Evolution of anti-predator traits in response to a flexible targeting strategy by predators
Bibliographic record
Abstract
Anti-predator benefits increase with vigilance rate and group size in many species of animal, while simultaneously resource intake rates usually decrease. This implies that there is an optimal group size and vigilance rate that will maximize individual fitness. While this basic theory of vigilance has been modelled and tested extensively, it has often been assumed that the predator represents a 'fixed-risk' such that groups of prey are essentially independent entities that exert little or no effect on one another either directly or indirectly. We argue that this is an over-simplification, and propose that the behaviour of one group of prey will likely affect the fitness of another local group of prey if the predator preferentially attacks the most vulnerable group-rather than attack both with constant rates. Using a numerical simulation model, we make the first examination of this game and allow the prey to dynamically evolve both optimal group size distributions between two habitats and vigilance rates in response to a predator with a preference for whichever group is the more vulnerable. We show that the density of prey in the population and the sensitivity of a predator to differences in prey vulnerability are likely to drive the dynamics of such a game. This novel approach to vigilance theory opens the door to several challenging lines of future research, both experimental and theoretical.
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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 | 0.004 |
| 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".