ArticleScientific reports2025
A method for preliminary assessment of the vulnerability to climate change of tree species for urban afforestation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Optimizing the structure of urban woody plantings for a warming climate: a frequency-weighted exposure framework for woody species in Chongqing, China.Frontiers in plant science · 2026Article
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6 authors.
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Abstract
Nature-based solutions are now a key part in climate change adaptation, particularly for urban environments. Urban forests are one of the most used methods for adding ecosystem services to an urban environment and at the same time address urban-specific climate change challenges such as heat-island effect, intense rainfall and water management. However, the effects of climate change in the long-term on urban forests are seldom taken into account when planning interventions such as afforestation. Species selection for urban forests should, among other factors, be based on an assessment of local present and future climatic conditions, so to ensure the long-term viability of the adaptation action. Here we propose a methodology to aid the species selection process, which is readily applicable to any place in Europe. We use data from publicly available gridded datasets of climate (ClimateEU) and tree species distribution (European Tree Atlas) in order to estimate realised trees' climatic niches. These are then compared to local climatic data, in order to obtain vulnerability scores that ranks the species according to their vulnerability to projected future climate conditions. As an example, we apply the method to a pilot city in central Italy, L'Aquila, where planning of urban afforestation is undergoing. Our analysis suggests that some species considered locally native will be less vulnerable than others (e.g. Quercus pubescens, Castanea sativa), while some species not locally native will result even less vulnerable (e.g. Quercus ilex, Pinus nigra). This information may be used as an initial input to the tree species selection decision process.
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