Temperate marine reserves: global ecological effects and guidelines for future networks
Bibliographic record
Abstract
Abstract Marine reserves, areas closed to all fishing and other extractive activities, provide a refuge for species of commercial and conservation importance. Given the considerable resources committed to designing temperate reserve networks, we synthesized data from temperate reserves worldwide to determine their ecological effects. In common with other studies, we found higher density, biomass, and species richness in temperate marine reserves compared to adjacent exploited areas. However, there was considerable heterogeneity in magnitude of effect among reserves, variability which was largely unexplained by species or reserve characteristics. Our analytical approach allowed for formal power analyses, indicating that detection of large reserve effects in temperate systems globally requires monitoring at least 37 reserves. These results must be qualified by the limitations of data available and will undoubtedly vary at different spatio‐temporal scales and for different focal species, but provide guidance for the design and monitoring of future marine conservations plans. International commitments toward establishment of multiple reserves offer a unique opportunity to assess reserve effectiveness; this opportunity can only be realized if reserves are designed to achieve clear and quantifiable objectives and are adequately monitored before and after establishment, based on appropriate power analyses, to assess how well those objectives are achieved.
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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 | 0.030 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".