Four centuries of commercial whaling eroded 11,000 years of population stability in bowhead whales
Bibliographic record
Abstract
Summary The bowhead whale, an Arctic endemic, was heavily overexploited during commercial whaling between the 16th-20th centuries 1 . Current climate warming, with Arctic amplification of average global temperatures, poses a new threat to the species 2 . Assessing the vulnerability of bowhead whales to near-future predictions of climate change remains challenging, due to lacking data on population dynamics prior to commercial whaling and responses to past climatic change. Here, we integrate palaeogenomics and stable isotope ( δ 13 C and δ 15 N) analysis of 201 bowhead whale fossils from the Atlantic Arctic with palaeoclimate and ecological modelling based on 823 radiocarbon dated fossils, 151 of which are new to this study. We find long-term resilience of bowhead whales to Holocene environmental perturbations, with no obvious changes in genetic diversity or population structure, despite large environmental shifts and centuries of whaling by Indigenous peoples prior to commercial harvests. Leveraging our empirical data, we simulated a time-series model to quantify population losses associated with commercial whaling. Our results indicate that commercial exploitation induced population subdivision and losses of genetic diversity that are yet to be fully realised; declines in genetic diversity will continue, even without future population size reductions, compromising the species’ resilience to near-future predictions of Arctic warming.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".