The impact of livestock grazing on plant diversity: an analysis across dryland ecosystems and scales in southern Africa
Bibliographic record
Abstract
A general understanding of grazing effects on plant diversity in drylands is still missing, despite an extensive theoretical background. Cross-biome syntheses are hindered by the fact that the outcomes of disturbance studies are strongly affected by the choice of diversity measures, and the spatial and temporal scales of measurements. The aim of this study is to overcome these weaknesses by applying a wide range of diversity measures to a data set derived from identical sampling in three distinct ecosystems. We analyzed three fence-line contrasts (heavier vs. lighter grazing intensity), representing different degrees of aridity (from arid to semiarid) and precipitation regimes (summer rain vs. winter rain) in southern Africa. We tested the impact of grazing intensity on multiple aspects of plant diversity (species and functional group level, richness and evenness components, alpha and beta diversity, and composition) at two spatial scales, and for both 5-yr means and interannual variability. Heavier grazing reduced total plant cover and substantially altered the species and functional composition at all sites. However, a significant decrease in species alpha diversity was detected at only one of the three sites. By contrast, alpha diversity of plant functional groups responded consistently across ecosystems and scales, with a significant decrease at heavier grazing intensity. The cover-based measures of functional group diversity responded more sensitively and more consistently than functional group richness. Beta diversity of species and functional types increased under heavier grazing, showing that at larger scales, the heterogeneity of the community composition and the functional structure were increased. Heavier grazing mostly increased interannual variability of alpha diversity, while effects on beta diversity and cover were inconsistent. Our results suggest that species diversity alone may not adequately reflect the shifts in vegetation structure that occur in response to increased grazing intensity in the dryland biomes of southern Africa. Compositional and structural changes of the vegetation are better reflected by trait-based diversity measures. In particular, measures of plant functional diversity that include evenness represent a promising tool to detect and quantify disturbance effects on ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".