Caribbean Cleaning Gobies Prefer Client Ectoparasites Over Mucus
Bibliographic record
Abstract
Abstract If cooperation often involves investment, then what specific conditions prevent selection from acting on cheaters that do not invest? The mutualism between the Indo‐Pacific cleaner wrasseLabroides dimidiatusand its reef fish clients has been a model system to study conflicts of interest and their resolution. These cleaners prefer client mucus over ectoparasites – that is, they prefer to cheat – but punishment and partner switching by clients enforce cooperative behaviour by cleaners. By contrast, clients of Caribbean cleaning gobies (Elacatinusspp.) do not to use punishment or partner switching. Here, we test the hypothesis that the behavioural differences between these two cleaner fish systems are caused by differences in cleaner foraging preferences. In foraging choice experiments, we offered broadstripe cleaning gobiesElacatinus prochilosclient‐derived parasitic isopods, client mucus and a control food item. The cleaning gobies significantly preferred ectoparasites over mucus or the control item, which contrasts with cleaner wrasses. We propose that the low level of cleaner–client conflict arising from cleaning goby foraging preferences explains the observed lack of strategic partner control behaviour in the clients of cleaning gobies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".