Dilution games: use of protective cover can cause a reduction in vigilance for prey in groups
Bibliographic record
Abstract
Antipredatory vigilance usually decreases in groups. The generally accepted “collective detection” explanation implies that because there are more eyes to scan the surroundings for predators, individuals in a group can lower their personal investment in vigilance without increasing their predation risk. The role of other factors, such as numerical risk dilution caused by the mere presence of companions, has been neglected. In a model, we explore a dilution game when foragers in groups have access to protective cover. We show that foragers can take advantage of risk dilution and that this leads to changes in vigilance with group size without the need to invoke collective detection. We identify a cost to maintaining high levels of vigilance as less vigilant foragers gather food faster and so depart the group sooner (to reach cover) leaving more vulnerable stragglers behind. In groups, there is a scramble to reach safe sites that can induce a reduction in vigilance levels. Such a mechanism operates less forcefully in large groups because individuals in these groups are less vulnerable to the departure of an individual. We also demonstrate that individuals should adopt lower levels of vigilance, to reach safe sites sooner, when predator evasion is compromised or when the rate of food intake is high. The model provides new insights into the mechanisms underlying changes in vigilance with group size in animals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".