Productivity and resilience of Chinook salmon compromised by ‘Mixed-Maturation’ fisheries in marine waters
Bibliographic record
Abstract
Abstract Most Chinook salmon ( Oncorhynchus tshawytscha ) in the northeast Pacific Ocean are harvested in mixed-stock marine fisheries. Here, multiple populations with varying abundance and productivities are encountered. In addition, many of these fisheries generally encounter both mature and immature Chinook. Hence, these fisheries are better described as mixed-stock and “mixed-maturation” (MM) fisheries. Harvest of immature fish can skew the age composition of Chinook populations towards younger, and hence smaller, individuals. Older Chinook are generally larger and contribute disproportionately to the productivity of their populations. We developed an individual-based demographic-genetic model of ocean-type Chinook to evaluate the effects of fisheries that harvest immature Chinook. We then compared those effects to terminal fisheries that harvest only mature fish. Our model provides the ability to assess the benefits of terminal Chinook fisheries to both landed catch and Chinook rebuilding. Recovered populations show a more archetypal age– and sex-structure than contemporary ocean-type Chinook subject to marine mixed-maturation fisheries. In our modeled scenarios of mixed-maturation fisheries, we found that immature Chinook can comprise up to 59% of the total numbers of fish caught, and 47% of the total weight of the catch. If instead, these Chinook were not harvested until they mature and reach terminal fisheries, they would contribute greater biomass to landed catches. These terminal fisheries allow a higher percentage of larger, older Chinook to escape, and would increase the fecundity and productivities of their populations. The benefits of terminal fisheries would accrue to fishers, sustainable wild harvesting, wildlife, and the rebuilding of depleted Chinook runs.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".