Ecological drivers of song evolution in birds: Disentangling the effects of habitat and morphology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environmental differences influence the evolutionary divergence of mating signals through selection acting either directly on signal transmission ("sensory drive") or because morphological adaptation to different foraging niches causes divergence in "magic traits" associated with signal production, thus indirectly driving signal evolution. Sensory drive and magic traits both contribute to variation in signal structure, yet we have limited understanding of the relative role of these direct and indirect processes during signal evolution. Using phylogenetic analyses across 276 species of ovenbirds (Aves: Furnariidae), we compared the extent to which song evolution was related to the direct influence of habitat characteristics and the indirect effect of body size and beak size, two potential magic traits in birds. We find that indirect ecological selection, via diversification in putative magic traits, explains variation in temporal, spectral, and performance features of song. Body size influences song frequency, whereas beak size limits temporal and performance components of song. In comparison, direct ecological selection has weaker and more limited effects on song structure. Our results illustrate the importance of considering multiple deterministic processes in the evolution of mating signals.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it