Song complexity in suboscine birds: evolutionary drivers and ecological constraints
Bibliographic record
Abstract
Acoustic signal complexity varies widely in animals, from single notes to highly sophisticated vocal displays. In birds, vocal complexity can evolve as an honest signal of individual quality driven by sexual selection. However, this hypothesis is rarely explored in conjunction with alternative drivers, including competition for ecological resources (social selection) and intra-group communication, both of which may favour increased signal complexity. Using Bayesian phylogenetic models, we test whether these alternative mechanisms predict the complexity of innate songs in 1288 species of suboscine passerine birds, while accounting for ecological constraints on sound production, transmission and detection. We found that overall song complexity was reduced by sexual selection (estimated from mating systems) and declined with body size and vegetation density. Conversely, note count and song length increased in territorial species, particularly those using song to defend year-round territories during the non-breeding season. These findings challenge the common assumption that sexual selection is the main driver of increased signal complexity and highlight the role of social selection via territorial competition as a factor increasing the temporal complexity of songs. Our results suggest that signal complexity depends on social, cultural and ecological contexts, reflecting a combination of multiple inter-related drivers and constraints.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".