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Record W4406120282 · doi:10.1007/s10071-024-01929-8

Current predation risk has opposing effects on social learning of foraging locations across two guppy populations

2025· article· en· W4406120282 on OpenAlexafffund
Mélanie F. Guigueno, Adrian C. K. Foster, Simon M. Reader

Bibliographic record

VenueAnimal Cognition · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaL'Oreal USARoyal SocietyRoyal Society of CanadaCanada Foundation for InnovationUnited Nations Educational, Scientific and Cultural Organization
KeywordsGuppyPredationSocial learningForagingPoeciliaBiologyEcologyALARMPsychologySocial psychologyZoologyFish <Actinopterygii>Fishery

Abstract

fetched live from OpenAlex

Social learning, where animals learn from other individuals, occurs in many diverse species. The influential but debated 'costly information' hypothesis posits that animals will rely more on social information in high-risk contexts, such as under increased predation risk. We examined and compared the effects of perceived predation risk on social learning of foraging sites in female Trinidadian guppies from wild and domestic populations raised in common-garden environments. We used a demonstrator-observer pairing where a subject could observe conspecific 'demonstrators' feeding from one of two feeders, and measured whether the observer subsequently spent more time at a demonstrated or non-demonstrated feeder. We manipulated perceived predation risk using alarm cue (conspecific skin extract). Stress responses and social learning differed between the two populations. Most notably, high predation risk enhanced social learning in the wild-type guppies, but depressed it in the domestic guppies. Thus, fish from both populations were able to socially learn, but under opposing contexts. These results suggest social learning propensities are the product of multiple interacting systems, and biases to favour social learning can emerge dependent on evolutionary history and current conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.352
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes2
Has abstractyes

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