Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. Here we propose a general mathematical framework to quantify ecological stochasticity under different situations in which deterministic factors drive the communities more similar or dissimilar than null expectation. An index, normalized stochasticity ratio (NST), was developed with 50% as the boundary point between more deterministic (<50%) and more stochastic (>50%) assembly. NST was tested with simulated communities by considering abiotic filtering, competition, environmental noise, and spatial scales. All tested approaches showed limited performance at large spatial scales or under very high environmental noise. However, in all of the other simulated scenarios, NST showed high accuracy (0.90 to 1.00) and precision (0.91 to 0.99), with averages of 0.37 higher accuracy (0.1 to 0.7) and 0.33 higher precision (0.0 to 1.8) than previous approaches. NST was also applied to estimate stochasticity in the succession of a groundwater microbial community in response to organic carbon (vegetable oil) injection. Our results showed that community assembly was shifted from more deterministic (NST = 21%) to more stochastic (NST = 70%) right after organic carbon input. As the vegetable oil was consumed, the community gradually returned to be more deterministic (NST = 27%). In addition, our results demonstrated that null model algorithms and community similarity metrics had strong effects on quantifying ecological stochasticity.

With knowledge on microbial composition and diversity, investigation of within-community interactions is a further step to elucidate microbial ecological functions, such as the biodegradation of hazardous contaminants. In this work, microbial functional molecular ecological networks were studied in both contaminated and uncontaminated soils to determine the possible influences of oil contamination on microbial interactions and potential functions. Soil samples were obtained from an oil-exploring site located in South China, and the microbial functional genes were analyzed with GeoChip, a high-throughput functional microarray. By building random networks based on null model, we demonstrated that overall network structures and properties were significantly different between contaminated and uncontaminated soils (P < 0.001). Network connectivity, module numbers, and modularity were all reduced with contamination. Moreover, the topological roles of the genes (module hub and connectors) were altered with oil contamination. Subnetworks of genes involved in alkane and polycyclic aromatic hydrocarbon degradation were also constructed. Negative co-occurrence patterns prevailed among functional genes, thereby indicating probable competition relationships. The potential "keystone" genes, defined as either "hubs" or genes with highest connectivities in the network, were further identified. The network constructed in this study predicted the potential effects of anthropogenic contamination on microbial community co-occurrence interactions.

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Patterns in the spatial distribution of organisms provide important information about mechanisms underlying biodiversity and the complexity of ecosystems. One of the most well-documented spatial patterns is the distance-decay relationship, which is a universal biogeographic pattern observed repeatedly for plant and animal communities, particularly for microorganisms in natural ecosystems such as soil, ocean, and salt marsh sediment. However, it is uncertain whether the microorganisms exhibit a distance-decay pattern in engineered ecosystems. Therefore, we measured the distance-decay relationship across various microbial functional and phylogenetic groups in 26 biological wastewater treatment plants (WWTPs) in China using a functional gene array (GeoChip 4.2). We found that microbial communities of activated sludge in WWTPs exhibited a significant but very weak distance-decay relationship. The taxon-area z values for different functional and phylogenetic groups were <0.0065, which is about 1 to 2 orders of magnitude lower than those observed in microbial communities elsewhere. Variation-partitioning analysis (VPA) showed that the relationships were driven by both environmental heterogeneity and geographic distance. Collectively, these results provided new insights into the spatial scaling of microbial communities in engineering ecosystems and highlighted the importance of environmental heterogeneity and geographic distance in shaping biogeographic patterns.

Importance

Determining the distance-decay relationship of microbial biodiversity is important but challenging in microbial ecology. All studies to date are based on natural environments; thus, it remains unclear whether there is such a relationship in an engineered ecosystem. The present study shows that there is a very weak distance-decay relationship in an engineered ecosystem (WWTPs) at the regional-to-continental scale. This study makes fundamental contributions to a mechanistic, predictive understanding of microbial biogeography.

Patterns of variation in plant and animal diversity along precipitation gradients have been extensively studied, but much less is known about how and to what extent precipitation affects the biogeographic distribution of microbial diversity in arid areas across large spatial scales. Here we collected soils from 54 sites along a 3700 km transect covering a wide range of grassland ecosystems with distinct aridity gradients. We quantified the bacterial community diversity and the effects of climate, edaphic parameter and geographic distance on the bacterial community structure using high-throughput 16S rRNA gene sequencing. Of the 35 phyla detected, 6 were dominant: Actinobacteria, Acidobacteria, Alphaproteobacteria, Deltaproteobacteria, Bacteroidetes and Planctomycetes. Aridity was a major factor influencing bacterial diversity, community composition and taxon abundance. Although the pattern of bacterial species richness is markedly different from that of plant species richness, most soil bacteria were endemic to particular bioregions like macro-organisms. Community similarity significantly declined with environmental distance and geographic distance (r = -0.579 and -0.773, respectively). Geographic distance (historical contingencies) contributed more to bacterial community variation (36.02%) than combined environmental factors (24.06%). Overall, our results showed that geographic distance and climatic factors concurrently govern bacterial biogeographic patterns in arid and semi-arid grassland.

