Global Ecosystem Nitrogen Cycling Reciprocates Between Land‐Use Conversion and Its Reversal
Bibliographic record
Abstract
Anthropogenic land-use practices influence ecosystem functions and the environment. Yet, the effect of global land-use change on ecosystem nitrogen (N) cycling remains unquantified despite that ecosystem N cycling plays a critical role in maintaining food security. Here, we analysed 2430 paired observations globally to show that converting natural to managed ecosystems increases ratios of autotrophic nitrification to ammonium immobilisation and nitrate to ammonium, but decreases soil immobilisation of mineral N, causing increased N losses via leaching and gaseous N emissions, such as nitrous oxide (e.g., via denitrification), resulting in a leaky N cycle. Changing land use from intensively managed to one that resembles natural ecosystems reversed N losses by 108% on average, resulting in a more conservative N cycle. Structural equation modelling revealed that changes in soil organic carbon, pH and carbon to N ratio were more important than changes in soil moisture content and temperature in predicting ecosystem N retention capacities following land-use conversion and its reversion. The hotspots of leaky N cycles were mostly in equatorial and tropical regions, as well as in Western Europe, the United States and China. Our results suggest that whether an ecosystem exhibits a conservative N cycle after land-use reversion depends on management practices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".