Investigating the spatiotemporal dynamics of apple tree phyllosphere bacterial and fungal communities across cultivars in orchards
Bibliographic record
Abstract
The phyllosphere, a reservoir of diverse microbial life associated with plant health, harbors microbial communities that are subject to various complex ecological processes acting at multiple scales. In this study, we investigated the determinants of the spatiotemporal variation in bacterial and fungal communities within the apple tree phyllosphere, employing 16S and ITS amplicon sequencing. Our research assessed the impact of key factors—plant compartment, site, time, and cultivar—on the composition and diversity of leaf and flower microbial communities. Our analyses, based on samples collected from three cultivars in three orchards in 2022, revealed that site and time are the strongest drivers of apple tree phyllosphere microbial communities. Conversely, plant compartment and cultivar exhibited minor roles in explaining community composition and diversity. Predominantly, bacterial communities comprised Hymenobacter (25%) and Sphingomonas (10%), while the most relatively abundant fungal genera included Aureobasidium (27%) and Sporobolomyces (10%). Additionally, our results show a gradual decrease in alpha-diversity throughout the growth season. These findings emphasize the necessity to consider local microbial ecology dynamics in orchards, especially as many groups worldwide aim for the development of biocontrol strategies (e.g., by manipulating plant–microbe interactions). More research is needed to improve our understanding of the determinants of time and site-specific disparities within apple tree phyllosphere microbial communities across multiple years, locations, and cultivars.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".