Predicting the drivers of incursion and expansion of vector‐borne diseases as part of early‐warning strategies (EWS) is a major challenge for geographically extensive diseases where spread is mediated by spatial heterogeneity in climate and other environmental drivers. Geospatial data on these environmental drivers are increasingly available affording opportunities for application to a predictive disease ecology paradigm provided the data can be synthesized and harmonized with fine‐scale, highly resolved data on vector and host responses to their environment. Here, we apply a multi‐scale big data–model integration approach using human‐guided machine learning to objectively evaluate the importance of a large suite of spatially distributed environmental variables (>400) to develop EWS for vesicular stomatitis (VS), a common viral vector‐borne vesicular disease affecting livestock throughout the Americas. Two temporally and phylogenetically distinct events were used to develop disease occurrence–environment relationships in incursion (2004) and expansion years (2005), and then to test those relationships (2014, 2015) at two scales: (1) local and (2) landscape to regional. Our results show that VS occurrence at a local scale of individual landowners was related to conditions that can be monitored (rainfall, temperatures, streamflow) or modified (vegetation). On‐site green vegetation during the month of occurrence and higher rainfall four months prior combined with either cool daytime (expansion) or nighttime (incursion) temperatures one month prior were indicators of VS occurrence. Distance to running water (incursion) and host density based on neighboring ranches (expansion) with infected animals were also important in individual years. At landscape‐to‐regional scales, conditions that favor specific VSV biological vectors were indicated, either black flies in incursion years or biting midges in expansion years. Changes in viral genetic lineage were less important to patterns in VS occurrence than factors affecting the host–vector–environment interactions. In combination with our onset map based on latitude, elevation, and long‐term annual precipitation, this year‐ and scale‐specific information can be used to develop strategies to minimize effects of future VS events. This big data approach coupled with expert knowledge and machine learning can be applied to other emerging diseases for improvement in understanding, prediction, and management of vector‐borne diseases.

Vesicular stomatitis (VS) is a vector-borne livestock disease caused by vesicular stomatitis New Jersey virus (VSNJV) or vesicular stomatitis Indiana virus (VSIV). The disease circulates endemically in northern South America, Central America, and Mexico and only occasionally causes outbreaks in the United States. Over the past 20 years, VSNJV outbreaks in the southwestern and Rocky Mountain regions occurred with incursion years followed by virus overwintering and subsequent expansion outbreak years. Regulatory response by animal health officials is deployed to prevent spread from lesioned animals. The 2019 VS incursion was the largest in 40 years, lasting from June to December 2019 with 1144 VS-affected premises in 111 counties in eight states (Colorado, Kansas, Nebraska, New Mexico, Oklahoma, Texas, Utah, and Wyoming) and was VSIV serotype, last isolated in 1998. A subsequent expansion occurred from April to October 2020 with 326 VS-affected premises in 70 counties in eight states (Arizona, Arkansas, Kansas, Missouri, Nebraska, New Mexico, Oklahoma, and Texas). The primary serotype in 2020 was VSIV, but a separate incursion of VSNJV occurred in south Texas. Summary characteristics of the outbreaks are presented along with VSV-vector sampling results and phylogenetic analysis of VSIV isolates providing evidence of virus overwintering.

