Eco-evolutionary dynamics in evolving communities

在同一时间尺度上确定生态和进化变化之间的相互作用和反馈动力学是一个持续的兴趣。这种对生态进化动力学的兴趣是由于需要了解人口和社区如何适应快速的环境变化，如变暖、入侵和污染。尽管迫切需要了解生态进化动力学，但它们在复杂系统中还没有得到很好的理解。在这里提出的项目中，我们的目标是(1)确定不同食物网中共同进化的宿主-病毒种群的快速适应性变化，这些变化在物种相互作用类型（从拮抗到互惠相互作用）和复杂性方面存在差异；(2)理解适应性变化的动态如何改变生态动态和潜在的反馈。我们将结合可控的恒化实验，不同时间点种群的全基因组测序和建模来表征和比较几十代不同食物网中的适应动态及其后果。我们将使用焦点物种方法，我们将重点放在不同食物网中宿主和病毒之间的相互作用上。通过对不同时间点的（宿主和病毒）种群进行表型上的进化动态和全基因组测序，将使我们能够确定选择下的候选突变。这些试验是重复的，可以评估不同时间的群体和个体无性系的适合度。此外，突变可以随着时间的推移进行追踪，估计适应性变化的概率和固定时间，在相互作用的种群中提供适应性变化的动态图景。与该项目有关的研究结果将对我们了解群落如何演变及其对人口和社区稳定以及维持多样性的影响具有广泛的意义。

There is a continuing interest to identify the interactions and feedback dynamics between ecological and evolutionary changes at the same time scale. This interest in eco-evolutionary dynamics is fuelled by the need to understand how populations and communities could adapt to rapid environmental change such as warming, invasion and pollution. Despite this pressing need to understand eco-evolutionary dynamics, they are not well understood in complex systems. In the project proposed here, we aim to (1) identify rapid adaptive changes in coevolving host-virus populations in different food webs that differ in the types of species interactions (ranging form antagonistic to mutualistic interactions) and complexity and to (2) comprehend how the dynamics of adaptive changes alter the ecological dynamics and potential feedbacks. We will combine controlled chemostat experiments, whole genome sequencing of populations across different time points and modelling to characterize and compare the adaptive dynamics and their consequences within the different food webs over dozens of generations. We will use a focal species approach where we focus on the interaction between host and virus in different food webs. Following the evolutionary dynamics phenotypically and with whole genome sequencing of both (host and virus) populations across different time points will allow us to identify candidate mutations under selection. The experiments are replicated and fitness of the populations and individual clones at different time can be assessed. In addition, mutations can be traced over time estimating the probability and time to fixation of adaptive changes, providing a picture of the dynamics of adaptive change, in both interacting populations. The findings related to this project will have broad implications for our understanding of how communities evolve and its consequences for population and community stability as well as for the maintenance of diversity.