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Abstract(s)
To mitigate the effects of climate change on urban green spaces, adaptive strategies are required, particularly regarding tree species. Lisbon is a most vulnerable city to extreme climate events and the performance and management of the numerous and diverse trees found in its gardens and parks are of much concern. We evaluated the vulnerability of such trees to predicted future Lisbon climates using the Climate Assessment Tool (Climate Change Alliance of Botanic Gardens, 2024) and inventories from 63 historical gardens and parks, including three botanical gardens. With the extensive information of the latter we: (i) identified and classified the species according to biogeographical origins; (ii) pinpointed those most vulnerable to, or favoured by climate change; and (iii) assessed the vulnerability of each studied site, according to the species present, their risk level and abundance. Among the selected 318 taxa (corresponding to 19,579 trees), the Palaearctic biogeographical region predominates. The highest risk levels were found in 50 % (77 %, for the most pessimistic climate scenario) of the Palaearctic species, which include many native taxa. Conversely, about 56 % of the Neotropical species are predicted to perform better under the worst climate change scenario. Only one of the ten most abundant tree species in Lisbon – Jacaranda mimosifolia – might remain invulnerable. Management plans for most Lisbon green spaces must therefore reexamine the use of Palaearctic species and tackle challenging adjustments, including the gradual replacement of familiar species (increasingly more difficult to maintain) by others with better performance under future climate conditions.
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Citation
Ana Raquel Cunha, Ana Luísa Soares, Sílvia Catarino, Maria Cristina Duarte, Maria M. Romeiras, Assessing the vulnerability of urban tree species to climate change: The case study of Lisbon gardens, Urban Forestry & Urban Greening, Volume 104, 2025, 128664, ISSN 1618-8667, https://doi.org/10.1016/j.ufug.2024.128664.
Publisher
Elsevier