Assessing the factors that contribute to Marine Protected Area (MPA) effectiveness and whether global protected areas of 30% can be achieved by 2030
Bibliographic record
Abstract
The urgency for effective conservation to mitigate the rapid deterioration of our marine habitats, which provide ecological, social, and economic benefits, is widely recognised. Marine Protected Areas (MPAs) have emerged as a pivotal tool in marine conservation. However, global MPA performance varies, and employed management schemes differ. This report aims to investigate the factors which contribute to MPA effectiveness, therefore success, and assess the feasibility of achieving the global target to conserve 30% of habitats by 2030; outlined in the Kunming-Montreal Global Biodiversity Framework (GBF, Target 3). To achieve this, a systematic literature review was conducted, employing search-strings, inclusion/exclusion criteria, screening, eligibility reading and finally data extraction, to assemble relevant information. The primary database used was Web of Science (WOS) supplemented by Google Scholar and websites searches, leading to 49 studies in this review. MPA effectiveness was identified to be significantly driven by enforcement, MPA design and stakeholder engagement. Accordingly, effective MPA designs are characterized by NEOLI (no-take, enforcement, old (>10 years), large (>100km2), isolated) features, whereby the presence of more features resulted in higher species biomass increases. Adequate enforcement correlates with ‘Overall Management Success’ (OMS), ensuring adherence to rules and regulations within MPAs. Stakeholder engagement is associated with high compliance and positive perceptions of MPAs thus legitimacy. The development of global MPA coverage, currently at 8.19%, is seen by increasing trends. Whilst MPA spatial variations are significantly positively correlated with coastline length, Human Development Index (HDI) and conservation investment. By elucidating the factors contributing to MPA success, enhancement for both existing and future designations is expedited, supporting GBF Target 3. Overall, the analysis of factors contributing to MPA effectiveness reveal overlapping complexities.
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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 | 0.018 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".