Sympatry drives colour and song divergence in wood-warblers (Parulidae)
Bibliographic record
Abstract
Closely related species often exhibit similarities in appearance and behaviour, yet when related species exist in sympatry, signals may diverge to enhance species recognition. Prior comparative studies provided mixed support for this hypothesis, but the relationship between sympatry and signal divergence is likely non-linear. Constraints on signal diversity may limit signal divergence, especially when large numbers of species are sympatric. We tested the effect of sympatric overlap on plumage colour and song divergence in wood-warblers (Parulidae), a speciose group with diverse visual and vocal signals. We also tested how number of sympatric species influences signal divergence. Allopatric species pairs had overall greater plumage and song divergence compared to sympatric species pairs. However, among sympatric species pairs, plumage divergence positively related to degree of sympatric overlap in males and females, while male song bandwidth and syllable rate divergence negatively related to sympatric overlap. In addition, as the number of species in sympatry increased, average signal divergence among sympatric species decreased, which likely due to constraints on warbler perceptual space and signal diversity. Our findings reveal that sympatry influences signal evolution in warblers, though not always as predicted, and that number of sympatric species can limit sympatry’s influence on signal evolution.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".