Mapping global soil acidification under N deposition
Bibliographic record
Abstract
Soil pH is critically important in regulating soil nutrients and thus influencing the biodiversity and ecosystem functions of terrestrial ecosystems. Despite the ongoing threat of nitrogen (N) pollution especially in the fast-developing regions, it remains unclear how increasing N deposition affects soil pH across global terrestrial ecosystems. By conducting a global meta-analysis with paired observations of soil pH under N addition and control from 634 studies spanning major types of terrestrial ecosystems, we show that soil acidification increases rapidly with N addition amount and is most severe in neutral-pH soils. Grassland soil pH decreases most strongly under high N addition while wetlands are the least acidified. By extrapolating these relationships to global mapping, we reveal that atmospheric N deposition leads to a global average soil pH decline of -0.16 in the past 40 years and regions encompassing Eastern United States, Southern Brazil, Europe, and South and East Asia are the hotspots of soil acidification under N deposition. Our results highlight that anthropogenically amplified atmospheric N deposition has profoundly altered global soil pH and chemistry. They suggest that atmospheric N deposition is a major threat to global terrestrial biodiversity and ecosystem functions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".