Understanding how biodiversity responds to urbanization is challenging, due in part to the single‐city focus of most urban ecological research. Here, we delineate continent‐scale patterns in urban species assemblages by leveraging data from a multi‐city camera trap survey and quantify how differences in greenspace availability and average housing density among 10 North American cities relate to the distribution of eight widespread North American mammals. To do so, we deployed camera traps at 569 sites across these ten cities between 18 June and 14 August. Most data came from 2017, though some cities contributed 2016 or 2018 data if it was available. We found that the magnitude and direction of most species' responses to urbanization within a city were associated with landscape‐scale differences among cities. For example, eastern gray squirrel (Sciurus carolinensis), fox squirrel (Sciurus niger), and red fox (Vulpes vulpes) responses to urbanization changed from negative to positive once the proportion of green space within a city was >~20%. Likewise, raccoon (Procyon lotor) and Virginia opossum (Didelphis virginiana) responses to urbanization changed from positive to negative once the average housing density of a city exceeded about 700 housing units/km2. We also found that local species richness within cities consistently declined with urbanization in only the more densely developed cities (>~700 housing units/km2). Given our results, it may therefore be possible to design cities to better support biodiversity and reduce the negative influence of urbanization on wildlife by, for example, increasing the amount of green space within a city. Additionally, it may be most important for densely populated cities to find innovative solutions to bolster wildlife resilience because they were the most likely to observe diversity losses of common urban species.

Research on urban wildlife can help promote coexistence and guide future interactions between humans and wildlife in developed regions, but most such investigations are limited to short‐term, single‐species studies, typically conducted within a single city. This restricted focus prevents scientists from recognizing global patterns and first principles regarding urban wildlife behavior and ecology. To overcome these limitations, we have designed a pioneering research network, the Urban Wildlife Information Network (UWIN), whereby partners collaborate across several cities to systematically collect data to populate long‐term datasets on multiple species in urban areas. Data collected via UWIN support analyses that will enable us to build basic theory related to urban wildlife ecology. An analysis of mammals in seven metropolitan regions suggests that common species are similar across cities, but relative rates of occupancy differ markedly. We ultimately view UWIN as an applied tool that can be used to connect the public to urban nature at a continental scale, and provide information critical to urban planners and landscape architects. Our network therefore has the potential to advance knowledge and to improve the ability to plan and manage cities to support biodiversity.

Urban biodiversity provides critical ecosystem services and is a key component to environmentally and socially sustainable cities. However, biodiversity varies greatly within and among cities, leading to human communities with changing and unequal experiences with nature. The "luxury effect," a hypothesis that predicts a positive | 5447 MAGLE Et AL.

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Context. Cities are increasingly recognized for their biodiversity conservation potential. Incomplete understanding of urban species habitat requirements and of the areal extents over which species-habitat interactions occur (“scale of effect”), however, restricts such conservation.Objectives.Better-understanding scales of effect for urban species could improve urban conservation by identifying areal extents to target in management. We sought to identify scales of effect for urban mammals and if and how these scales differ among species and environmental covariates.Methods. We constructed Eastern fox squirrel (Sciurus niger), red fox (Vulpes vulpes), and Virginia opossum (Didelphis virginianus) occupancy models using data from trail cameras in Iowa City, Iowa, USA. We compared models constructed using environmental covariates estimated over different extents and identified appropriate scales of effect for each covariate based on model fit.Results. Scales of effect varied among species and covariates. Environmental covariates based on home ranges sometimes provided the best model fit; however, best-fit model covariates were often estimated over other extents. The best-fit fox squirrel model included covariates estimated at different, but relatively local scales (<250 m). Both opossum and red fox models included local to landscape-level covariates. Thus, scales of effect differed among species, possibly with body size, and environmental attributes, offering clear evidence that urban mammals exhibit a multi-scalar response to their environments.Conclusions. Our findings suggest that habitat models should include covariates estimated over a range of biologically-informed extents to more accurately identify habitat relationships. Such modeling will inform the choice of extent in urban habitat management.

Summary Cities are becoming increasingly important to biodiversity conservation, conservation that could also benefit urban people given the importance of nature to human well-being. Urban conservation is challenging, however, given cities’ primary role as human habitats and the need to simultaneously support heterogeneous human and wild species communities in similarly heterogeneous environments. We demonstrate a framework for identifying conservation zones within cities and human and species habitat preferences within them, thereby identifying habitat attributes that management could target to support human well-being and conservation objectives. The framework first categorizes conservation zones within a city, then develops species indicator communities for each zone. Habitat preferences are identified for each indicator community using richness modelling, and human habitat preferences within zones are identified using one of several approaches. Lastly, habitat preferences are compared to identify commonalities and differences within zones. We demonstrate our framework in Iowa City (IA, USA) using songbirds, identifying similarities in human and bird habitat preferences within conservation zones that management could target to support human well-being and species conservation and differences in preferences that could be proactively managed to reduce conflict. This framework can thus identify key habitat attributes and approaches to inform conservation planning targeted to specific settings within cities.