From design to implementation: Lessons from planning the first marine protected area network in Canada
Bibliographic record
Abstract
The social-ecological outcomes of marine protected areas (MPAs) can be amplified when sites are designed in networks that function cooperatively and synergistically. From 2015–2024, seventeen First Nations, the Province of British Columbia, and the Government of Canada collaboratively developed a Network Action Plan to establish an MPA Network for the Northern Shelf Bioregion (NSB). The NSB is a diverse and complex social-ecological region in British Columbia that spans an area of 102,000 km 2 from Northern Vancouver Island to the Alaska border. Here, we describe the MPA Network planning process and the transition from Network planning to implementation. We discuss key elements of success that enabled the Network Action Plan’s collaborative development and lessons learned to inform and improve regional MPA Network implementation and global MPA Network planning and implementation. All phases of the planning process, including governance, development, engagement, design, and analysis, were guided by the following principles: collaborative governance, multiple ways of knowing, participatory engagement, and adaptation to new information. The Network Action Plan’s dynamic design process resulted in innovative approaches and tools to protect conservation objectives throughout the NSB. As the NSB MPA Network process transitions from planning toward implementation, collaborative governance, network monitoring, and ongoing participatory engagement processes will play a central role in advancing the goals and objectives articulated in the Network Action Plan and ensuring equitable social-ecological outcomes. Overall, the NSB MPA Network planning process provides a model for collaborative design of MPA Networks and can inform co-governance and co-management processes throughout the world. • The proposed Network spans 30 % of the Northern Shelf Bioregion and 357 zones. • Indigenous, local, and western knowledge informed MPA Network planning. • The proposed Network protects ecologically and culturally important species, habitats, and features.
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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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.019 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".