Efficient and equitable design of marine protected areas in Fiji through inclusion of stakeholder-specific objectives in conservation planning
Bibliographic record
Abstract
The efficacy of protected areas varies, partly because socioeconomic factors are not sufficiently considered in planning and management. Although integrating socioeconomic factors into systematic conservation planning is increasingly advocated, research is needed to progress from recognition of these factors to incorporating them effectively in spatial prioritization of protected areas. We evaluated 2 key aspects of incorporating socioeconomic factors into spatial prioritization: treatment of socioeconomic factors as costs or objectives and treatment of stakeholders as a single group or multiple groups. Using as a case study the design of a system of no-take marine protected areas (MPAs) in Kubulau, Fiji, we assessed how these aspects affected the configuration of no-take MPAs in terms of trade-offs between biodiversity objectives, fisheries objectives, and equity in catch losses among fisher stakeholder groups. The achievement of fisheries objectives and equity tended to trade-off concavely with increasing biodiversity objectives, indicating that it is possible to achieve low to mid-range biodiversity objectives with relatively small losses to fisheries and equity. Importantly, the extent of trade-offs depended on the method used to incorporate socioeconomic data and was least severe when objectives were set for each fisher stakeholder group explicitly. We found that using different methods to incorporate socioeconomic factors that require similar data and expertise can result in plans with very different impacts on local stakeholders.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".