A Field-Based Study of Phyllosphere Mycobiomes in Apple Orchards Under Varying Agricultural Management Strategies
Bibliographic record
Abstract
Abstract Microbial communities in the phyllosphere are key players in plant health and disease resistance, yet their response to agricultural management strategies remains poorly understood under field conditions. Here, we compare fungal community composition and diversity across conventional and organic apple orchards using ITS amplicon sequencing. Leaf samples were collected from six sites at three distinct time points during the 2023 growing season (in May, July, and August) corresponding approximately to monthly intervals throughout the summer. Flower samples were collected from the same trees in May. Our analyses reveal that agricultural management strategies are significantly associated with fungal community structure, with effects intensifying from May to July. Both types of management strategies showed enrichment for different genera known to include common apple tree pathogens: Alternaria and Podosphaera were associated with conventional sites, while Didymella and Ramularia were associated with organic sites. Although fungal alpha diversity was higher in May at conventional orchards compared to organic orchards, it declined over time at conventional sites while it remained stable at organic sites. Together, these patterns indicate that distinct management interventions impose contrasting selective pressures on the apple tree phyllosphere mycobiome, thus shaping both broad fungal community composition and the dominance dynamics of key fungal taxa. Our findings underscore the ecological relevance and inherent challenges of field-based microbiome research, and provide insights to inform the development of sustainable orchard management strategies grounded in fungal community dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".