Mathematic methods for the evaluation of microbial diversity and their applications in the research on wastewater treatment systems

Abstract:

Microbial communities play central roles in global climate regulation, human health and industrial biotechnology. The quantification of microbial diversity is important for the understanding of ecological characteristics of communities, their dynamics and functions. Herein, we introduced the commonly used alpha-diversity indices, including richness, Shannon-Weaver index, Simpson diversity indices and Hill’s diversity number. Also, we summarized diversity evaluation ways which are used in estimating the coverage of molecular methods (e.g., rarefaction curve and good’s coverage) and community richness (e.g., Chao1 and ACE indices and taxa abundance distribution curve). Then we showed the application of mathematic methods in the research on microbial diversity by taking wastewater treatment plant (WWTP) as an example which is the largest application of bioprocess engineering. Current investigations showed that taxa richness and Shannon diversity of activated sludge microbial communities in full-scale WWTPs increased with the increase in sampling sizes of different methods. However, the disparity between sample size and community size is a common problem in microbial investigations. By reconstructing microbial communities using DNA sampling data based on certain taxa abundance distribution curves, diversity of the communities was evaluated. It was shown that microbial richness was characterized with large uncertainty. Shannon diversity, and especially Simpson diversity indices which are weakly dependent on low-abundance taxa could be estimated accurately. They are good tools to evaluate and compare microbial taxonomic diversity. Developing novel modeling approaches and advances in sequencing technology can improve the accuracy of microbial richness evaluation. In addition, clarifying phylogenetic and functional diversity of microbial communities are also of substantial importance for the understanding of microbial ecology.