Climate change introduces threatened killer whale populations and conservation challenges to the Arctic
Bibliographic record
Abstract
Abstract The Arctic is the fastest-warming region on the planet, and sea ice loss has opened new habitat for sub-Arctic species such as the killer whale ( Orcinus orca ). As apex predators, killer whales can cause significant ecosystem-scale changes, however, we know very little about killer whales in the Arctic. Setting conservation priorities for killer whales and their Arctic prey species requires knowledge of their evolutionary history and demography. We found that there are two highly genetically distinct, non-interbreeding populations of killer whales using the eastern Canadian Arctic—one population is newly identified as globally distinct. The effective sizes of both populations recently declined, and both are vulnerable to inbreeding and reduced adaptive potential. Furthermore, we present evidence that human-caused mortalities, particularly ongoing harvest, pose an ongoing threat to these populations. The certainty of substantial environmental change in the Arctic complicates conservation and management significantly. Killer whales bring top-down pressure to Arctic food webs, however, they also merit conservation concern. The opening of the Arctic to killer whales exemplifies the magnitude of complex decisions surrounding local peoples, wildlife conservation, and resource management as the effects of climate change are realized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".