Making protected areas effective for biodiversity, climate and food
Bibliographic record
Abstract
The spatial extent of marine and terrestrial protected areas (PAs) was among the most intensely debated issues prior to the decision about the post-2020 Global Biodiversity Framework (GBF) of the Convention on Biological Diversity. Positive impacts of PAs on habitats, species diversity and abundance are well documented. Yet, biodiversity loss continues unabated despite efforts to protect 17% of land and 10% of the oceans by 2020. This casts doubt on whether extending PAs to 30%, the agreed target in the Kunming-Montreal GBF, will indeed achieve meaningful biodiversity benefits. Critically, the focus on area coverage obscures the importance of PA effectiveness and overlooks concerns about the impact of PAs on other sustainability objectives. We propose a simple means of assessing and visualising the complex relationships between PA area coverage and effectiveness and their effects on biodiversity conservation, nature-based climate mitigation and food production. Our analysis illustrates how achieving a 30% PA global target could be beneficial for biodiversity and climate. It also highlights important caveats: (i) achieving lofty area coverage objectives alone will be of little benefit without concomitant improvements in effectiveness, (ii) trade-offs with food production particularly for high levels of coverage and effectiveness are likely and (iii) important differences in terrestrial and marine systems need to be recognized when setting and implementing PA targets. The CBD's call for a significant increase in PA will need to be accompanied by clear PA effectiveness goals to reduce and revert dangerous anthropogenic impacts on socio-ecological systems and biodiversity.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".