Molecular Soil Biology 2026, Vol.17, No.1, 12-25 http://bioscipublisher.com/index.php/msb 18 structure of the co-occurrence network will also show differences (Zhou et al., 2019). This indicates that the irrigation changes are not just the increase or decrease of certain species, but the original cooperative or mutual response relationships between species may also be rearranged. Cross-regional studies have also shown that some topological features of the methane-producing archaea network are closely related to the methane generation process, and the network structure itself can even help explain the flux differences (Shi et al., 2020). In other words, when comparing different irrigation modes, if only looking at single-point indicators such as mcrA or pmoA, the information may not be sufficient; treating network complexity as a response feature at the system level and combining it with environmental factors such as Eh, DOC, and nitrogen forms for analysis often helps to better understand the underlying ecological mechanisms. Figure 3 Illustration of compositional bias in amplicon sequencing data. Because microbial abundance data are constrained to relative proportions, direct correlation analysis may generate spurious associations among taxa (Adapted from Faust & Raes, 2016) 5.3 Identification of key groups and ecological function analysis In microbial network analysis, the identification of "key groups" often involves referring to some centrality indicators, such as degree centrality, betweenness centrality, or determining which nodes may be the "functional pivots" of the system based on whether they belong to module hubs or connectors (Banerjee et al., 2018). Some studies across Asian paddy fields have identified multiple potential key genera in the methane-producing archaea-related networks, and found that the co-occurrence connections of these groups and the contribution to methane production show quantifiable differences. This also reminds us that key nodes are not necessarily important just because they have many connections; sometimes they are exactly at the critical links in the substrate transformation chain. However, it should be noted that changes in key nodes in the network do not necessarily mean that the interactions themselves have become stronger or weaker. For example, under water-saving irrigation conditions, the groups related to methane production may have fewer connections, while the groups related to nitrification or iron cycling are more active, seemingly indicating a shift in the "functional center" of the network (Xue et al., 2022). Therefore, when interpreting key groups, it is usually necessary to analyze together with functional gene information (such as mcrA, pmoA) or metabolic pathway enrichment results, rather than making overly strong causal conclusions based solely on centrality indicators.
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