The influence of ultraviolet reflectance differs between conspicuous aposematic signals in neotropical butterflies and poison frogs
Bibliographic record
Abstract
Warning signals are often characterized by highly contrasting, distinctive, and memorable colors. Greater chromatic (hue) and achromatic (brightness) contrast have both been found to contribute to greater signal efficacy, making longwave colored signals (e.g., red and yellow), that are perceived by both chromatic and achromatic visual pathways, particularly common. Conversely, shortwave colors (e.g., blue and ultraviolet) do not contribute to luminance perception yet are also commonly found in warning signals. Our understanding of the role of UV in aposematic signals is currently incomplete as UV perception is not universal, and evidence for its utility is at best mixed. We used visual modeling to quantify how UV affects signal contrast in aposematic heliconiian butterflies and poison frogs both of which reflect UV wavelengths, occupy similar habitats, and share similar classes of predators. Previous work on butterflies has found that UV reflectance does not affect predation risk but is involved in mate choice. As the butterflies, but not the frogs, have UV-sensitive vision, the function of UV reflectance in poison frogs is currently unknown. We found that despite showing up strongly in UV photographs, UV reflectance only appreciably affected visual contrast in the butterflies. As such, these results support the notion that although UV reflectance is associated with intraspecific communication in butterflies, it appears to be nonfunctional in frogs. Consequently, our data highlight that we should be careful when assigning a selection-based benefit to the presence of UV reflectance.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".