Response mechanisms of sediment microbial communities in different habitat types in a shallow lake

A bacteria-based index of biotic integrity indicates aquatic ecosystem restoration

Intensive ecological interventions have been carried out in highly polluted shallow lakes to improve their environments and restore their ecosystems. However, certain treatments, such as dredging polluted sediment and stocking fish, can impact the aquatic communities, including benthos and fishes. These impacts can alter the composition and characteristics of aquatic communities, which makes community-based ecological assessments challenging. Here we develop a bacteria-based index of biotic integrity (IBI) that can clearly indicate the restoration of aquatic ecosystems with minimal artificial interventions. We applied this method to a restored shallow lake during 3-year intensive ecological interventions. The interventions reduced nutrients and heavy metals by 27.1% and 16.7% in the sediment, while the total organic carbon (TOC) increased by 8.0% due to the proliferation of macrophytes. Additionally, the abundance of sulfur-related metabolic pathways decreased by 10.5% as the responses to improved ecosystem. The score of bacteria-based IBI, which is calculated based on the diversity, composition, and function of benthic bacterial communities, increased from 0.62 in 2018 to 0.81 in 2021. Our study not only provides an applicable method for aquatic ecological assessment under intensive artificial interventions but also extends the application of IBI to complex application scenarios, such as ecosystems with significantly different aquatic communities and comparisons between different basins.

The function and keystone microbiota in typical habitats under the influence of anthropogenic activities in Baiyangdian Lake

Microbe is an essential driver in regulating the biochemical cycles of carbon, nitrogen, and sulfur. In freshwater lake, microbial communities and functions are influenced by multiple factors, especially anthropogenic activities. Baiyangdian Lake consisted of various habitats, and was frequently interfered with human activities. In this study, 16 S rRNA sequencing and metagenomic sequencing were performed to characterize the microbial communities, determine keystone taxa and reveal dominated metabolic functions in typical habitats in Baiyangdian Lake. The results showed that the diversity of microbial community was significantly higher in sediment compared with corresponding water sample. Microbial community showed strong spatial heterogeneity in sediment, and temporal heterogeneity in water. As for different habitats, significantly higher alpha diversity was observed in ecotone, where the interference of human activities was relatively weak. The shared OTUs were distinguished from the keystone taxa, which indicated the uniqueness of microbiota in different ecological habitat. Moreover, the interactions of microbial in ecological restoration area (abandoned fish pond) were relatively simple, suggesting that this ecosystem was relatively fragile compared with others. Based on the metagenomic sequencing, we recognized that the canal, open water, and abandoned fish pond were beneficial for methanogenic and the ecotone might be a hot zone for the oxidation of methane. Notably, most of the microbes that participated in these predominant metabolisms were unclassified, which indicated the hug potential for exploring functional microorganisms in Baiyangdian Lake. This study provided a comprehensive understanding of the ecology characteristics of microbiota in habitats undergoing various human interference in Baiyangdian Lake.

Bio-fertilizer Affects Structural Dynamics, Function, and Network Patterns of the Sugarcane Rhizospheric Microbiota

Fertilizers and microbial communities that determine fertilizer efficiency are key to sustainable agricultural development. Sugarcane is an important sugar cash crop in China, and using bio-fertilizers is important for the sustainable development of China's sugar industry. However, information on the effects of bio-fertilizers on sugarcane soil microbiota has rarely been studied. In this study, the effects of bio-fertilizer application on rhizosphere soil physicochemical indicators, microbial community composition, function, and network patterns of sugarcane were discussed using a high-throughput sequencing approach. The experimental design is as follows: CK: urea application (57 kg/ha), CF: compound fertilizer (450 kg/ha), BF1: bio-fertilizer (1500 kg/ha of bio-fertilizer + 57 kg/ha of urea), and BF2: bio-fertilizer (2250 kg/ha of bio-fertilizer + 57 kg/ha of urea). The results showed that the bio-fertilizer was effective in increasing sugarcane yield by 3-12% compared to the CF treatment group, while reducing soil acidification, changing the diversity of fungi and bacteria, and greatly altering the composition and structure of the inter-root microbial community. Variance partitioning canonical correspondence (VPA) analysis showed that soil physicochemical variables explained 80.09% and 73.31% of the variation in bacteria and fungi, respectively. Redundancy analysis and correlation heatmap showed that soil pH, total nitrogen, and available potassium were the main factors influencing bacterial community composition, while total soil phosphorus, available phosphorus, pH, and available nitrogen were the main drivers of fungal communities. Volcano plots showed that using bio-fertilizers contributed to the accumulation of more beneficial bacteria in the sugarcane rhizosphere level and the decline of pathogenic bacteria (e.g., Leifsonia), which may slow down or suppress the occurrence of diseases. Linear discriminant analysis (LDA) and effect size analysis (LEfSe) searched for biomarkers under different fertilizer treatments. Meanwhile, support vector machine (SVM) assessed the importance of the microbial genera contributing to the variability between fertilizers, of interest were the bacteria Anaerolineace, Vulgatibacter, and Paenibacillus and the fungi Cochliobolus, Sordariales, and Dothideomycetes between CF and BF2, compared to the other genera contributing to the variability. Network analysis (co-occurrence network) showed that the network structure of bio-fertilizers was closer to the network characteristics of healthy soils, indicating that bio-fertilizers can improve soil health to some extent, and therefore if bio-fertilizers can be used as an alternative to chemical fertilizers in the future alternative, it is important to achieve green soil development and improve the climate.

Omics-based ecosurveillance uncovers the influence of estuarine macrophytes on sediment microbial function and metabolic redundancy in a tropical ecosystem

Vertical zonation within estuarine ecosystems can strongly influence microbial diversity and function by regulating competition, predation, and environmental stability. The degree to which microbial communities exhibit horizontal patterns through an estuary has received comparatively less attention. Here, we take a multi-omics ecosurveillance approach to study environmental gradients created by the transition between dominant vegetation types along a near pristine tropical river system (Wenlock River, Far North Queensland, Australia). The study sites included intertidal mudflats fringed by saltmarsh, mangrove or mixed soft substrata habitats. Collected sediments were analyzed for eukaryotes and prokaryotes using small sub-unit (SSU) rRNA gene amplicons to profile the relative taxonomic composition. Central carbon metabolism metabolites and other associated organic polar metabolites were analyzed using established metabolomics-based approaches, coupled with total heavy metals analysis. Eukaryotic taxonomic information was found to be more informative of habitat type. Bacterial taxonomy and community composition also showed habitat-specificity, with phyla Proteobacteria and Cyanobacteria strongly linked to mangroves and saltmarshes, respectively. In contrast, metabolite profiling was critical for understanding the biochemical pathways and expressed functional outputs in these systems that were tied to predicted microbial gene function (16S rRNA). A high degree of metabolic redundancy was observed in the bacterial communities, with the metabolomics data suggesting varying degrees of metabolic criticality based on habitat type. The predicted functions of the bacterial taxa combined with annotated metabolites accounted for the conservative perspective of microbial community redundancy against the putative metabolic pathway impacts in the metabolomics data. Coupling these data demonstrates that habitat-mediated estuarine gradients drive patterns of community diversity and metabolic function and highlights the real redundancy potential of habitat microbiomes. This information is useful as a point of comparison for these sensitive ecosystems and provides a framework for identifying potentially vulnerable or at-risk systems before they are significantly degraded.