Biological market effects predict cleaner fish strategic sophistication
Bibliographic record
Abstract
Abstract Market-like situations emerge in nature when trading partners exchange goods and services. However, how partner choice option contributes to the expression of social strategic sophistication (i.e., the ability to adjust behavior flexibly given the specifics of a situation) is still poorly understood. A suitable study system to explore this question is the “cleaner” fish Labroides dimidiatus. Cleaners trade parasite removal in exchange for food with a variety of “client” species. Previous research documented strong interindividual variation in two features of their strategic sophistication, namely, the ability to adjust service quality to the presence of an audience and to give priority to clients with access to alternative cleaners (“visitor clients”) over clients lacking such choice options (“resident clients”). Here, we sampled various demes (i.e., group of individuals) of the same population of cleaner fish in order to investigate the extent to which factors describing fish densities and cleaning interaction patterns predict the strategic sophistication in two laboratory experiments. These experiments tested whether cleaners could increase their food intake through reputation management and/or learning to provide service priority to a visitor-like ephemeral food plate. We found that high “outbidding competition,” characterized by high densities of cleaners and visitor clients, along with visitor’s behavior promoting such competition, consistently predicted high strategic sophistication in cleaners. A better understanding of the role of learning versus potential genetic factors, interacting with local market conditions to affect strategic sophistication, is needed to clarify how natural selection has promoted the evolution and maintenance of cooperation in this cleaning mutualism.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".