Range-wide whole-genome resequencing of the brown bear reveals drivers of intraspecies divergence
Bibliographic record
Abstract
Population-genomic studies can shed new light on the effect of past demographic processes on contemporary population structure. We reassessed phylogeographical patterns of a classic model species of postglacial recolonisation, the brown bear (Ursus arctos), using a range-wide resequencing dataset of 128 nuclear genomes. In sharp contrast to the erratic geographical distribution of mtDNA and Y-chromosomal haplotypes, autosomal and X-chromosomal multi-locus datasets indicate that brown bear population structure is largely explained by recent population connectivity. Multispecies coalescent based analyses reveal cases where mtDNA haplotype sharing between distant populations, such as between Iberian and southern Scandinavian bears, likely results from incomplete lineage sorting, not from ancestral population structure (i.e., postglacial recolonisation). However, we also argue, using forward-in-time simulations, that gene flow and recombination can rapidly erase genomic evidence of former population structure (such as an ancestral population in Beringia), while this signal is retained by Y-chromosomal and mtDNA, albeit likely distorted. We further suggest that if gene flow is male-mediated, the information loss proceeds faster in autosomes than in X chromosomes. Our findings emphasise that contemporary autosomal genetic structure may reflect recent population dynamics rather than postglacial recolonisation routes, which could contribute to mtDNA and Y-chromosomal discordances.
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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".