Host-Specificity and Core Taxa of Seagrass Leaf Microbiome Identified Across Tissue Age and Geographical Regions
Bibliographic record
Abstract
The seagrass Zostera marina is a widespread foundational species in temperate coastal ecosystems that supports diverse communities of epiphytes and grazers. Bacteria link the production of seagrass to higher trophic levels and are thought to influence seagrass biology and health. Yet, we lack a clear understanding of the factors that structure the seagrass microbiome, or whether there is a consistent microbial community associated with seagrass that underpins functional roles. We sampled surface microbiome (epibiota) from new and old growth seagrass leaves and the surrounding seawater in eight meadows among four regions along the Central Coast of British Columbia, Canada to assess microbiome variability across space and as leaves age. We found that the seagrass leaf microbiome differs strongly from seawater. Microbial communities in new and old growth leaves are different from each other and from artificial seagrass leaves we deployed in one meadow. The microbiome on new leaves is less diverse and there is a small suite of core OTUs (operational taxonomic units) consistently present across regions. The overall microbial community for new leaves is more dispersed but with little regional differentiation, while the epiphytes on old leaves are regionally distinct. Many core OTUs on old leaves are commonly associated with marine biofilms. Together these observations suggest a stronger role for host filtering in new compared to old leaves, and a stronger influence of the environment and environmental colonization in old leaves. We found 11 core microbial taxa consistently present on old and new leaves and at very low relative abundance on artificial leaves and in the water column. These 11 taxa appear to be strongly associated with Z. marina . These core taxa may perform key functions important for the host such as detoxifying seagrass waste products, enhancing plant growth, and controlling epiphyte cover.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".