Tracing the Drivers of Range‐Wide Bowhead Whale Genomic Structure and Diversity
Bibliographic record
Abstract
In a rapidly warming Arctic, genetic variation might serve to buffer organisms against the effects of environmental change, such as sea ice loss and ocean warming. Yet, this concept remains largely unexplored because comprehensive genome-wide studies across the full ranges of Arctic marine taxa are rare. Bowhead whales (Balaena mysticetus) have a strong association with sea ice and polar water masses, a long history of human exploitation, and a circumpolar distribution, making them a valuable model for evaluating how past environmental and anthropogenic factors have shaped contemporary population variation. We analysed both nuclear and mitochondrial genomes from bowhead whales sampled across the species' range, encompassing all four recognised stocks. Firstly, our results indicate the existence of three genetic groupings instead of four: (1) Okhotsk Sea; (2) East Greenland-Svalbard-Barents Sea; and (3) a population containing two recognised stocks-the Bering-Chukchi-Beaufort and East Canada-West Greenland. We utilised high-resolution ecological niche modelling and bowhead whale movement data to reconstruct inter-stock habitat connectivity over the last 11,700 years, finding that this explains our identified genetic groupings. Bowhead whale populations exhibit little-to-no evidence of recent inbreeding and retain high genetic diversity relative to other mammalian species despite centuries of intensive commercial whaling. The most vulnerable population is that in the Okhotsk Sea, which has the lowest genetic diversity, most inbreeding, and the highest realised genetic load. Collectively, our findings elucidate the recent history and dynamics of bowhead whales, offering valuable baseline data and context on present-day genetic structure and diversity to support effective conservation and management strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".