The interplay between satiation and temptation drives cleaner fish Labroides dimidiatus foraging behaviour and service quality towards client reef fish
Bibliographic record
Abstract
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.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".