NSF Postdoctoral Research Fellowship in Biology

本行动资助美国国家科学基金会2008年度生物学博士后奖学金。该奖学金支持一项名为“利用系统发育聚类和过度分散分析检查微生物多样性和时空分布”的研究和培训计划。研究员是Kathryn M. Docherty，赞助科学家是俄勒冈大学生态和进化生物学中心的Jessica L. Green教授。一个非常多样化的微生物群落在生物地球化学循环和生态系统功能中起着至关重要的作用，但我们对这些作用在空间、时间和环境变化中的联系还没有深入的了解。本项目利用两种强大而先进的信息学工具——系统发育聚类分析和过度分散分析，研究与氮循环相关的微生物多样性，比较不同空间和季节梯度水平上的微生物群落。与传统的多样性测量方法相比，使用这种高度计算方法的优势在于，系统发育相关性可以与生态相似性进行比较，因此可以在环境和进化历史的背景下检查生态特征的保守性和冗余性，并探索功能性微生物多样性。直到最近，微生物被认为是全球分布的，并且相对于宏观生物具有根本不同的空间尺度关系。然而，最近的研究表明，微生物的生物多样性与高等生物相似，结垢可能是由环境异质性和扩散历史造成的。该项目使用在广泛的空间、时间和环境尺度上收集的各种数据集，并使用新的生物信息学方法来检查和比较几种不同的功能微生物群落，以及植物和非生物数据。培训计划提供了一个独特的机会，在生物统计，数学和理论建模，以及专业网络，指导和职业发展的机会。

This action funds an NSF Postdoctoral Fellowship in Biology for FY 2008. The fellowship supports a research and training plan entitled "Examining microbial biodiversity and spatial-temporal distribution using phylogenetic clustering and overdispersion analyses." The Fellow is Kathryn M. Docherty and the sponsoring scientist is Professor Jessica L. Green at the University of Oregon Center for Ecology and Evolutionary Biology. A very diverse community of microorganisms plays critical roles in biogeochemical cycling and ecosystem function yet we do not have an in-depth knowledge of these roles in linking spatial, temporal and environmental variation. This project examines nitrogen-cycle related microbial biodiversity using two powerful and cutting-edge informatics tools, phylogenetic clustering and over-dispersion analyses, to compare microbial communities across several levels of spatial and seasonal gradients. The advantage of using this highly computational approach over traditional diversity measures is that phylogenetic relatedness can be compared to ecological similarity, so that ecological trait conservatism and redundancy can be examined and functional microbial diversity explored, within the context of environmental and evolutionary history. Until recently, microorganisms were assumed to be globally distributed and have fundamentally different spatial scaling relationships relative to macro-organisms. However, recent studies suggest that microbial biodiversity is similar to higher organisms and that scaling can result from environmental heterogeneity and dispersal history. This project uses various datasets collected over broad spatial, temporal and environmental scales and uses novel bioinformatics approaches to examine and compare several different functional microbial communities to each other, as well as to plant and abiotic data. The training plan provides a unique opportunity in biostatistical, mathematical and theoretical modeling as well as professional networking, mentoring and career-development opportunities.