Protected area management has significant spillover effects on vegetation
Bibliographic record
Abstract
The Kunming–Montreal Global Biodiversity Framework calls for rapid global expansion of protected areas in response to ongoing biodiversity loss and ecosystem degradation1. One of its strongest selling points is the benefits protected areas provide to adjacent human communities2,3. However, little attention has been paid to how policy and management can support such benefits. Here, to address this gap, I explored influences on the effect sizes of vegetation spillovers from a candidate 12,513 Australian protected areas, defining spillovers as the difference in vegetation outside a protected area that occurs as a consequence of the existence of the protected area4. In 2020, 71% (2,189) out of the 3,063 protected areas for which full analysis was possible had a positive spillover effect of 0.1 or greater on at least 1 of 10 vegetation cover classes. Many protected area types were significant predictors of spillover magnitude. The covariance explained by protected area type with local and contextual variables was 14%, suggesting that internal management moderates adjacent locations. These findings highlight the potential to include spillover effects explicitly in global policy frameworks and suggest a pathway to an empirical basis for monitoring and accounting schemes that support biodiversity conservation and ecosystem service provision adjacent to protected areas. Analysis of effect sizes of vegetation spillovers from protected areas in Australia shows that 71% of over 3,063 analysed protected areas had a positive spillover effect, and that these effects covary with protected area management type.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".