Microbial mechanisms underlying differences of methane emissions between urban and rural wetlands
Bibliographic record
Abstract
ABSTRACT Methane (CH 4 ) emissions differ between urban and rural wetlands, while the microbial mechanisms associated with these differences have not been clearly identified. Here, we characterized the CH 4 -cycling microbial communities and their functional function metabolic pathways between urban and rural wetlands by using 16S rRNA amplicon sequencing, metagenomes and CH 4 flux measurements. Results showed that rural wetlands primarily utilized acetate/CO 2 -dependent methanogenic pathway and complete carbon oxidation to CO 2 in methanotrophic pathway. Whereas, urban wetlands were dominated by the coenzyme M-dependent methanogenic pathway and trimethylamine catabolism, with methanotrophic pathway characterized by enhanced carbon assimilation capacity. In wetland water, while the abundances of methanogens in urban water were 5-fold lower than in rural water, urban water exhibited stronger microbial cooperation and higher metabolic flexibility, which were associated with an 85% higher water-atmosphere CH 4 flux compared to rural counterparts. In wetland soil, key environmental factors (e.g. higher pH and lower organic matter content compared to rural sites) shaped distinct microbial community structures and CH 4 metabolic traits. These differences were shown as higher functional gene diversity, more stable co-occurrence networks, and greater metabolic flexibility, which were linked to a 6-fold higher soil CH 4 emissions than in rural soil. This study describes the microbial mechanisms underlying CH 4 emission differences between urban and rural wetlands, providing insights into microbially mediated CH 4 cycling in urban wetland ecosystems.
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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 | 0.000 | 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".