Using fire to enhance rewilding when agricultural policies fail
Bibliographic record
Abstract
Rewilding has been proposed as an opportunity for biodiversity conservation in abandoned landscapes. However, rewilding is challenged by the increasing fire risk associated with more flammable landscapes, and the loss of open-habitat specialist species. Contrastingly, supporting High Nature Value farmlands (HNVf) has been also highlighted as a valuable option, but the effective implementation of agricultural policies often fails leading to uncertain scenarios wherein the effects of wildfire management remain largely unexplored. Herein, we simulated fire-landscape dynamics to evaluate how fire suppression scenarios affect fire regime and biodiversity (102 species of vertebrates) under rewilding and HNVf policies in the future (2050), in a transnational biosphere reserve (Gerês-Xurés Mountains, Portugal-Spain). Rewilding and HNVf scenarios were modulated by three different levels of fire suppression effectiveness. Then, we quantified scenario effects on fire regime (burned and suppressed areas) and biodiversity (habitat suitability change for 2050). Simulations confirm HNVf as a long-term opportunity for fire suppression (up to 30,000 ha of additional suppressed areas between 2031 and 2050 in comparison to rewilding scenario) and for conservation (benefiting around 60% of species). Rewilding benefits some species (20%), including critically endangered, vulnerable and endemic taxa, while several species (33%) also profit from open habitats created by fire. Although HNVf remains the best scenario, rewilding reinforced by low fire suppression management may provide a nature-based solution when societal support through agricultural policies fails.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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".