Unraveling the Genetic Legacy of Commercial Whaling and Population Dynamics in Arctic Bowhead Whales and Narwhals
Bibliographic record
Abstract
Assessing genetic structure and diversity in wildlife is particularly important in the context of climate change. The Arctic is rapidly warming, and endemic species must adapt quickly or face significant threats to persistence. Bowhead whales (Balaena mysticetus) and narwhals (Monodon monoceros) are two long-lived Arctic species with similar habitat requirements and are often seen together in the Canadian Arctic. Although their ranges overlap extensively, bowhead whales experienced significantly greater commercial whaling mortality than narwhals over several centuries. The similar habitat requirements but different harvest histories of these two species provide an opportunity to examine present-day genetic diversity and the demographic and genetic consequences of commercial whaling. We whole-genome resequenced contemporary Canadian Arctic bowhead whales and narwhals to delineate population structure and reconstruct demographic history. We found higher genetic diversity in bowhead whales compared to narwhals. However, bowhead whale effective population size sharply declined contemporaneously with the intense commercial whaling period. Narwhals, in contrast, exhibited recent growth in effective population size, likely reflecting exposure to limited opportunistic commercial harvest. Bowhead whales will likely continue to experience significant genetic drift in the future, leading to the erosion of genetic diversity. In contrast, narwhals do not seem to be at imminent risk of losing their current levels of genetic variation due to their long-term low effective population size and lack of evidence for a recent decline. This work highlights the importance of considering population trajectories in addition to genetic diversity when assessing the genetics of populations for conservation and management purposes.
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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 | 0.001 | 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".