Aerenchyma in emergent plants and rhizospheric microbial communities promote methane fluxes in wetlands
Bibliographic record
Abstract
Abstract Wetlands are the largest natural source of CH 4 globally, yet our understanding of how environmental parameters and microorganisms affect the production and emission of CH 4 in emergent plant–sediment systems remains limited. In this study, CH 4 fluxes were investigated in a wetland with Canna indica for 42 d, as well as nutrients and microbial community. It was found that the chimney effect formed by aerenchyma in roots, stems, and leaves of C. indica promoted the emission and oxidation of CH 4 in the wetland and reduced the CH 4 concentration in sediments. Canna indica reduced the nutrient release from surface sediments into the overlying water. Pearson correlation analysis showed that temperature, pH, and oxidation–reduction potential were the main influencing factors for CH 4 production and oxidation in the wetland. Canna indica inhibited the diversity of archaeal community but promoted the diversity of bacterial community in the rhizosphere. Stochastic processes had a greater impact on bacterial and archaeal succession trajectories in wetland sediments. Network analysis showed that C. indica promoted interactions among bacteria and archaea that enhanced their ability to resist environmental interference. The well‐developed aerenchyma of C. indica provided an important passage for the transport of CH 4 from sediments to the atmosphere and shaped the microbial community structure in the rhizosphere. Meanwhile, CH 4 emissions were also constrained by several variables, such as temperature and physiological adaptation in the long term. Thus, it is necessary to plant emergent plants in areas with low CH 4 emissions and optimize plant configuration in the context of global warming.
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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) | 0.000 | 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".