Seagrass (<i>Zostera marina</i>) transplant experiment reveals core microbiota and resistance to environmental change
Bibliographic record
Abstract
Zostera marina (seagrass) is a coastal marine angiosperm that sustains a diverse and productive ecosystem. Seagrass-associated microbiota support host health, yet the ecological processes that maintain biodiversity and stability of the seagrass leaf microbiota are poorly understood. We tested two hypotheses: (1) Microbes select seagrass leaves as habitat such that they consistently host distinct microbiota and/or core taxa in comparison to nearby substrates, and (2) seagrass leaf microbiota are stable once established and are resistant to change when transplanted to a novel environment. We reciprocally transplanted replicate seagrass shoots (natural and surface sterilized/dead tissue treatments) among four meadows with different environmental conditions and deployed artificial seagrass treatments in all four meadows. At the end of the 5-day experiment, the established microbiota on natural seagrass partially turned over to resemble microbial communities in the novel meadow, and all experimental treatments hosted distinct surface microbiota. We consistently found that natural and sterilized/dead seagrass hosted more methanol-utilizing bacteria compared to artificial seagrass and water, suggesting that seagrass core microbiota are shaped by taxa that metabolize seagrass exudates coupled with minor roles for host microbial defence and/or host-directed recruitment. We found evidence that the local environment strongly influenced the seagrass leaf microbiota in natural meadows and that transplant location explained more variation than experimental treatment. Transplanting resulted in high turnover and variability of the seagrass leaf microbiota, suggesting that it is flexibly assembled in a wide array of environmental conditions which may contribute to resilience of seagrass in future climate change scenarios.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".