The goal of this project is to understand the nonlinear dynamics underlying the mixing of airborne populations of microorganisms, via testing of hypotheses that blend theoretical considerations of dynamical atmospheric structures with aerobiological sampling and analysis. The research program is built on the observation that in environmental flows, chaotic dynamical structure makes efficient movement and dispersal of agents possible, whether these agents are pathogens of plants and animals, chemical pollutants, or engineered devices like sensor platforms or delivery vehicles. We focus on understanding the atmospheric transport of fungi in the genus Fusarium, which are causal agents of a number of devastating plant and animal diseases. Using dynamical systems methods such as Lagrangian coherent structures and almost-invariant sets, we will compute, track, and predict atmospheric transport barriers governing the motion of microorganisms such as Fusarium between habitats. By comparison with results of microbiological analysis, we expect to reveal how dynamical structures partition and mix airborne populations of microorganisms, and relatedly, how mixtures of microorganisms encode their recent history of large-scale atmospheric mixing.
The resulting new framework will likely provide a new approach to aerobiological modeling and may assist farmers by providing an early warning system for high risk plant pathogens in the future. To this end, students working on this project will intern at a company conducting aerobiological modeling related to agricultural and environmental decision-making. Moreover, the developed framework could aid in improving modeling of transport of pathogens in more general geophysical environments, paving the way for more effective management strategies for the spread of infectious diseases affecting plants, domestic animals, and humans. Online materials will be generated in the form of multimedia tutorials to instruct the public on the relevance of chaotic dynamics to ecology, as well as computational tools for use by other researchers.
The goal of this project was to understand the nonlinear dynamics underlying the mixing of airborne populations of microorganisms, via testing of hypotheses that blend theoretical considerations of atmospheric dynamics with airborne biological sampling and analysis. Our research program was built on the observation that in environmental flows, chaotic dynamics makes efficient movement and dispersal of agents possible, whether these agents are pathogens of plants and animals, chemical pollutants, or engineered devices like sensor platforms or delivery vehicles. We focused on understanding the atmospheric transport of fungi in the genus Fusarium, which are causal agents of a number of devastating plant diseases such as Fusarium head blight, a devastating wheat disease which has led to 3 billion dollars in crop losses in the US over the past decade. Fusaria are also producers of mycotoxins that threaten the health of domestic animals and humans. Using advanced analysis, we tracked the motion of microorganisms such as Fusarium in the atmosphere. The resulting new framework provides a new approach to aerobiological modeling and may assist farmers by providing an early warning system for high risk plant pathogens in the future. Moreover, the developed framework could aid in improving modeling of transport of pathogens in more general geophysical environments, paving the way for more effective management strategies for the spread of natural or introduced infectious diseases affecting plants, domestic animals, and humans. Thousands of colonies of Fusarium were collected with drones and ground-based sampling devices from 2009-2012 from unknown sources. Spore concentrations were higher in the fall, spring, and summer, and lower in the winter. Samples collected during the winter were likely coming from more distant sources (several kilometers away as compared to just one kilometer during the other seasons). Our studies suggest that there are large dispersed ‘‘clouds’’ of Fusarium with horizontal dimensions on the scale of 20 to 100 kilometers which travel with the wind. An analysis of the temporal variation in the collections of Fusarium showed that the similarity between collections decreased smoothly over time on average. Thus, inherent fluctuations over short periods of time can be excluded as potential contributors to occasional rapid changes in the atmospheric counts of Fusarium. Instead, this work supports the idea that atmospheric populations of fusaria are well mixed, and rapid changes in concentrations of fusaria in the lower atmosphere will be rare and may be attributed to large-scale atmospheric phenomena or strong local sources, both of which can lead to sharp gradients. A combination of field experiments and mathematical models were used to understand the local spread of Fusarium from a known source of inoculum. A large source of inoculum of a unique strain of the fungus Fusarium graminearum was established at a research farm in VA, and an array of fixed sensors was used to trap spores from the source up to one kilometer downwind from the center of the source. The strength of the source showed a spiky pattern, which corresponded to predominantly nightly releases of spores over a period of about two weeks. Using our sensor array, we measured the amount of disease spread within a kilometer from the center of the source. This provided an experimental means to directly measure a curve, known as the dispersal kernel, which is an important input for epidemic models of disease spread.
