Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties
Bibliographic record
Abstract
Abstract Soil nitrogen (N) availability is critical for grassland functioning. However, human activities have increased the supply of biologically limiting nutrients, and changed the density and identity of mammalian herbivores. These anthropogenic changes may alter net soil N mineralization (soil net Nmin), that is, the net balance between N mineralization and immobilization, which could severely impact grassland structure and functioning. Yet, to date, little is known about how fertilization and herbivore removal individually, or jointly, affect soil net Nmin across a wide range of grasslands that vary in soil and climatic properties. Here we collected data from 22 grasslands on five continents, all part of a globally replicated experiment, to assess how fertilization and herbivore removal affected potential (laboratory‐based) and realized (field‐based) soil net Nmin. Herbivore removal in the absence of fertilization did not alter potential and realized soil net Nmin. However, fertilization alone and in combination with herbivore removal consistently increased potential soil net Nmin. Realized soil net Nmin, in contrast, significantly decreased in fertilized plots where herbivores were removed. Treatment effects on potential and realized soil net Nmin were contingent on site‐specific soil and climatic properties. Fertilization effects on potential soil net Nmin were larger at sites with higher mean annual precipitation (MAP) and temperature of the wettest quarter (T.q.wet). Reciprocally, realized soil net Nmin declined most strongly with fertilization and herbivore removal at sites with lower MAP and higher T.q.wet. In summary, our findings show that anthropogenic nutrient enrichment, herbivore exclusion and alterations in future climatic conditions can negatively impact soil net Nmin across global grasslands under realistic field conditions. This is an important context‐dependent knowledge for grassland management worldwide.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".