Higher-dimensional ordination analysis teases out impacts of Bradyrhizobium bioaugmentation on native soil microbial...

Microbial inoculants are increasingly being used to enhance plant growth and soil environments, yet their ecological effects on native microbial communities remain unclear. We investigated the long-term dynamics of soybean-nodulating Bradyrhizobium strains carrying the nosZ gene encoding N_₂O reductase in microcosms comprising three different soils. Analysis of 16S rRNA amplicons revealed that bacterial community compositions were primarily driven by soil type and time, with inoculation having only minor effects. In contrast, nosZ clade I amplicon analysis showed high survivability of the dominant nosZ-sequence groups corresponding to the inoculant strains, which accounted for 20%-50% of the total nosZ community even 249 days after inoculation; despite this dominance, the overall community structures of indigenous nosZ-harboring Bradyrhizobium remained largely unchanged. This observation was further supported by a permutational multivariate analysis of variance, which showed a 90% reduction in R² when the inoculant sequence groups were excluded. Thus, it is possible to enhance N₂O-reducing function through inoculation without drastically disrupting the indigenous microbial community. To explore the landscapes of inoculation effects obscured in two-dimensional mapping, we applied dimensionality-reduction tools such as principal coordinates analysis beyond their typical use for visualization by extending analyses into higher-dimensional spaces. Varying the number of dimensions revealed that the signal-to-noise ratio and clustering tendency peaked at 4-10 dimensions, with further increases in dimensions leading to homogenization of community patterns. This intermediate dimensional space also revealed differences in community succession by soil type. These findings demonstrate that careful selection of dimensionality can enhance the discovery of ecological patterns.IMPORTANCEDimensionality reduction is widely used to visualize microbiome data in two- or three-dimensional ordination spaces. However, its application in higher-dimensional analysis remains underexplored. We highlighted the use of dimensionality reduction not only as a visualization tool, but as a means of projecting microbiome data into ordination spaces for geometric analyses, which are not limited to two or three dimensions. Communities were projected beyond three dimensions to examine how dimensionality affects evaluation of inoculation effects through geometric analyses. Our findings show that dimension selection strongly influences the ability to detect and resolve ecological signals, which were distorted in low-dimensional spaces or homogenized in extremely high-dimensional spaces. Intermediate dimensionalities better retained the spatial fidelity needed to resolve soil-dependent responses to inoculation. By enhancing the resolution of small but meaningful effects, this approach provides a robust framework for guiding strain selection, application strategies, and risk assessment in microbial inoculation studies.