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A lack of information about urban habitats, and a lack of professionally-collected species occurrence data are often cited as major impediments to completing bioassessments in urban landscapes. We developed an urban biodiversity assessment framework that addresses these challenges. The proposed framework combines a customized hierarchical urban habitat classification scheme with citizen science-generated species occurrence data, such as iNaturalist, and eBird. It integrates publicly available data on the physical and anthropogenic environment with species occurrence information and serves as a novel method for conducting urban biodiversity assessments. This framework provides insights into how species occurrences within an urban landscape are associated with spatial variation in the physical and anthropogenic environment. It can also yield information useful for urban planning and conservation management aimed at maintaining and enhancing the abundance and diversity of native and other desirable species in urban areas. This framework requires minimal taxonomic expertise on the part of those who employ it, and it can be implemented in urban areas worldwide, wherever adequate data exist. We demonstrate the application of this framework in the highly urbanized portion of Los Angeles County, California, USA. Our demonstration used 18 physical and anthropogenic variables to classify our study area into nine urban habitat types. We then assessed relationships between these urban habitat types with species occurrences using research-grade data from iNaturalist This analysis detected significant differences in distributions of some species between these nine urban habitat types and demonstrated that the proposed framework can be used to conduct urban biodiversity assessments. With increasing availability of remote sensing data and publicly-generated biodiversity data, this framework may be used for analysis of urban areas around the globe.

An Urban Biodiversity Assessment Framework That Combines an Urban Habitat Classification Scheme and Citizen Science Data