Vertebrate Grazers: Shapers of Spatial and Temporal Vegetation Patterns in East African Savannahs
Bibliographic record
Abstract
Vertebrate grazers, such as African elephants (Loxodonta africana), plains zebras (Equus quagga), African buffalo (Syncerus caffer), wildebeest (Connochaetes taurinus), and various antelope species, play a crucial role in shaping both spatial and temporal patterns of vegetation in East African savannahs. Through selective foraging, trampling, and nutrient deposition via dung and urine, these herbivores create and maintain distinct vegetation structures, including short-grass “grazing lawns” and taller stands of less palatable grasses. These spatial patterns arise from feedback loops: by repeatedly grazing nutrient-rich patches, herbivores stimulate regrowth of high-quality forage, reinforcing their own feeding preferences. At broader scales, the seasonal migrations of large grazers, such as wildebeest and zebra across the Serengeti–Mara ecosystem, track rainfall and forage availability, producing temporal shifts in vegetation productivity and composition. Grazers also influence fire regimes by reducing fine fuel loads in heavily grazed zones, which limits fire spread and alters the competitive balance between grasses and woody plants, which helps maintain the savannah’s tree–grass coexistence. Additionally, resource partitioning among species of different body sizes and feeding strategies enhances habitat heterogeneity across the landscape. These multiscale processes, combining direct plant–herbivore interactions with indirect effects on disturbance regimes, show the resilience and biodiversity of East African savannah ecosystems, with important implications for conservation management in the face of climate change and megafauna declines.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".