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。环境的变化影响着物种的生存，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进化方式。理解和预测复杂环境中的进化有许多重要的应用。这些措施包括管理受威胁物种和防治迅速演变的病原体，如：该项目使用数学模型和细菌实验室实验来探索环境变化如何影响进化。这项工作的结果将使我们更好地理解进化是如何以及何时可以预测的。该项目还为本科生和研究生提供研究培训，扩展本科课程，并为农村高中学生提供强调生物学和数学之间重要联系的丰富课程。在复杂的自然界中，环境变化和生物之间的局部相互作用可能在进化适应和多样化中发挥关键作用。该项目的目标是利用荧光假单胞菌种群的进化实验，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。首先，将开发基于主体的模型，并将其用于建立生活在不同类型空间异质环境中的种群进化的期望。然后，以这些期望为指导，空间异质性对这些细菌种群进化的影响将使用独特的半固体琼脂实验室环境进行测试。基因组序列数据将被收集，然后使用新的分子进化统计模型进行分析。种群基因组数据分析将提供一个独特的第一次看分子进化的细节在空间结构的种群。这项研究的结果将有助于更好地理解复杂环境中驱动进化的过程，最终建立对经济、环境和人类健康具有重要意义的物种进化的更好的预测模型。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。

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.