Responses of vervet monkeys in large troops to terrestrial and aerial predator alarm calls
Bibliographic record
Abstract
The extent to which animal vocalizations convey specific information about events in the environment is subject to continued debate. The alarm-calls of vervet monkeys have played a pivotal role in this debate as they represent the classic example of a predator-specific call production system combined with a set of equally specific responses by receivers. Here, we revisit the vervet alarm-calling system, and assess the hypothesis that these acoustically distinct calls trigger context- and predator-appropriate behavior. We investigated responses in 2 groups of free-ranging vervet monkeys (Chlorocebus pygerythrus) to both natural encounters with predators and experimental presentations of aerial and terrestrial predator alarm calls. Our results show that the modal natural and experimental response was not to initiate escape behavior, either immediately or in the 10s following an alarm call, but to look at the sound source. When monkeys did take evasive action, contextually inappropriate behavior (i.e., behavior that was not appropriate for evading the specific predator type) was as likely to occur as contextually appropriate behavior. The distance at which calls were heard was negatively correlated with the probability of evasive action. Larger group size, and the greater mean distance at which natural calls were heard, may explain why our animals displayed less predator-appropriate evasion or vigilance than expected. We conclude that the broader social and ecological framework in which calls occur, rather than a simple contextually regular linkage between call types and specific predators, shapes animals’ responses to calls in this species.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".