhodobacterales, Altermonadales, Rhizobiales, Rhodospirillales and Desulfovibrionales) and others declining (e.g. Oceanosprillales). Emerging evidence also suggests that stress may increase the microbial beta diversity amongst coral colonies, potentially reflecting a reduced ability of the coral host to regulate its microbiome. Moving forward, studies will need to discern the implications of stress-induced shifts in microbiome diversity for the coral hosts and may be able to use microbiome community structure to identify resilient corals. The evidence we present here supports the hypothesis that microbial communities play important roles in ecological resilience, and we encourage a focus on the microbial contributions to resilience for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".