Dynamical Mechanisms Influencing the Population Structure of Airborne Pathogens: Theory and Observations

本项目的目标是通过测试将动态大气结构的理论考虑与好氧生物学采样和分析相结合的假设，了解空气中微生物种群混合的非线性动力学。该研究计划是建立在观察，在环境流，混沌动力学结构使有效的运动和传播的代理可能，无论这些代理是植物和动物的病原体，化学污染物，或工程设备，如传感器平台或运载工具。我们专注于了解镰刀菌属真菌的大气运输，这是一些毁灭性的植物和动物疾病的致病因子。使用动力系统方法，如拉格朗日相干结构和几乎不变集，我们将计算，跟踪和预测大气传输障碍，控制微生物的运动，如镰刀菌栖息地之间。通过与微生物分析结果的比较，我们希望揭示动力结构如何划分和混合空气中的微生物种群，以及相关的，微生物的混合物如何编码其最近的大规模大气混合的历史，由此产生的新框架可能会提供一个新的方法来空气生物学建模，并可能帮助农民提供一个早期预警系统的高风险植物病原体在未来。为此，从事该项目的学生将在一家公司实习，该公司进行与农业和环境决策相关的空气生物学建模。此外，开发的框架可以帮助改进病原体在更一般的地球物理环境中的运输建模，为更有效的管理策略铺平道路，以控制影响植物，家畜和人类的传染病的传播。将以多媒体教程的形式生成在线材料，以指导公众了解混沌动力学与生态学的相关性，以及供其他研究人员使用的计算工具。

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.