Experimental tests of selection against heterospecific aggression as a driver of avian colour 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 colour patterns than those that live apart, but the selective pressures driving sympatric divergence in colour 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 colour 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 colour pattern. These results suggest that either the concerted evolution of additional traits (e.g. discrimination), or interactions in sympatry that promote learning, is required if colour pattern divergence among sympatric species is to reduce heterospecific aggression. Alternatively, colour pattern divergence among sympatric species may be caused by other selective pressures, such as selection against hybridization or habitat partitioning and secondary signal adaptation.
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 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".