How Useful Are Plant Traits in Explaining Variation in Phyllosphere Microbial Abundance and Composition Across Hosts?
Bibliographic record
Abstract
Plant taxonomy has emerged as a key driver of plant-microbe associations, but the mechanisms underlying these associations remain poorly understood. By defining selective environmental gradients for microbial taxa, plant traits can provide more proximate explanations of microbial taxonomic turnover across plants than plant taxonomy alone. Whether key plant traits may generally predict plant-microbe associations, however, remains unknown. Here, we conducted a systematic review of the phyllosphere literature to evaluate whether specific plant traits consistently explained variation in the abundance and composition of leaf microbes within and among plant species. Drawing on results from over 100 studies, we showed that plant traits linked to development, primary metabolism and defence consistently shaped the composition of leaf bacterial and fungal communities, highlighting the relevance of these traits in predicting plant-microbe associations in the phyllosphere. Since most plant traits tested did not influence leaf microbial composition more frequently than expected by chance, our study underscores the importance of: (1) rethinking the scale and selection of plant traits used to investigate microbiome assembly; (2) refining the taxonomic resolution at which microbial communities are analysed and (3) considering alternative explanations such as stochastic processes or historical factors for improving our understanding of plant-microbe associations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".