Changes in antipredator vigilance over time caused by a war of attrition between predator and prey
Bibliographic record
Abstract
Prey animals often move from patch to patch in search of food and must evaluate the likelihood that a predator is present in each patch to adjust their antipredator behavior. This is important because a prey animal might have inadvertently arrived at a patch on which a sit-and-wait predator is lurking, a common situation in many species of animals. In a simulation model, we explore how long the ambushing predator should wait before attacking the prey animal and how the prey animal can adjust its vigilance in response. We adopted a war-of-attrition framework where the predator selects randomly an attack time from a distribution to keep the prey guessing and where the prey also keep the predator guessing by selecting randomly a time at which to switch from high to low vigilance. We found that an evolutionarily stable solution can emerge in this game and has the following form under a broad range of ecological conditions: the predator attacks early and the prey adopts a high vigilance early and then switches later to a lower vigilance. The results indicate that antipredator vigilance may change as a function of time rather than being a constant value as assumed in most vigilance models. We conclude that the uncertainty that ambushing predators and their prey must plant in the minds of the other can have important consequences for the evolution of predator and prey tactics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".