Dietary specialization on elasmobranchs and seasonal foraging patterns of offshore killer whales Orcinus orca in the northeastern Pacific
Bibliographic record
Abstract
Dietary specialization allows species with overlapping ranges and similar trophic levels to coexist by reducing competition. Offshore killer whales are sympatric with other populations of Orcinus orca in the northeastern Pacific, but their feeding preferences are poorly known. Using genetic, drone videography, photographic, and observational evidence collected during 84 predation events, we found that elasmobranchs comprised almost 90% of all prey sampled, revealing that the offshore killer whale ecotype has a specialized diet. Sharks are energetically profitable prey because they are predictably available, large-bodied, and have a large, lipid-rich liver. In addition to previously documented prey, we identified 4 new species: salmon shark, Pacific electric ray, albacore tuna, and broadnose sevengill shark. Targeted species differed depending on the time of year and region, matching the known seasonal availability and migratory behaviour of sharks. Pacific sleeper sharks (45.2% of identified prey) and salmon sharks (6.0%) were primarily taken in Alaska, USA, and northern British Columbia (BC), Canada, in spring and early summer, whereas blue sharks (17.9%) and Pacific spiny dogfish (16.7%) were typically caught in southerly locations along the continental shelf edge off BC in late summer and fall. Blue, broadnose sevengill, and shortfin mako sharks were hunted off California, USA, in winter. Teleosts comprised only 10.7% of predation events. We conclude that in addition to fish-eating and marine mammal-eating ecotypes, offshore killer whales represent another example of pronounced, likely culturally transmitted, dietary specialization among the killer whales of the northeastern Pacific.
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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".