Modeling no-take marine reserves in regulated fisheries: assessing the role of larval dispersal
Bibliographic record
Abstract
We explored the effects of larval dispersal distance on the impact of no-take marine reserves (NTMRs) implemented in fisheries with catch regulations. NTMRs exist in many fisheries with harvest regulated by annual catch limits. In these fisheries, catch is taken from outside NTMRs, potentially resulting in reduced abundance outside NTMRs and an overall reduction in catch. We used a spatial model with two life stages (larvae and adults) to evaluate the effects of larval dispersal distance for fisheries managed by a total allowable catch (TAC) and an NTMR. We examined effects of the timing of density-dependent mortality in relation to larval movement. Abundance reached similar values for populations with long and short larval dispersal distances. Catch declined substantially for stocks with short larval dispersal distances. When larval dispersal distances were long, catch declined to values below maximum sustainable yield (MSY), but stabilized. Catch per unit effort (CPUE) declined to 9% of CPUE at MSY for stocks with short distance larval dispersal after the implementation of an NTMR; with long distance larval dispersal, CPUE declined to approximately 50% or less of the CPUE at MSY. The CPUE did not reflect trends in abundance after the implementation of an NTMR.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".