Contrasting effects of altered precipitation regimes on soil nitrogen cycling at the global scale
Bibliographic record
Abstract
Changes in precipitation regimes can strongly affect soil nitrogen (N) cycling in terrestrial ecosystems. However, whether altered precipitation regimes may differentially affect soil N cycling between arid and humid biomes at the global scale is unclear. We conducted a meta-analysis using 1036 pairwise observations collected from 194 publications to assess the effects of increased and decreased precipitation on the input (N return from plants), storage (various forms of N in soil), and output (gaseous N emissions) of soil N in arid versus humid biomes at the global scale. We found that (1) increased precipitation significantly increased N input (+12.1%) and output (+34.9%) but decreased N storage (-13.7%), while decreased precipitation significantly decreased N input (-10.7%) and output (-34.8%) but increased N storage (+11.1%); (2) the sensitivity of soil N cycling to increased precipitation was higher in arid regions than in humid regions, while that to decreased precipitation was lower in arid regions than in humid regions; (3) the effect of altered precipitation regimes on soil N cycling was independent of precipitation type (i.e., rainfall vs. snowfall); and (4) the mean annual precipitation regulated soil N cycling in precipitation alteration experiments at the global scale. Overall, our results clearly show that the response of soil N cycling to increased versus decreased precipitation differs between arid and humid regions, indicating the uneven effect of climate change on soil N cycling between these two contrasting climate regions. This implies that ecosystem models need to consider the differential responses of N cycling to altered precipitation regimes in different climatic conditions under future global change scenarios.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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".