CULTURAL AND GENETIC EVOLUTION IN MOUNTAIN WHITE-CROWNED SPARROWS: SONG DIALECTS ARE ASSOCIATED WITH POPULATION STRUCTURE
Bibliographic record
Abstract
Bird song often varies geographically within a species; when this geographic variation has distinct boundaries, the shared song types are referred to as song dialects. How dialects are produced and their adaptive significance are longstanding problems in biology, with implications for the role of culture in the evolution and ecology of diverse organisms, including humans. Here we test the hypothesis that song dialect, a culturally transmitted trait, is related to the population genetic structure of mountain white-crowned sparrows (Zonotrichia leucophrys oriantha). To address this, we compared microsatellite allele frequencies from 18 sample sites representing eight dialect regions in the Sierra Nevada. Pairwise genetic distances were not significantly correlated with geographic distances either within or between dialects, nor did dialect groups form distinct genetic groups according to neighbor-joining or UPGMA analysis, and most variation in allele frequencies occurred among individuals rather than at higher levels. However, most of the remaining variation was attributable to differences among, rather than within, dialect regions, and this among-dialect component of variance was statistically significant. Moreover, when controlling for the effect of geographic distance, song dissimilarity and genetic distance between site pairs were significantly correlated. Thus, song dialects appear to be associated with reductions in, but not strict barriers to, gene flow among dialect regions.
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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 | 0.001 |
| 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".