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ABSTRACT MotivationSNAPSHOT USA is an annual, multicontributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables ContainedSNAPSHOT USA 2019–2023 contains 987,979 records of camera trap image sequence data and 9694 records of camera trap deployment metadata. Spatial Location and GrainData were collected across the United States of America in all 50 states, 12 ecoregions and many ecosystems. Time Period and GrainData were collected between 1st August and 29th December each year from 2019 to 2023. Major Taxa and Level of MeasurementThe dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software FormatSNAPSHOT USA 2019–2023 comprises two .csv files. The original data can be found within the SNAPSHOT USA Initiative in the Wildlife Insights platform. 

Abstract AimThe assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities. LocationConterminous United States. Time Period2010–2021. Major Taxa StudiedTwenty‐five species of mammals. MethodsWe analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities. ResultsClimate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard‐mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine‐scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west. Main ConclusionsOur results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa. 

Abstract Reliable maps of species distributions are fundamental for biodiversity research and conservation. The International Union for Conservation of Nature (IUCN) range maps are widely recognized as authoritative representations of species’ geographic limits, yet they might not always align with actual occurrence data. In recent area of habitat (AOH) maps, areas that are not habitat have been removed from IUCN ranges to reduce commission errors, but their concordance with actual species occurrence also remains untested. We tested concordance between occurrences recorded in camera trap surveys and predicted occurrences from the IUCN and AOH maps for 510 medium‐ to large‐bodied mammalian species in 80 camera trap sampling areas. Across all areas, cameras detected only 39% of species expected to occur based on IUCN ranges and AOH maps; 85% of the IUCN only mismatches occurred within 200 km of range edges. Only 4% of species occurrences were detected by cameras outside IUCN ranges. The probability of mismatches between cameras and the IUCN range was significantly higher for smaller‐bodied mammals and habitat specialists in the Neotropics and Indomalaya and in areas with shorter canopy forests. Our findings suggest that range and AOH maps rarely underrepresent areas where species occur, but they may more often overrepresent ranges by including areas where a species may be absent, particularly at range edges. We suggest that combining range maps with data from ground‐based biodiversity sensors, such as camera traps, provides a richer knowledge base for conservation mapping and planning. 

Abstract Site occupancy models (SOMs) are a common tool for studying the spatial ecology of wildlife. When observational data are collected using passive monitoring field methods, including camera traps or autonomous recorders, detections of animals may be temporally autocorrelated, leading to biased estimates and incorrectly quantified uncertainty. We presently lack clear guidance for understanding and mitigating the consequences of temporal autocorrelation when estimating occupancy models with camera trap data.We use simulations to explore when and how autocorrelation gives rise to biased or overconfident estimates of occupancy. We explore the impact of sampling design and biological conditions on model performance in the presence of autocorrelation, investigate the usefulness of several techniques for identifying and mitigating bias and compare performance of the SOM to a model that explicitly estimates autocorrelation. We also conduct a case study using detections of 22 North American mammals.We show that a join count goodness‐of‐fit test previously proposed for identifying clustered detections is effective for detecting autocorrelation across a range of conditions. We find that strong bias occurs in the estimated occupancy intercept when survey durations are short and detection rates are low. We provide a reference table for assessing the degree of bias to be expected under all conditions. We further find that discretizing data with larger windows decreases the magnitude of bias introduced by autocorrelation. In our case study, we find that detections of most species are autocorrelated and demonstrate how larger detection windows might mitigate the resulting bias.Our findings suggest that autocorrelation is likely widespread in camera trap data and that many previous studies of occupancy based on camera trap data may have systematically underestimated occupancy probabilities. Moving forward, we recommend that ecologists estimating occupancy from camera trap data use the join count goodness‐of‐fit test to determine whether autocorrelation is present in their data. If it is, SOMs should use large detection windows to mitigate bias and more accurately quantify uncertainty in occupancy model parameters. Ecologists should not use gaps between detection periods, which are ineffective at mitigating temporal structure in data and discard useful data. 

Abstract Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence. 

Abstract Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap‐derived Big Data are becoming increasingly solvable with the help of scalable cyber‐infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media‐based and event‐based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in‐depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard. 

