Aposematism increases acoustic diversification and speciation in poison frogs
Bibliographic record
Abstract
Multimodal signals facilitate communication with conspecifics during courtship, but they can also alert eavesdropper predators. Hence, signallers face two pressures: enticing partners to mate and avoiding detection by enemies. Undefended organisms with limited escape abilities are expected to minimize predator recognition over mate attraction by limiting or modifying their signalling. Alternatively, organisms with anti-predator mechanisms such as aposematism (i.e. unprofitability signalled by warning cues) might elaborate mating signals as a consequence of reduced predation. We hypothesize that calls diversified in association with aposematism. To test this, we assembled a large acoustic signal database for a diurnal lineage of aposematic and cryptic/non-defended taxa, the poison frogs. First, we showed that aposematic and non-aposematic species share similar extinction rates, and aposematic lineages diversify more and rarely revert to the non-aposematic phenotype. We then characterized mating calls based on morphological (spectral), behavioural/physiological (temporal) and environmental traits. Of these, only spectral and temporal features were associated with aposematism. We propose that with the evolution of anti-predator defences, reduced predation facilitated the diversification of vocal signals, which then became elaborated or showy via sexual selection.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".