ArticleMethodsX2025
Integrating fire predisposition assessment into decision support systems for mountain forest management.
Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
Strategic long-term planning of mountain forests in the European Alps requires a balancing act between sustaining forest biodiversity and ecosystem services (BES) and mitigating disturbance risks, particularly under climate change. Multi-criteria decision support systems (DSSs) address this challenge by integrating climate-sensitive forest modelling into frameworks for the evaluation of BES provision under simulated climate and management trajectories. Recent developments incorporate assessments of disturbance predisposition into DSSs, accounting for risks from bark beetle infestations and windthrow. These DSS frameworks have proven flexible applicability across various forest models, spatial scales, forest types, and environmental conditions. However, climate-change-induced transitions of disturbance regimes require adaptations of existing DSS frameworks by accounting for emerging disturbance agents, such as forest fires. Here, we introduce the integration of a fire predisposition assessment system (PAS) into a DSS, incorporating factors such as topography, climatic conditions, wildland-urban interface, and stand structural characteristics. Particularly in the context of long-term planning in mountain forests, the expanded DSS could enable the identification of conflicting forest management objectives related to BES provision and disturbance mitigation efforts under climate change, leading to more informed management decisions.•A predisposition assessment system for assessing multiple predisposing factors of forest fires is presented.•The fire PAS enables an integrated evaluation with disturbance predisposition to bark beetle infestations and windthrow, as well as BES provision.•The modular structure of the fire PAS enables adaptation and application to various spatial scales, as well as integration with different forest models.
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