Plant traits and taxonomy drive host associations in tropical phyllosphere fungal communities
Bibliographic record
Abstract
The aerial surface of plants, known as the phyllosphere, represents a widespread and diverse habitat for microbes, but the fungal communities colonizing the surface of leaves are not well characterized, and how these communities are assembled on hosts is unknown. We used high-throughput sequencing of fungal communities on the leaves of 51 tree species in a lowland tropical rainforest in Panama to examine the influence of host plant taxonomy and traits on the fungi colonizing the phyllosphere. Fungal communities on leaves were dominated by the phyla Ascomycota (79% of all sequences), Basidiomycota (11%), and Chytridiomycota (5%). Host plant taxonomic identity explained more than half of the variation in fungal community composition across trees, and numerous host functional traits related to leaf morphology, leaf chemistry, and plant growth and mortality were significantly associated with fungal community structure. Differences in fungal biodiversity among hosts suggest that individual tree species support unique fungal communities and that diverse tropical forests also support a large number of fungal species. Similarities between phyllosphere and decomposer communities suggest that fungi inhabiting living leaves may have significant roles in ecosystem functioning in tropical forests.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".