Assessments of expected MPA outcomes can inform and improve biodiversity conservation: Case studies using The MPA Guide
Bibliographic record
Abstract
Global, regional, and national targets have been set to protect and conserve at least 30 % of the ocean by 2030, in recognition of the important benefits of healthy ocean ecosystems, including for human well-being. Many of these targets recognize the importance of the quality, not just quantity, of areas that are included in the 30 %, such as marine protected areas (MPAs). For example, the Convention on Biological Diversity’s Global Biodiversity Framework Target 3 calls for areas to be effectively conserved and managed, ecologically representative, well-connected, and equitably governed. Protecting a percent area is not the sole goal – protection must be effective and equitable. To better understand the quality of biodiversity conservation afforded, in addition to the quantity of area protected, we looked at MPAs across 13 studies that used The MPA Guide and related tools to track Stage of Establishment and Level of Protection as measures of expected biodiversity conservation outcomes across diverse locations, scales, and cultural, political, and conservation contexts. We show that standardized assessments of MPA quality can help to (1) evaluate and improve existing MPAs; (2) plan new MPAs; (3) compare the quality of MPA protection across various scales; (4) track MPA quality, including progress towards coverage targets; (5) enable clear communication and collaboration, and (6) inform actions needed to achieve policy targets and their underlying environmental and social goals, among others. We share common opportunities, challenges, and recommendations for tracking MPA quality at various scales, and using these quality assessments to measure progress towards global targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".