Conservation Risk and Uncertainty in Recovery Prospects for a Collapsed and Culturally Important Salmon Population in a Mixed-Stock Fishery
Bibliographic record
Abstract
Abstract Mixed-stock fisheries simultaneously exploit populations that may differ in their conservation status, and uncertainty in stock-specific harvest rates can hamper evaluations of recovery prospects for depressed populations. These difficulties are exemplified in the Sockeye Salmon population from the Atnarko watershed, which collapsed in the early 2000s, causing cultural and economic hardship. A recovery plan identified the incidental harvest of Sockeye Salmon by mixed-stock fisheries in the Atnarko as a potential, but poorly understood, impediment to recovery. We reconstructed harvest rates for salmon in Indigenous and commercial fisheries and used an age-structured state-space model of stock–recruit dynamics to predict how a range of future mixed-stock harvest rates would influence recovery. Under recent harvest rates, there is a 50–60% chance that the population will grow to exceed a recovery goal of 15,000 spawners over the next four generations. Eliminating the harvest of Sockeye Salmon altogether increased predicted recovery prospects to a maximum of 69%, suggesting that factors other than fisheries are contributing to the lack of recovery (e.g., ocean conditions) and that harvest management alone is unlikely to lead to recovery with a high degree of certainty. We developed a generalized migration, harvest, and catch monitoring simulation model to quantify how different monitoring scenarios might improve estimates for mixed-stock harvest rates. Increasing the number of specimens collected for genetic samples improved the harvest rate estimates for each stock caught in the mixed-stock fisheries, particularly for the smallest stocks, and relative to single sampling events conducted near the peak of the return migration, weekly sampling improved estimates only slightly but provided insurance against missing the peak of the return migration. Our study highlights collaborative research initiated and directed by the Nuxalk Nation to promote the recovery of a depressed stock that is inherent to traditional foods, thereby contributing to a global effort to integrate Indigenous cultural values with biological conservation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".