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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".