An Evaluation of Rebuilding Policies for U.S. Fisheries
Bibliographic record
Abstract
Rebuilding depleted fish populations is a priority of modern fisheries management. In the U.S., strong statutory mandates extend to both the goals and process by which stocks are to be rebuilt. However, the National Standard Guidelines that govern the implementation of the Magnuson-Stevens Fishery Conservation and Management Act may change to increase flexibility in rebuilding requirements. In this study we evaluate performance of the status quo approach to fish stock rebuilding in the United States against 3 alternatives that have been proposed to improve rebuilding outcomes. These alternatives either simplify the analytical requirements of rebuilding analyses or apply 'best practices' in fisheries management, thereby avoiding the need for rebuilding analyses altogether. We use a Management Strategy Evaluation framework to evaluate rebuilding options across 6 fish life history types and 5 possible real-world fishery scenarios that include options for stock assessment quality, multiple fleets, and the degree to which the stocks are overfished at the start of the analysis. We show that the status quo rebuilding plan and a harvest control rule that reduces harvest rates at low stock size generally achieve the best rebuilding outcomes across all life-history types and fishery scenarios. Both approaches constrain fishing in the short term, but achieve high catches in the medium and long term as stocks rebuild to productive levels. These results support a growing body of literature that indicates that efforts to end overfishing early pay off in the medium- to long-term with higher cumulative catches than the alternative.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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".