Protected areas, drought, and grazing regimes influence fire occurrence in a fire-prone Mediterranean region
Bibliographic record
Abstract
Abstract Background Extreme fire seasons in the Mediterranean basin have received international attention due to the damage caused to people, livelihoods, and vulnerable ecosystems. There is a body of literature linking increasingly intense, large fires to a build-up of fuel from rural land abandonment exacerbated by climate change. However, a better understanding of the complex factors driving fires in fire-prone landscapes is needed. We use a global database based on the MODIS Fire CCI51 product, and the Greater Côa Valley, a 340,000-ha area in Portugal, as a case study, to investigate the environmental drivers of fire and potential tools for managing fires in a landscape that has undergone changing agricultural and grazing management. Results Between 2001 and 2020, fires burned 32% (1881.45 km 2 ) of the study area. Scrublands proportionally burnt the most, but agricultural land and forests were also greatly impacted. The risk of large fires (> 1 km 2 ) was highest in these land cover types under dry conditions in late summer. Areas with higher sheep densities were more likely to burn, while cattle density had no apparent relationship with fire occurrence. There was also a 15% lower probability of a fire occurring in protected areas. Conclusion Future climatic changes that increase drought conditions will likely elevate the risk of large fires in the Mediterranean basin, and abandoned farmland undergoing natural succession towards scrubland will be at particularly high risk. Our results indicate that livestock grazing does not provide a simple solution to reducing fire risk, but that a more holistic management approach addressing social causes and nature-based solutions could be effective in reducing fire occurrence.
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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 | 0.000 | 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 | 0.000 |
| 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".