Background: Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.Results: Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA).Conclusions: The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

Biodiversity and its responses to environmental changes are central issues in ecology and for society. Almost all microbial biodiversity research focuses on “species” richness and abundance but not on their interactions. Although a network approach is powerful in describing ecological interactions among species, defining the network structure in a microbial community is a great challenge. Also, although the stimulating effects of elevated CO ₂ (eCO ₂ ) on plant growth and primary productivity are well established, its influences on belowground microbial communities, especially microbial interactions, are poorly understood. Here, a random matrix theory (RMT)-based conceptual framework for identifying functional molecular ecological networks was developed with the high-throughput functional gene array hybridization data of soil microbial communities in a long-term grassland FACE (free air, CO ₂ enrichment) experiment. Our results indicate that RMT is powerful in identifying functional molecular ecological networks in microbial communities. Both functional molecular ecological networks under eCO ₂ and ambient CO ₂ (aCO ₂ ) possessed the general characteristics of complex systems such as scale free, small world, modular, and hierarchical. However, the topological structures of the functional molecular ecological networks are distinctly different between eCO ₂ and aCO ₂ , at the levels of the entire communities, individual functional gene categories/groups, and functional genes/sequences, suggesting that eCO ₂ dramatically altered the network interactions among different microbial functional genes/populations. Such a shift in network structure is also significantly correlated with soil geochemical variables. In short, elucidating network interactions in microbial communities and their responses to environmental changes is fundamentally important for research in microbial ecology, systems microbiology, and global change. IMPORTANCE Microorganisms are the foundation of the Earth's biosphere and play integral and unique roles in various ecosystem processes and functions. In an ecosystem, various microorganisms interact with each other to form complicated networks. Elucidating network interactions and their responses to environmental changes is difficult due to the lack of appropriate experimental data and an appropriate theoretical framework. This study provides a conceptual framework to construct interaction networks in microbial communities based on high-throughput functional gene array hybridization data. It also first documents that elevated carbon dioxide in the atmosphere dramatically alters the network interactions in soil microbial communities, which could have important implications in assessing the responses of ecosystems to climate change. The conceptual framework developed allows microbiologists to address research questions unapproachable previously by focusing on network interactions beyond the listing of, e.g., the number and abundance of species. Thus, this study could represent transformative research and a paradigm shift in microbial ecology.

Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO ₂ , is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO ₂ enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO ₂ levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management. IMPORTANCE The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO ₂ level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research.

Significance: The study of ecological succession remains at the core of ecology. Understanding the trajectories and mechanisms controlling ecological succession is crucial to predicting the responses of ecosystems to environmental change and projecting their future states. By definition, deterministic succession is expected under homogeneous abiotic and biotic starting conditions. This study, however, shows that the succession of groundwater microbial communities in response to nutrient amendment is primarily stochastic, but that the drivers controlling biodiversity and succession are dynamic rather than static. By identifying the mechanisms controlling microbial community assembly and succession, this study makes fundamental contribution to the mechanistic understanding essential for a predictive microbial ecology of many systems ranging from microbiomes of humans and plants to natural and managed ecosystems.

Climate warming is increasingly leading to marked changes in plant and animal biodiversity, but it remains unclear how temperatures affect microbial biodiversity, particularly in terrestrial soils. Here we show that, in accordance with metabolic theory of ecology, taxonomic and phylogenetic diversity of soil bacteria, fungi and nitrogen fixers are all better predicted by variation in environmental temperature than pH. However, the rates of diversity turnover across the global temperature gradients are substantially lower than those recorded for trees and animals, suggesting that the diversity of plant, animal and soil microbial communities show differential responses to climate change. To the best of our knowledge, this is the first study demonstrating that the diversity of different microbial groups has significantly lower rates of turnover across temperature gradients than other major taxa, which has important implications for assessing the effects of human-caused changes in climate, land use and other factors.