Migrant and resident birds adjust antipredator behavior in response to social information accuracy
Bibliographic record
Abstract
Animals can reduce their uncertainty of predation risk by attuning to antipredator behavior of others or assessing the risk for themselves. Although it has never been empirically examined in the context of predation, we predicted that animals combine information gleaned from others with their own sampling experience to estimate risk. To test this prediction, we assessed the state-dependent mobbing responses of migrant and resident songbirds at a fall migration stopover site in eastern Canada to stimuli simulating a range of predation risk situations. We presented individuals with social cues in the form of playbacks of black-capped chickadee (Poecile atricapillus) mob-calls conveying graded information about predator size in combination with a predator model (one of two owl species) that rendered the social information either correct or incorrect. The response did not differ based on migratory state; both migrant and resident birds stayed longer at experimental trials when presented with erroneous social information. In particular, response duration of birds presented with a low-threat chickadee mob-call and a high-threat model (understating the risk) was substantially longer than the response to other low-threat call trials, suggesting that individuals were capable of Bayesian updating by devaluing the social cue and acting on their own assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".