Reproducing plant microbiome research reveals site and time as key drivers of apple tree phyllosphere bacterial communities
Bibliographic record
Abstract
Manipulating plant microbiomes is foreseen as a key biocontrol avenue to tackle the accelerating challenges of global change in agriculture. Several recent studies have identified the spatiotemporal dynamics of phyllosphere microbial communities, stressing the need to understand plant microbiome drivers to design efficient biocontrol interventions. Yet, these works are often performed on small sample counts, rarely provide sufficient information on the relative impact of time or local environment, and are seldom repeated to assess reproducibility. To address these limits, we performed a longitudinal sampling across multiple orchards of contrasting agricultural practices to study the ecological drivers of phyllosphere bacterial communities of apple tree (Malus domestica, Borkh.). We sampled up to eight apple cultivars at six orchards (three conventional, three organic) in the Eastern Townships (Canada) in 2022 and 2023. In contrast with common cross-sectional microbiome studies, our work builds on a two-year sampling design, thus allowing for the evaluation of the reproducibility of previous plant microbiome research. Our results support previous findings indicating that site and time are major drivers of apple tree bacterial community structure, yet their relative influence vary across the two sampling years. In addition, our data showed that leaf and flower bacterial alpha diversity is lower at organic sites compared to conventional sites. Overall, this study provides a comprehensive longitudinal multi-site study design highlighting the value of assessing reproducibility in plant microbiome studies and paving the way for future research in this field.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".