The signal environment is more important than diet or chemical specialization in the evolution of warning coloration
Bibliographic record
Abstract
Aposematic coloration, or warning coloration, is a visual signal that acts to minimize contact between predator and unprofitable prey. The conditions favoring the evolution of aposematic coloration remain largely unidentified. Recent work suggests that diet specialization and resultant toxicity may play a role in facilitating the evolution and persistence of warning coloration. Using a phylogenetic approach, we investigated the evolution of larval warning coloration in the genus Papilio (Lepidoptera: Papilionidae). Our results indicate that there are at least four independent origins of aposematic larval coloration within Papilio. Controlling for phylogenetic relatedness among Papilio taxa, we found no evidence supporting the hypothesis that either diet specialization or chemical specialization facilitated the origin of aposematic larvae. However, there was a significant relationship between the signal environment and the evolution of aposematic larvae. Specifically, Papilio lineages feeding on herbaceous or narrow-leaved plants, regardless of the plants' taxonomic affiliation, were more likely to evolve aposematic larvae than were lineages feeding only on trees/shrubs or broad-leaved plants. These results demonstrate that factors other than diet specialization, such as the signal environment of predator-prey interactions, may play a large role in the initial evolution and persistence of aposematic coloration.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".