Climate and competition influence sockeye salmon population dynamics across the Northeast Pacific Ocean
Bibliographic record
Abstract
Pacific salmon productivity is influenced by ocean conditions and interspecific interactions, yet their combined effects are poorly understood. Using data from 47 North American sockeye salmon (Oncorhynchus nerka) populations, we present evidence that the magnitude and direction of climate and competition effects vary over large spatial scales. In the south, a warm ocean and abundant salmon competitors combined to strongly reduce sockeye productivity, whereas in the north, a warm ocean substantially increased productivity and offset the negative effects of competition at sea. From 2005 to 2015, the approximately 82 million adult pink salmon (Oncorhynchus gorbuscha) produced annually from hatcheries were estimated to have reduced the productivity of southern sockeye salmon by ∼15%, on average. In contrast, for sockeye at the northwestern end of their range, the same level of hatchery production was predicted to have reduced the positive effects of a warming ocean by ∼50% (from a ∼10% to a ∼5% increase in productivity, on average). These findings reveal spatially dependent effects of climate and competition on sockeye productivity and highlight the need for international discussions about large-scale hatchery production.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".