Ocean acidification (OA), caused by seawater CO2 uptake, has significant impacts on marine calcifying organisms and phototrophs. However, the response of bacterial communities, who play a crucial role in marine biogeochemical cycling, to OA is still not well understood. Previous studies have shown that the diversity and structure of microbial communities change undeterminably with elevated pCO2. Here, novel phylogenetic molecular ecological networks (pMENs) were employed to investigate the interactions of native bacterial communities in response to OA in the Arctic Ocean through a mesocosm experiment. The pMENs results were in line with the null hypothesis that elevated pCO2/pH does not affect biogeochemistry processes. The number of nodes within the pMENs and the connectivity of the bacterial communities were similar, despite increased pCO2 concentrations. Our results indicate that elevated pCO2 did not significantly affect microbial community structure and succession in the Arctic Ocean, suggesting bacterioplankton community resilience to elevated pCO2. The competitive interactions among the native bacterioplankton, as well as the modular community structure, may contribute to this resilience. This pMENs-based investigation of the interactions among microbial community members at different pCO2 concentrations provides a new insight into our understanding of how OA affects the microbial community.

Understanding the structure, functions, activities and dynamics of microbial communities in natural environments is one of the grand challenges of 21st century science. To address this challenge, over the past decade, numerous technologies have been developed for interrogating microbial communities, of which some are amenable to exploratory work (e.g., high-throughput sequencing and phenotypic screening) and others depend on reference genes or genomes (e.g., phylogenetic and functional gene arrays). Here, we provide a critical review and synthesis of the most commonly applied "open-format" and "closed-format" detection technologies. We discuss their characteristics, advantages, and disadvantages within the context of environmental applications and focus on analysis of complex microbial systems, such as those in soils, in which diversity is high and reference genomes are few. In addition, we discuss crucial issues and considerations associated with applying complementary high-throughput molecular technologies to address important ecological questions.

The elevational and latitudinal diversity patterns of microbial taxa have attracted great attention in the past decade. Recently, the distribution of functional attributes has been in the spotlight. Here, we report a study profiling soil microbial communities along an elevation gradient (500-2200 m) on Changbai Mountain. Using a comprehensive functional gene microarray (GeoChip 5.0), we found that microbial functional gene richness exhibited a dramatic increase at the treeline ecotone, but the bacterial taxonomic and phylogenetic diversity based on 16S rRNA gene sequencing did not exhibit such a similar trend. However, the β-diversity (compositional dissimilarity among sites) pattern for both bacterial taxa and functional genes was similar, showing significant elevational distance-decay patterns which presented increased dissimilarity with elevation. The bacterial taxonomic diversity/structure was strongly influenced by soil pH, while the functional gene diversity/structure was significantly correlated with soil dissolved organic carbon (DOC). This finding highlights that soil DOC may be a good predictor in determining the elevational distribution of microbial functional genes. The finding of significant shifts in functional gene diversity at the treeline ecotone could also provide valuable information for predicting the responses of microbial functions to climate change.

Microbial functional potential in high arsenic (As) groundwater ecosystems remains largely unknown. In this study, the microbial community functional composition of nineteen groundwater samples was investigated using a functional gene array (GeoChip 5.0). Samples were divided into low and high As groups based on the clustering analysis of geochemical parameters and microbial functional structures. The results showed that As related genes (arsC, arrA), sulfate related genes (dsrA and dsrB), nitrogen cycling related genes (ureC, amoA, and hzo) and methanogen genes (mcrA, hdrB) in groundwater samples were correlated with As, SO₄^2-, NH₄⁺ or CH₄ concentrations, respectively. Canonical correspondence analysis (CCA) results indicated that some geochemical parameters including As, total organic content, SO₄^2-, NH₄⁺, oxidation-reduction potential (ORP) and pH were important factors shaping the functional microbial community structures. Alkaline and reducing conditions with relatively low SO₄^2-, ORP, and high NH₄⁺, as well as SO₄^2- and Fe reduction and ammonification involved in microbially-mediated geochemical processes could be associated with As enrichment in groundwater. This study provides an overall picture of functional microbial communities in high As groundwater aquifers, and also provides insights into the critical role of microorganisms in As biogeochemical cycling.