Long-term nutrient addition increased CH4 emission from a bog through direct and indirect effects
Bibliographic record
Abstract
Abstract Peatlands are globally significant sources of atmospheric methane (CH 4 ). While several studies have examined the effects of nutrient addition on CH 4 dynamics, there are few long-term peatland fertilization experiments, which are needed to understand the aggregated effects of nutrient deposition on ecosystem functioning. We investigated responses of CH 4 flux and production to long-term field treatments with three levels of N (1.6–6.4 g m −2 yr −1 as NH 4 NO 3 ), potassium and phosphorus (PK, 5.0 g P and 6.3 g K m −2 yr −1 as KH 2 PO 4 ), and NPK in a temperate bog. Methane fluxes were measured in the field from May to August in 2005 and 2015. In 2015 CH 4 flux was higher in the NPK treatment with 16 years of 6.4 g N m −2 yr −1 than in the control (50.5 vs . 8.6 mg CH 4 m −2 d −1 ). The increase in CH 4 flux was associated with wetter conditions derived from peat subsidence. Incubation of peat samples, with and without short-term PK amendment, showed that potential CH 4 production was enhanced in the PK treatments, both from field application and by amending the incubation. We suggest that changes in this bog ecosystem originate from long-term vegetation change, increased decomposition and direct nutrient effects on microbial dynamics.
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 | 0.001 | 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".