Antibiotic-resistant microorganisms (ARMs) are widespread in natural environments, animals (wildlife and livestock), and humans, which has reduced our capacity to control life threatening infectious disease. Yet, little is known about their transmission pathways, especially at the wildlife-livestock interface. This study investigated the potential transmission of ARMs and antibiotic resistance genes (ARGs) between cattle and wildlife by comparing gut microbiota and ARG profiles of feral swine (Sus scrofa), coyotes (Canis latrans), cattle (Bos taurus), and environmental microbiota. Unexpectedly, wild animals harbored more abundant ARMs and ARGs compared to grazing cattle. Gut microbiota of cattle was significantly more similar to that of feral swine captured within the cattle grazing area where the home range of both species overlapped substantially. In addition, ARMs against medically important antibiotics were more prevalent in wildlife than grazing cattle, suggesting that wildlife could be a source of ARMs colonization in livestock.

Extensive, severe wildfires, and wildfire‐induced smoke occurred across the western and central United States since August 2020. Wildfires resulting in the loss of habitats and emission of particulate matter and volatile organic compounds pose serious threatens to wildlife and human populations, especially for avian species, the respiratory system of which are sensitive to air pollutions. At the same time, the extreme weather (e.g., snowstorms) in late summer may also impact bird migration by cutting off their food supply and promoting their migration before they were physiologically ready. In this study, we investigated the environmental drivers of massive bird die‐offs by combining socioecological earth observations data sets with citizen science observations. We employed the geographically weighted regression models to quantitatively evaluate the effects of different environmental and climatic drivers, including wildfire, air quality, extreme weather, drought, and land cover types, on the spatial pattern of migratory bird mortality across the western and central US during August‐September 2020. We found that these drivers affected the death of migratory birds in different ways, among which air quality and distance to wildfire were two major drivers. Additionally, there were more bird mortality events found in urban areas and close to wildfire in early August. However, fewer bird deaths were detected closer to wildfires in California in late August and September. Our findings highlight the important impact of extreme weather and natural disasters on bird biology, survival, and migration, which can provide significant insights into bird biodiversity, conservation, and ecosystem sustainability.

Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems [“GPS”]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous‐time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions—individuals occurring at the same location, but at different times—while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease‐relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30‐min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM‐Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine‐scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer–resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.

Bacillus anthracis, the causative pathogen of anthrax, is a spore-forming, environmentally maintained bacterium that continues to be a veterinary health problem with outbreaks occurring primarily in wildlife and livestock. Globally, the genetic populations of B. anthracis include multiple lineages, and each may have different ecological requirements and geographical distributions. It is, therefore, essential to identify environmental associations within lineages to predict geographical distributions and risk areas with improved accuracy. Here, we model the ecological niche and predict the geography of the most widespread sublineage of B. anthracis in the continental United States using updated MERRA-derived (Modern Era Retrospective analysis for Research and Applications; the NASA atmospheric data reanalysis of satellite information with multiple data products) bioclimate variables (i.e., MERRAclim data) and updated soil variables. We filter the occurrence data associated with the A1.a/Western North American sub-lineage of B. anthracis from historical anthrax outbreaks using the multiple-locus variable-number tandem repeat system. In addition, we also incorporate recent cases associated with B. anthracis A1.a sub-lineage from 2008 to 2012 in Montana, Colorado, and Texas. Our results provide the predicted distribution of the A1.a sub-lineage of B. anthracis for the United States with better predictive accuracy and higher spatial resolution than previous estimates. Our prediction serves as an improved disease risk map to better inform anthrax surveillance and control in the United States, particularly the Dakotas and Montana where this sub-lineage is persistent.