Understanding spontaneous biodiversity in informal urban green spaces: A local-landscape filtering framework with a test on wall plants

In densely urbanized areas, small pockets of vegetated areas such as street verges, vacant lots, and walls can be rich in biodiversity. In spite of their small size, these ‘informal urban greenspaces’ can provide critical ecosystem services to urban residents. Maintaining and enhancing the provisioning of ecosystem services requires a systematic understanding of biodiversity patterns and drivers in informal urban green spaces. The ‘environmental filtering’ (a process of certain species selected by specific environmental conditions) concept in community ecology theory may serve as a useful tool for this goal. We tested a multi-scale filtering framework by examining the spontaneous plant diversity patterns (from 83 surveyed sites) on the vertical surfaces of the ancient city wall of Nanjing, China. We found that the variables representing local-habitat filtering (e.g., wall substrates and aspect) and landscape filtering (including spatial configuration of urban land cover, and nighttime light intensity surrounding the local habitats) can jointly explain substantial fractions of variations in taxonomic diversity (up to ca. 60%) and functional diversity (up to ca. 40%). The explanatory power was stronger in the repaired wall habitats than in the unrepaired counterparts, in line with the prediction that environmental filtering is more pronounced during the early stages of community assembly. While the strength of landscape filtering showed clear scale-dependency, its relative importance consistently outweighs local-habitat filtering across all study scales of 200–1600 m, suggesting that configuration of neighboring landscape context can play an important role in shaping local-scale biodiversity of informal urban green spaces. Our results have useful implications for the study, design, and management of informal urban green spaces. Well-tailored multi-scale filtering frameworks may contribute to understanding urban biodiversity patterns in a systematic way.