Reaching Global Marine Biodiversity Conservation Goals With Area-Based Fisheries Management: A Typology-Based Evaluation
Bibliographic record
Abstract
In 2010, the Parties to the Convention on Biological Diversity (CBD) adopted the Aichi Biodiversity Target 11, calling for conserving 10% of the ocean through marine protected areas (MPAs) and “other effective area-based conservation measures” (OECMs), explicitly recognizing that other types of spatial conservation measures beyond areas designated as MPAs may also achieve biodiversity gains. Eight years later, CBD Parties adopted a definition and criteria for OECMs, and by early 2022, only a few OECMs had been reported. The OECM definition clearly requires that the measures be area-based and likely to contribute to conservation. However, conservation need not be their primary objective. Guidance on the extent and limits of what these “area measures” might include is needed. Clarity would assist countries in delivering on the CBD’s Post-2020 Global Biodiversity Framework, with decadal goals incorporating an area-based conservation target, in which OECMs will play a crucial role. To achieve greater recognition of OECMs, countries require sector-specific guidance to guide recognition, listing, and ongoing implementation of OECMs. Here, we evaluate how well area-based fisheries management measures meet the OECM criteria as well as sustainable use principles, broader ecosystem management objectives, and more general biodiversity conservation goals. We systematically review case studies across a broad range of spatial management approaches to provide evidence of correspondence with the OECM criteria, arguing that many with primary objectives related to fisheries sustainability provide co-benefits for biodiversity, and hence biodiversity conservation and sustainable development. This review highlights how fisheries measures can help achieve a number of Sustainable Development Goals alongside the global targets for biodiversity of CBD.
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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 | 0.081 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".