Abstract
Bacteria have been inferred to exhibit relatively weak biogeographic patterns. To what extent such findings reflect true biological phenomena or methodological artifacts remains unclear. Here, we addressed this question by analyzing the turnover of soil bacterial communities from three data sets. We applied three methodological innovations: (i) design of a hierarchical sampling scheme to disentangle environmental from spatial factors driving turnover; (ii) resolution of 16S rRNA gene amplicon sequence variants to enable higher-resolution community profiling; and (iii) application of the new metric zeta diversity to analyze multisite turnover and drivers. At fine taxonomic resolution, rapid compositional turnover was observed across multiple spatial scales. Turnover was overwhelmingly driven by deterministic processes and influenced by the rare biosphere. The communities also exhibited strong distance decay patterns and taxon-area relationships, with z values within the interquartile range reported for macroorganisms. These biogeographical patterns were weakened upon applying two standard approaches to process community sequencing data: clustering sequences at 97% identity threshold and/or filtering the rare biosphere (sequences lower than 0.05% relative abundance). Comparable findings were made across local, regional, and global data sets and when using shotgun metagenomic markers. Altogether, these findings suggest that bacteria exhibit strong biogeographic patterns, but these signals can be obscured by methodological limitations. We advocate various innovations, including using zeta diversity, to advance the study of microbial biogeography.IMPORTANCE It is commonly thought that bacterial distributions show lower spatial variation than for multicellular organisms. In this article, we present evidence that these inferences are artifacts caused by methodological limitations. Through leveraging innovations in sampling design, sequence processing, and diversity analysis, we provide multifaceted evidence that bacterial communities in fact exhibit strong distribution patterns. This is driven by selection due to factors such as local soil characteristics. Altogether, these findings suggest that the processes underpinning diversity patterns are more unified across all domains of life than previously thought, which has broad implications for the understanding and management of soil biodiversity.
Highlights
Bacteria have been inferred to exhibit relatively weak biogeographic patterns
The processing of 16S rRNA gene amplicon sequencing data typically used to profile communities can reduce data set resolution; reads are usually clustered into operational taxonomic units (OTUs) based on an arbitrary identity threshold, and the rare biosphere is regularly removed [52, 53]
This sampling scheme was designed to enable the analysis of microbial community turnover at multiple spatial scales, capture a wide spectrum of distance classes (Fig. S1c), and discriminate underlying spatial and environmental drivers
Summary
Bacteria have been inferred to exhibit relatively weak biogeographic patterns. To what extent such findings reflect true biological phenomena or methodological artifacts remains unclear. Through leveraging innovations in sampling design, sequence processing, and diversity analysis, we provide multifaceted evidence that bacterial communities exhibit strong distribution patterns This is driven by selection due to factors such as local soil characteristics. As is the case in the field of macroecology, the relative importance of deterministic and stochastic processes in shaping contemporary distributions of microorganisms continues to be debated and there is a large body of often divergent literature in this area In this regard, a major methodological challenge is to perform sampling and analysis that sufficiently disentangles the autocorrelation between environmental and spatial factors in soil ecosystems [12, 25,26,27]. Compounding these issues, the pairwise analyses generally used to quantify community turnover inadequately partition variation from all community members: incidence-based measures are highly sensitive to the rare biosphere, and abundance-based measures focus on the common few [54, 55]
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