Biodiversity alleviates the decrease of grassland multifunctionality under grazing disturbance: A global meta‐analysis
Bibliographic record
Abstract
Abstract Aim Biodiversity drives the delivery of multiple ecosystem functions related to carbon and nutrient cycling (ecosystem multifunctionality, EMF), and biodiversity and ecosystem functioning are strongly threatened by intensive grazing in grasslands. However, it remains unclear how biodiversity regulates EMF changes in response to intensive grazing. Location Global. Time period From 1992 to 2018. Major taxa studied Grassland. Methods Here, we conducted a global synthesis using 373 paired observations from 90 published studies to address the responses of plant diversity, EMF and their relationships to grazing disturbance depending on grazing intensity, livestock type, grazing duration, and climatic aridity. Results Our results showed that EMF significantly decreased with increasing grazing intensity, but the negative EMF response was alleviated at high levels of plant diversity. The grazing‐induced changes in EMF increased linearly with the changes in plant diversity, with the slopes increasing by 78.9% from light to heavy grazing. In addition, the grazing‐induced decrease of EMF was stronger with longer grazing duration and more arid climates. Structural equation models suggested that grazing intensity reduced EMF largely via decreasing plant diversity, whereas upsizing livestock type promoted EMF by increasing plant diversity. Main conclusions This study highlights the key role of biodiversity in mediating EMF in response to intensifying grazing disturbance. We call for conservation of biodiversity to maintain grassland multifunctionality and services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".