Marine protected areas and fisheries: bridging the divide
Bibliographic record
Abstract
ABSTRACT Long‐term and well‐managed marine protected areas (MPAs) can, under the right circumstances, contribute to biodiversity conservation and fisheries management, thus contributing to food security and sustainable livelihoods. This article emphasizes (1) the potential utility of MPAs as a fisheries management tool, (2) the costs and benefits of MPAs for fishing communities, and (3) the foundations of good governance and management processes for creating effective MPAs with a dual fisheries and conservation mandate. This article highlights case studies from numerous regions of the world that demonstrate practical and often successful solutions in bridging the divide between MPA management and fisheries sustainability, with a focus on small‐scale coastal fisheries in order to emphasize lessons learned. To be an effective fisheries management tool, MPAs should be embedded in broader fisheries management and conservation plans. MPAs are unlikely to generate benefits if implemented in isolation. The spatial and temporal distribution of benefits and costs needs to be taken into account since proximal fishery‐dependent communities may experience higher fishing costs over the short and long‐term while the fisheries benefits from MPAs may only accrue over the long‐term. Key lessons for effectively bridging the divide between biodiversity conservation and fisheries sustainability goals in the context of MPAs include: creating spaces and processes for engagement, incorporating fisheries in MPA design and MPAs into fisheries management, engaging fishers in management, recognizing rights and tenure, coordinating between agencies and clarifying roles, combining no‐take‐areas with other fisheries management actions, addressing the balance of costs and benefits to fishers, making a long‐term commitment, creating a collaborative network of stakeholders, taking multiple pressures into account, managing adaptively, recognizing and addressing trade‐offs, and matching good governance with effective management and enforcement. © 2014 The Authors. Aquatic Conservation: Marine and Freshwater Ecosystems published by John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".