OECMs in marine capture fisheries: Key implementation issues of governance, management, and biodiversity
Bibliographic record
Abstract
The ‘Other Effective Area-based Conservation Measure’ (OECM) concept was first introduced in 2010, by the Conference of the Parties of the Convention on Biological Diversity (CBD COP) in the CBD Strategic Plan for Biodiversity conservation 2011–2020. The concept acknowledged that a range of spatial measures other than protected areas were appropriate for reaching Aichi Target 11 spatial conservation coverage. The OECM definition was adopted in 2018 in CBD COP Decision 14/8, which calls on States to mainstream OECMs into economic sectors, to recognize the current biodiversity conservation benefits and co-benefits from their area-based management measures and enhance them as much as possible. In the marine capture fisheries sector, the identification of OECMs is a work in progress and the issues addressed in this paper are key implementation issues that States and fisheries authorities are or will be encountering regarding their governance, management, and biodiversity outcomes. The purpose of the paper is to draw attention to some key OECM implementation issues arising in marine capture fisheries and to suggest possible approaches to address them. The governance issues addressed relate to enabling frameworks, industrial fisheries, legitimate authorities, long-term commitments, cross-sectoral coordination and planning, and contribution to the Post-2020 Global Biodiversity Framework of the CBD. The management issues considered relate to effectiveness in achieving expected outcomes, costs and benefits of OECMs, spatial relations between OECMs and fisheries, and the role of OECMs in the Ecosystem Approach to Fisheries (EAF). Regarding the significant positive biodiversity outcomes expected from OECMs, issues relate to the type of outcomes expected, their current or intended nature, the level of evidence required, and their relationship to area-based conservation standards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".