Climate change introduces threatened killer whale populations and conservation challenges to the Arctic
Bibliographic record
Abstract
The Arctic is the fastest-warming region on the planet, and the lengthening ice-free season is opening Arctic waters to sub-Arctic species such as the killer whale (Orcinus orca). As apex predators, killer whales can cause significant ecosystem-scale changes. Setting conservation priorities for killer whales and their Arctic prey species requires knowledge of their evolutionary history and demographic trajectory. Using whole-genome resequencing of 24 killer whales sampled in the northwest Atlantic, we first explored the population structure and demographic history of Arctic killer whales. To better understand the broader geographic relationship of these Arctic killer whales to other populations, we compared them to a globally sampled dataset. Finally, we assessed threats to Arctic killer whales due to anthropogenic harvest by reviewing the peer-reviewed and gray literature. We found that there are two highly genetically distinct, non-interbreeding populations of killer whales using the eastern Canadian Arctic. These populations appear to be as genetically different from each other as are ecotypes described elsewhere in the killer whale range; however, our data cannot speak to ecological differences between these populations. One population is newly identified as globally genetically distinct, and the second is genetically similar to individuals sampled from Greenland. The effective sizes of both populations recently declined, and both appear vulnerable to inbreeding and reduced adaptive potential. Our survey of human-caused mortalities suggests that harvest poses an ongoing threat to both populations. The dynamic Arctic environment complicates conservation and management efforts, with killer whales adding top-down pressure on Arctic food webs crucial to northern communities' social and economic well-being. While killer whales represent a conservation priority, they also complicate decisions surrounding wildlife conservation and resource management in the Arctic amid the effects of climate change.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".