Competition and coexistence among short-grass grazers in the Hluhluwe-iMfolozi Park, South Africa
Bibliographic record
Abstract
Coexistence among grazing ungulates has been related to differences in grass height and grassland types selected, underlain by morphological distinctions. Nevertheless, resource competition may arise when smaller species depress grass height below that suitable for larger species, whereas competition may be counteracted by facilitation when larger species increase the extent of high-quality grassland available. We investigated resource-use overlap between white rhinoceros ( Ceratotherium simum (Burchell, 1817)), blue wildebeest ( Connochaetes taurinus (Burchell, 1823)), and Burchell’s zebra (Equus burchelli (Gray, 1824)) in the Hluhluwe-iMfolozi Park in South Africa. We recorded the grassland type favoured, grass species utilized, grass height grazed, and greenness selected during the dry seasons of 2 years. Blue wildebeest shifted their grazing away from lawn grassland in the drier year, whereas Burchell’s zebra favoured lawn grassland only in the relatively wet year. White rhinoceros concentrated their feeding on lawn grassland throughout the dry seasons of both years, and favoured shorter grass than the other two grazers. Species characterizing grazing lawns contributed relatively more to the grass used by white rhinoceros in the drier year. Resource competition was potentially ameliorated by widened availability of lawn grassland promoted by white rhinoceros grazing. This counterbalancing of feeding competition and habitat facilitation enables the coexistence of these grazers despite similar food requirements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".