Tree tissues and species traits modulate the microbial methane-cycling communities of the tree phyllosphere
Bibliographic record
Abstract
Abstract Background Methanogenic and methanotrophic communities (i.e., the microbial communities involved in methane production and consumption) of the tree phyllosphere remain uncharacterized for most tree species despite increasing evidence of their role in regulating tree methane fluxes. Using 16S rRNA gene sequencing, we studied the methanogenic and methanotrophic communities of leaves, wood and bark of five tree species (Acer saccharinum, Fraxinus nigra, Ulmus americana, Salix nigra, and Populus tremuloides) growing in the floodplain of Lake St-Pierre (Québec). Results Methanogenic and methanotrophic communities differed mostly between tree tissues (leaf, wood and bark) but also between tree species according to different traits (e.g., leaf, heartwood and bark pH, leaf and heartwood humidity). Methanogens were prevalent in the wood of trees, while facultative methanotrophs were found in higher proportions than methanogens in leaves and bark, suggesting different potential role of these microbial communities in methane regulation. Tree species differing in key traits could also be associated with differential microbial production/consumption of methane. Tissue pH was a particularly important trait in modulating methanogen-methanotroph community composition and the relative abundance of methanogens and methanotrophs in the different phyllosphere compartments. Conclusion Our study shows that methanogens and methanotrophs are prevalent in the phyllosphere of several tree species, suggesting a potential widespread role in the regulation of tree methane fluxes. Tree species traits are important in determining the composition and abundance of phyllosphere methane-cycling microbial communities. Better understanding these microbial communities and their drivers can help assess their potential contribution to methane mitigation strategies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".