Nitrogen use aggravates bacterial diversity and network complexity responses to temperature

Nitrogen fertilization practices alter microbial communities driven by clonal integration in Moso bamboo

Nitrogen (N) fertilization is crucial for maintaining plant productivity. Clonal plants can share resources between connected ramets through clonal integration influencing microbial communities and regulating soil biogeochemical cycling, especially in the rhizosphere. However, the effect of various N fertilization practices on microbial communities in the rhizosphere of clonal ramets remain unknown. In this study, clonal fragments of Moso bamboo (Phyllostachys edulis), consisting of a parent ramet, an offspring ramet, and an interconnecting rhizome, were established in the field. NH4NO3 solution was applied to the parent, offspring ramets or rhizomes to investigate the effect of fertilization practices on the structure and function of rhizosphere microbial communities. The differences in N availability, microbial biomass and community composition, and abundance of nitrifying genes among rhizosphere soils of ramets gradually decreased during the rapid growth of Moso bamboo, irrespective of fertilization practice. The soil N availability variation, particularly in NO3-, caused by fertilization practices altered the rhizosphere microbial community. Soil N availability and stable microbial biomass N in parent fertilization were the highest, being 9.0 % and 18.7 %, as well as 60.8 % and 90.4 % higher than rhizome and offspring fertilizations, respectively. The microbial network nodes and links in rhizome fertilization were 1.8 and 7.5 times higher than in parent and offspring fertilization, respectively. However, the diversity of bacterial community and abundance of nitrifying and denitrifying genes were the highest in offspring fertilization among three practices, which may be associated with increased N loss. Collectively, the rhizosphere microbial community characteristics depended on fertilization practices by altering the clonal integration of N in Moso bamboo. Parent and rhizome fertilization were favorable for N retention in plant-soil system and resulted in more stable microbial functions than offspring fertilization. Our findings provide new insights into precision fertilization for the sustainable Moso bamboo forest management.

Systematic tracking of nitrogen sources in complex river catchments: Machine learning approach based on microbial metagenomics

Tracking nitrogen pollution sources is crucial for the effective management of water quality; however, it is a challenging task due to the complex contaminative scenarios in the freshwater systems. The contaminative pattern variations can induce quick responses of aquatic microorganisms, making them sensitive indicators of pollution origins. In this study, the soil and water assessment tool, accompanied by a detailed pollution source database, was used to detect the main nitrogen pollution sources in each sub-basin of the Liuyang River watershed. Thus, each sub-basin was assigned to a known class according to SWAT outputs, including point source pollution-dominated area, crop cultivation pollution-dominated area, and the septic tank pollution-dominated area. Based on these outputs, the random forest (RF) model was developed to predict the main pollution sources from different river ecosystems using a series of input variable groups (e.g., natural macroscopic characteristics, river physicochemical properties, 16S rRNA microbial taxonomic composition, microbial metagenomic data containing taxonomic and functional information, and their combination). The accuracy and the Kappa coefficient were used as the performance metrics for the RF model. Compared with the prediction performance among all the input variable groups, the prediction performance of the RF model was significantly improved using metagenomic indices as inputs. Among the metagenomic data-based models, the combination of the taxonomic information with functional information of all the species achieved the highest accuracy (0.84) and increased median Kappa coefficient (0.70). Feature importance analysis was used to identify key features that could serve as indicators for sudden pollution accidents and contribute to the overall function of the river system. The bacteria Rhabdochromatium marinum, Frankia, Actinomycetia, and Competibacteraceae were the most important species, whose mean decrease Gini indices were 0.0023, 0.0021, 0.0019, and 0.0018, respectively, although their relative abundances ranged only from 0.0004 to 0.1 %. Among the top 30 important variables, functional variables constituted more than half, demonstrating the remarkable variation in the microbial functions among sites with distinct pollution sources and the key role of functionality in predicting pollution sources. Many functional indicators related to the metabolism of Mycobacterium tuberculosis, such as K24693, K25621, K16048, and K14952, emerged as significant important factors in distinguishing nitrogen pollution origins. With the shortage of pollution source data in developing regions, this suggested approach offers an economical, quick, and accurate solution to locate the origins of water nitrogen pollution using the metagenomic data of microbial communities.

Impacts of Thermal Drainage on Bacterial Diversity and Community Construction in Tianwan Nuclear Power Plant

As one of the low-carbon and high-efficient energy sources, nuclear power is developing vigorously to alleviate the crisis of global climate warming and realize carbon neutrality goals. Meanwhile, the ecological effect of thermal drainage in the nuclear power plant is significantly remarkable, which environmental assessment system has not yet referred to microorganisms. The rapid response of microbial diversity and community structure to environmental changes is crucial for ecosystem stability. This study investigated the bacterial diversity, community construction, and the co-occurrence patterns by 16S rRNA gene amplicon sequencing among gradient warming regions in Tianwan Nuclear Power Plant. The alpha diversity of the high warming region was the lowest in summer, which was dominated by Proteobacteria, whereas the highest bacterial diversity presented in high warming regions in winter, which harbored higher proportions of Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes. The spatial distribution of bacterial communities showed clear separation especially in summer. Strong correlations were between community compositions and environmental factors, such as salinity, DO, TN, and temperature in summer. Furthermore, remarkable seasonality in bacterial co-occurrence patterns was discovered: the robustness of the bacterial co-occurrence network was promoted in winter, while the complexity and robustness were decreased in summer due to the warming of thermal drainage. These findings reveal the potential factors underpinning the influence of thermal drainage on bacterial community structure, which make it possible to predict the ecological effect of the nuclear power plants by exploring how the microbial assembly is likely to respond to the temperature and other environmental changes.

Differentiated responses of the phyllosphere bacterial community of the yellowhorn tree to precipitation and temperature regimes across Northern China

Introduction

As an ephemeral and oligotrophic environment, the phyllosphere harbors many highly diverse microorganisms. Importantly, it is known that their colonization of plant leaf surfaces is considerably influenced by a few abiotic factors related to climatic conditions. Yet how the dynamics of phyllosphere bacterial community assembly are shaped by detailed climatological elements, such as various bioclimatic variables, remains poorly understood.

Methods

Using high-throughput 16S rRNA gene amplicon sequencing technology, we analyzed the bacterial communities inhabiting the leaf surfaces of an oilseed tree, yellowhorn (Xanthoceras sorbifolium), grown at four sites (Yinchuan, Otogqianqi, Tongliao, and Zhangwu) whose climatic status differs in northern China.

Results and Discussion

We found that the yellowhorn phyllosphere's bacterial community was generally dominated by four phyla: Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. Nevertheless, bacterial community composition differed significantly among the four sampled site regions, indicating the possible impact of climatological factors upon the phyllosphere microbiome. Interestingly, we also noted that the α-diversities of phyllosphere microbiota showed strong positive or negative correlation with 13 bioclimatic factors (including 7 precipitation factors and 6 temperature factors). Furthermore, the relative abundances of 55 amplicon sequence variants (ASVs), including three ASVs representing two keystone taxa (the genera Curtobacterium and Streptomyces), exhibited significant yet contrary responses to the precipitation and temperature climatic variables. That pattern was consistent with all ASVs' trends of possessing opposite correlations to those two parameter classes. In addition, the total number of links and nodes, which conveys community network complexity, increased with rising values of most temperature variables. Besides that, remarkably positive relevance was found between average clustering coefficient and most precipitation variables. Altogether, these results suggest the yellowhorn phyllosphere bacterial community is capable of responding to variation in rainfall and temperature regimes in distinctive ways.