Nitrogen deposition alleviates phosphorus-induced imbalances in soil enzyme stoichiometry
Bibliographic record
Abstract
Soil extracellular enzymes are critical drivers of carbon (C) and nutrient cycling in terrestrial ecosystems. However, the effects of phosphorus (P) additions on soil enzyme activities and stoichiometries, particularly under varying nitrogen (N) addition regimes worldwide, are not well comprehended. Here, we conducted a meta-analysis based on 155 field studies across various ecosystems (forests, croplands and grasslands), which shows that P enrichment conditions enhances C-acquiring enzymes but has no effect on the enzymatic C:N ratio. P addition alone reduces P-acquiring enzymes by 14% without affecting N-acquiring enzymes. In contrast, P combined with N addition boosts N-acquiring enzymes by 21% while leaving P-acquiring enzymes unchanged. Notably, the combined effect of P and N addition on microbial C limitation (assessed via vector length) and enzymatic stoichiometries is less pronounced than that of P addition alone. Key drivers of these responses include mean annual precipitation, soil microbial biomass, and its stoichiometries. These results suggest that N addition mitigates the stoichiometric imbalance and microbial C limitation induced by P addition, potentially promoting soil organic C accumulation. Our findings emphasize the critical need to account for such interactive effects in models predicting soil biogeochemical cycles under future changes in global exogenous N and P inputs. Phosphorus addition alone reduces phosphorus-acquiring enzyme activities by 14%, while phosphorus combined with nitrogen addition boosts nitrogen-acquiring enzyme activities by 21%, according to a meta-analysis based on 155 field studies across various ecosystems.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".