The effects of livestock grazing on biodiversity are multi‐trophic: a meta‐analysis
Bibliographic record
Abstract
Anthropogenic disturbance has generated a significant loss of biodiversity worldwide and grazing by domestic herbivores is a contributing disturbance. Although the effects of grazing on plants are commonly explored, here we address the potential multi-trophic effects on animal biodiversity (e.g. herbivores, pollinators and predators). We conducted a meta-analysis on 109 independent studies that tested the response of animals or plants to livestock grazing relative to livestock excluded. Across all animals, livestock exclusion increased abundance and diversity, but these effects were greatest for trophic levels directly dependent on plants, such as herbivores and pollinators. Detritivores were the only trophic level whose abundance decreased with livestock exclusion. We also found that the number of years since livestock was excluded influenced the community and that the effects of grazer exclusion on animal diversity were strongest in temperate climates. These findings synthesise the effects of livestock grazing beyond plants and demonstrate the indirect impacts of livestock grazing on multiple trophic levels in the animal community. We identified the potentially long-term impacts that livestock grazing can have on lower trophic levels and consequences for biological conservation. We also highlight the potentially inevitable cost to global biodiversity from livestock grazing that must be balanced against socio-economic benefits.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".