Incorporating harvest–population diversity trade-offs into harvest policy analyses of salmon management in large river basins
Bibliographic record
Abstract
Accounting for population diversity can be critical to the sustainable management of mixed-stock fisheries because harvest rates that can be sustained by productive populations may come at the cost of overfishing less productive ones. While these harvest–diversity trade-offs are well-recognized, their consequences for harvest policy performance are not often explicitly evaluated in contemporary fisheries management. We use closed-loop simulations to evaluate the ability of alternative harvest policies to meet population diversity and fishery objectives for one of the largest subsistence Chinook salmon (Oncorhynchus tshawytscha) fisheries in the world (Kuskokwim River Basin in western Alaska). We found clear evidence of population diversity that resulted in asymmetric trade-offs among fishery and conservation objectives whereby policies that forgo relatively small amounts of harvest result in relatively large increases in equitable access to Chinook and elimination of risk of weak stock extirpation. The performance of alternative harvest policies, and the magnitude of trade-offs, were sensitive to regime shifts and uncertainty in the drivers of recruitment variation. However, we found that harvest policies that prioritized meeting minimum subsistence needs were unlikely to jeopardize long-term sustainability.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".