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Record W3193399144 · doi:10.1093/beheco/arab096

Predation risk shapes the use of conflicting personal risk and social safety information in guppies

2021· article· en· W3193399144 on OpenAlexafffund
Laurence E A Feyten, Adam L. Crane, Indar W. Ramnarine, G. E. Brown

Bibliographic record

VenueBehavioral Ecology · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPredationNeophobiaALARMBiologyPoeciliaForagingSocial cueAlarm signalSocial learningPoeciliidaeEcologyFish <Actinopterygii>Social psychologyPsychologyFishery

Abstract

fetched live from OpenAlex

Abstract When faced with uncertainty, animals can benefit from using multiple sources of information in order to make an optimal decision. However, information sources (e.g., social and personal cues) may conflict, while also varying in acquisition cost and reliability. Here, we assessed behavioral decisions of Trinidadian guppies (Poecilia reticulata), in situ, when presented with conflicting social and personal information about predation risk. We positioned foraging arenas within high- and low-predation streams, where guppies were exposed to a personal cue in the form of conspecific alarm cues (a known indicator of risk), a novel cue, or a control. At the same time, a conspecific shoal (a social safety cue) was either present or absent. When social safety was absent, guppies in both populations showed typical avoidance responses towards alarm cues, and high-predation guppies showed their typical avoidance of novel cues (i.e., neophobia). However, the presence of social safety cues was persuasive, overriding the neophobia of high-predation guppies and emboldening low-predation guppies to ignore alarm cues. Our experiment is one of the first to empirically assess the use of safety and risk cues in prey and suggests a threshold level of ambient risk which dictates the use of conflicting social and personal information.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.203

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 studies0.0000.000
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 head0.057
GPT teacher head0.273
Teacher spread0.216 · 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

Citations26
Published2021
Admission routes2
Has abstractyes

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