Science should not be left behind during the design of a marine protected area: meeting conservation priorities while integrating stakeholder interests
Bibliographic record
Abstract
Marine protected areas (MPAs) design is a complex process that typically involves diverse stakeholders, requiring compromise between diverging priorities. Such compromises, when not carefully understood, can threaten the ecological effectiveness of MPAs. Using the example of the Canadian Laurentian Channel MPA, we studied a planning process from initial scientific advice to the final MPA. We analysed the impacts of successive boundary modifications to the draft MPA, often made to accommodate extractive industries, on the protection of seven species initially identified as potential conservation priorities. We also quantified the potential economic impacts of changes in boundary modifications on the fisheries industry. Results show that reducing the proposed MPA size by 33.4% helped reduce the potential economic impact on the fishing industry by 65.5%, but it resulted in up to 43% decrease in protection of species of conservation priority. Changes in MPA boundary delineation during the design were not subjected to formal scientific reviews, raising questions on the potential effectiveness of this MPA. Better integration of science in MPA design is required to help assess the impacts that trade-offs made during stakeholder consultations can have on the MPA ecological effectiveness.
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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 | 0.041 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".