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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.

Data and code that can be used to reproduce the analyses underlying 'Mitigating pseudoreplication and bias in resource selection functions with autocorrelation-informed weighting' by Alston, Fleming, et al. (Preprint: https://doi.org/10.1101/2022.04.21.489059) For more detailed information, please visit the README file.

Data from: Estimating encounter location distributions from animal tracking data

1. Seed dispersal is a key biological process that remains poorly documented because dispersing seeds are notoriously hard to track. While long-distance dispersal is thought to be particularly important, seed-tracking studies typically yield incomplete data sets that are biased against long-distance movements. 2. We evaluate an analytical procedure developed by Jansen, Bongers & Hemerik (2004) to infer the tail of a seed dispersal kernel from incomplete frequency distributions of dispersal distances obtained by tracking seeds. This ‘censored tail reconstruction’ (CTR) method treats dispersal distances as waiting times in a survival analysis and censors nonretrieved seeds according to how far they can reliably be tracked. We tested whether CTR can provide unbiased estimates of longdistance movements which typically cannot be tracked with traditional field methods. 3. We used a complete frequency distribution of primary seed dispersal distances of the palm Astrocaryum standleyanum, obtained with telemetric thread tags that allow tracking seeds regardless of the distance moved. We truncated and resampled the data set at various distances, fitted kernel functions on CTR estimates of dispersal distance and determined how well this function approximated the true dispersal kernel. 4. Censored tail reconstruction with truncated data approximated the true dispersal kernel remarkably well but only when the best-fitting function (lognormal) was used. We were able to select the correct function and derive an accurate estimate of the seed dispersal kernel even after censoring 50–60% of the dispersal events. However, CTR results were substantially biased if 5% or more of seeds within the search radius were overlooked by field observers and erroneously censored. Similar results were obtained using additional simulated dispersal kernels. 5. Our study suggests that the CTR method can accurately estimate the dispersal kernel from truncated seed-tracking data if the kernel is a simple decay function. This method will improve our understanding of the spatial patterns of seed movement and should replace the usual practice of omitting nonretrieved seeds fromanalyses in seed-tracking studies

1. Activity level (the proportion of time that animals spend active) is a behavioural and ecological metric that can provide an indicator of energetics, foraging effort and exposure to risk. However, activity level is poorly known for free-living animals because it is difficult to quantify activity in the field in a consistent, cost-effective and non-invasive way.2. This article presents a new method to estimate activity level with time-of-detection data from camera traps (or more generally any remote sensors), fitting a flexible circular distribution to these data to describe the underlying activity schedule, and calculating overall proportion of time active from this.3. Using simulations and a case study for a range of small- to medium-sized mammal species, we find that activity level can reliably be estimated using the new method.4. The method depends on the key assumption that all individuals in the sampled population are active at the peak of the daily activity cycle. We provide theoretical and empirical evidence suggesting that this assumption is likely to be met for many species, but may be less likely met in large predators, or in high-latitude winters. Further research is needed to establish stronger evidence on the validity of this assumption in specific cases; however, the approach has the potential to provide an effective, non-invasive alternative to existing methods for quantifying population activity levels.

The distribution of suitable rest sites is considered to be a key determinant of spatial patterns in animal activity. However, it is not immediately evident which landscape features satisfy rest site requirements or how these sites are configured within the home range. We used Global Positioning System (GPS)/accelerometer telemetry to investigate rest site selection at the home-range scale for northern tamanduas Tamandua mexicana on Barro Colorado Island (BCI), Panama. We developed models specifying each tamandua as the individual experimental unit and averaged coefficients to produce population-level estimates. Tamanduas had on average 17.8 (±¿8.1) rest sites within their home range and used 1.36 (±¿0.51) on any given day. These rest sites tended to be located in the core of tamandua home ranges, with active locations associated with the periphery of the home range. Rest sites were positively associated with (1) a high density of Attalea butyracea palm trees; (2) elevation; (3) tall vegetation. There was a slight negative relationship between the distribution of rest sites and slope, and no apparent relationship between rest site selection and relative distance to forest canopy gaps. From focal animal observations, we identified that tamandua rest sites were typically located in trees (90%), with 25% (12 of 49) occurring in palms. We contend that northern tamanduas on BCI selected vegetated arboreal rest sites because of reduced likelihood of detection from terrestrial predators in these sites. Our models identified considerable individual variation in rest site selection, which suggests that the practice of pooling individuals and fitting models at an aggregate level may be inappropriate for certain types of habitat selection research.

Striped Hog-nosed Skunk Conepatus semistriatus was camera-trapped in central Panama. The photographs, taken in a densely forested area, probably belong to a single, wandering, individual. These photographs represent the easternmost record of C. semistriatus in Central America and confirm an earlier, unvouchered, report that its distribution in Panama is larger than previously thought. The record is in the centre of the 700-km wide gap between two sub-ranges, suggesting that the species has a continuous distribution across Central and northern South America.