Designing Effective Marine Protected Areas in Seaflower Biosphere Reserve, Colombia, Based on Biological and Sociological Information
Bibliographic record
Abstract
Abstract: Ecologists have paid increasing attention to the design of marine protected areas (MPAs), and their design advice consistently recommends representing all habitat types within MPAs or MPA networks as a means to provide protection to all parts of the natural ocean system. Recent developments of new habitat‐mapping techniques make this advice more achievable, but the success of such an approach depends largely on our ability to define habitat types in a way that is ecologically relevant. We devised and tested the ecological relevance of a set of habitat‐type definitions through our participation in a stakeholder‐driven process to design a network of MPAs, focusing on no‐take marine reserves in the Seaflower Biosphere Reserve, San Andrés Archipelago, Colombia. A priori definitions of habitat types were ecologically relevant, in that our habitat‐type definitions corresponded to identifiable and unique characteristics in the ecological communities found there. The identification of ecological pathways and connectivity among habitats also helped in designing ecologically relevant reserve boundaries. Our findings contributed to the overall design process, along with our summary of other general principles of marine reserve design. Extensive stakeholder input provided information concerning the resources and their patterns of use. These inputs also contributed to the reserve design process. We anticipate success for the Seaflower Biosphere Reserve at achieving conservation and social goals because its zoning process includes detailed yet flexible scientific advice and the participation of stakeholders at every step .
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".