Alternative states maintained by feedbacks are notoriously difficult, if not impossible, to reverse. Although positive interactions that modify soil conditions may have the greatest potential to alter self‐reinforcing feedbacks, the conditions leading to these state change reversals have not been resolved. In a 9‐yr study, we modified horizontal connectivity of resources by wind or water on different geomorphic surfaces in an attempt to alter plant–soil feedbacks and shift woody‐plant‐dominated states back toward perennial grass dominance. Modifying connectivity resulted in an increase in litter cover regardless of the vector of transport (wind, water) followed by an increase in perennial grass cover 2 yr later. Modifying connectivity was most effective on sandy soils where wind is the dominant vector, and least effective on gravelly soils on stable surfaces with low sediment movement by water. We found that grass cover was related to precipitation in the first 5 yr of our study, and plant–soil feedbacks developed following 6 yr of modified connectivity to overwhelm effects of precipitation on sandy, wind‐blown soils. These feedbacks persisted through time under variable annual rainfall. On alluvial soils, either plant–soil feedbacks developed after 7 yr that were not persistent (active soils) or did not develop (stable soils). This novel approach has application to drylands globally where desertified lands have suffered losses in ecosystem services, and to other ecosystems where connectivity‐mediated feedbacks modified at fine scales can be expected to impact plant recovery and state change reversals at larger scales, in particular for wind‐impacted sites.

West Nile virus (WNV) is the most common arthropod-borne virus (arbovirus) in the United States (US) and is the leading cause of viral encephalitis in the country. The virus has affected tens of thousands of US persons total since its 1999 North America introduction, with thousands of new infections reported annually. Approximately 1% of humans infected with WNV acquire neuroinvasive West Nile Disease (WND) with severe encephalitis and risk of death. Research describing WNV ecology is needed to improve public health surveillance, monitoring, and risk assessment. We applied Bayesian joint-spatiotemporal modeling to assess the association of vector surveillance data, host species richness, and a variety of other environmental and socioeconomic disease risk factors with neuroinvasive WND throughout the conterminous US. Our research revealed that an aging human population was the strongest disease indicator, but climatic and vector-host biotic interactions were also significant in determining risk of neuroinvasive WND. Our analysis also identified a geographic region of disproportionately high neuroinvasive WND disease risk that parallels the Continental Divide, and extends southward from the US–Canada border in the states of Montana, North Dakota, and Wisconsin to the US–Mexico border in western Texas. Our results aid in unraveling complex WNV ecology and can be applied to prioritize disease surveillance locations and risk assessment.

Vesicular stomatitis viruses (VSVs) cause a condition known as vesicular stomatitis (VS), which results in painful lesions in equines, cattle, swine, and camelids, and when transmitted to humans, can cause flu-like symptoms. When animal premises are affected by VS, they are subject to a quarantine. The equine industry more broadly may incur economic losses due to interruptions of animal trade and transportation to shows, competitions, and other events. Equine owners, barn managers, and veterinarians can take proactive measures to reduce the risk of equines contracting VS. To identify appropriate risk management strategies, it helps to understand which biting insects are capable of transmitting the virus to animals, and to identify these insect vectors' preferred habitats and behaviors. We make this area of science more accessible to equine owners, barn managers, and veterinarians, by (1) translating the most relevant scientific information about biting insect vectors of VSV and (2) identifying practical management strategies that might reduce the risk of equines contracting VSV from infectious biting insects or from other equines already infected with VSV. We address transmission risk at four different spatial scales-the animal, the barn/shelter, the barnyard/premises, and the surrounding environment/neighborhood-noting that a multiscale and spatially collaborative strategy may be needed to reduce the risk of VS.

Climate change will bring more extremes in temperature and precipitation that will impact productivity and ecosystem resilience throughout agroecosystems worldwide. Historical events can be used to identify drivers that impact future events. A catastrophic drought in the US in the 1930s resulted in an abrupt boundary between areas severely impacted by the Dust Bowl and areas that were less severely affected. Historical primary production data confirmed the location of this boundary at the border between two states (Nebraska and Iowa). Local drivers of weather and soils explained production responses across the boundary before and after the drought (1926–1948). During the drought, however, features at the landscape scale (soil properties and wind velocities) and regional scale (the Missouri River, its floodplain, and the nearby Loess Hills) explained most of the observed variance in primary production. The impact of future extreme events may be affected by land surface properties that either accentuate or ameliorate the effects of these events. Consideration of large‐scale geomorphic processes may be necessary to interpret and manage for catastrophic events.