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.

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.

Aim: We test a new species distribution modelling (SDM) framework, while comparing results to more common distribution modelling techniques. This framework allows for the combination of presence‐only (PO) and presence‐absence (PA) data and accounts for imperfect detection and spatial bias in presence data. The new framework tested here is based on a Poisson point process model, which allows for predictions of population size. We compared these estimates to those provided by experts on the species.Species and Location: Presence data on Baird's tapir (Tapirus bairdii) throughout its range from southern México to northern Colombia were used in this research, primarily from the years 2000 to 2016.Methods: Four SDM frameworks are compared as follows: (1) Maxent, (2) a presence‐only (PO) SDM based on a Poisson point process model (PPM), (3) a presence‐absence (PA) SDM also based on a PPM and (4) an Integrated framework which combines the previous two models. Model averaging was used to produce a single set of coefficient estimates and predictive maps for each model framework. A hotspot analysis (Gi*) was used to identify habitat cores from the predicted intensity of the Integrated model framework.Results: Important variables to model the distribution of Baird's tapir included land cover, human pressure and topography. Accounting for spatial bias in the presence data affected which variables were important in the model. Maxent and the Integrated model produced predictive maps with similar patterns and were considered to be more in agreement with expert knowledge compared to the PO and PA models.Main conclusions: Total abundance as predicted by the model was higher than expert opinion on the species, but local density estimates from our model were similar to available independent assessments. We suggest that these results warrant further validation and testing through collection of independent test data, development of more precise predictor layers and improvements to the model framework.

While the conservation role of remaining natural habitats in anthropogenic landscapes is clear, the degree to which agricultural matrices impose limitations to animal use is not well understood, but vital to assess species’ resilience to land use change. Using an occupancy framework, we evaluated how oil palm plantations affect the occurrence and habitat use of terrestrial mammals in the Colombian Llanos. Further, we evaluated the effect of undergrowth vegetation and proximity to forest on habitat use within plantations. Most species exhibited restricted distributions across the study area, especially in oil palm plantations. Habitat type strongly influenced habitat use of four of the 12 more widely distributed species with oil palm negatively affecting species such as capybara and naked-tailed armadillo. The remaining species showed no apparent effect of habitat type, but oil palm and forest use probabilities varied among species. Overall, generalist mesocarnivores, white-tailed deer, and giant anteater were more likely to use oil palm while the remaining species, including ocelot and lesser anteater, showed preferences for forest. Distance to nearest forest had mixed effects on species habitat use, while understory vegetation facilitated the presence of species using oil palm. Our findings suggest that allowing undergrowth vegetation inside plantations and maintaining nearby riparian corridors would increase the likelihood of terrestrial mammals’ occurrence within oil palm landscapes.

With the extirpation of apex predators from many North American systems, coyotes Canis latrans have become the de facto top predator and are ubiquitous members of most ecosystems. Keystone predators aid in maintaining ecosystem function by regulating the mammal community through direct predation and instilling the landscape of fear, yet the value of coyotes regulating systems to this capacity is understudied and likely variable across environments. Since coyotes are common in the Midwestern United States, we utilized camera traps and occupancy analyses to assess their role in regulating the distribution of mammalian herbivores in a fragmented suburban ecosystem. Forest cover was a strong positive predictor of white-tailed deer Odocoileus virginianus detection, while coyote occurrence had a negative effect. Coyotes exerted a negative effect on squirrel (Sciurus spp.) and eastern cottontail rabbit Sylvilagus floridanus occurrence, while urban cover was a positive predictor for the prey species’ occurrence. These results suggest all 3 species behaviorally avoid coyotes whereby deer seek denser forest cover and squirrels and cottontails mitigate risk by increasing use of urban areas. Although previous studies reveal limited influence of coyote on the rest of the carnivore guild in suburban systems, we suggest coyotes play an important role in regulating the herbivorous mammals and hence may provide similar ecological benefits in urban/suburban forest fragments through trophic cascades. Furthermore, since hunting may not be allowed in urban and suburban habitats, coyotes might also serve as the primary regulator of nuisance species occurring at high abundance such as white-tailed deer and squirrels.