Data from: Cultural isolation is greater than genetic isolation across an avian hybrid zone
Bibliographic record
Abstract
AbstractElucidating the relationship between genetic and cultural evolution is important in understanding speciation, as learned premating barriers might be involved in maintaining species differences. Here we test this relationship by examining a widely recognized premating barrier, bird song, in a hybrid zone between black-throated green (Setophaga virens) and Townsend's warblers (S. townsendi). We use song analysis, genomic techniques and playback experiments to characterize the cultural and genetic backgrounds of individuals in this region, expecting that if song is an important reproductive barrier between these species, there should be a strong relationship between song and genotype. We show that songs in the hybrid zone correspond to the distinctly different songs found in allopatry but that song and genotype are not tightly coupled in sympatry. Allopatric individuals responded only to local songs, indicating that individuals may have learned to respond to songs they commonly hear. We observed discordance between song and genotype clines; a narrower cline suggests that cultural selection on song is stronger than natural selection on genotype. These findings indicate that song is unlikely to play a role in reproductive isolation between these species, and we suggest that spatial variation in song may nonetheless be maintained by frequency-dependent cultural selection. This decoupling of genes and culture may contribute to hybridization in this region.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.016 |
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".