Evaluation of Potential Factors Predisposing Livestock to Predation by Jaguars
Bibliographic record
Abstract
ABSTRACT Depredation of livestock by large carnivores is an important but poorly understood source of human‐carnivore conflict. We examined patterns of livestock depredation by jaguars ( Panthera onca ) and pumas ( Puma concolor ) on a ranch‐wildlife reserve in western Brazil to assess factors contributing to prey mortality. We predicted jaguars would kill a greater proportion of calves than yearling and adult cattle and that proximity to suitable habitat would increase mortality risk. We further speculated that exposure to predation risk would promote livestock grouping and increased movement distance. We recorded 169 cattle mortality incidents during 2003–2004, of which 19% were due to predation by jaguars and pumas. This level of mortality represented 0.2–0.3% of the total livestock holdings on the ranch. Jaguars caused most (69%) cattle predation events, and survival in allotments was lower for calves than for other age classes. Forest proximity was the only variable we found to explain patterns of livestock mortality, with predation risk increasing as distance to forest cover declined. Due to low predation risk, cattle movement patterns and grouping behavior did not vary relative to level of spatial overlap with radiocollared jaguars. The overall effect of predation on cattle was low and livestock likely constituted an alternative prey for large cats in our study area. However, selection of calves over other age cohorts and higher predation risk among cattle in proximity to forest cover is suggestive of selection of substandard individuals. Cattle ranchers in the Pantanal region may reduce cattle mortality rates by concentrating on losses due to nonpredation causes that could be more easily controlled.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".