Microdiversity characterizes prevalent phylogenetic clades in the glacier-fed stream microbiome

Abstract

Glacier-fed streams (GFSs) are extreme and rapidly vanishing ecosystems, and yet they harbor diverse microbial communities. Although our understanding of the GFS microbiome has recently increased, we do not know which microbial clades are ecologically successful in these ecosystems, nor do we understand potentially underlying mechanisms. Ecologically successful clades should be more prevalent across GFSs compared to other clades, which should be reflected as clade-wise distinctly low phylogenetic turnover. However, methods to assess such patterns are currently missing. Here we developed and applied a novel analytical framework, “phyloscore analysis”, to identify clades with lower spatial phylogenetic turnover than other clades in the sediment microbiome across twenty GFSs in New Zealand. These clades constituted up to 44% and 64% of community α-diversity and abundance, respectively. Furthermore, both their α-diversity and abundance increased as sediment chlorophyll a decreased, corroborating their ecological success in GFS habitats largely devoid of primary production. These clades also contained elevated levels of putative microdiversity than others, which could potentially explain their high prevalence in GFSs. This hitherto unknown microdiversity may be threatened as glaciers shrink, urging towards further genomic and functional exploration of the GFS microbiome.

Disturbance by soil mixing decreases microbial richness and supports homogenizing community assembly processes

“…deterministic and stochastic processes) could simultaneously be influencing community assembly. Other recent approaches have developed models to assign community assembly processes separately to different ‘bins’ of OTUs (Ning et al 2020; Fodelianakis et al 2021), though these methods risk overstating relationships between community processes and perhaps idiosyncratic OTU bins.…”

Inferring the Contribution of Microbial Taxa and Organic Matter Molecular Formulas to Ecological Assembly

“…However, while capable of identifying assembly processes at levels below the entire community, this approach still investigates processes impacting assembly at the subcommunity level rather than measuring the degree which an individual feature impacts or is impacted by assembly. Fodelianakis et al (2021) instead took an approach, called "phyloscore analysis" that focuses instead on the ecological contributions of specific taxa within a microbial community. Likewise, βNTI feat focuses on individual features to measure their ecological contribution to community dynamics and highlights a point of complementarity across community, subcommunity, and feature-level foci (Table 1).…”

“…Importantly, given that βNTI feat does not rely on abundance or taxonomicbased distance metrics, it is able to overcome many of the limitations associated with SIMPER (Warton et al, 2012). When compared to the phyloscore metric described by Fodelianakis et al (2021), it is much more similar though differs in some mathematical specifics (namely the null implementation) and in its application across different scales.…”

Genomic and metabolic adaptations of biofilms to ecological windows of opportunities in glacier-fed streams

“…Metabarcoding library preparation and sequencing. The prokaryotic 16S rRNA gene metabarcoding library preparation was performed as described in Fodelianakis et al 70 , targeting the V3-V4 hypervariable region of the 16S rRNA gene with the 341F/785R primers and following Illumina guidelines for 16S metagenomic library preparation for the MiSeq system. The eukaryotic 18S rRNA gene metabarcoding library preparation was performed likewise but using the TAReuk454F-TAReukREV3 primers to target the 18S rRNA gene V4 loop 71 .…”

“…Metabarcoding analyses. The 16S rRNA gene metabarcoding data were analysed using a combination of Trimmomatic 72 and QIIME2 73 as described in Fodelianakis et al 70 , with the exception that here the latest SILVA database 74 v138.1 was used for taxonomic classification of 16S rRNA and 18S rRNA gene amplicons. Non-bacterial ASVs including those affiliated to archaea, chloroplasts and mitochondria were discarded from the 16S rRNA amplicon dataset in all downstream analyses.…”