YY-EEID US-UK XXXX: Eco-Evolutionary dynamics of infectious diseases in host population networks.

自然宿主种群通常是分散的，由几个小种群组成，这些小种群通过动物运动相互联系。破碎的种群结构可能是自然发生的，由于适合的栖息地的斑块分布，或由于人类活动和景观的变化。了解人口网络拓扑结构的变化（例如人口之间的规模和连通程度）如何影响疾病传播是一个紧迫的优先事项，因为我们正在不断地（尽管往往是无意中）改变网络拓扑结构。当我们考虑这些网络中的主机之间的感染传播时，这一点尤为重要。我们拟议的工作将联合收割机从野生沙漠大角羊（DBH）收集的数据与新的理论方法（例如网络模型）相结合，以研究不同破碎程度的人群中感染风险如何变化。此外，由于动物感染的种类会随着进化时间的推移而改变它们能够应对的感染类型，我们还将确定网络拓扑结构如何影响免疫防御的遗传适应。这一点特别重要，因为宿主群体的免疫反应会影响该群体对新出现的传染病的脆弱性，因此不同网络中的动物可能具有不同的抵抗新“新出现”感染的能力。我们提出，种群之间的连通性和动物运动水平将改变哪些寄生虫和微生物能够在每个网络中持续存在。此外，由于不止一种寄生虫可以感染动物，这些寄生虫物种往往可以相互作用，我们建议，在个别主机的寄生虫群落的结构将由这些驱动。为了研究我们的假设，我们将从绵羊粪便中提取样本，并在较长的时间内进行跟踪，以揭示沙漠大角牛在三个不同的碎片化种群中的寄生虫群落模式。然后集中在充分研究的网络从莫哈韦沙漠，我们将联合收割机这些纵向观测数据与实验方法，以确定如何寄生虫相互作用结构内的主机寄生虫群落。我们还将测量免疫反应和调查绵羊的免疫遗传学概况，以估计不同的寄生虫群落如何推动14个大角羊种群的自然选择。然后，我们将使用我们的经验数据来参数化和测试数学网络模型，探索生态和宿主进化过程如何塑造特别是大角牛的疾病动态，以及一般的种群网络。拟议工作的广泛范围和雄心勃勃的目标是可以实现的，因为原因：（i）DBH提供复制宿主种群，这些种群在种群连接性和寄生虫群落方面有所不同，但在其他方面相似。(ii)我们可以利用新型分子技术的力量来追踪不同寄生虫群体。(iii)我们将开发创新的建模方法，将我们关于微生物和寄生虫传播的现场数据与宿主免疫力的详细测量相结合。我们的建模框架将使我们能够探索两个一般性问题（例如，宿主种群碎片化如何影响哪些寄生虫持续存在和传播？）以及更多的战术问题（例如，景观连通性的特定变化-例如公路建设/动物移动限制-如何影响感染风险？）。宿主种群网络无处不在--从山顶上的沙漠大角羊，到保护区网络，再到农场和城市。这项拟议的研究将使我们能够开发和测试一个数学框架，用于探索传染病在不同宿主群体网络中的生态和进化动力学，这可能会改变我们对人群暴露风险随时间和空间变化的看法。

Natural host populations are often fragmented, consisting of several small populations that are linked to one another by animal movement. Fragmented population structures may occur naturally, due to patchy distributions of suitable habitat, or result from human activity and transformations of the landscape. Understanding how changes in population network topology (e.g. size and degree of connectivity between populations) affects disease transmission is an urgent priority, because we are continuously, though often inadvertently, changing network topology. This is particularly important when we consider the transmission of infections across hosts in these networks. Our proposed work will combine data collected from wild desert bighorn sheep (DBH) with new theoretical approaches (e.g. network models) to investigate how infection risks change in populations with different levels of fragmentation. Further, because the kinds of infections animals have will, over evolutionary time, alter the types of infection they are able to respond to, we will also determine how network topology affects the genetic adaptation of immune defense. This is particularly important because, the immune responses in host populations will affect that populations' vulnerability to emerging infectious diseases and so animals in different networks are likely to have different abilities to resist new 'emerging' infections. We propose that the level of connectivity and animal movement between populations will change which parasites and microbes are able to persist within each network. Further, as more than one species of parasite can infect an animal and these parasite species can often interact, we propose that the structure of the parasite community in individual hosts will then be driven by these. To investigae our hypotheses, we will take faecal samples from sheep followed over extended periods, to uncover the landscape-level parasite community patterns in desert bighorns across three differently fragmented populations. Then focusing in on the well-studied network from the Mojave desert, we will combine these longitudinal observational data with experimental approaches to determine how parasite interactions structure the within-host parasite communities. We will also measure immune responses and survey immunogenetic profiles of sheep to estimate how different parasite communities may drive natural selection across 14 bighorn sheep populations. We will then use our empirical data to parameterize and test mathematical network models exploring how ecological and host evolutionary processes shape disease dynamics in bighorns in particular, and across population networks in general. The broad scope and ambitious goals of the proposed work are attainable because reasons: (i) the DBH provid replicate host populations that vary in population connectivity and parasite communities, but are otherwise similar. (ii) We can harness the power of novel molecular techniques to track communities of different groups of parasite. (iii) We will develop innovative modeling approaches, which will integrate our field data on the transmission of microbes and parasites with detailed measured of host immunity. Our modeling framework will allow us to explore both general questions (e.g. How does host population fragmentation impact which parasites persist and spread?) and more tactical concerns (e.g. How will particular changes in landscape connectivity -- e.g. highway construction / animal movement restrictions - affect infection risk?). Host population networks are everywhere - from desert bighorn sheep on mountain tops, to networks of protected areas, through to farms and cities. The proposed study would allow us to develop and test a mathematical framework for exploring ecological and evolutionary dynamics of infectious diseases in different host population networks, potentially transforming how we think about variation in exposure risks among populations over space and time.