Taking Stock of Chinook Salmon Energy Densities has Implications for Resident Killer Whales Meeting Their Energy Needs
Bibliographic record
Abstract
Chinook salmon (Oncorhynchus tshawytscha) rely on large reserves of energy accumulated at sea to complete their journey upstream, mature reproductively, and spawn. In part due to these energy reserves, Chinook are the primary prey species for resident killer whales. However, energy density has been shown to vary significantly among Chinook populations, indicating that data on stock specific energy density are necessary to assess whether available prey can meet resident killer whale energy requirements. In this study, we sought to derive stock specific estimates of Southern British Columbia Chinook energy density. To begin, we evaluated a microwave energy meter as a non-lethal, rapid method for assessing lipid content (a proxy for energy density) in Chinook. Energy meter readings were collected from 60 Chinook which were then fully homogenized and lipid extracted to calibrate the device to measure whole-body lipid content. Our analysis provided a strong linear regression relationship between energy meter measurements and whole-body lipid content (R²=0.88, p < 0.001). Following this analysis we deployed the energy meter at the Albion Fraser River Test Fishery in Maple Ridge, BC Canada in 2020. We collected energy meter readings from 1568 individual Chinook encompassing members of all 5 Fraser management units. We identified three distinct groups of these management units based on average lipid level: Fall-41 (6.7% ± 1.8), Summer-41 (10.8% ± 2.2) and a group containing Spring-42, Spring-52 and Summer-52 (13.0% ± 2.8). Our results show that the Summer-41 group contained 25% more kcal/kg than the Fall-41 group and that the Spring-42, Spring-52 and Summer-52 group contained 40% more kcal/kg than the Fall-41 group. This study indicates how Chinook life history drives energy accumulation and provides values which can be used in conjunction with known Chinook and resident killer whale distribution to more accurately assess whether available prey meet predator energy needs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".