A cold-water fish striving in a warming ocean: Insights from whole-genome sequencing of the Greenland halibut in the Northwest Atlantic
Bibliographic record
Abstract
Characterizing the extent of genetic differentiation among individuals and its distribution across the genome is increasingly important to inform both conservation and management of exploited species. The Greenland Halibut is one of the main demersal fish species to be commercially exploited in Eastern Canada, and accurate information on geographic population structure and local adaptation is required to ensure the long-term presence of this species. We generated high-quality whole-genome sequencing data for 1,297 Greenland Halibut sampled across 32 locations throughout the Northwest Atlantic (from Arctic Canadian and Greenlandic coasts to the Gulf of St Lawrence). Population genetic structure was analyzed, revealing an absence of population differentiation between Canada and west Greenland but significant genetic differentiation between the Gulf of Saint Lawrence and the remainder of the Northwest Atlantic. Except for Gulf of Saint Lawrence, Greenland Halibut thus appear to be panmictic throughout the Northwest Atlantic. Environmental Association Analyses revealed that the environment explained up to 51 % might be replaced by 51% of the differentiation observed between the two stocks, with both ocean-bottom and surface variables ( e.g. , temperature and oxygen) involved in the observed genomic differentiation. Altogether, these results indicate that phenotypic differences previously observed between the Gulf of Saint Lawrence and the Northwest Atlantic likely resulted from functional adaptive divergence to their respective environmental conditions. Using coalescent simulations, we also assessed how high levels of migration between the two stocks would allow Greenland Halibut to potentially escape unfavorable environmental conditions in the Gulf of Saint Lawrence. In addition to supporting the management of this important exploited species, this work highlights the utility of using comprehensive genomic datasets to characterize the effects of climate change across a wider range of species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".