Quantitative fatty acid signature analysis reveals a high level of dietary specialization in killer whales across the North Atlantic
Bibliographic record
Abstract
Quantifying the diet composition of apex marine predators such as killer whales (Orcinus orca) is critical to assessing their food web impacts. Yet, with few exceptions, the feeding ecology of these apex predators remains poorly understood. Here, we use our newly validated quantitative fatty acid signature analysis (QFASA) approach on nearly 200 killer whales and over 900 potential prey to model their diets across the 5000 km span of the North Atlantic. Diet estimates show that killer whales mainly consume other whales in the western North Atlantic (Canadian Arctic, Eastern Canada), seals in the mid-North Atlantic (Greenland), and fish in the eastern North Atlantic (Iceland, Faroe Islands, Norway). Nonetheless, diet estimates also varied widely among individuals within most regions. This level of inter-individual feeding variation should be considered for future ecological studies focusing on killer whales in the North Atlantic and other oceans. These estimates reveal remarkable population- and individual-level variation in the trophic ecology of these killer whales, which can help to assess how their predation impacts community and ecosystem dynamics in changing North Atlantic marine ecosystems. This new approach provides researchers with an invaluable tool to study the feeding ecology of oceanic top predators.
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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 | 0.000 | 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".