Adaptations to prey base in the hypercarnivorous leopard cat <i>Prionailurus bengalensis</i>
Bibliographic record
Abstract
Investigating biogeographical variations in diet composition can help understand the adaptability and generalism of species. Although the dietary adaptability of omnivorous mesocarnivores is well established, far less work has explored how more specialist hypercarnivores optimise their diets. By reviewing 11 studies of the leopard cat (Prionailurus bengalensis), we quantitatively examined how dietary composition varies over the wide range of biomes they occupy in Asia. Specifically, we contrasted the diet of the Iriomote Island sub-species (south-western Japan), where native rodents are absent, with that of the mainland. Leopard cat diet typically comprised mammals, birds, amphibians, reptiles, and invertebrates. In Iriomote Island, however, the low relative frequency of occurrence of small mammals (only introduced rats) was compensated by higher frequencies of reptiles and amphibians compared to the mainland. Consequently, trophic diversity and dietary niche breadth were higher for leopard cats in Iriomote Island than for the mainland. This shows that even hypercarnivorous species can use trophic plasticity to adapt to local prey availability. Given that rodent numbers often fluctuate substantially over time, the availability of alternative prey, such as herptiles, may be vital for the conservation of the leopard cat, and especially the critically endangered Iriomote cat. More generally, the trophic versatility of hypercarnivores must be considered when assessing their vulnerability to environmental change.
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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 | 0.000 | 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) | 0.001 | 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".