Consistent waves of collective vigilance in groups using public information about predation risk
Bibliographic record
Abstract
Consistent waves of collective vigilance in prey groups using public information about predation risk. Antipredator vigilance models have long assumed that individuals in groups monitor threats independently from one another. This assumption has been challenged recently, both theoretically and empirically. In particular, recent models predict that individuals should pay attention to the vigilance state of their neighbors and become increasingly vigilant when the proportion of vigilant neighbors is higher. Such copying can lead to temporal waves of collective vigilance in groups rather than random fluctuations. Here, we investigated the robustness of these predicted waves under varying ecological situations. Using an individual-based modeling approach, we show that such waves are predicted to occur in small and large groups, when copying only involves the nearest neighbor or the radius of copying is small or large or when the shape of the group is square or rectangular. However, when the influence of neighbors was restricted (e.g., by reducing the radius of influence, by only considering the nearest neighbor, or in more elongated groups), waves involved a smaller proportion of the group. In general, collective patterns were more organized when the copying tendency was strong and the frequency at which individuals can switch state was not too high. Our results show that collective waves of vigilance are a robust phenomenon emerging from the individual social behavior of group members, thus encouraging empirical scrutiny of the connection between individual and group vigilance.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".