Planning for success: Serengeti lions seek prey accessibility rather than abundance
Bibliographic record
Abstract
Summary We used long‐term radio‐telemetry data to investigate how Serengeti lions (Panthera leo) distribute themselves with respect to hunting opportunities. Specifically, we investigate whether lions hunt in areas where prey are easy to capture or where prey are locally abundant. We used resource‐selection functions (logistic regressions) to measure the location of kills/carcasses with respect to five different habitats: the view‐sheds from large rocky outcrops, river confluences, woodland vegetation, erosion embankments and water sources. As expected for a sit‐and‐wait predator, resting lions spent more time in areas with good cover. On a broad‐scale, lions shifted their ranges according to the seasonal movement of prey, but at a finer scale (< 100 m) lions fed in areas with high prey ‘catchability’ rather than high prey density. Plains lions selected erosion embankments, view‐sheds from rocky outcrops, and access to free water. Woodland lions tended to use erosion embankments, and woody vegetation. The results emphasize the importance of fine‐scale landscape and habitat features when assessing predator–prey theory and conservation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".