THE ROLE OF ECOLOGICAL CONSTRAINT IN DRIVING THE EVOLUTION OF AVIAN SONG FREQUENCY ACROSS A LATITUDINAL GRADIENT
Bibliographic record
Abstract
Just as features of the physical and biotic environment constrain evolution of ecological and morphological traits, they may also affect evolution of communication systems. Here we analyze constraints on rates of vocal evolution, using a large dataset of New World avian sister taxa. We show that species breeding in tropical forests sing at generally lower frequencies and across narrower bandwidths than species breeding in open habitats, or at high latitudes. We attribute these restrictions on birdsong frequency to the presence of high-frequency insect noise and greater degradation of high-frequency sounds in tropical forests. We fit Ornstein-Uhlenbeck models to show that recent evolution of song frequency has been more greatly constrained in tropical forests than elsewhere, that is, songs have shown less tendency to diverge over time in tropical forests, consistent with inferred acoustic restrictions. In addition, we find that song frequency has evolved more rapidly overall at high latitudes in both forest and open habitats. Besides a larger available sound window, other factors contributing to more rapid divergence at high latitudes may include an overall increased intensity of sexual selection, occupation of more divergent habitats, and the presence of fewer competing species.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".