Vegetation composition regulates the interaction of warming and nitrogen deposition on net carbon dioxide uptake in a boreal peatland
Bibliographic record
Abstract
Abstract Peatlands are carbon sinks and have the potential to mitigate global warming. However, it is unclear whether peatlands will remain carbon sinks or switch to carbon sources under global changes, such as climate warming, elevated nitrogen (N) deposition and vegetation composition change. In this study, these global changes were mimicked in a boreal peatland for 7 years to explore the interactions of climate warming, elevated N deposition and vegetation composition on the carbon sink function of peatlands. The results showed that warming has a limited effect on net ecosystem production (NEP), while N addition decreased NEP by 65% owing to the detrimental effect on Sphagnum mosses. The negative impact of N addition on NEP could be mitigated by warming under intact vegetation. Under the treatment of graminoid removal, warming and elevated N addition (WN) decreased NEP by 80%–106%. Under the treatment of shrub removal, 7 years of WN treatment did not affect NEP. These results highlight the importance of vegetation composition in regulating net CO2 flux in peatlands. If peatlands shift to shrub‐dominated ecosystems, the net CO2 uptake in peatlands would be decreased under climate warming and elevated N deposition. If peatlands shift to graminoid‐dominated ecosystems, the net CO2 uptake in peatlands would be unaltered under climate warming and elevated N deposition. This study sheds new light on the interactions of climate warming, elevated N deposition, and vegetation composition change on the CO2 uptake of peatlands; and could help accurately evaluate the carbon sink function of peatlands under future global change. Read the free Plain Language Summary for this article on the Journal blog.
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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".