A decision-support tool to facilitate discussion of no-take boundaries for Marine Protected Areas during stakeholder consultation processes
Bibliographic record
Abstract
Marine Protected Areas (MPAs) are proposed to help conserve marine biodiversity and ecological integrity. There is much guidance on the optimal design of MPAs but once potential MPAs are identified there is little guidance on defining the final no-take boundaries. This is especially problematic in temperate zones where ecological boundaries are “fuzzy”, which can be quite complicated during a consultation process involving the government and divergent stakeholder groups. More decision-support tools are needed to help stakeholders and government agencies objectively compare conservation and socio-economic trade-offs among proposed boundary options. To that end, we developed a method to identify which boundary minimizes spatial overlap of highly vulnerable species and a dominant stressor. We used the recently proposed boundary options of a candidate MPA in Atlantic Canada to illustrate our method. We evaluated the vulnerability of 23 key species to bottom trawling, the most prevalent stressor in the area. We then compared the spatial overlap of the most vulnerable species and the 2002–2011 footprint of bottom trawling among boundary options. The best boundary option was identified as that which minimized spatial overlap and total area. This approach identifies boundary options which provide the greatest protection of vulnerable species from their most significant stressor, at limited socio-economic cost. It is an objective decision-support tool to help stakeholders agree on final boundaries for MPAs.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.015 |
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".