NSF Postdoctoral Fellowship in Biology FY 2013

开发新的方法来测试共享进化历史的模型理解产生生物多样性的过程是生物学的一个基本目标。为了实现这一目标，至关重要的是要解释影响共生物种整个群落进化的机制，例如环境的变化。下一代测序（NGS）技术的进步提供了使用来自多个物种的全基因组数据以前所未有的细节检查进化历史的机会。然而，目前还没有方法在一个比较的框架内分析NGS数据，以测试有关共享进化历史的假设。该项目的目标是开发贝叶斯统计模型，该模型可以利用NGS数据来估计共分布分类群中物种形成事件的时间模式。这些新方法将应用于来自非洲中西部物种的NGS数据，以推断过去干旱化周期对非洲热带雨林动物多样性的影响。更广泛的影响包括开发新的计算工具，作为开源软件免费提供。这些工具将在多个学科，包括生物多样性科学和进化医学的普遍利益，通过提供统计方法来估计跨景观和宿主类群及其相关微生物之间的共同进化动力学的共同多样化模型。前者对于理解全球生物多样性至关重要，后者对于理解宿主相关微生物群（包括病原体）的进化历史和组装至关重要。培训目标包括掌握推进数学，统计学和生物学研究所需的数学，计算和生物信息学技能。该项目还将使用基于证据的方法来制定课堂策略，以改善本科生在进化和科学方法的基本概念方面的教育。该项目将遵循开放式笔记本科学的原则;所有进展将通过版本控制软件进行真实的记录，并在互联网上公开提供。

Developing novel methods for testing models of shared evolutionary historyUnderstanding the processes that generate biodiversity is a fundamental goal of biology. To achieve this goal, it will be critical to account for mechanisms that influence the evolution of entire communities of co-occurring species, such as changes to the environment. The advancement of next-generation sequencing (NGS) technology provides opportunities to examine evolutionary history in unprecedented detail using genome-wide data from multiple species. However, there are currently no methods for analyzing NGS data in a comparative framework to test hypotheses about shared evolutionary history. The goal of this project is to develop Bayesian statistical models that can utilize NGS data for estimating the temporal pattern of speciation events across co-distributed taxa. These novel methods will be applied to NGS data from species across West-Central Africa to infer the effect of past aridifi cation cycles on the diversifi cation ofAfro-tropical rainforest fauna.Broader impacts include the development of novel computational tools made freely available as open-source software. These tools will be of general interest across multiple disciplines, including biodiversity science and evolutionary medicine, by providing statistical approaches to estimate models of co-diversifi cation across landscapes and co-evolutionary dynamics between host taxa and their associated microbiomes. The former is critical for understanding global biodiversity, and the latter is important for understanding the evolutionary history and assembly of host-associated microbiota, including pathogens. Training objectives include mastering the mathematical, computational, and bioinformatic skills necessary for advancing research at the nexus of mathematics, statistics, and biology. This project will also use an evidence-based approach to develop classroom strategies for improving undergraduate education in the foundational concepts of evolution and the scienti fic method. This project will follow the principles of Open Notebook Science; all progress will be recorded in real time via version-control software and made publicly available on the Internet.