Repeated behavioral evolution is associated with targeted convergence of gene expression in cavity-nesting songbirds
Bibliographic record
Abstract
Uncovering the genomic bases of phenotypic adaptation is a major goal in biology, but this has been hard to achieve for complex behavioral traits. Here, we leverage the repeated, independent evolution of obligate cavity-nesting in birds to test the hypothesis that pressure to compete for a limited breeding resource has facilitated convergent evolution in behavior, hormones, and gene expression. We used an integrative approach, combining aggression assays in the field, testosterone measures, and transcriptome-wide analyses of the brain in wild-captured females and males. Our experimental design compared species pairs across five avian families, each including one obligate cavity-nesting species and a related species with a more flexible nest strategy. We find behavioral convergence, with higher levels of territorial aggression in obligate cavity-nesters, particularly among females. Across species, levels of testosterone in circulation were not associated with nest strategy, nor aggression. Phylogenetic analyses of individual genes and co-regulated gene networks revealed more shared patterns of brain gene expression than expected by drift, but the scope of convergent gene expression evolution was limited to a small percent of the genome. When comparing our results to other studies that did not use phylogenetic methods, we suggest that accounting for shared evolutionary history may reduce the number of genes inferred as convergently evolving. Altogether, we find that behavioral convergence in response to shared ecological pressures is associated with largely independent gene expression evolution across different avian families, punctuated by a narrow set of convergently evolving genes.
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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 | 0.000 | 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) | 0.001 | 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".