Detectability of a poison frog and its Batesian mimic depends on body posture and viewing angle
Bibliographic record
Abstract
Abstract Aposematic signals warn predators that prey should be avoided due to dangerous secondary defences. However, as warning signals do not always produce avoidance, warning colors may evolve as a trade-off balancing detectability against signal saliency. For Batesian mimics, which display salient signals but lack secondary defenses, the costs of predator encounters are greater, potentially increasing the benefit of crypsis. This raises the question of whether imperfect mimicry may reduce detectability while retaining mimetic efficacy. We tested this hypothesis with the poisonous frog Ameerega bilinguis and undefended Batesian mimic Allobates zaparo, using computational visual modeling and screen-based detection trials with human participants. We found that both species incorporate camouflage into their warning colors, but to different degrees depending on viewing angle and behavior. Contrary to expectation, we found differences in detectability between model and mimic that do not adhere to the hypothesized cryptic mimetic phenotype. To aerial observers, we found the mimic to be more detectable than the model. To terrestrial observers, likely owing to the model’s bright ventral color, we found the model more detectable in viewing angles that highlight the ventral coloration, whereas the mimic was more detectable in viewing angles that highlight the dorsal coloration. Consequently, we suggest that in addition to being the result of perceptual or developmental constraints, imperfect mimicry may also evolve as an adaptive strategy which balances camouflage with different signaling functions. Our findings complement the emerging view that aposematic signals may evolve in response to a multitude of selection pressures beyond aversion alone.
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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 | 0.002 |
| 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) | 0.002 | 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".