Increasing vulnerability to predation increases preference for the scrounger foraging tactic
Bibliographic record
Abstract
When animals forage in groups, individuals can search for food themselves (producer tactic) or they can search for and join other individuals that have located food (scrounger tactic). The scrounger tactic may provide greater antipredator benefits than the producer tactic because “scroungers” hop with their heads up and tend to occupy central positions in a group, whereas “producers” hop with their heads down and tend to occupy edge positions. We tested whether increasing an individual's vulnerability to predation (using wing-loading manipulations) causes an increased preference for the scrounger tactic in zebra finches (Taeniopygia guttata). Wing-loading manipulations were effective at increasing focal individuals' perception of vulnerability to predation; treatment individuals increased their total time allocated to vigilance, whereas control individuals did not. Treatment individuals also increased their use of the scrounger tactic (proportion of hops with head up) and scrounged a greater proportion of patches, whereas control individuals exhibited no changes. Our results are consistent with the hypothesis that the scrounger tactic confers greater antipredator benefits than the producer tactic, although whether antipredator benefits are achieved through differences in head orientation, spatial position, or both, remains unclear. Our finding that individuals adjust their use of the scrounger tactic according to changes in their phenotype provides evidence for phenotype-limited allocation strategies in producer–scrounger games.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".