Spatially explicit estimate of nitrogen effects on soil respiration across the globe
Bibliographic record
Abstract
Abstract Soil respiration (Rs), as the second largest flux of carbon dioxide (CO 2 ) between terrestrial ecosystems and the atmosphere, is vulnerable to global nitrogen (N) enrichment. However, the global distribution of the N effects on Rs remains uncertain. Here, we compiled a new database containing 1282 observations of Rs and its heterotrophic component (Rh) in field N manipulative experiments from 317 published papers. Using this up‐to‐date database, we first performed a formal meta‐analysis to explore the responses of Rs and Rh to N addition, and then presented a global spatially explicit quantification of the N effects using a Random Forest model. Our results showed that experimental N addition significantly increased Rs but had a minimal impact on Rh, not supporting the prevailing view that N enrichment inhibits soil microbial respiration. For the major biomes, the magnitude of N input was the main determinant of the spatial variation in Rs response, while the most important predictors for Rh response were biome specific. Based on the key predictors, global mapping visually demonstrated a positive N effect in the regions with higher anthropogenic N inputs (i.e., atmospheric N deposition and agricultural fertilization). Overall, our analysis not only provides novel insight into the N effects on soil CO 2 fluxes, but also presents a spatially explicit assessment of the N effects at the global scale, which are pivotal for understanding ecosystem carbon dynamics in future scenarios with more frequent anthropogenic activities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".