Species interactions and seasonal transmission of fungal diseases in plant populations: experimental tests of historical contingency

该项目的目标是了解病原体传播如何受到宿主内病原体相互作用和环境的影响。在人类、非人类动物和植物中，许多流行病是季节性的——也就是说，它们是由季节性环境的变化开始或加速的。更精确地了解季节环境的变化如何引起流行病，可以改进对未来流行病的预测。了解季节性流行病原因的一个挑战是，这些流行病往往涉及生态相互作用，例如多物种生物之间的竞争。为了应对这一挑战，该项目将在田间条件下进行一系列实验，研究感染同一植物寄主的多种真菌病原体之间的相互作用。为了增加公众对科学的参与，项目科学家将与科学博物馆合作，应用现有的方法有效地吸引公众参与，将项目的方法和发现融入K-12教育课程，并为公众开发互动展览。该项目将使用一个模型系统来开发可在其他系统中应用的创新方法，以了解季节性流行病的原因，改进农业实践，并提高我们预测流行病的能力。最近的理论预测，宿主个体内病原体或寄生虫之间的物种相互作用的结果可能受到它们所处的群落环境的影响。随着时间的推移，生态群落聚集在一起，这些依赖于环境的相互作用创造了历史偶然性。为了解决物种相互作用和环境条件在季节性流行病中的作用，该项目将在季节性变化的环境条件下操纵宿主内部相互作用并量化疾病传播。该项目将利用一种实验可处理的田间系统：两种真菌病原体（Rhizoctoniasolani和Colletotrichum cereale），它们感染分布广泛且具有重要农业意义的禾草物种高羊茅（Lolium arundinaceum）的叶子。利用这个系统，研究人员将进行现场实验，操纵疾病传播的历史。该项目将采用一种综合方法，利用在两个生态层面（宿主个体和种群）进行的实地实验，并用动态模型在这些层面之间架起桥梁。在这个综合框架中，该项目将研究生物复杂性的其他层面，包括寄生虫遗传谱系的季节性动态，以及宿主微生物组对疾病传播的影响。最终，这个项目将进一步加深我们对宿主物种内部相互作用与环境条件相一致时，可以控制疾病传播的季节性变化的理解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this project is to understand how pathogen transmission can be influenced both by interactions of pathogens within hosts and by the environment. Across humans, non-human animals, and plants, many epidemics are seasonal – that is, they are started or accelerated by shifts in the seasonal environment. Prediction of future epidemics may be improved by a more precise understanding of how shifts in the seasonal environment cause epidemics. A challenge for understanding the causes of seasonal epidemics is that these epidemics often involve ecological interactions such as competition among multiple species of organisms. To address this challenge, this project will conduct a series of experiments under field conditions on interactions between multiple fungal pathogens infecting a shared plant host. To increase the public’s involvement in science, the project scientists will partner with a science museum to apply current methods for effective engagement of the public, to integrate the project’s approaches and findings into K-12 educational curricula, and to develop interactive exhibits for the public. This project will use a model system to develop innovative approaches that can be applied in other systems to understand the causes of seasonal epidemics, improve agricultural practices, and improve our capacity to predict epidemics. Recent theory predicts that the outcome of species interactions between pathogens or parasites within a host individual can be influenced by the community context in which they are embedded. As ecological communities assemble over time, these context-dependent interactions create historical contingency. To resolve the roles of species interactions and environmental conditions in seasonal epidemics, this project will manipulate within-host interactions and quantify disease transmission in the context of seasonally varying environmental conditions. The project will leverage an experimentally tractable field system: two fungal pathogens (Rhizoctoniasolani and Colletotrichum cereale) that infect leaves of the widespread and agriculturally important grass species tall fescue (Lolium arundinaceum). Using this system, the researchers will conduct field experiments that manipulate the history of disease transmission. The project will employ an integrative approach that uses field experiments conducted at two ecological levels – host individuals and populations – and bridges across those levels with a dynamical model. In this integrative framework, the project will investigate additional layers of biological complexity, including the seasonal dynamics of parasite genetic lineages, and influences of the host microbiome on disease transmission. Ultimately, this project will further our understanding of when within-host species interactions, in concert with environmental conditions, can govern seasonal shifts in disease transmission.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.