Comparing the Performance of Four Very Large Marine Protected Areas with Different Levels of Protection
Bibliographic record
Abstract
In the last decades, several targets for marine conservation were set to counter the effects of increasing fishing pressure, e.g., protecting 10% of the sea by 2020, and establishing large-scale marine protected areas (LSMPAs). Using the ‘reconstructed’ catch data for 1950 to 2018 made available by the Sea Around Us initiative, we show that the declaration of an exclusive economic zone (EEZ) in 1983 by the U.S.A. and its protection by the U.S. Coast Guard had a much bigger impact on catches around the Northwestern Hawaiian Islands than the subsequent creation of a LSMPA. This is similar to Pitcairn Islands, a UK territory. Trends differed sharply in the Galapagos and New Caledonia, where neither their EEZ declaration nor the LSMPA (by Ecuador in 1988 and by France in 2014) stopped local fisheries from continuous expansion. Our results also demonstrate that in the studied multizone LSMPAs continued local fishing induces a ‘fishing down’ effect wherein the mean trophic level (TL) declined, especially in the Galapagos, by 0.1 TL per decade. Stakeholders’ responses to a short questionnaire and satellite imagery lent support to these results in that they documented substantial fishing operations and ‘fishing the line’ within and around multizone LSMPAs. In the case of EEZs around less populated or unpopulated islands, banning foreign fishing may reduce catch much more than a subsequent LSMPA declaration. This confirms that EEZs are a tool for coastal countries to protect their marine biodiversity and that allowing fishing in an MPA, while politically convenient, may result in ‘paper parks’ within which fishing can cause the same deleterious effects as in wholly unprotected areas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".