Protected areas as hotspots of wildfire activity in fire-prone Temperate and Mediterranean biomes
Bibliographic record
Abstract
The European Union has recently passed the Nature Restoration Law which, among others, seeks to increase the cover of forest reserves protected for biodiversity and, globally, the Kunming-Montreal Global Biodiversity Framework similarly seeks to expand protected areas. Here we test whether a trade-off exists between protected areas expansion and fire activity, leading to a higher exposure to fire for the population in protected areas, because they often harbor more biomass and occur in remote areas. We analyzed forest fires affecting 14,892,174 ha, and intersecting 10,999 protected areas, across fire-prone European Temperate and Mediterranean forest biomes, and in similar ecosystems within California, Chile and Australia. Protected areas were being disproportionally affected by fire within most Temperate biomes, and fire severity was 20 % higher within protected areas also in Mediterranean biomes. Population in the periphery of forest areas was up to 16 times more likely to be exposed to large wildfires when their environment was within, or near, protected areas. Differences in manageable factors such as fuel loads and road density were primary drivers of the divergence in burned area across protection status, with abiotic factors playing also significant roles. The importance of fuel loads indicates that current plans for expanding strictly protected areas, where no human intervention is allowed, may be particularly problematic from a fire perspective. Wildfire prevention and mitigation must be central goals in the development of conservation/restoration programs to diminish population exposure and fire severity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".