The expansion of coyotes (Canis latrans) into the northeastern United States is a major challenge to wildlife professionals, especially in suburban and urban areas where reports of human—coyote interaction (HCI) are on the rise. To assist wildlife professionals in identifying potential hot spots of interaction and homeowners in evaluating their risk of a backyard encounter, we used the techniques of citizen science to build a landscape model of HCI for suburban residential properties in Westchester County, New York, USA. We distributed surveys via school children (kindergarten to grade 12) as part of a voluntary class assignment, to maximize the number of homeowners participating in our study and to provide learning experiences for students. Of 6,000 surveys distributed to schools, >1,500 students interviewed their parents on whether a coyote had been seen or heard on their property from 2003 to 2006. Although surveys could not be distributed randomly owing to the participatory process of individual schools, we did receive responses from across Westchester County, representing the spectrum from the most rural to the most urban towns. Homeowners who encountered (i.e., seen or heard) a coyote on their property were on average 50% closer to forest, 36% closer to grassland, and 66% farther from medium‐ to high‐intensity development, complementing existing knowledge on urban coyote habitat use. Our model seemed robust in predicting an independent set of coyote observations (r = 0.88). Based on this model, we generated a map describing the probability of HCI that can be used by both wildlife professionals and homeowners. Regarding the former, state wildlife agencies could more precisely target education campaigns on how to live with coyotes where the possibility of HCI was greatest. Homeowners, in turn, could evaluate their own risk and modify behaviors that would make their property less attractive to coyotes. Furthermore, in creating a descriptive model of HCI from citizen‐generated data, we demonstrated how citizen science can be a useful exploratory tool, generating a wealth of data over a large geographic area in a short period, especially when the inquest is appropriate to stakeholder participation in data collection.

With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14‐week period (17 August–24 November of 2019). We sampled wildlife at 1,509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian’s eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the United States. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban–wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot‐usa, as will future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species‐specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.

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