Abstract
Recent discussion focuses on the best method for delineating microbial taxa, based on either exact sequence variants (ESVs) or traditional operational taxonomic units (OTUs) of marker gene sequences. We sought to test if the binning approach (ESVs versus 97% OTUs) affected the ecological conclusions of a large field study. The data set included sequences targeting all bacteria (16S rRNA) and fungi (internal transcribed spacer [ITS]), across multiple environments diverging markedly in abiotic conditions, over three collection times. Despite quantitative differences in microbial richness, we found that all α and β diversity metrics were highly positively correlated (r > 0.90) between samples analyzed with both approaches. Moreover, the community composition of the dominant taxa did not vary between approaches. Consequently, statistical inferences were nearly indistinguishable. Furthermore, ESVs only moderately increased the genetic resolution of fungal and bacterial diversity (1.3 and 2.1 times OTU richness, respectively). We conclude that for broadscale (e.g., all bacteria or all fungi) α and β diversity analyses, ESV or OTU methods will often reveal similar ecological results. Thus, while there are good reasons to employ ESVs, we need not question the validity of results based on OTUs.IMPORTANCE Microbial ecologists have made exceptional improvements in our understanding of microbiomes in the last decade due to breakthroughs in sequencing technologies. These advances have wide-ranging implications for fields ranging from agriculture to human health. Due to limitations in databases, the majority of microbial ecology studies use a binning approach to approximate taxonomy based on DNA sequence similarity. There remains extensive debate on the best way to bin and approximate this taxonomy. Here we examine two popular approaches using a large field-based data set examining both bacteria and fungi and conclude that there are not major differences in the ecological outcomes. Thus, it appears that standard microbial community analyses are not overly sensitive to the particulars of binning approaches.