Urban Biodiversity Tool: a Decision Support System (DSS) for automated mapping of biodiversity patterns in cities

• The study presents a Decision Support System (DSS) that standardizes the evaluation of urban tree biodiversity utilizing native, alien, and invasive species ratios, enabling scalable and replicable analyses across diverse cities. • A harmonized database of 7,377 tree taxa was developed by consolidating fragmented regional and global datasets, addressing critical gaps in species’ origin and invasiveness data for the European urban context. • Application in six cities revealed urban-rural gradients, with city centers presenting lower native-to-alien ratios (e.g., Barcelona: 0.32) and suburban areas exhibiting higher ecological integrity (e.g., Rennes: 1.48). • The tool matched >85% of inventory entries (e.g., 94% in Barcelona) despite incomplete municipal records, demonstrating robustness for practical use by local authorities. • UBI4T supports targeted conservation, invasive species management, and climate-resilient urban planning, with potential for expansion via citizen science and machine learning to include fauna, traits, and temporal trends. Urban biodiversity is correlated with ecosystem resilience, climate adaptation, and human well-being, yet consistent and systematic assessments remain a challenge. This study presents the Urban Biodiversity Tool, a Decision Support System (DSS) for evaluating and mapping urban tree diversity via two indicators comprising from the Urban Biodiversity Index for Trees (UBI4T): the native-to-alien species ratio (UBI4T 1 ) and the invasive-to-alien species ratio (UBI4T 2 ). Integrating a harmonized European-wide woody plant species and traits database with inventories of local municipalities, the tool streamlines species’ classification and quantifies biodiversity patterns across spatial scales. Application in six European cities revealed substantial variation in native species dominance (UBI4T 1 : 0.32–1.48) and invasive species pressure (UBI4T 2 : 5–20% of alien trees), with clear urban-rural gradients. Despite data gaps, matching efficiency exceeded 85%, confirming strong performance. UBI4T facilitates cities to identify conservation priorities, invasions, and inform green infrastructure planning, offering a scalable, cost-effective framework for global urban biodiversity monitoring and policy support.