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Record W4389307412 · doi:10.32942/x21s5r

The interplay between satiation and temptation drives cleaner fish Labroides dimidiatus foraging behaviour and service quality towards client reef fish

2023· preprint· en· W4389307412 on OpenAlexaff
Zegni Triki, Xaing yi Li Richter, Ana Isabel Pinto, Antoine Baud, Sandra A. Binning, Mélisande Aellen, Yasmin Emery, Virginie Staubli, Nichola Raihani, Redouan Bshary

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversité de Montréal
FundersUniversité de Neuchâtel
KeywordsTemptationForagingCoral reef fishWrasseCheatingMutualism (biology)BusinessFisheryEcologyBiologyFish <Actinopterygii>PsychologySocial psychology

Abstract

fetched live from OpenAlex

Supply and demand affect the values of goods exchanged in cooperative trades where high demand typically leads to a higher price. An exception has been described in the marine cleaning mutualism involving the cleaner fish Labroides dimidiatus and its variety of ‘client’ coral reef fishes. Cleaner fish feed on clients’ ectoparasites but prefer eating clients’ mucus instead, which constitutes cheating. Here, we provide field observations, followed by a set of laboratory experiments with real clients and Plexiglas feeding plates as surrogates for clients. In the field and in three experiments with real clients, we found that satiated cleaner fish were more cooperative, even though low hunger levels should make them less dependent on cleaning interactions. Similarly, the more abstract version of the experiments using Plexiglas plates offering two food types mimicking client ectoparasites and mucus showed that satiation led cleaner fish to feed more against their preferences – an indicator of cooperative behaviour. However, this outcome occurred only if the temptation to eat the preferred food was low. When the temptation to cheat was high, cleaners did so. We provide further general support to these findings with a game-theoretic model. Many mutualisms involve food as a commodity. Thus, identifying foraging decision rules will enhance our understanding of how individuals adjust to real-time market conditions rather than playing evolved strategies adapted to the average market 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)0.002
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 score0.058
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.083
GPT teacher head0.408
Teacher spread0.325 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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Same topicExperimental Behavioral Economics StudiesFrench-language works237,207