Understanding the characteristics of wildfire-affected communities and the importance of particular factors of different dimensions, is paramount to improve prevention and mitigation strategies, tailored to people's needs and abilities. In this study, we explored different combinations of biophysical and social factors to characterize wildfire-affected areas in Portugal. By means of machine-learning methods based on classification trees, we assessed the predictive ability of various models to discriminate different levels of wildfire incidence at the local scale. The model with the best performance included a reduced set of both biophysical and social variables and we found that, oveall, the exclusion of specific variables improved prediction rates of group classification. The most important variables were related to landcover; the civil parishes covered by more than 20% of shrublands were more fire-prone, whereas those parishes with at least 40% of agricultural land were less affected by wildfires. Regarding social variables, the most-affected parishes showed a lower proportion of foreign residents and lower purchasing power, conditions likely associated with the socioeconomic context of inland low-density rural areas, where rural abandonment, depopulation and ageing trends have been observed in the last decades. Further research is needed to investigate how other particular parameters representing the social context, and its evolution, can be integrated in wildfire occurrence modelling, and how these interact with the biophysical conditions over time.
Keywords: Biophysical factors; Local communities; Portugal; Risk mitigation; Sociodemographic variables; Wildfire incidence.
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