Stakeholder perceptions of wildfire management strategies as nature-based solutions in two Iberian biosphere reserves
Bibliographic record
Abstract
Increased large and high-intensity wildfires cause large socioeconomic and ecological impacts, which demand improved landscape management approaches in which both ecological and societal dimensions are integrated. Engaging society in fire management requires a better understanding of stakeholder perceptions of wildfires and landscape management. We analyze stakeholder perceptions about wildfire-landscape interactions in abandoned rural landscapes of southern Europe, and how fire and the land should be managed to reduce wildfire hazard and ensure the long-term supply of ecosystem services in these fire-prone regions. To do so, a structured online questionnaire was sent to the stakeholders of two transboundary biosphere reserves in Spain-Portugal. Our analysis also questioned to what extent fuel management strategies can be considered nature-based solutions (NbS) using the IUCN standard. Overall, stakeholders state that fire should be managed and support fire prevention in lieu of fire suppression policies. Rural abandonment is perceived as the main cause of large wildfires, with high-intensity fires impacting the study regions more than in the recent past, a trend which they expect to continue in the future in the absence of management. All the suggested fuel management strategies, except chemical treatments, were accepted by the stakeholders who perceive more positive than negative effects of fuel management on forest ecosystem services. Transboundary coordination was rated as inadequate or even nonexistent. We did not find differences among stakeholder sectors and biosphere reserves, indicating that in the study area, there is a general agreement on perceptions about wildfire and associated impacts at the landscape level. Finally, we showed that promoting agricultural and livestock uses, modifying forest species composition to increase fire resistance, and introducing large herbivores have the potential to become effective NbS in the regions. This study represents a first-step analysis representing a base for future co-design and implementation of NbS to improve fuel management, contributing to the understanding of the stakeholder support for their application in addressing the socioeconomic challenges in high fire-risk areas.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".