Does Foraging or the Avoidance of Predation Determine Habitat Selection by Selective Resident Grazers in the Serengeti Woodlands? A Mixed Strategy with Season
Bibliographic record
Abstract
Savanna systems are characterised by a community of large mammal herbivores with up to 30 species; coexistence is based on resource partitioning. In this paper we analyse the features of the landscape and plant structure which lead herbivores to use particular locations, a key to resource partitioning. The processes involved, top-down versus bottom-up, are well known for large species and small ones but not for medium-sized ones. We use two resident, medium-sized species, topi (Damaliscus lunatus jimela) and kongoni, (Alcelaphus buselaphus cokei) in the central woodlands of the Serengeti; selection of habitat by the residents is important for predator-prey interactions and for interactions among the grazers. Using Principal Component Analysis and Multiple Regression we develop highly predictive models which show that resource availability is the critical determinant of habitat selection in the dry season; and reduction in predation risk appears to be important in the wet season. These results show for the first time that habitat selection by the medium-sized herbivores is driven by different strategies in the two seasons. This contributes to understanding the processes involved in the dynamics of this globally important savanna system, a necessary foundation for management.
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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 | 0.000 |
| 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) | 0.000 | 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".