Assembly and maintenance of phyllosphere microbial diversity during rubber tree leaf senescence
Bibliographic record
Abstract
Phyllosphere microorganisms execute important ecological functions including supporting host plant growth, enhancing host resistance to abiotic stresses, and promoting plant diversity. How leaf developmental stages affect plant-microbiome interactions and phyllosphere microbial community assembly and diversity is poorly understood. In this study, we utilized amplicon sequencing of 16S rRNA and ITS genes to investigate the composition and diversity of microbial communities across different leaf developmental stages of rubber trees. Our findings reveal that endophytic microbial communities, particularly bacterial communities, are more influenced by leaf senescence than by epiphytic communities. The high abundance of metabolism genes in the endosphere of yellow leaves contributes to the degradation and nutrient relocation processes. Nutrient loss leads to a higher abundance of α-Proteobacteria (r-selected microorganisms) in the yellow leaf endosphere, thereby promoting stochastic community assembly. As leaves age, the proportion of microorganisms entering the inner layer of leaves increases, consequently enhancing the diversity of microorganisms in the inner layer of leaves. These results offer insights into the mechanisms governing community assembly and diversity of leaf bacteria and fungi, thereby advancing our understanding of the evolving functions of microbial communities during leaf senescence in general, and for an important tropical crop species in particular.
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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 | 0.000 |
| 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 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".