Quantifying the immense diversity of microbial life poses significant new challenges. Statistical methods currently available to estimate microbial diversity are, at best, unproven, because they are not compatible with the highly skewed species abundance curves that are characteristic of most microbial communities. The objectives of this research are to 1) develop novel statistical approaches to estimate biodiversity, 2) test these methods using existing data sets, and 3) make these methods available to the community in the form of freely accessible, easy to use, sophisticated statistical software. The software tools will implement a wide variety of older and novel methods for estimating species richness, including parametric and nonparametric procedures and interactive graphical displays. These tools will be tested by applying them to existing data sets, and they will be used to analyze emerging global patterns of microbial biodiversity. The intellectual merit of the project is that these tools have the potential to transform microbial biodiversity research by providing reliable biodiversity metrics with meaningful standard errors. The broader impacts of the project include student training and the creation of tools that will serve a growing cadre of biostatisticians, bioinformaticians, and microbial ecologists.
This study represents a synthesis between state-of-the-art microbial biology, mathematical and computational statistics, bioinformatics, and molecular approaches to microbial ecology. By building bridges between these disciplines, this collaborative project helped develop and implement statistical and computational tools for estimating biodiversity, quantified as species richness, from samples of abundance or incidence data. This opens a means to reliably study not only the extent of microbial diversity, but also its patterns over time and space. We believe that the new software tools created will help transform microbial biodiversity research because the researchers for the first time are now able to assess biodiversity metrics that are reliable and general, have biologically meaningful standard errors, and meet other fundamental statistical standards. The approach we developed can be an essential tool in biodiversity research because it is based on a well-studied foundation of state-of-the-art statistics, and provides a readily accessible, user-friendly, and freely available computational platform. The estimates of microbial richness we obtained contribute to the knowledge of the extent of microbial diversity and serve as a new baseline in microbial diversity studies. The impact in the development of human resources is also sizable: we have trained three PhD students and two postdoctoral fellows in interdisciplinary sciences, setting a successful career path to all of them.
