Managing wildlife populations in the face of global change requires regular data on the abundance and distribution of wild animals, but acquiring these over appropriate spatial scales in a sustainable way has proven challenging. Here we present the data from Snapshot USA 2020, a second annual national mammal survey of the USA. This project involved 152 scientists setting camera traps in a standardized protocol at 1485 locations across 103 arrays in 43 states for a total of 52,710 trap‐nights of survey effort. Most (58) of these arrays were also sampled during the same months (September and October) in 2019, providing a direct comparison of animal populations in 2 years that includes data from both during and before the COVID‐19 pandemic. All data were managed by the eMammal system, with all species identifications checked by at least two reviewers. In total, we recorded 117,415 detections of 78 species of wild mammals, 9236 detections of at least 43 species of birds, 15,851 detections of six domestic animals and 23,825 detections of humans or their vehicles. Spatial differences across arrays explained more variation in the relative abundance than temporal variation across years for all 38 species modeled, although there are examples of significant site‐level differences among years for many species. Temporal results show how species allocate their time and can be used to study species interactions, including between humans and wildlife. These data provide a snapshot of the mammal community of the USA for 2020 and will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, and the impacts of species interactions on daily activity patterns. There are no copyright restrictions, and please cite this paper when using these data, or a subset of these data, for publication.

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Eastern spotted skunks Spilogale putorius are an understudied species that has experienced range-wide declines. Over the past 16 years, camera traps have become an increasingly common tool to monitor and understand their current distribution. To inform best surveying practices, we reviewed 16 camera trap studies specifically targeting this species. We focused on reported latency to initial detection and three main aspects of study design: seasonality of detections, baits and lures, and camera trap brands. Latency to initial detection ranged from 1–82 days with a mean of 17.1 days (SD = 9.1). Attractants varied among projects, but most (75%) used sardines as bait. The percentage of skunk detections tended to vary across the year, with the highest percentage of skunk detections occurring in March (92%). We conclude by suggesting best practices and directions for future research techniques that will aid in developing more efficient methods to address key knowledge gaps for this elusive species. Given the long timeframes for latency to initial detection monitoring individual sites for at least four weeks, with the use of bait, is likely the best strategy to detect Eastern spotted skunks. We encourage further experimental approaches on the effectiveness of different baits and lures, and how to increase latency to initial detection. Collectively, we hope this leads to the development of a standardized monitoring approach that could be implemented across studies and states within the Eastern spotted skunk’s range.

Camera traps are becoming an increasingly important tool to survey wildlife populations. However, the application of camera trapping for reliable species identification between nondistinctive, morphologically similar sympatric species is untested for most small mammals, including North American flying squirrels (Glaucomys spp.). Camera traps are a successful monitoring technique where flying squirrel species are allopatric, however there are zones of sympatry between Humboldt's flying squirrel (HFS, G. oregonensis) and northern flying squirrel (NFS, G. sabrinus) in the Pacific Northwest and NFS and southern flying squirrels (SFS, G. volans) in eastern North America. We used camera trap data collected during flying squirrel surveys in 2013-2020 at 59 sites in California, North Carolina, and Virginia, USA, to determine if a reliable method could be used to differentiate the species.With a subset of 100 high-quality, independent capture events per species (50 of dorsal views, 50 of lateral views), we used body measurements and pelage characteristics to differentiate species using random forest classification models. Our models predicted species identification accuracy rates of 90.9% for dorsal views and 68.2% for lateral views. Species misclassification rates between HFS and NFS were 23.5% for dorsal views and 26.5% for lateral views, whereas misclassification rates between NFS and SFS were 16.6% for dorsal views and 5.7% for lateral views.Although misclassification rates were lower than we expected between NFS and SFS, we are cautious about recommending camera trapping as opposed to ultrasonic acoustics for North