An equilibrium model for predicting the efficacy of marine protected areas in coastal environments
Bibliographic record
Abstract
Quantitative models of marine protected area (MPA) proposals can be used to compare outcomes given current biological knowledge. We used a model of a linear coastline, with 200 discrete cells each spanning 1.6 km of coast. This model is used to evaluate alternative proposals for marine protected area networks, predicting equilibrium changes in abundances and harvests while accounting for dispersal of larvae and older fish, changes in fecundity with reduced mortality in reserves, impacts of displaced fishing effort on abundances outside reserves, and compensatory (stock–recruitment) changes in postsettlement juvenile survival. The model demonstrates that modest dispersal rates of older fish can substantially reduce abundance within protected areas compared with predictions from models that ignore such dispersal. The strength of compensatory improvements in postsettlement juvenile survival is the most critical factor in determining whether a reserve network can rescue populations from the impacts of severe overharvesting. We use the model to compare specific alternative proposals for protected area networks along the California coast, as mandated through California's Marine Life Protection Act, and show that achieving the goals of the Act depends primarily on the fisheries management regulations outside of protected areas and that the size and configuration of MPAs has little impact.
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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 | 0.008 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".