Warming Amplifies Responses of Soil Organic Carbon to Multiple Global Change Drivers
Bibliographic record
Abstract
ABSTRACT Soil organic carbon (SOC) is the largest terrestrial C reservoir on Earth, which plays a critical role in climate regulation. While global warming is a defining feature of anthropogenic climate change, its interactive effects with other global change drivers on the content of SOC remain unclear. For this study we conducted a global meta‐analysis of 2349 observations from 363 studies, which revealed that warming alone reduced SOC by 7.2% while synergistically amplifying responses to other drivers. It enhanced the effects of elevated CO 2 by 240% and those of nitrogen addition by 350%, while exacerbating drought‐induced losses by 340%. Noticeably, while warming revealed synergistic interactions with other drivers, the interactions between elevated CO 2 , nitrogen addition, and drought were additive. The responses of SOC consistently strengthened with treatment intensity and duration across diverse ranges of ecosystems, climates, and soil textures. These findings establish warming as a catalytic force that reshapes SOC dynamics under ongoing global change, with profound implications for terrestrial C‐climate feedbacks.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".