Predator-prey interactions between harbour seals (Phoca vitulina) and Pacific salmon (Oncorhynchus spp.) in the Salish Sea
Bibliographic record
Abstract
Populations of Chinook (Oncorhynchus tshawytscha) and coho salmon (O. kisutch) have experienced significant declines in abundance and productivity over the last 50 years in the Salish Sea as harbour seals (Phoca vitulina) recovered from hunting and culling. Some have hypothesized that increased predation by seals may be responsible for the declines in salmon survival, and their failure to recover after reductions in fishing effort. However, it is not known if these correlations exist for every population of salmon in the Salish Sea, or how many young Chinook and coho salmon are consumed by seals each year. I developed mathematical and statistical models to investigate the potential causal relationship between seal predation and declines in Chinook and coho salmon populations in the Salish Sea. I also used simulation modeling to evaluate outcomes that may result if managers reduced British Columbia’s harbour seal population to promote the recovery of salmon populations. I found that harbour seal densities were strongly negatively associated with productivity of most wild Chinook salmon populations in the Salish Sea and Washington Coast that were included in the study. Integrating recently collected seal diet data with a novel predation model indicates that large numbers of juvenile Chinook and coho salmon are eaten by seals, and that predation-related mortality has likely increased significantly over the last 50 years. The results of my simulation model suggest both lethal removals and contraception could reduce the seal population, but that important tradeoffs exist between the two approaches. Overall, my findings increase understanding of the role that marine mammal predation plays in the early marine life stage of juvenile salmon, and identifies potential outcomes and tradeoffs of actively managing predator populations.
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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 | 0.000 |
| 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".