Data and code for spillover analysis described in Cumming, G.S. (2025). <b>Protected area management has significant spillover effects on vegetation</b>. <i>Nature, </i>https://doi.org/10.1038/s41586-025-09837-8
Bibliographic record
Abstract
Manuscript abstract: The Kunming-Montreal Global Biodiversity Framework (GBF) calls for rapid global expansion of protected areas in response to ongoing biodiversity loss and ecosystem degradation. One of its strongest selling points is the benefits protected areas provide to adjacent human communities. However, little attention has been paid to how policy and management can support such benefits. 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 protected area’s existence. In 2020, 71% (2189) of the 3063 protected areas for which full analysis was possible had a positive spillover effect of 0.1 or greater on at least one 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 protected area-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.Notes: the manuscript has a lengthy Supplementary Materials file that is published on the Nature web site with the article. The Supplementary Materials include examples of all code and an explanation of which routines were run for each step of the analysis. The current on-line publication includes the remaining items that would be needed to fully replicate the analysis:Extracted data by sampling polygon and CAPAD protected area boundaryShapefiles giving boundaries of sampling polygonsA full set of the R code that would be needed to re-run the original analysis.If there are any missing items, error, or inoperable code, please notify the author to post an update. Please note also that the author is not responsible for updating this code if it becomes outdated, or for helping individuals to resolve platform- or analysis-specific problems. This code was written and tested in R Studio between 02/2023 and 08/2025 using R 4.2.1.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.185 | 0.029 |
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; both teacher heads agree on what is shown here.
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".