Population differences in how wild Trinidadian guppies use social information and socially learn
Bibliographic record
Abstract
Abstract Animals have access to information produced by the behaviour of other individuals, which they may use (“social information use”) and learn from (“social learning”). The benefits of using such information differ with socio-ecological conditions. Thus, population differences in social information use and social learning should occur. We tested this hypothesis with a comparative study across five wild populations of Trinidadian guppies ( Poecilia reticulata ) known to differ in their ecology and social behaviour. Using a field experiment, we found population differences in how guppies used and learned from social information, with only fish from one of the three rivers studied showing evidence of social information use and social learning. Within this river, populations differed in how they employed social information: fish from a high-predation regime where guppies exhibit high shoaling propensities chose the same foraging location than conspecifics, while fish from a low-predation regime with reduced shoaling propensities chose and learned the opposite foraging location than conspecifics. We speculate that these differences are due to differences in predation risk and conspecific competition, possibly mediated via changes in grouping tendencies. Our results provide evidence that social information use and social learning can differ across animal populations and are influenced by socio-ecological factors.
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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 | 0.000 |
| 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".