Experimental tests of selection against heterospecific aggression as a driver of avian color pattern divergence
Bibliographic record
Abstract
Signal divergence is thought to reduce the costs of co-occurrence for closely related species and may thereby be important in the generation and maintenance of new biodiversity. In birds, closely related, sympatric species are more divergent in their color patterns than those that live apart, but the selective pressures driving sympatric divergence in color pattern are not well understood. Here, we conducted field experiments on naïve birds using spectrometer-matched, painted 3D-printed models to test whether selection against heterospecific aggression might drive color pattern divergence in the genus Poecile. We found that territorial male black-capped chickadees (P. atricapillus) are equally likely to attack sympatric and allopatric congeners, and wintering flocks are equally likely to visit feeders occupied by sympatric and allopatric congeners, despite sympatric congeners being more divergent in color pattern. These results suggest that either the concerted evolution of additional traits (e.g., discrimination), or interactions in sympatry that promote learning, are required if color pattern divergence among sympatric species is to reduce heterospecific aggression. Alternatively, color pattern divergence among sympatric species may be caused by other selective pressures, such as selection against hybridization or habitat partitioning and secondary signal adaptation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".