Spatial Scaling of Microbial Diversity in the Environment: Biostatistical Challenges

生物的空间分布模式提供了关于调节生命多样性和生态系统复杂性的机制的重要信息。虽然微生物可能构成地球生物多样性的大部分，并在地球化学和生态系统功能中发挥关键作用，但对其空间多样性知之甚少。人们普遍认为，在微生物样品中观察到的多样性仅代表真正多样性的一小部分。根据样本数据估计两个地点共有微生物类群的真实数量的问题提出了一个困难但重要的挑战。为了更准确地量化两个微生物群落之间的相似性，从而更可靠地比较微生物和大型生物空间生物多样性模式，需要新的生物统计技术。拟议的生物信息学研究将利用标记-释放-再捕获统计数据，开发新的方法来估计环境微生物样品中共有类群的真实数量。利用概率论和模拟建模，将量化微生物群落中潜在的类群丰度分布如何影响观察到的和估计的共享类群的数量。这些新开发的方法将应用于探索加州春季池栖息地的微生物多样性的空间格局。

Patterns in the spatial distribution of organisms provide important information about mechanisms that regulate the diversity of life and the complexity of ecosystems. Although microorganisms may comprise much of Earths biodiversity and play critical roles in biogeochemistry and ecosystem functioning, little is known about their spatial diversification. It is widely believed that the observed diversity in a microbial sample represents only a small portion of the true diversity. The problem of estimating the true number of shared microbial taxa by two sites based on sample data presents a difficult but important challenge. To more accurately quantify the similarity between two microbial communities, and thus make more reliable comparisons of micro- and macro-organism spatial biodiversity patterns, novel biostatistical techniques areneeded. The proposed bioinformatics study will draw upon mark-release-recapture statistics to develop new methods for estimating the true number of shared taxa in environmental microbial samples. Using probability theory and simulation modeling, how the underlying taxa-abundance distribution in microbial communities influences the number of observed and estimated shared taxa will be quantified. These newly developed approaches will be applied to explore spatial patterns of microbial diversity in Californian vernal pool habitats.