Modelling the effects of establishing a marine reserve for mobile fish species
Bibliographic record
Abstract
We present a model of the effects of a marine reserve on spawning stock biomass (SSB) and short- and long-term yield for a size-structured species that exhibits seasonal movements. The model considers the effects of protecting nursery and (or) spawning grounds under a range of fishing mortalities and fish mobility rates. We consider two extremes of effort redistribution following reserve establishment and analyze the effects of a reserve when the fishery targets either mature or immature fish. We apply the model to the Mediterranean hake (Merluccius merluccius) and show that a marine reserve could be highly beneficial for this species. We demonstrate benefits from reserves not just for overexploited stocks of low-mobility species, but also (to a lesser extent) for underexploited stocks and high-mobility species. Greatly increased resilience to overfishing is also found in the majority of cases. We show that a reserve provides benefits additional to those obtained from simple effort control. Benefits from reserves depend to a major extent on the amount of effort redistribution following reserve establishment and on fishing selectivity; hence, these factors should be key components of any evaluation of reserve effectiveness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".