A long-term, spatially replicated experimental test of the effect of marine reserves on local fish yields
Bibliographic record
Abstract
Do no-take marine reserves affect fishery yields? Manipulations of reserve status, and yield estimates, were made at two Philippine islands over two decades. Twenty-five percent and ten percent, respectively, of the coral reefs at Sumilon and Apo islands were made no-take reserves in 1974 and 1982. Biomass of target fish increased inside the no-take reserves 3- to 4.5-fold over 918 years. Biomass did not increase outside each reserve. Protection of the Sumilon reserve ceased in 1984. Biomass of targeted fish in the reserve and trap and gillnet catches of these fish declined by 42.7% and 40%, respectively, by 1985. The reserve was reprotected from 1987 to 1991 and from 1995 to 2001. Fish biomass increased in the reserve by 27.2%. Trap and gillnet catches outside the reserve increased 26.9% by 2001. The Apo reserve was protected from 1982 to 2001. Total catch of major fish families was significantly higher after (19852001) than before (1981) reserve establishment at Apo, increasing 41.3% between 1981 and 19982001. These experiments, plus spillover evidence, suggest that marine reserves may help maintain, or even enhance, local fishery yields in the long-term.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".