Microbiomes of Thalassia testudinum throughout the Atlantic Ocean, Caribbean Sea, and Gulf of Mexico are influenced by site and region while maintaining a core microbiome
Bibliographic record
Abstract
Plant microbiomes are known to serve several important functions for their host, and it is therefore important to understand their composition as well as the factors that may influence these microbial communities. The microbiome ofThalassia testudinumhas only recently been explored, and studies to-date have primarily focused on characterizing the microbiome of plants in a single region. Here, we present the first characterization of the composition of the microbial communities ofT. testudinumacross a wide geographical range spanning three distinct regions with varying physicochemical conditions. We collected samples of leaves, roots, sediment, and water from six sites throughout the Atlantic Ocean, Caribbean Sea, and the Gulf of Mexico. We then analyzed these samples using 16S rRNA amplicon sequencing. We found that site and region can influence the microbial communities ofT. testudinum, while maintaining a plant-associated core microbiome. A comprehensive comparison of available microbial community data fromT. testudinumstudies determined a core microbiome composed of 14 ASVs that consisted mostly of the family Rhodobacteraceae. The most abundant genera in the microbial communities included organisms with possible plant-beneficial functions, like plant-growth promoting taxa, disease suppressing taxa, and nitrogen fixers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".