Bacterial Community Diversity Dynamics Highlight Degrees of Nestedness and Turnover Patterns

Abstract

Bacterial communities change their structure rapidly due to short generation times of their members. How bacteria assemble to certain structures provides insight into ecological mechanisms that shape a bacterial community. Microbial community flow cytometry was used to create community fingerprints based on subcommunity distributions and to visualize the dynamic variations of 10 independently grown communities under equal conditions. Inventory diversity values were recorded by α‐ and γ‐diversity whereas the degree of subsistence of subcommunities (nestedness) and the degree of gain or loss of subcommunities (turnover) was calculated as multi‐sites ß‐diversity terms ßNES and ßSIM. Numbers of unique subcommunities of pairwise samples were determined by intra‐ and inter‐community ß‐diversity values. Although all communities were exposed to niche‐differentiating conditions they assembled to disparate structures. In our study, the turnover coefficients were high (> 0.6), while the nestedness coefficients were complementary low in the separate 10 bioreactors. Intra‐ and inter‐community ß‐diversity values indicated fast community shifts. Microbial community flow cytometry straightforwardly identifies the dominance and subsistence of subsets of cells in a community or the degree of their replacement. The calculation of either turnover or nestedness patterns might have implications in medical, biotechnological, or environmental research. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Predicting the Presence and Abundance of Bacterial Taxa in Environmental Communities through Flow Cytometric Fingerprinting

“…The flow cytometric toolbox for monitoring environmental communities already contains algorithms for estimating community-level diversity ( 16 , 40 ), stability ( 15 ), and turnover ( 41 ), as well as algorithms that allow one to associate population dynamics with environmental or experimental parameters ( 13 ) and pipelines that are designed for community-level classification into different categories (e.g., diseased/healthy, etc.) ( 42 ).…”

Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting

“…The flow cytometric toolbox for monitoring environmental communities already contains algorithms for estimating community level diversity (Props et al ., 2016; Wanderley et al ., 2019), stability (Liu et al, 2018) and turnover (Liu and Müller, 2020), as well as algorithms that allow to associate population dynamics with environmental or experimental parameters (Koch, Fetzer, Harms, and Müller, 2013) and pipelines that are designed for community-level classification into different categories (e.g. diseased/healthy, etc.)…”

The Impact of the Antibiotic Fosfomycin on Wastewater Communities Measured by Flow Cytometry

“…The partitioning of β-diversity (βSOR, Sørensen pairwise dissimilarity) to nestedness (βNES) and turnover (βSIM) components was conducted using the “betapart” R package. For batch experiment, βNES and βSIM were analyzed intracommunity pairwise at successive time points and intercommunity pairwise at different augmentation proportions using the function “beta.pair.” For the continuous bioreactor experiment, samples from phase 4 (432–672 h) were analyzed intracommunity wise using the function “beta.multi” ( Baselga, 2010 ; Liu and Müller, 2020 ).…”

“…For batch experiment, βNES and βSIM were analyzed intracommunity pairwise at successive time points and intercommunity pairwise at different augmentation proportions using the function "beta.pair." For the continuous bioreactor experiment, samples from phase 4 (432-672 h) were analyzed intracommunity wise using the function "beta.multi" (Baselga, 2010;Liu and Müller, 2020).…”