Estimating diversity and relative abundance in microbial communities
High-throughput sequencing has advanced our understanding of the role that bacteria and archaea play in marine, terrestrial and host-associated health. Microbial community ecology differs in many ways from macroecology, and therefore new statistical methods are required to analyze microbiome data. In this talk I will present two new statistical methods for the analysis of microbiome data. The first, DivNet, estimates the diversity of microbial communities, and the second, corncob, estimates the relative abundance of microbial strains, metabolites, or genes. Both methods explicitly model microbe-microbe interactions, resulting in larger (but more accurate) estimates of variance compared to classical models. The methods will be illustrated with an analysis of the effects of wildfire on soil microbial communities in the northwestern Canadian boreal forest.