Conservation risks and portfolio effects in mixed‐stock fisheries
Bibliographic record
Abstract
Abstract Fish biodiversity sustains the resilience and productivity of fisheries, yet this biodiversity can be threatened by overharvest and depletion in mixed‐stock fisheries. Thus, the biodiversity that provides benefits may also make sustainable resource extraction more difficult, a key challenge in fisheries management. We simulated a mixed‐stock fishery to explore relationships between different dimensions of biodiversity and fishery performance relative to conservation and fishery objectives. Different dimensions of biodiversity (number of stocks, evenness, asynchrony among stocks, heterogeneity in stock productivity) exacerbated trade‐offs between fishery and conservation objectives. For example, fisheries targeting stock‐complexes with greater asynchrony, and to a lesser extent richness, had greater stability in harvest through time but also greater risks of overfishing weak stocks and reduced yield compared to less biodiverse stock‐complexes. These trade‐offs were ameliorated by increasing management control—the capacity of fishery managers to harvest specific stocks. To explore these trade‐offs in real‐world fisheries, we contrasted the fishing and population status of individual stocks within three major mixed‐stock sockeye salmon (Oncorhynchus nerka, Salmonidae) fisheries—Bristol Bay, Fraser River, and Skeena River. In general, the fisheries with lower management control had individual stocks that were more often being over‐ or under‐fished, compared with those with higher management control, though variation among regions in biodiversity, scale of management, and magnitude of habitat alteration likely also contribute to these relationships. Collectively, our findings emphasize that there is a need to extract less or regulate better in order to conserve and benefit from biodiversity in fisheries and other natural resource management systems.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".