Bibliographic record
Abstract
Abstract It has long been understood that species that are profitable for predators to attack can gain protection if they resemble unprofitable species (Batesian mimicry), and that unprofitable species may face selection to evolve a common warning signal (Müllerian mimicry). Here we suggest that there may be widespread selection for another form of protective mimicry, so far unrecognized, that can arise even among profitable prey. Specifically, when predators adopt species-specific attack strategies, then co-occurring prey species that are caught in different ways may be selected to resemble one another. This is because the mimicry may increase the chance that the predator deploys an inappropriate attack strategy, thereby increasing the probability the prey will escape. We refer to this phenomenon as “mutualistic deceptive mimicry”, since the mimicry misleads the predator yet potentially benefits all co-mimics. We show that this hypothesis is quantitatively plausible. We then provide an empirical ‘proof of concept’ demonstrating that predators can learn to attack distinct prey types in specific ways and that this behaviour readily generates selection for mimicry. Finally, we discuss how this unrecognized form of mimicry fits into an earlier classification of protective mimicry and suggest a number of potential examples in the natural world.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".