Disentangling the mechanisms of signal evolution in Tyrannidae flycatchers, part I: song is constrained by morphology and covaries with ecological factors
Bibliographic record
Abstract
Acoustic signals mediate key animal interactions and can evolve through a variety of factors. Signal divergence can reinforce pre-zygotic barriers and minimize costly hybridizations among closely related species or partition acoustic space to avoid signal interference. To unravel the drivers of song evolution, it is critical to simultaneously test multiple evolutionary axes leading to heterospecific song variation (e.g., the role of morphology, ecology, and heterospecific recognition). Tyrannidae is the largest Passeriformes family and occurs across a broad environmental gradient. Tyrannids are suboscines, so song variation represents evolutionary differences that are not confounded by social learning. Several genera show conserved plumage coloration, but exhibit pronounced vocal differences. In the first of our two-part paper on signal ecology and evolution, we leveraged a large-scale song dataset to unravel song divergence in Tyrannidae (n = 282 species) by testing the relative influence of morphology (body and bill size), ecology (vegetation cover, diet, climate), and heterospecific proximity on song evolution. Based on phylogenetically controlled analyses, we found Tyrannidae song evolution was primarily driven by morphological adaptation, where larger-bodied birds with heftier bills sang lower frequency and slower paced songs. Pairwise song differences were weakly related to heterospecific proximity of some genera, lending support to the species recognition hypothesis or drift. Given that many flycatchers are habitat specialists, natural selection acting on bill morphology and body size in specific environmental-contexts may shape song among tyrannids. By simultaneously testing the relative roles of morphological, ecological, and geographical factors on song evolution, our study highlights the complexity of suboscine song evolution and the importance of large-scale comparative studies that test multiple evolutionary hypotheses.
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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 | 0.001 |
| 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) | 0.001 | 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".