Contextualizing ecological performance: Rethinking monitoring in marine protected areas
Bibliographic record
Abstract
Abstract The global extent of marine protected areas (MPAs) has increased rapidly in the last decade, and monitoring and evaluation are now required for effective and adaptive management of these areas. We classify monitoring in MPAs into four categories and identify a critically important, but undervalued category: human pressure monitoring that targets human activities and their impacts. Human pressure monitoring is fundamental for interpreting the results of ecological performance monitoring and for evaluating MPA management effectiveness. The consequences of ecological performance monitoring that show unsuccessful MPA performance while falsely assuming successful mitigation of human pressures could jeopardize MPA performance analysis and adaptive management, and thus be worse than not monitoring at all. Human pressure monitoring enables using MPAs as reference areas where the effects of global or regional pressures (e.g. climate change) can be disentangled from the effects of local human activities, as well as to minimize the shifting baseline phenomenon in defining healthy stocks. These benefits cannot be realized without active human pressure monitoring integrated into an adaptive management cycle that ensures effective MPA protection. In the absence of human pressure monitoring, all ecological monitoring within MPAs falls in the ambient monitoring category: monitoring that is not intended to measure conservation outcomes. We discuss the implications for monitoring programme design and provide a structure for decision‐makers on how to prioritize monitoring activities within MPAs that place greater emphasis on improving MPAs as biodiversity conservation tools over proving MPA performance.
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 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 | 0.024 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".