Predator inadvertent social information use favours reduced clumping of its prey
Bibliographic record
Abstract
When animals forage socially, individuals can obtain prey from their own searching (producer tactic) or by using the behaviour of others (scrounger tactic) when it provides inadvertent social information (ISI) that food has been located. This ISI may either indicate the location of food (social information, SI), or it may indicate the quality of the resource (public information, PI). To date, few studies have explored the selective consequences for prey of being exploited by predators that use ISI. Prey exploited by such predators should evolve traits that favour high levels of ISI use (scrounging) because this would result in lower predator search efficiency given that fewer predators would be searching directly for the prey. Our simulations confirm that ISI‐using predators should increase their use of ISI when their prey form larger clumps resulting in higher prey survival. Our objective therefore is to explore whether prey will evolve towards higher clumpiness when their predators use ISI, using genetic algorithm simulation. The prey were subjected to one of three types of predators for over 500 prey generations. The predators either used: (1) no social information (NS), (2) SI only, or (3) PI. Surprisingly, the prey evolved the highest clumpiness for NS predators. Prey evolved towards smaller clump sizes with SI predators and the clumps were marginally larger when predators used PI. The result is due to the prey evolving the minimum clumpiness required to cause maximal ISI use by their predators. We discuss how this response by prey may favour the use of PI over SI in their ISI‐using predators.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".