Ecological implications of changing hatchery practices for Chinook salmon in the Salish Sea
Bibliographic record
Abstract
Abstract For over a century, hatchery programs have been used to subsidize natural salmon populations in order to increase fisheries opportunities and, more recently, to conserve declining natural populations. While an extensive literature has described the impacts of large‐scale hatchery operations on freshwater ecosystems, less attention has been given to ecosystem interactions within the marine environment. We analyzed records of hatchery‐released Chinook salmon in the Salish Sea to assess temporal and spatial changes in hatchery practices since 1950, with the goal of identifying potential implications for ecosystem dynamics and conservation efforts in the region. Over the past 65 yr, we found significant changes in the size and time at which juvenile salmon are released, resulting in decreased diversity of these traits. Research suggests that predation on juvenile salmon by other fish, avian, and marine mammal species could be size‐dependent, and our results indicate that current hatchery practices are releasing Chinook salmon in the size range preferred by these predators. With current marine survival rates at chronically low levels, and increasing demand for hatchery subsidies, it is important to consider how modifying existing hatchery programs intended to reduce homogenization may promote more natural marine food web dynamics, with potential benefits to both hatchery and natural Chinook 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".