Abstract SNAPSHOT USA is a multicontributor, long‐term camera trap survey designed to survey mammals across the United States. Participants are recruited through community networks and directly through a website application (https://www.snapshot-usa.org/). The growing Snapshot dataset is useful, for example, for tracking wildlife population responses to land use, land cover, and climate changes across spatial and temporal scales. Here we present the SNAPSHOT USA 2021 dataset, the third national camera trap survey across the US. Data were collected across 109 camera trap arrays and included 1711 camera sites. The total effort equaled 71,519 camera trap nights and resulted in 172,507 sequences of animal observations. Sampling effort varied among camera trap arrays, with a minimum of 126 camera trap nights, a maximum of 3355 nights, a median 546 nights, and a mean 656 ± 431 nights. This third dataset comprises 51 camera trap arrays that were surveyed during 2019, 2020, and 2021, along with 71 camera trap arrays that were surveyed in 2020 and 2021. All raw data and accompanying metadata are stored on Wildlife Insights (https://www.wildlifeinsights.org/), and are publicly available upon acceptance of the data papers. SNAPSHOT USA aims to sample multiple ecoregions in the United States with adequate representation of each ecoregion according to its relative size. Currently, the relative density of camera trap arrays varies by an order of magnitude for the various ecoregions (0.22–5.9 arrays per 100,000 km²), emphasizing the need to increase sampling effort by further recruiting and retaining contributors. There are no copyright restrictions on these data. We request that authors cite this paper when using these data, or a subset of these data, for publication. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government. 

There is an urgent need to develop global observation networks to quantify biodiversity trends for evaluating achievements of targets of Kunming-Montreal Global Biodiversity Framework. Camera traps are a commonly used tool, with the potential to enhance global observation networks for monitoring wildlife population trends and has the capacity to constitute global observation networks by applying a unified sampling protocol. The Snapshot protocol is simple and easy for camera trapping which is applied in North America and Europe. However, there is no regional camera-trap network with the Snapshot protocol in Asia. We present the first dataset from a collaborative camera-trap survey using the Snapshot protocol in Japan conducted in 2023. We collected data at 90 locations across nine arrays for a total of 6162 trap-nights of survey effort. The total number of sequences with mammals and birds was 7967, including 20 mammal species and 23 avian species. Apart from humans, wild boar, sika deer and rodents were the most commonly observed taxa on the camera traps, covering 57.9% of all the animal individuals. We provide the dataset with a standard format of Wildlife Insights, but also with Camtrap DP 1.0 format. Our dataset can be used for a part of the global dataset for comparing relative abundances of wildlife and for a baseline of wildlife population trends in Japan. It can also used for training machine-learning models for automatic species identifications. 

Addressing the ongoing biodiversity crisis requires identifying the winners and losers of global change. Species are often categorized based on how they respond to habitat loss; for example, species restricted to natural environments, those that most often occur in anthropogenic habitats, and generalists that do well in both. However, species might switch habitat affiliations across time and space: an organism may venture into human-modified areas in benign regions but retreat into thermally buffered forested habitats in areas with high temperatures. Here, we apply community occupancy models to a large-scale camera trapping dataset with 29 mammal species distributed over 2,485 sites across the continental United States, to ask three questions. First, are species’ responses to forest and anthropogenic habitats consistent across continental scales? Second, do macroclimatic conditions explain spatial variation in species responses to land use? Third, can species traits elucidate which taxa are most likely to show climate-dependent habitat associations? We found that all species exhibited significant spatial variation in how they respond to land-use, tending to avoid anthropogenic areas and increasingly use forests in hotter regions. In the hottest regions, species occupancy was 50% higher in forested compared to open habitats, whereas in the coldest regions, the trend reversed. Larger species with larger ranges, herbivores, and primary predators were more likely to change their habitat affiliations than top predators, which consistently affiliated with high forest cover. Our findings suggest that climatic conditions influence species’ space-use and that maintaining forest cover can help protect mammals from warming climates. 

Growing threats to biodiversity demand timely, detailed information on species occurrence, diversity and abundance at large scales. Camera traps (CTs), combined with computer vision models, provide an efficient method to survey species of certain taxa with high spatio-temporal resolution. We test the potential of CTs to close biodiversity knowledge gaps by comparing CT records of terrestrial mammals and birds from the recently released Wildlife Insights platform to publicly available occurrences from many observation types in the Global Biodiversity Information Facility. In locations with CTs, we found they sampled a greater number of days (mean = 133 versus 57 days) and documented additional species (mean increase of 1% of expected mammals). For species with CT data, we found CTs provided novel documentation of their ranges (93% of mammals and 48% of birds). Countries with the largest boost in data coverage were in the historically underrepresented southern hemisphere. Although embargoes increase data providers' willingness to share data, they cause a lag in data availability. Our work shows that the continued collection and mobilization of CT data, especially when combined with data sharing that supports attribution and privacy, has the potential to offer a critical lens into biodiversity. This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’.