Microbial networks are an increasingly popular tool to investigate microbial community structure, as they integrate multiple types of information and may represent systems-level behaviour. Interpreting these networks is not straightforward, and the biological implications of network properties are unclear. Analysis of microbial networks allows researchers to predict hub species and species interactions. Additionally, such analyses can help identify alternative community states and niches. Here, we review factors that can result in spurious predictions and address emergent properties that may be meaningful in the context of the microbiome. We also give an overview of studies that analyse microbial networks to identify new hypotheses. Moreover, we show in a simulation how network properties are affected by tool choice and environmental factors. For example, hub species are not consistent across tools, and environmental heterogeneity induces modularity. We highlight the need for robust microbial network inference and suggest strategies to infer networks more reliably.

From hairballs to hypotheses-biological insights from microbial networks.

Microbial interactions shape the structure and function of microbial communities; microbial co-occurrence networks in specific environments have been widely developed to explore these complex systems, but their interconnection pattern across microbiomes in various environments at the global scale remains unexplored. Here, we have inferred an Earth microbial co-occurrence network from a communal catalog with 23,595 samples and 12,646 exact sequence variants from 14 environments in the Earth Microbiome Project dataset. This non-random scale-free Earth microbial co-occurrence network consisted of 8 taxonomy distinct modules linked with different environments, which featured environment specific microbial co-occurrence relationships. Different topological features of subnetworks inferred from datasets trimmed into uniform size indicate distinct co-occurrence patterns in the microbiomes of various environments. The high number of specialist edges highlights that environmental specific co-occurrence relationships are essential features across microbiomes. The microbiomes of various environments were clustered into two groups, which were mainly bridged by the microbiomes of plant and animal surface. Acidobacteria Gp2 and Nisaea were identified as hubs in most of subnetworks. Negative edges proportions ranged from 1.9% in the soil subnetwork to 48.9% the non-saline surface subnetwork, suggesting various environments experience distinct intensities of competition or niche differentiation. This investigation highlights the interconnection patterns across microbiomes in various environments and emphasizes the importance of understanding co-occurrence feature of microbiomes from a network perspective.

/pdf/earth-microbial-co-occurrence-network-reveals-2lbxu4ks4t.pdf

Earth microbial co-occurrence network reveals interconnection pattern across microbiomes

Significant differences in the microbial biomass and diversity in soil have been observed following long-term fertilization, but little research has explored the co-occurrence patterns among microbial taxa or functions. Soil samples from four long-term experiments in China were collected, and an Illumina amplicon sequencing of 16S rDNA amplicon was performed to decipher the differences in interactions and network organization arising from fertilization with organic amendment (OA) or chemical fertilizer (CF) across the sampling sites. We thus aimed to extend the analysis beyond the basic inventory descriptions of the composition and diversity of the microbiome and toward an assessment of relationships and effects. Nonmetric multidimensional scaling analysis revealed a clear difference between the soil microbiomes associated with the OA and CF treatments. Fertilization can interact with the soil chemical properties and sampling site, thereby significantly impacting the associated soil microbiome. Distinct network structures indicated that the OA network was characterized by more functionally interrelated operational taxonomic units (OTUs) than the CF network. The topological roles of individual OTUs and key microbial populations were distinctly different, although the generalists mainly belonging to Actinobacteria and Proteobacteria were observed in both networks. The different fertilization treatments had distinct effects on the soil pH, which represented a key variable to impact the associated microbial community modularity. Most functional groups involving carbon-, nitrogen- and phosphorus-related metabolism and recycling and antibiotic biosynthesis were present at significantly high abundance in the organic-amended soils. All of the functions with statistical significance could be traced to corresponding responsible major modules in the CF network, but it was not the case in OA soil. These results suggested that the soil functions were decentralized and assigned to small groups in OA soil. Overall, we demonstrated that long-term organic amendment supports stronger functional potentials and more interactions within soil community relative to long-term chemical-only fertilization, which may be related to OA benefits to soil stability and buffering capacity. These results explore the correlations, inter-taxa and between major groups and functional traits, to gain a more integrated understanding of microbiome and ecological rules guiding soil community fostered by fertilization regimes.

Insight into how organic amendments can shape the soil microbiome in long-term field experiments as revealed by network analysis

Biochar amendments have been widely proposed as a conventional and efficient strategy to promote soil organic carbon (SOC) sequestration via negative priming. Unfortunately, the extent and biological mechanisms responsible for biochar-induced negative priming are still not fully understood. Despite traditional explanations focused on the environmental filtering mechanisms of biochar amendments on microbial biomass and community composition underlying the priming effect on SOC dynamics, whether and how a biochar-induced competitive interaction with keystone taxa determines SOC mineralization in natural ecosystems has been minimally explored. Here, we paid particular attention to the relationships between the diversity and network structure of soil bacterial and fungal communities and SOC mineralization. A 3-year field experiment was conducted comprising five treatments: no fertilization, conventional fertilization, and conventional fertilization with three rates of biochar amendments. Biochar amendments considerably increased soil moisture capacity and pH and subsequently shaped the composition and co-occurrence networks of soil bacterial and fungal communities. Importantly, network analysis revealed that the biochar amendments triggered the competitive interaction with putative keystone taxa in the bacterial and fungal networks. Structural equation modeling suggested that the competitive interaction with keystone taxa promoted bacterial and fungal diversity and consequently reduced carbohydrate catabolism and soil metabolic quotient. Stable isotope probing incubations further provided consistent evidence of competition by keystone taxa with the increases in bacterial and fungal diversity under the biochar amendments. We found that biochar-induced competition with keystone taxa stimulated the bacterial and fungal diversity and consequently decreased SOC mineralization. The comprehensive understanding of the unexplored biological mechanisms underlying the biochar-induced negative priming may provide crucial implications for enabling SOC sequestration.

