WARNING SIGNALS EVOLVE TO DISENGAGE BATESIAN MIMICS
Bibliographic record
Abstract
Prey that are unprofitable to attack are typically conspicuous in appearance. Conventional theory assumes that these warning signals have evolved in response to predator receiver biases. However, such biases might be a symptom rather than a cause of warning signals. We therefore examine an alternative theory: that conspicuousness evolves in unprofitable prey to avoid confusion with profitable prey. One might wonder why unprofitable prey do not find a cryptic means to be distinct from profitable prey, reducing both their risk of confusion with profitable prey and their rate of detection by predators. Here we present the first coevolutionary model to allow for Batesian mimicry and signals with different levels of detectability. We find that unprofitable prey do indeed evolve ways of distinguishing themselves using cryptic signals, particularly when appearance traits can evolve in multiple dimensions. However, conspicuous warning signals readily evolve in unprofitable prey when there are more ways to look different from the background than to match it. Moreover, the more unprofitable the prey species, the higher its evolved conspicuousness. Our results provide strong support for the argument that unprofitable species evolve conspicuous signals to avoid confusion with profitable prey and indicate that peak shift in conspicuousness-linked traits is a major factor in its establishment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".