Surplus killing by pumas <i>Puma concolor</i>: rumours and facts
Bibliographic record
Abstract
Abstract Surplus killing (i.e. predation in which predators kill more prey than necessary to satiate their hunger) appears to be widespread in carnivores and has the potential to exacerbate human–carnivore conflict. Nevertheless, little is known about the frequency of surplus killing or about its impact on livestock. We review the information available on surplus killing by pumas Puma concolor and use data from central Argentina to quantify its impact on ranching and to analyse both its causes and its implications for puma–human conflicts. We reviewed 73 publications and found nine mentions of surplus killing events from six countries. The sheep was by far the most commonly affected livestock species. In central Argentina, surplus killing was reported by 25–33% of the ranchers. In this region, the number of livestock killed during each event ranged from seven to 160 (median = 23) for the literature reports and from two to 70 (median = 7) in the records we personally collated. The number of individual animals killed per event was greater for interview‐based second‐hand reports than for first‐hand reports and verified events. Our results indicate that although surplus killing by pumas is uncommonly reported in the literature, it may be locally recurrent. Although surplus killing may be overestimated in interview‐based reports, it can produce significant losses for sheep and goat ranchers, may strongly exacerbate puma–human conflicts, and should be considered in puma–human conflict mitigation strategies. Ranchers typically attributed surplus killing to female pumas teaching kittens to hunt. However, there is little evidence supporting this interpretation. Surplus killing by pumas may be more likely to occur in situations where the predator's ‘normal’ hunting sequence is disrupted by the accessibility of large numbers of easy prey. Confinement, stormy weather and poor antipredator behaviour may favour the occurrence of surplus killing events on livestock.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".