Models and indicators for assessing conservation and fisheries-related effects of marine protected areas
Bibliographic record
Abstract
Two kinds of approaches have been used for assessing conservation and fisheries-related effects of marine protected areas (MPAs): (i) statistical modelling based on field data and (ii) mathematical modelling quantifying the consequences of MPAs on the dynamics of populations, communities, and fisheries. Statistical models provide a diagnostic on the impact of MPAs on the ecosystem and resources; they are also needed for devising and assessing sampling designs for monitoring programs. Dynamic models enable exploration of the consequences of MPA designs and other management policies. We briefly review how each of these approaches has been implemented up to now in the literature and identify potential indicators of MPA effects that can be obtained from each approach to provide scientific advice for managers. Methodological gaps that impede the assessment of MPA effects and the construction of appropriate indicators are then discussed, and recent developments in this respect are presented. We finally propose ways to reconcile the two approaches based on their complementarity to derive suitable indicators to support decision making. In this respect, we suggest in addition that MPA managers should be associated from the beginning to the design and construction of indicators.
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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 | 0.001 |
| 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".