Multi-diversity strengthens multifunctionality in grasslands with intensive grazing pressure
Bibliographic record
Abstract
Livestock grazing strongly affects biodiversity and ecosystem functioning in grasslands. However, it remains unclear how different grazing impact multiple biodiversity, ecosystem multifunctionality (EMF), and their relationship with the interactions of grazing duration, livestock type and climatic factors. Here, we conducted a global synthesis from 104 published studies. Our results showed that light and moderate grazing improved multi-diversity, but heavy grazing significantly decreased multi-diversity and EMF. The grazing-induced decrease of EMF intensified with grazing duration, and the reduction of multi-diversity and EMF under intensive grazing was stronger in more arid climates. The response of EMF increased linearly with that of multi-diversity under all grazing intensities. Moreover, grazing intensity reduced EMF largely via decreasing multi-diversity, whereas a shift of livestock type from small to large size promoted EMF by increasing multi-diversity. This study provides first empirical evidence and new insights into the relationship between multi-diversity and EMF under grazing in global grasslands.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".