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机制的重要信息。尽管微生物可能构成地球生物多样性的大部分，并在生物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只代表了真正多样性的一小部分。基于样本数据估算两个站点共享微生物类群的真实数量是一个困难而又重要的挑战。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观和宏观生物空间生物多样性模式，需要新的生物统计技术。提出的生物信息学研究将利用标记释放-再捕获统计来开发新的方法来估计环境微生物样品中共享分类群的真实数量。利用概率论和模拟模型，量化微生物群落中潜在类群丰度分布对观测和估计的共享类群数量的影响。这些新开发的方法将用于探索加州春池生境中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.