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Creators/Authors contains: "Pavoine, Sandrine"

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  1. Abstract Whether cities are more or less diverse than surrounding environments, and the extent to which non‐native species in cities impact regional species pools, remain two fundamental yet unanswered questions in urban ecology. Here we offer a unifying framework for understanding the mechanisms that generate biodiversity patterns across taxonomic groups and spatial scales in urban systems. One commonality between existing frameworks is the collective recognition that species co‐occurrence locally is not simply a function of natural colonization and extinction processes. Instead, it is largely a consequence of human actions that are governed by a myriad of social processes occurring across groups, institutions, and stakeholders. Rather than challenging these frameworks, we expand upon them to explicitly consider how human and non‐human mechanisms interact to control urban biodiversity and influence species composition over space and time. We present a comprehensive theory of the processes that drive biodiversity within cities, between cities and surrounding non‐urbanized areas and across cities, using the general perspective of metacommunity ecology. Armed with this approach, we embrace the fact that humans substantially influence β‐diversity by creating a variety of different habitats in urban areas, and by influencing dispersal processes and rates, and suggest ways how these influences can be accommodated to existing metacommunity paradigms. Since patterns in urban biodiversity have been extensively described at the local or regional scale, we argue that the basic premises of the theory can be validated by studying the β‐diversity across spatial scales within and across urban areas. By explicitly integrating the myriad of processes that drive native and non‐native urban species co‐occurrence, the proposed theory not only helps reconcile contrasting views on whether urban ecosystems are biodiversity hotspots or biodiversity sinks, but also provides a mechanistic understanding to better predict when and why alternative biodiversity patterns might emerge. 
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