Promoting native biodiversity: An evaluation of multifactorial and bioclimatic selection criteria for street trees in Italian cities

Street trees provide numerous benefits (or ecosystem services) to citizens, as they reduce urban heat, improve air quality, and mitigate intense rainfall events. On the other end, the urban environment is challenging for trees for various reasons, such as water availability, humidity, and increased temperatures (currently exacerbated by current climatic changes). Street trees, however, are rarely chosen according to the benefits they provide or their ecological needs. In Italy, for instance, despite its high biodiversity, species used as urban street trees are relatively limited due to the limited availability of species on the market, historical or cultural preferences, and economic reasons. This study aims to develop a preliminary methodology for selecting native tree species, considering their ecological requirements and potential suitability for the urban environment, factoring in the future warming of temperatures. Our proposed methodology consists of culling species from floristic databases and applying a series of ecological and bioclimatic filters to obtain a list of species that can potentially be used as street trees. When we applied our methodology to Italian cities, we obtained a list of 190 native species (among which 44 species are already used as street trees) that can potentially be recommended for the urban habitat. On the other hand, our analysis highlighted an underutilization of several native species (39 species) and that many species (177) are not suited for the climatic context in which they are used. Our results also suggest a pool of species (22 already used as street trees and 19 not used) that, in the Italian context, can be better suited to withstand the expected temperature increases. The selection of species should primarily be based on their being native but also on their ecological requirements to increase urban biodiversity, reduce tree mortality, and reduce management costs.