Seasonal variation in the lipid content of Fraser River Chinook Salmon (Oncorhynchus tshawytscha) and its implications for Southern Resident Killer Whale (Orcinus orca) prey quality
Bibliographic record
Abstract
Abstract In Southern British Columbia (BC), Canada, declines in southern resident killer whale (SRKW—Orcinus orca) populations have been linked to declines in numbers and average size of their preferred prey, Chinook salmon (Oncorhynchus tshawytscha). However, the life history diversity of Chinook suggests that there is a need to assess stock-specific differences in energy density to evaluate prey quality as a factor in SRKW declines. In this study, we calibrated a Distell fat meter to estimate Chinook whole-body lipid content, a proxy for energy density. The fat meter was deployed at the Fraser River, BC, Chinook test fishery during 2020, collecting lipid, weight, and length measurements from 1566 genetically stock identified individuals encompassing all major Fraser River Chinook population units (management units, MUs) at river entry. We found that MU-specific lipid content increased with distance and elevation to spawning grounds and was highest in the Spring-52 (12.8%) and Summer-52 (12.7%) MUs, intermediate in the Summer-41 MU (10.8%), and lowest in the Fall-41 MU (7.3%). Lipid content also decreased by up to 6 percentage points within MUs from the beginning to end of their migration period. Our data revealed SRKWs’ most endangered prey sources, the Spring-52 and Summer-52 MUs, are also its most energy rich. It also indicated SRKWs have access to progressively lower energy density Chinook through the year, requiring up to ~ 30% more fish to meet energy demands in the fall than in the spring.
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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".