CAREER: The effects of spatial structure and heterogeneity on local adaptation, diversification, and dispersal evolution: Experimental tests and statistical models

大多数物种生活在复杂多变的环境中。环境中的变化会影响物种的生活地点，它们如何移动，以及它们如何相互作用。这反过来又会影响它们的发展。了解和预测复杂环境中的进化具有许多重要的应用。这些措施包括管理受威胁的物种和打击迅速演变的病原体，如。该项目使用数学模型和细菌实验室实验来探索环境中的变化如何影响进化。这项工作的结果将有助于更好地理解进化如何以及何时是可预测的。该项目还为本科生和研究生提供研究培训，扩大本科生课程，并为农村高中生提供强化课程，强调生物学和数学之间的重要联系。在复杂的自然世界中，环境变化和生物体之间的局部相互作用有可能在进化适应和多样化中发挥关键作用。该项目的目标是确定空间结构和可变环境对局部适应，进化多样化和扩散的影响，使用细菌，荧光假单胞菌的种群进化实验。首先，基于代理的模型将被开发和使用，以建立生活在不同类型的空间异质性环境中的人口的进化的期望。然后，以这些预期为指导，将使用独特的半固体琼脂实验室环境测试空间异质性对这些细菌种群进化的影响。将收集基因组序列数据，然后使用分子进化的新统计模型进行分析。人口基因组数据分析将提供一个独特的第一次看在空间结构人口的分子进化的细节。这项研究的结果将导致更好地了解复杂环境中驱动进化的过程，最终允许更好地预测经济，环境和人类健康重要性物种的进化模型。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Most species live in complex and variable environments. Variation in within an environment impacts where species live, how they move around, and how they interact with each other. This, in turn, can influence how they evolve. Understanding and predicting evolution in complex environments has many important applications. These include the management of threatened species and combating rapidly evolving pathogens such. This project uses mathematical models and lab experiments with bacteria to explore how variation in an environment impacts evolution. Results from this work will allow for a better understanding of how and when evolution is predictable. The project also provides research training for undergraduate and graduate students, expanded undergraduate curriculum, as well as an enrichment course for rural high school students emphasizing important connections between biology and math.In the complex natural world, environmental variation and local interactions between organisms have the potential to play a key role in evolutionary adaptation and diversification. The goal of this project is to determine the effects of spatially structured and variable environments on local adaptation, evolutionary diversification, and dispersal using evolution experiments with populations of the bacterium, Pseudomonas fluorescens. First, agent-based models will be developed and used to build expectations for evolution of populations living in different types of spatially heterogeneous environments. Then, using those expectations as a guide, the effects of spatial heterogeneity on the evolution of those bacterial populations will be tested using a unique semi-solid agar lab environment. Genome sequence data will be collected and then be analyzed using novel statistical models of molecular evolution. The population genome data analysis will provide a unique first look at the details of molecular evolution in spatially structured populations. Results from this research will lead to a better understanding of the processes driving evolution in complex environments, ultimately allowing for better predictive models of evolution in species of economic, environmental, and human health importance.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.