Nitrogen addition decreases methane uptake caused by methanotroph and methanogen imbalances in a Moso bamboo forest
Bibliographic record
Abstract
Abstract Forest soils play an important role in controlling global warming by reducing atmospheric methane (CH 4 ) concentrations. However, little attention has been paid to how nitrogen (N) deposition may alter microorganism communities that are related to the CH 4 cycle or CH 4 oxidation in subtropical forest soils. We investigated the effects of N addition (0, 30, 60, or 90 kg N ha −1 yr −1 ) on soil CH 4 flux and methanotroph and methanogen abundance, diversity, and community structure in a Moso bamboo ( Phyllostachys edulis ) forest in subtropical China. N addition significantly increased methanogen abundance but reduced both methanotroph and methanogen diversity. Methanotroph and methanogen community structures under the N deposition treatments were significantly different from those of the control. In N deposition treatments, the relative abundance of Methanoculleus was significantly lower than that in the control. Soil pH was the key factor regulating the changes in methanotroph and methanogen diversity and community structure. The CH 4 emission rate increased with N addition and was negatively correlated with both methanotroph and methanogen diversity but positively correlated with methanogen abundance. Overall, our results suggested that N deposition can suppress CH 4 uptake by altering methanotroph and methanogen abundance, diversity, and community structure in subtropical Moso bamboo forest soils.
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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".