Post‐glacial recolonization of the North American Arctic by Arctic char (<i>Salvelinus alpinus</i>): genetic evidence of multiple northern refugia and hybridization between glacial lineages
Bibliographic record
Abstract
Abstract Aims We investigated post‐glacial recolonization of the North American Arctic by Arctic char (Salvelinus alpinus) and examined potential hybridization between different glacial lineages upon secondary contact. Location North American Arctic and adjacent areas. Methods We collected mtDNA sequence data from 1355 individuals from 110 sampling locations and data from nine microsatellite loci from 931 individuals from 37 locations. We assessed the phylogenetic relationships and geographical distribution of mtDNA haplotypes and conducted historical demographic analyses. We used a Bayesian clustering analysis method to detect potential hybridization between glacial lineages. Results Two highly divergent mtDNA lineages were identified in the Arctic region with distinct but overlapping geographic distributions: one in Beringia and the other over the entire Arctic Archipelago and coastal mainland east of Alaska. The microsatellite data also implied the existence of these two lineages. Evidence of hybridization was detected between the Arctic lineage and an Atlantic lineage in eastern North America. Main conclusions Our data suggested survival and recolonization from two northern glacial refugia: one in Beringia and another in a smaller refugium, perhaps in the Arctic Archipelago itself or a separate refugium within Beringia. Patterns of hybridization detected supported the presence of a secondary contact zone between glacial lineages in the eastern Canadian Arctic.
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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 | 0.001 | 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".