The biodiversity adaptation gap: Management actions for marine protected areas in the face of climate change
Bibliographic record
Abstract
Abstract Marine protected areas (MPAs) are a crucial component of international biodiversity conservation commitments, yet are increasingly affected by climate change. No synthesis or analysis exists of the specific on‐the‐ground management actions that have been taken by MPA managers in response to climate change. Here, we extract, evaluate, classify, and analyze adaptation responses from 646 existing, English‐language MPA management plans preselected for their consideration of climate change. Our synthesis documents 213 unique management actions, of which only a fraction (4.7%) were on‐the‐ground adaptative measures directed at enhancing biodiversity conservation; in contrast, almost half (45.5%) were monitoring measures. Our analysis highlights the apparent paucity of documented management actions addressing the challenging task of limiting climate change impacts on biodiversity within MPAs—a “biodiversity adaptation gap”. By compiling a community resource of adaptation approaches that can be further expanded and disseminated, we hope to contribute to the effort to adapt MPA networks to climate change.
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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 | 0.000 |
| 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".