A Social–Ecological Systems Approach to Assessing Conservation and Fisheries Outcomes in Fijian Locally Managed Marine Areas
Bibliographic record
Abstract
Locally managed marine areas (LMMAs) are often recommended as a strategy to achieve conservation and fisheries management, though few studies have evaluated their performance against these objectives. We assessed the effectiveness of eight periodically harvested closures (PHCs), the most common form of management within Fijian LMMAs, focusing on two outcomes: protection of resource units and biodiversity conservation. Of the eight PHCs, only one provided biodiversity benefits, whereas three were moderately successful in protecting resource units (targeted fish biomass). Protection of resource units was more likely when PHCs were harvested less frequently, less recently, and when total fish biomass in open areas was lower. Our findings further suggest that monitoring, enforcement, and clearly defined boundaries are critical, less frequent harvesting regimes are advised, and culturally appropriate management incentives are needed. Although PHCs have some potential to protect resource units, they are not recommended as a single strategy for broad-scale biodiversity conservation.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".