Shared governance increases marine protected area effectiveness
Bibliographic record
Abstract
Marine protected areas (MPAs) are widely used to conserve and manage coastal resources. Protected areas are governed by a variety of institutional arrangements, yet little is known concerning the relative performance of different governance approaches. This research draws upon a unique dataset that combines details on the reported International Union for Conservation of Nature (IUCN) governance categories of 217 global MPAs and their ecological outcomes to compare the performance of alternative governance arrangements. We find that MPAs with shared governance arrangements, where management authority is shared among multiple government and non-government actors, are 98% more likely to have higher fish biomass than MPAs governed by state agencies (i.e., primarily government) alone (mean effect size and 95% C.I = 0.32 ± 0.31). We also find higher biomass in older MPAs, those in countries with higher gross domestic product (GDP), and those with a higher proportion of no-take area. With targets to protect 30% of our oceans driving new commitments to expand MPA coverage globally, our results suggest that multi-stakeholder participation and collaboration facilitated by shared and decentralized governance arrangements can play an important role in achieving conservation outcomes.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".