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Abstract Urban ecosystems are expanding rapidly, significantly altering natural landscapes and impacting biodiversity.Here we explore seasonal variation in mammal diversity using environmental DNA (eDNA) from soil samples collected during winter and summer across 21 urban parks in Detroit, Michigan. We estimated gamma (regional), alpha (local) and beta (compositional change) diversity to determine if seasonal shifts, reflecting winter scarcity and summer abundance in mammal community composition and human activity, could be detected using eDNA. We expected that larger parks would exhibit greater diversity and higher seasonal turnover, consistent with the species‐area relationship (SAR) and hypothesised that increased summer resource availability would lead to decreased network density as species disperse more broadly.We found that urban parks show subtle, park‐specific changes in community composition influenced by both ecological and anthropogenic factors, with species including striped skunk, brown rat and groundhog responsible for the observed seasonal variation. Consistent with the SAR, larger parks supported higher species richness and diversity. Ecological network analysis, focusing on metrics such as clustering coefficient and network density, revealed a decrease in the overall connectivity and cohesiveness of species interactions from winter to summer, supporting our hypothesis of broader species dispersal during resource‐rich periods. Notably, human DNA was prevalent in all parks, alongside detections of pig and cow eDNA, potentially reflecting human disturbance and anthropogenic food inputs.Our findings underscore the efficacy of eDNA analysis in capturing urban mammal community dynamics, the impact of human activities on biodiversity and its potential as a valuable tool for urban ecological research. Ultimately, enhancing monitoring capacity aids in conservation and urban planning efforts that will promote human‐wildlife coexistence and preserve the socio‐ecological benefits stemming from biodiversity across cityscapes. 

ABSTRACT Camera trap studies have become a popular medium to assess many ecological phenomena including population dynamics, patterns of biodiversity, and monitoring of endangered species. In conjunction with the benefit to scientists, camera traps present an unprecedented opportunity to involve the public in scientific research via image classifications. However, this engagement strategy comes with a myriad of complications. Volunteers vary in their familiarity with wildlife, thus, the accuracy of user‐derived classifications may be biased by the commonness or popularity of species and user‐experience. From an extensive multi‐site camera trap study across Michigan, U.S.A, we compiled and classified images through a public science platform called Michigan ZoomIN. We aggregated responses from 15 independent users per image using multiple consensus methods to assess accuracy by comparing to species identification completed by wildlife experts. We also evaluated how different factors including consensus algorithms, study area, wildlife species, user support, and camera type influenced the accuracy of user‐derived classifications. Overall accuracy of user‐derived classification was 97%; although, several canid (e.g.,Canis lupus, Vulpes vulpes) and mustelid (e.g.,Neovison vison) species were repeatedly difficult to identify by users and had lower accuracy. When validating user‐derived classification, we found that study area, consensus method, and user support best explained accuracy. To overcome hesitancy associated with data collected by untrained participants, we demonstrated their value by showing that the accuracy from volunteers was comparable to experts when classifying North American mammals. Our hierarchical workflow that integrated multiple consensus methods led to more image classifications without extensive training and even when the expertise of the volunteer was unknown. Ultimately, adopting such an approach can harness broader participation, expedite future camera trap data synthesis, and improve allocation of resources by scholars to enhance performance of public participants and increase accuracy of user‐derived data. © 2021 The Wildlife Society.