Effects of management objectives and rules on marine conservation outcomes
Bibliographic record
Abstract
Understanding the relative effectiveness and enabling conditions of different area-based management tools is essential for supporting efforts that achieve positive biodiversity outcomes as area-based conservation coverage increases to meet newly set international targets. We used data from a coastal social-ecological monitoring program in 6 Indo-Pacific countries to analyze whether social, ecological, and economic objectives and specific management rules (temporal closures, fishing gear-specific, species-specific restrictions) were associated with coral reef fish biomass above sustainable yield levels across different types of area-based management tools (i.e., comparing those designated as marine protected areas [MPAs] with other types of area-based management). All categories of objectives, multiple combinations of rules, and all types of area-based management had some sites that were able to sustain high levels of reef fish biomass-a key measure for coral reef functioning-compared with reference sites with no area-based management. Yet, the same management types also had sites with low biomass. As governments advance their commitments to the Kunming-Montreal Global Biodiversity Framework and the target to conserve 30% of the planet's land and oceans by 2030, we found that although different types of management can be effective, most of the managed areas in our study regions did not meet criteria for effectiveness. These findings underscore the importance of strong management and governance of managed areas and the need to measure the ecological impact of area-based management rather than counting areas because of their designation.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".