Molecular Soil Biology 2026, Vol.17, No.1, 12-25 http://bioscipublisher.com/index.php/msb 17 practice nowadays is to analyze the gene abundance obtained by qPCR together with transcriptional level information, while monitoring environmental factors such as Eh, DOC, NH4 +, andNO3 - (Deng et al., 2021). This makes it easier to establish a complete explanatory framework. One important point to note is that the number of genes and the actual gas flux do not simply correspond. Therefore, it is often necessary to combine process measurements and use statistical methods such as structural equation or mediation analysis to more clearly define the relationship between "genes - processes - fluxes". 4.3 The impact of irrigation modes on the composition of functional microbial communities Numerous field studies have shown that when the irrigation method changes, the microbial community structure in paddy fields also adjusts accordingly. The key factor is often the reconfiguration of the redox environment: after changes in water management, the dominance relationships of methanogenic archaea, methane-oxidizing bacteria, and the fermentation and synthesis bacteria that provide substrates for them all change (Liu et al., 2019). Comparative studies using metagenomic data have demonstrated that compared to long-term flooding irrigation (FI), water-saving irrigation methods such as alternate wetting and drying or intermittent irrigation often lead to a decrease in the relative abundance of methanogenic groups of archaea; at the functional level, the enrichment of genes related to methane metabolism also decreases, while pathways related to carbohydrate decomposition and nitrification become more active (Wang et al., 2021). These changes suggest that AWD not only may inhibit methane production but also may cause the nitrogen cycle to develop in a more oxidized direction. However, in some field experiments, another situation has also occurred: although water-saving irrigation significantly reduces CH4 emissions and affects the comprehensive warming potential of greenhouse gases, the changes in community abundance at the 16S level are not significant. That is to say, the community structure may only have undergone slight adjustments, while the functional expression has changed significantly. This is also the reason why many subsequent studies have introduced network analysis and multi-omics methods. 5 Structure Characteristics of Soil Microbial Co-occurrence Network 5.1 Network construction methods and topological parameters There is a common problem with the amplicon data from paddy fields, which is that the "constitutive" features are quite obvious. If a direct correlation analysis is conducted, it is easy to obtain some false correlations. To solve this problem, some specialized methods have been developed later, such as SparCC, which is based on the logarithmic ratio concept and estimates the correlation structure between variables under the sparse assumption; and SPIEC-EASI, which combines the composition data transformation and sparse graph model to infer the potential association network through conditional independence relationships (Kurtz et al., 2015). Generally, when constructing such a network, there is a set of basic steps: first, filter out low-abundance nodes, then perform data normalization or transformation, then select an appropriate inference algorithm, and conduct significance tests and multiple comparison corrections; if one wants to compare the networks between different treatments, it is necessary to ensure comparability, such as using the same node set or a unified sparsity threshold. The network structure is usually described by some topological indicators, such as the number of nodes and edges, average degree, clustering coefficient, average path length, modularity, and the proportion of positive and negative associations. Among them, modularity is often used to observe possible functional partitions, while average degree and clustering coefficient reflect whether the network connections are tight. However, it should be noted that the co-occurrence network only shows statistical correlations and cannot directly indicate real interaction relationships. Therefore, it is usually necessary to combine environmental factors and functional evidence for analysis (Figure 3) (Faust and Raes, 2016). 5.2 Comparison of network complexity under different irrigation modes When comparing microbial networks under different irrigation conditions, the main intention is to see if the changes in water content will cause adjustments to the overall organizational structure of the system. Some macro-genomic studies in the field have found that between continuous flooding (FI) and the two water-saving irrigation methods (AI/AWD, RI), not only will the microbial composition be significantly separated, but the
RkJQdWJsaXNoZXIy MjQ4ODYzNA==