Aggressive mimics profit from a model–signal receiver mutualism
Bibliographic record
Abstract
Mimetic species have evolved to resemble other species to avoid predation (protective mimicry) or gain access to food (aggressive mimicry). Mimicry systems are frequently tripartite interactions involving a mimic, model and 'signal receiver'. Changes in the strength of the relationship between model and signal receiver, owing to shifting environmental conditions, for example, can affect the success of mimics in protective mimicry systems. Here, we show that an experimentally induced shift in the strength of the relationship between a model (bluestreak cleaner fish, Labroides dimidiatus) and a signal receiver (staghorn damselfish, Amblyglyphidodon curacao) resulted in increased foraging success for an aggressive mimic (bluestriped fangblenny, Plagiotremus rhinorhynchos). When the parasite loads of staghorn damselfish clients were experimentally increased, the attack success of bluestriped fangblenny on damselfish also increased. Enhanced mimic success appeared to be due to relaxation of vigilance by parasitized clients, which sought cleaners more eagerly and had lower overall aggression levels. Signal receivers may therefore be more tolerant of and/or more vulnerable to attacks from aggressive mimics when the net benefit of interacting with their models is high. Changes in environmental conditions that cause shifts in the net benefits accrued by models and signal receivers may have important implications for the persistence of aggressive mimicry systems.
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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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".