The contribution of other effective area-based conservation measures (OECMs) to protecting global biodiversity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nations recently agreed to set aside 30% of the planet by 2030 as conservation areas (the “30 × 30” goal) necessitating major expansions, not just of traditional protected areas like national parks, but also of ‘other effective area-based conservation measures’ (OECMs) – areas that provide de facto benefits to biodiversity despite conservation not being the primary management objective. But evidence for whether OECMs achieve positive biodiversity outcomes remains critically needed. Here we quantify how OECMs contribute to biodiversity conservation in the three high-biodiversity countries in which they have been extensively trialed. OECM performance varies across countries; those in South Africa align better with areas that a priori strategic planning identified as important for species conservation and key ecosystem services than those in Colombia and the Philippines. OECMs tend not to cover areas supporting regional connectivity in any of the countries. OECMs have potential to assist conservation, but policy, planning, and coordination at national and international levels would help ensure that new OECMs are strategically established and effectively managed to enhance outcomes for biodiversity conservation and ecosystem service provisioning. Many countries aim to protect 30% of their land and seas by 2030 using a mix of traditional protected areas and “other effective area-based conservation measures” (OECMs), which often prioritize local or non-conservation goals. This study shows that while OECMs reduced deforestation in Colombia and aligned well with conservation priorities in South Africa, they were less effective in the Philippines and generally did not enhance connectivity, highlighting the need for more strategic deployment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it