Addressing distribution equity in spatial conservation prioritization for small-scale fisheries
Bibliographic record
Abstract
Spatial conservation prioritization is used worldwide for designing marine protected areas (MPA) that achieve set conservation objectives with minimal impacts to marine users. People involved in small-scale fisheries (SSF) may incur negative and disproportionate impacts from implementing MPAs, yet limited available data often restricts their representation in MPA planning. Using a Philippines case study, we focus here on the systematic design of a MPA network that aims to minimize and distribute costs equitably for SSF whilst achieving representation targets for biodiversity conservation. The objectives of the study are to: (1) document a participatory mapping approach for collecting SSF data for prioritization using the local knowledge of fishers; and (2) examine how the completeness and resolution of SSF data may affect prioritization outputs in terms of biodiversity representation, spatial efficiency, and distribution equity. In the data-poor region, we conducted participatory mapping workshops with fishers in 79 communities to collect data on the spatial distribution patterns of different SSF fisheries and communities, and employed remote sensing techniques to define coastal habitats, which were targeted for inclusion in MPAs. The datasets were integrated within the decision-support tool Marxan with Zones to develop three scenarios. The SSF data incorporated in each scenario varied based on their completeness (considered all fishing methods or only dominant methods) and resolution (fishing methods itemized by community or municipality). All scenarios derived MPA plans that met representation targets with similar area coverage. The outputs, however, varied in terms of distribution equity, measured by the distribution of opportunity costs (loss of fishing grounds) across different fisheries and communities. Scenarios that did not include minority fisheries or variations between communities, led to inequitable costs. These results highlight the need to incorporate detailed data on SSF at appropriate resolutions, and how this can be achieved through participatory approaches.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".