No abstract

No abstract

Extreme wildfires are expected to increase in Southern Europe, due to climate change and rural abandonment. Fire management is focused on suppression, which accelerates the transition to more flammable landscapes. Here, we synthesise the knowledge acquired over the 'FirESmart' project (https://firesmartproject.wordpress.com). Our findings show how agroforestry policies could benefit biodiversity while providing further fire suppression opportunities. The EU Green Deal offers an opportunity to incorporate 'fire-smartness' into upcoming agroforestry policies. Still, if these policies fail at reversing rural abandonment, the use of fire could enhance rewilding and tree-planting as 'climate-smart' strategies in the fire-prone mountains of Southern Europe.

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.

The spread of invasive alien species promotes ecosystem structure and functioning changes, with detrimental effects on native biodiversity and ecosystem services, raising challenges for local management authorities. Predictions of invasion dynamics derived from modeling tools are often spatially coarse and therefore unsuitable for guiding local management. Accurate information on the occurrence of invasive plants and on the main factors that promote their spread is critical to define successful control strategies. For addressing this challenge, we developed a dual framework combining satellite image classification with predictive ecological modeling. By combining data from georeferenced invaded areas with multispectral imagery with 10-meter resolution from Sentinel-2 satellites, a map of areas invaded by the woody invasive Acacia longifolia in a municipality of northern Portugal was devised. Classifier fusion techniques were implemented through which eight statistical and machine-learning algorithms were ensembled to produce accurate maps of invaded areas. Through a Random Forest (RF) model, these maps were then used to explore the factors driving the landscape-level abundance of A. longifolia. RF models were based on explanatory variables describing hypothesized environmental drivers, including climate, topography/geomorphology, soil properties, fire disturbance, landscape composition, linear structures, and landscape spatial configuration. Satellite-based maps synoptically described the spatial patterns of invaded areas, with classifications attaining high accuracy values (True Skill Statistic, TSS: 0.895, Area Under the Receiver Operating Curve, ROC: 0.988, Kappa: 0.857). The predictive RF models highlighted the primary role of climate, followed by landscape composition and configuration, as the most important drivers explaining the species abundance at the landscape level. Our innovative dual framework—combining image classification and predictive ecological modeling—can guide decision-making processes regarding effective management of invasions by prioritizing the invaded areas and tackling the primary environmental and anthropogenic drivers of the species’ abundance and spread.