The implications of large home range size in a solitary felid, the Leopard (<i>Panthera pardus</i>)
Bibliographic record
Abstract
Abstract The size of the home range of a mammal is affected by numerous factors. However, in the normally solitary, but polygynous, Leopard (Panthera pardus), home range size and maintenance is complicated by their transitory social grouping behavior, which is dependent on life history stage and/or reproductive status. In addition, the necessity to avoid competition with conspecifics and other large predators (including humans) also impacts upon home range size. We used movement data from 31 sites across Africa, comprising 147 individuals (67 males and 80 females) to estimate the home range sizes of leopards. We found that leopards with larger home ranges, and in areas with more vegetation, spent longer being active and generally traveled faster, and in straighter lines, than leopards with smaller home ranges. We suggest that a combination of bottom-up (i.e., preferred prey availability), top-down (i.e., competition with conspecifics), and reproductive (i.e., access to mates) factors likely drive the variability in Leopard home range sizes across Africa. However, the maintenance of a large home range is energetically expensive for leopards, likely resulting in a complex evolutionary trade-off between the satisfaction of basic requirements and preventing potentially dangerous encounters with conspecifics, other predators, and people.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".