Habitat filtering shapes the differential structure of microbial communities in the Xilingol grassland

Abstract

The spatial variability of microorganisms in grasslands can provide important insights regarding the biogeographic patterns of microbial communities. However, information regarding the degree of overlap and partitions of microbial communities across different habitats in grasslands is limited. This study investigated the microbial communities in three distinct habitats from Xilingol steppe grassland, i.e. animal excrement, phyllosphere, and soil samples, by Illumina MiSeq sequencing. All microbial community structures, i.e. for bacteria, archaea, and fungi, were significantly distinguished according to habitat. A high number of unique microorganisms but few coexisting microorganisms were detected, suggesting that the structure of microbial communities was mainly regulated by species selection and niche differentiation. However, the sequences of those limited coexisting microorganisms among the three different habitats accounted for over 60% of the total sequences, indicating their ability to adapt to variable environments. In addition, the biotic interactions among microorganisms based on a co-occurrence network analysis highlighted the importance of Microvirga, Blastococcus, RB41, Nitrospira, and four norank members of bacteria in connecting the different microbiomes. Collectively, the microbial communities in the Xilingol steppe grassland presented strong habitat preferences with a certain degree of dispersal and colonization potential to new habitats along the animal excrement- phyllosphere-soil gradient. This study provides the first detailed comparison of microbial communities in different habitats in a single grassland, and offers new insights into the biogeographic patterns of the microbial assemblages in grasslands.

Diverse recruitment to a globally structured atmospheric microbiome

“…Dryland soils (dry deserts, polar/alpine and dry continental locations) were pronounced sources for bacteria globally and this may reflect the more readily aerosolised non-cohesive soils typical of these biomes 41 . This expands the influence of deserts to a global scale beyond the well-defined intercontinental desert dust transit routes for microbial dispersal 40 . For the fungi, polar and alpine soils were major sources and this is congruent with the notion that permanently cold surface substrates in these environments have been proposed to act as long-term reservoirs for inactive fungal propagules 4 .…”

“…Calculation of geographic distances were performed using R package geosphere 35 function distGeo with WGS84 ellipsoid. Source tracking was conducted using FEAST 36 with data from other studies (processed using dada2 following the same parameters as this study) as additional sources/sinks [37][38][39][40][41][42][43] and NCBI BioProject PRJEB42801. For correlation analysis between abiotic and biotic variables the Pearson correlation coefficient for multiple pairwise combinations were calculated using the R package Corrplot 44 .…”

“…Aerosolization of microorganisms not only occurs from soil but also from different terrestrial and aquatic surfaces, e.g. ocean surface waters 3,20 , the phyllosphere 33,39 and stochastic desert dust events 40,41 . We therefore employed a statistical classification algorithm to estimate recruitment to air microbiomes from the surface habitats of different climatic regions (Fig.…”

“…We envisage soil and other surface habitats act as a global reservoir for bacterial diversity, whereas fungi were more diverse in air than soil and so the atmosphere may be viewed as a global reservoir for fungal diversity. We have also shown that drylands were a major contributor to global atmospheric microbial diversity and because desert soils are characterised by stochastic emissions that travel inter-continental distances, we assume stochasticity punctuates regional patterns and accounts for some of the unexplained sources 40 . Communities in air were taxonomically and functionally structured and show clear signs of selective influence on their assembly, despite evidence for recruitment from diverse habitats and many taxa with undetermined source.…”

Diverse recruitment to a globally structured atmospheric microbiome

“…Calculation of geographic distances were performed using R package geosphere 89 function distGeo with WGS84 ellipsoid. Source tracking was conducted by fast expectation-maximization using FEAST 44 with data from other studies (processed using dada2 following the same parameters as this study) as additional sources/sinks [90][91][92][93][94][95][96] and NCBI BioProject PRJEB42801. For correlation analysis between abiotic and biotic variables the Pearson correlation coefficient for multiple pairwise combinations were calculated using the R package corrplot 97 , with P-value cut-off of 0.05 corrected for multiple tests using Bonferroni correction.…”