/pdf/competitive-interaction-with-keystone-taxa-induced-negative-589nh909j5.pdf

Competitive interaction with keystone taxa induced negative priming under biochar amendments

Nematode predation has important roles in determining bacterial community composition and dynamics, but the extent of the effects remains largely rudimentary, particularly in natural environment settings. Here, we investigated the complex microbial–microfaunal interactions in the rhizosphere of maize grown in red soils, which were derived from four long-term fertilization regimes. Root-free rhizosphere soil samples were separated into three aggregate fractions whereby the abundance and community composition were examined for nematode and total bacterial communities. A functional group of alkaline phosphomonoesterase (ALP) producing bacteria was included to test the hypothesis that nematode grazing may significantly affect specific bacteria-mediated ecological functions, that is, organic phosphate cycling in soil. Results of correlation analysis, structural equation modeling and interaction networks combined with laboratory microcosm experiments consistently indicated that bacterivorous nematodes enhanced bacterial diversity, and the abundance of bacterivores was positively correlated with bacterial biomass, including ALP-producing bacterial abundance. Significantly, such effects were more pronounced in large macroaggregates than in microaggregates. There was a positive correlation between the most dominant bacterivores Protorhabditis and the ALP-producing keystone 'species' Mesorhizobium. Taken together, these findings implicate important roles of nematodes in stimulating bacterial dynamics in a spatially dependent manner.

/pdf/nematode-grazing-promotes-bacterial-community-dynamics-in-ikfjrec3yl.pdf

Nematode grazing promotes bacterial community dynamics in soil at the aggregate level

A central aim of this microbial ecology research was to investigate the mechanisms shaping the assembly of soil microbial communities. Despite the importance of bacterial and fungal mediation of carbon cycling in forest ecosystems, knowledge concerning their distribution patterns and underlying mechanisms remains insufficient. Here, soils were sampled from six bamboo forests across the main planting area of Moso bamboo in southern China. The bacterial and fungal diversities were assessed by sequencing 16S rRNA and ITS gene amplicons, respectively, with an Illumina MiSeq. Based on structural equation modelling, dispersal limitation had strongest impact on bacterial beta diversity, while the mean annual precipitation had a smaller impact by directly or indirectly mediating the soil organic carbon density. However, only the mean annual temperature and precipitation played direct roles in fungal beta diversity. Moreover, the co-occurrence network analyses revealed a possibly much higher network connectivity in the fungal network than in the bacteria. With less dispersal limitation, stronger environmental selection and a potentially more connected network, the fungal community had more important roles in the soil carbon metabolisms in bamboo forests. Fungal beta diversity and the clustering coefficient explained approximately 14.4% and 6.1% of the variation in the carbon metabolic profiles among sites, respectively, but that of bacteria only explained approximately 1.7% and 1.8%, respectively. This study explored soil microbial spatial patterns along with the underlying mechanisms of dispersal limitation, selection and connectivity of ecological networks, thus providing novel insights into the study of the distinct functional traits of different microbial taxa.

Fungal community reveals less dispersal limitation and potentially more connected network than that of bacteria in bamboo forest soils

Nitrification plays a central role in global nitrogen cycle, which is affected by biological interaction between soil microfauna and microorganisms. However, the complexity of soil biotic communities made it difficult to reveal organizational principles of the community and the interactions among species. Here, we used the network analysis to decipher the interactions between nematodes and ammonia oxidizers within aggregate fractions under 10-year manure application, and examine their associations with soil variables and potential nitrification activity (PNA). Three aggregate fractions included large macroaggregates (>2000 μm, LA), small macroaggregates (250–2000 μm, SA), and inter-aggregate soil and space (

Aggregate-related changes in network patterns of nematodes and ammonia oxidizers in an acidic soil

Carbon storage estimation for fast-growing bamboos remains limited because of its wide distribution and spatial variation. This study estimates biomass production and carbon storage of a moso bamboo (Phyllostachys pubescens Mazel ex Houz.) forest in southern China. A diameter–age bivariate distribution model based on the Weibull distribution function and a single bamboo biomass estimation model based on diameter and age were used to predict the biomass and carbon storage. The parameters of the two models were obtained on the basis of the observation data. Results showed that moso bamboo biomass varied with age and diameter at breast height (DBH). The corresponding estimated biomass and carbon storage in the study area were 88.23 and 40.45 Mg ha, respectively. One-sixth of older culms from the entire stand were removed by selective cutting, and the annual yields of biomass and carbon were 14.07 and 6.74 Mg ha, respectively. The moso bamboo forest has higher carbon sequestration capacity than Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) forest, tropical mountain rain forest, loblolly pine (Pinus taeda L.) forest and oriental oak (Quercus variabilis Bl.). The present model could be used well to estimate accurately the biomass carbon of moso bamboo at regional scale in southern China.

/pdf/estimation-of-biomass-and-carbon-storage-of-moso-bamboo-1f6cf9yh02.pdf

Estimation of biomass and carbon storage of moso bamboo (Phyllostachys pubescens Mazel ex Houz.) in southern China using a diameter–age bivariate distribution model

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Fungal community reveals less dispersal limitation and potentially more connected network than that of bacteria in bamboo forest soils

Aggregate-related changes in network patterns of nematodes and ammonia oxidizers in an acidic soil

Estimation of biomass and carbon storage of moso bamboo (Phyllostachys pubescens Mazel ex Houz.) in southern China using a diameter–age bivariate distribution model