Tissue humidity and <scp>pH</scp> as important species traits regulating tree methane emissions in floodplain wetland forests
Bibliographic record
Abstract
Despite the increasing number of studies investigating tree methane fluxes, the relationships between tree methane fluxes and species traits remain mostly unexplored. We measured leaf and stem methane fluxes of five tree species (Acer saccharinum, Fraxinus nigra, Ulmus americana, Salix nigra, and Populus spp.) in the floodplain of Lake St-Pierre (Québec) and examined how these fluxes vary with species traits (wood density, humidity, pH; leaf water content, pH, stomatal conductance; methanogen and methanotroph relative abundances (RAs) in leaf, wood, and bark). Tree methane fluxes differed among tree species according to traits linked to the transport of soil-produced methane and chemical conditions associated with the regulation of methane-cycling microorganisms. Tree fluxes were correlated positively with heartwood and leaf pH and negatively with their humidity. Stem emissions were positively correlated with methanogen RA in heartwood, and leaf emissions were negatively correlated with the RA of leaf epiphytic methanotrophs, suggesting a contribution of tree microbiota in the regulation of methane fluxes. We demonstrated for the first time that tissue pH may be a particularly important trait influencing tree methane fluxes via the regulation of microbial mechanisms. Species with low tissue pH show potential for methane release mitigation.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".