Eco-evolutionary dynamics in evolving communities

在同一时间尺度上确定生态变化和进化变化之间的相互作用和反馈动力学一直是人们感兴趣的问题。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，这种对生态进化动力学的兴趣得到了推动。尽管迫切需要了解生态进化动力学，但它们在复杂系统中并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)识别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的类型(从拮抗相互作用到相互作用)和复杂性上有所不同，以及(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。我们将结合受控恒化器实验、不同时间点种群的全基因组测序和建模来表征和比较不同食物网中数十代人的适应动态及其后果。我们将使用焦点物种方法，重点研究不同食物网中宿主和病毒之间的相互作用。根据进化动态，通过表型和全基因组对(宿主和病毒)群体进行跨不同时间点的测序，将使我们能够识别在选择下的候选突变。这些实验是重复的，可以评估种群和个体克隆在不同时间的适应度。此外，可以随着时间的推移追踪突变，估计固定适应性变化的概率和时间，从而提供两个相互作用的种群中适应性变化的动态图景。与该项目有关的调查结果将对我们理解社区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影响。

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.