Evaluating approaches for scaling‐up community‐based marine‐protected areas into socially equitable and ecologically representative networks
Bibliographic record
Abstract
Marine-protected areas (MPAs) are vital to marine conservation, but their coverage and distribution is insufficient to address declines in global biodiversity and fisheries. In response, many countries have committed through the Aichi Target 11 of the Convention on Biological Diversity to conserve 10% of the marine environment through ecologically representative and equitably managed MPAs by 2020. The rush to fulfill this commitment has raised concerns on how increasing MPA coverage will affect other elements of Target 11, including representation and equity. We examined a Philippines case study to assess and compare 3 MPA planning approaches for biodiversity representation and equitable distribution of costs to small-scale fishers. In the opportunistic approach, MPAs were identified and supported by coastal communities. The donor-assisted approach used local knowledge to select MPAs through a national-scale and donor-assisted conservation project. The systematic conservation planning approach identified MPA locations with the spatial prioritization software Marxan with Zones to achieve biodiversity objectives with minimal costs to fishers. We collected spatial data on biodiversity and fisheries features and performed a gap analysis to evaluate MPAs derived from different approaches. We assessed representation based on the proportion of biodiversity features conserved in MPAs and distribution equity by the distribution of opportunity costs (fishing areas lost in MPAs) among fisher stakeholder groups. The opportunistic approach did not ineffectively represent biodiversity and resulted in inequitable costs to fishers. The donor-assisted approach affected fishers disproportionately but provided near-optimal regional representation. Only the systematic approach achieved all representation targets with minimal and equitable costs to fishers. Our results demonstrate the utility of systematic conservation planning to address key elements of Target 11 and highlight opportunities (e.g., integration of local and scientific knowledge can address representation and equity concerns) and pitfalls (e.g., insufficient stakeholder considerations can exacerbate social inequalities) for planning MPAs in similar contexts.
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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 | 0.037 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".