Changes in sea ice alter genetic structure of an iconic Arctic apex predator in less than three decades
Bibliographic record
Abstract
Abstract Climate change is having profound effects on biodiversity and species distributions worldwide. Nowhere are these effects potentially more pronounced than in the Arctic, where warming is almost two times the global average, and where year-round sea ice extent has significantly decreased, affecting many ice-dependent species. The polar bear ( Ursus maritimus ) is a circumpolar, apex Arctic predator, a sentinel of climate change, and a symbol of conservation. It is of immense cultural and spiritual importance to Inuit peoples and is hunted across the Arctic. Declines in sea ice have caused habitat fragmentation and loss, disrupting movement and prey access, potentially altering genetic structure and influencing polar bear’s potential to persist. Using samples collected from 1997 to 2020 by Inuit across much of the Canadian Arctic, 322 genome-wide autosomal DNA markers specifically designed to quantify polar bear genetic structure and a very stringent spatial-temporal method, we compare polar bear spatial genetic structure and landscape features between two periods across a consistent distribution:1997–2008 and 2009–2020. We observe marked changes in spatial genetic structure across the Arctic Archipelago over just two decades, shifting the boundaries between polar bear genetic clusters by ∼250 km. Landscape resistance models reveal the importance of sea ice and land cover type for each period, with spatial lag models showing that genetic change is best predicted by sea ice shifts between periods. Our study reveals rapid changes in genetic structure of polar bears in the Canadian Arctic, helps to inform conservation and management, and offers insight on future polar bear persistence across its immense, remote distribution.
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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 | 0.000 | 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".