The combined effects of predation, fishing, and ocean productivity on salmon species targeted by marine mammals in the northeast Pacific
Bibliographic record
Abstract
Along the northeast Pacific coast, the salmon-eating southern resident killer whale population (SRKW, Orcinus orca) have been at very low levels since the 1970s. Previous research have suggested that reduction in food availability, especially of Chinook salmon (Oncorhynchus tshawytscha), could be the main limiting factor for the SRKW population. Using the ecosystem modelling platform Ecopath with Ecosim (EwE), this study evaluated if the decline of the Pacific salmon populations between 1979 and 2020 may have been impacted by a combination of factors, including marine mammal predation, fishing activities, and climatic patterns. We found that the total mortality of most Chinook salmon populations has been relatively stable for all mature returning fish despite strong reduction in fishing mortality since the 1990s. This mortality pattern was mainly driven by pinnipeds, with increases in predation between 1979 and 2020 mortality ranging by factors of 1.8 to 8.5 across the different Chinook salmon population groups. The predation mortality on fall-run Chinook salmon smolts originating from the Salish Sea increased 4.6 times from 1979 to 2020, whereas the predation mortality on coho salmon (Oncorhynchus kisutch) smolts increased by a factor of 7.3. The model also revealed that the north Pacific gyre oscillation (NPGO) was the most important large-scale climatic index affecting the stock productivity of Chinook salmon populations from California to northern British Columbia. Overall, the model provided evidence that multiple factors may have affected Chinook salmon populations between 1979 and 2020, and suggested that predation mortality by marine mammals could be an important driver of salmon population declines during that time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".