Mountain farmland protection and fire-smart management jointly reduce fire hazard and enhance biodiversity and carbon sequestration
Bibliographic record
Abstract
The environmental and socio-economic impacts of wildfires are foreseen to increase across southern Europe over the next decades regardless of increasing resources allocated for fire suppression. This study aims to identify fire-smart management strategies that promote wildfire hazard reduction, climate regulation ecosystem service and biodiversity conservation. Here we simulate fire-landscape dynamics, carbon sequestration and species distribution (116 vertebrates) in the Transboundary Biosphere Reserve Gerês-Xurés (NW Iberia). We envisage 11 scenarios resulting from different management strategies following four storylines: Business-as-usual (BAU), expansion of High Nature Value farmlands (HNVf), Fire-Smart forest management, and HNVf plus Fire-Smart. Fire-landscape simulations reveal an increase of up to 25% of annual burned area. HNVf areas may counterbalance this increasing fire impact, especially when combined with fire-smart strategies (reductions of up to 50% between 2031 and 2050). The Fire-Smart and BAU scenarios attain the highest estimates for total carbon sequestered. A decrease in habitat suitability (around 18%) since 1990 is predicted for species of conservation concern under the BAU scenario, while HNVf would support the best outcomes in terms of conservation. Our study highlights the benefits of integrating fire hazard control, ecosystem service supply and biodiversity conservation to inform better decision-making in mountain landscapes of Southern Europe.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".