How does species association affect mixed stock fisheries management? A comparative analysis of the effect of marine protected areas, discard bans, and individual fishing quotas
Bibliographic record
Abstract
We developed a spatially explicit bioeconomic model of a mixed-stock fishery with an unproductive and a productive stock to examine how the spatial overlap between species affects the outcome of a fishery under alternative management methods. We considered a competitive total allowable catch (TAC) system, with and without a ban on discards, and an individual vessel quota (IVQ) fishery managed either to maximum sustainable yield (MSY) or maximum economic yield (MEY). We also evaluated the utility of marine protected areas (MPAs) designed to protect the unproductive species for each management scenario. Banning discarding (whether under a TAC or IVQ) created the biggest increase in profit regardless of species overlap as it moves the target species biomass toward Bmey. MPAs reduced the profit in most cases and were not always successful at conserving the unproductive stock above a target size. The IVQ system managed to MEY produced the most profit among all scenarios while preserving the populations above some target values in most cases, but an IVQ system managed to MSY produced lower profits than a competitive TAC with a discard ban at some levels of species overlap.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".