How far have we come? A review of MPA network performance indicators in reaching qualitative elements of Aichi Target 11
Bibliographic record
Abstract
Abstract Effective networks of marine protected areas (MPAs) are explicitly recognized and called for in international biodiversity conservation strategies such as the Aichi Targets. While various indicators have been proposed to assess effectiveness of individual MPAs, no comprehensive set of indicators exists for MPA networks, particularly for Aichi Target 11. The qualitative elements of this target recognize the value of social, economic, governance, and ecological factors in achieving effective biodiversity conservation. Here, we used a systematic literature review to identify indicators of MPA network effectiveness. We reviewed 64 publications, identifying 48 indicators that could be aligned with the qualitative elements. Results showed that assessments of MPA network effectiveness predominantly focused on effective management while neglecting equitable management and integration into the wider land and seascape. Indicators tended to focus on ecological characteristics, overlooking social, economic, and governance dimensions. Key challenges in addressing these gaps include identifying conflicting priorities and objectives in adjacent marine and land areas that interfere with cooperation and knowledge sharing, and ensuring diverse areas with distinct social and ecological contexts are considered. This study provides the first review of indicators for assessing MPA networks and adds to the literature assessing whether current and future targets can be met.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".