Jaguar’s Predation and Human Shield, a Tapir Story
Bibliographic record
Abstract
Despite the risks associated, some species choose to shield behind a predator to decrease predation risk by another predator. In this study, we demonstrate how Baird’s tapirs (Tapirus bairdii) use humans as a “shield” to reduce the risk of being preyed upon by jaguars (Panthera onca). We collected georeferenced photographic records of 23 tapirs (seven of them injured) sighted near human settlements (0 to 5 km) in the Calakmul region of Mexico from 2008 to 2019. Using multidimensional scale analysis, we determined which possible factors (tapir health status, injuries, distance to the settlement, as well as seasonality) are related to the decision of tapirs to approach human settlements. To support our claims of jaguars’ attacks, we described the pattern of injuries believed to have been inflicted by jaguars on tapirs, and we analysed photographs and videos of species of the genus Panthera attacking larger prey than themselves to establish a pattern of injuries and compare it to the injuries observed on tapirs. Our study shows that tapir sightings near human settlements are related to health deterioration, injuries by jaguars and seasonality. The injuries found on tapirs are similar to those caused by other big cats on large prey, providing strong support for jaguar-inflicted wounds. Further studies should investigate whether the increasing human presence in different habitats in the Neotropical region could be influencing the behaviour and distribution of prey and predators.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".