Dietary plasticity and broad North Atlantic origins inferred from bulk and amino acid‐specific <scp>δ<sup>15</sup>N</scp> and <scp>δ<sup>13</sup>C</scp> favour killer whale range expansions into Arctic waters
Bibliographic record
Abstract
Abstract Killer whales (Orcinus orca) occur seasonally in the eastern Canadian Arctic (ECA), where their range expansion associated with declining sea ice have raised questions about the impacts of increasing killer whale predation pressure on Arctic‐endemic prey. We assessed diet and distribution of ECA killer whales using bulk and compound‐specific stable isotope analysis (CSIA) of amino acids (AA) of 54 skin biopsies collected from 2009 to 2020 around Baffin Island, Canada. Bulk ECA killer whale skin δ15N and δ13C values did not overlap with potential Arctic prey after adjustment for trophic discrimination, and instead reflected foraging history in the North Atlantic prior to their arrival in the ECA. Adjusted killer whale stable isotope (SI) values primarily overlapped with several species of North Atlantic baleen whales or tuna. Amino acid (AA)‐specific δ15N values indicated the ECA killer whales fed primarily on marine mammals, having similar glutamic acid δ15N–phenylalanine δ15N (δ15NGlx‐Phe) and threonine δ15N (δ15NThr) as mammal‐eating killer whales from the eastern North Pacific (ENP) that served as a comparative framework. However, one ECA whale grouped with the fish‐eating ENP ecotype based δ15NThr. Distinctive essential AA δ13C of ECA killer whale groups, along with bulk SI similarity to killer whales from different regions of the North Atlantic, indicates different populations converge in Arctic waters from a broad source area. Generalist diet and long‐distance dispersal capacity favour range expansions, and integration of these insights will be critical for assessing ecological impacts of increasing killer whale predation pressure on Arctic‐endemic species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".