Riparian cottonwood trees and adjacent river sediments have different microbial communities and produce methane with contrasting carbon isotope compositions
Bibliographic record
Abstract
Rivers and their adjacent riparian forests are intimately linked by the exchange of water, nutrients, and organic matter. Both riparian cottonwood trees and adjacent river sediments host microbial communities including archaeal methanogens, supporting methane production and emission to the atmosphere. Here we combine microbial community and stable isotope analyses to characterize the drivers of methane cycling in distinct anoxic habitats (river sediments versus riparian cottonwood stems) in the Oldman River, southern Alberta (Canada). We demonstrate that, differences in the chemical characteristics of organic matter support divergent microbial communities that generate methane from distinct metabolic pathways. Organic matter in river sediments had C/N ratios approximately 50-fold lower than in tree stems and had more diverse dissolved organic components. Contrasting substrate availability between river sediment and tree stems was likely the primary mechanism for the observed differences in bacterial and methanogen community compositions, and greater microbial diversity in river sediments than in tree stems. The methane carbon isotope composition (δ13C values) differed for the tree stem (-103.6 to -70.6‰) and river sediment (-55.1 to -48.4‰) environments, suggesting that methane was primarily produced via CO2-reduction in tree stems by Methanobacteriales, while river sediments produced more methane through acetate fermentation primarily by Methanosarcinales. This study demonstrates the importance of organic matter quality and microbial community composition in driving metabolic processes contributing to methane production and emission in rivers and adjacent riparian 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 | 0.000 |
| 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 | 0.000 |
| 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".