Habitat structure and the presence of large carnivores shape the site use of an understudied small carnivore: caracal ecology in a miombo woodland
Bibliographic record
Abstract
Abstract Basic ecological knowledge on African small carnivores and how they interact with the wider carnivore guild are lacking for many species. The caracal (Caracal caracal) has a widespread distribution across Africa, yet there is a paucity of information on this species outside of savannah and agricultural landscapes. Using camera trap data from Kasungu National Park, Malawi, we provide novel information on caracal habitat use in a miombo woodland and compare the spatiotemporal dynamics between caracal and members of the large carnivore guild (leopard, Panthera pardus and spotted hyaena, Crocuta crocuta). We found that caracal were more likely to use sites with higher grass cover and further away from permanent water sources. Caracal site use increased in areas with lower spotted hyaena abundance and caracal exhibited different temporal activity patterns to spotted hyaena. In contrast, caracal did not exhibit spatial or temporal avoidance of leopard at the scale investigated here. However, the probability of detecting caracal at sites of higher leopard abundance was significantly lower, suggesting possible behavioural mechanisms to avoid interaction. Our study provides an insight into caracal ecology in a miombo woodland and improves our understanding of community dynamics between a lesser-studied small carnivore and the large carnivore guild.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".