Genetics of divergence in the Northern Saw-whet Owl (<i>Aegolius acadicus</i>)
Bibliographic record
Abstract
Differentiation, often leading to speciation, is common among island populations of birds.However, migratory species tend to differentiate less because increased movements often preclude the extended periods of allopatry thought to be required for speciation.This interaction of isolation and migration-gene flow is a complex issue in evolutionary biology.We examined the genetics of divergence in Northern Saw-whet Owls (Aegolius acadicus), in which a migratory form (A. a. acadicus) occurs during fall migration and winter, but not at other times of the year, in the range of a sedentary, island form (A. a. brooksi) on Haida Gwaii, British Columbia.We used 2,018 base pairs of mtDNA and genomic data from 405 amplified fragment-length polymorphisms to assess the population genetics and evolutionary history of these two subspecies.Both mtDNA and nuclear genomic markers showed significant divergence between these subspecies, and mtDNA gene flow between them was very low (,1 individual generation À1 ).These subspecies likely diverged in association with the Wisconsin glacial maximum, ~16,000 yr BP.The refugial history of the region, life-history traits, and the maintenance of lineage integrity despite current contact suggest that this divergence occurred or is maintained through heteropatric differentiation, probably driven by a loss of migratory behavior in brooksi accompanied by local adaptations.
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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".