Community assembly shapes the performance of Solanum nitidibaccatum, an emerging ruderal non-native

A growing number of emerging non-native species are expanding within their introduced ranges and may become increasingly problematic in the future. Among them, several Solanum taxa pose underappreciated ecological and economic risks. Solanum nitidibaccatum , a poorly studied annual native to South America and increasingly recorded across Europe, represents a suitable model for understanding the establishment of non-native ruderal and segetal Solanum species in a transformed landscape. We examined how the composition and functional structure of ruderal vegetation influence the occurrence of S. nitidibaccatum , hypothesizing that habitat filtering and high functional heterogeneity facilitate its establishment, whereas interspecific competition constrains it. Using community diversity metrics and functional structure indices, we related vegetation properties to the probability of this species occurrence. The probability of S. nitidibaccatum occurrence was higher in species-rich communities and in vegetation characterized by higher leaf dry matter content and greater functional dispersion. In contrast, it declined in assemblages with a stronger contribution of nitrogen-demanding species and taller vegetation with higher specific leaf area. Our findings indicate that the establishment of S. nitidibaccatum is shaped by the interplay between habitat filtering and functional heterogeneity in ruderal vegetation, which may create underutilized niche space that facilitates the spread of this emerging non-native species. Considering its potential capacity to colonize ecologically similar vegetation across a range of transformed ecosystems, S. nitidibaccatum should be regarded as a species with high potential to become a problematic invasive weed. Its continued spread, together with the emergence of other non-native Solanum species, underscores the need for further research to better anticipate ecological and economic consequences that are challenging to predict.