Prevalence of Chinook salmon is higher for southern than for northern resident killer whales in summer hot-spot feeding areas
Bibliographic record
Abstract
Differences in the availability of prey may explain the low numbers of southern resident killer whales and the increase in northern resident killer whales in British Columbia and Washington State. However, in-situ data on the availability of their preferred prey (Chinook salmon, Oncorhynchus tshawytscha) in the core feeding areas used by these two populations of fish-eating killer whales have been lacking to test this hypothesis. We used multi-frequency echosounders (38, 70, 120, and 200 kHz) to estimate densities of adult Chinook (age-4+, > 81 cm) within 16 hot-spot feeding areas used by resident killer whales during summer 2020 in the Salish Sea and North Island Waters. We found Chinook were generally concentrated within 50 m from the bottom in the deep waters, and tended to be absent near the surface in the shallow waters (< 50 m). In general, the densities of Chinook we encountered were highest as the fish entered the Salish Sea (from Swiftsure Bank in the south) and Johnstone Strait (from Queen Charlotte Strait to the north)-and declined as fish migrated eastward along the shoreline of Vancouver Island. Median densities of Chinook for all sampled areas combined were 0.4 ind.·1000 m-2 in northern resident foraging areas, and 0.9 ind.·1000 m-2 in southern resident killer whale areas (p < 0.05, Mann-Whitney U test). Thus, Chinook salmon were twice as prevalent within the hot-spot feeding areas of southern versus northern resident killer whales. This implies that southern resident killer whales have greater access to Chinook salmon compared to northern residents during summer-and that any food shortage southern residents may be encountering is occurring at other times of year, or elsewhere in their range.
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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) | 0.002 | 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".