Data from: A synthesis of the prevalence and drivers of non-compliance in marine protected areas
Bibliographic record
Abstract
Non-compliance regularly negates the effectiveness of marine protected areas (MPAs) worldwide. Understanding and addressing non-compliance is critical given continued efforts to establish MPAs to meet international milestones (e.g., Aichi targets). We conducted a literature review and meta-analysis to address five key questions and research gaps for MPAs: 1) how is non-compliance best measured? 2) what are common drivers of non-compliance? 3) what is the overall prevalence of non-compliance? 4) how frequently is ecological failure of MPAs attributed to non-compliance? and 5) are there measurable management impacts on regulated fishing in MPAs (i.e., effective reduction of fishing)? We found 151 papers that had some focus on non-compliant resource extraction in MPAs and 96 that quantified it. Insufficient enforcement was the most cited driver of non-compliance, followed by several socio-economic drivers including lack of awareness, livelihood/economic gain, social norms, and ineffective governance. Prohibited fishing in MPAs was often reduced compared to outside areas, as shown by our meta-analysis. However, we found frequent reports and measures of non-compliance globally, and many cases of failed ecological performance attributed primarily to non-compliance (57% of 67 relevant studies). Overall, our synthesis demonstrates that non-compliance continues to be a prevalent issue for MPAs. Reducing non-compliance and ensuring effective MPAs will rely on continuous evaluation of non-compliance to inform adaptive management, as well as addressing the complex, interrelated drivers that arise throughout MPA planning, establishment, and management.
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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 | 0.024 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.018 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".