Population genomics reveals historical divergence and local adaptation in polar bears
Bibliographic record
Abstract
Single-nucleotide polymorphisms (SNPs) have numerous advantages over microsatellites, including greater power to infer population structure and history and to detect loci undergoing selection. Here, we conduct the first continental-level SNP study of polar bears (Ursus maritimus) using genotypes from an array of 5441 SNP loci genotyped in 16–30 polar bears sampled in each of 16 geographic regions in Canada and West Greenland. Our study aimed to assess population history and genetic structure and to identify evidence of adaptive loci. Using these data, we confirmed the existence of four broad-scale genetic clusters in North America (FCT = 0.035) and identified nine fine-scale subclusters using more powerful spatial methods. An assessment of historical patterns of migration suggests that polar bears migrated into North America from the Beaufort Sea after the last glacial maximum. Using a conservative approach, we identified 17 loci that may represent adaptive variation, including one SNP in the 3’ untranslated region of PDLIM5 (PDZ And LIM Domain 5), a gene involved in cardiovascular function, which has undergone substantial selection in polar bears since their divergence from brown bears. Outlier loci differentiated the Norwegian Bay genetic cluster more strongly from remaining clusters than did our complete dataset, suggesting possible adaptive differences in the High Arctic. Through careful consideration of SNP loci, sample inclusion, and analytical approaches, we provide a comprehensive picture of polar bear population structure at a continental level. This study provides a model for the analysis of wide-ranging species that can contribute to their conservation and management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".