Adaptation and assembly of microbial communities under saline-alkaline stress in paddy ecosystems: Implications for nitrogen and carbon cycling

Rice cultivation is a pivotal strategy in mitigating soil salinity, with nitrogen (N) and carbon (C) being key elements in food production. However, the microbial mechanisms driving their transformation under varying saline-alkali conditions remain insufficiently understood. This study investigated dynamic changes in N and C across high (H), medium (M), and low (L) saline-alkali paddy soils, revealing microbial community assembly and the underlying mechanisms. Post-harvest, H treatment exhibited higher levels of nitrous-N (NO2--N), nitrate-N (NO3--N), and total-N (TN) compared to L. Total organic-C (TOC) increased across all treatments with the increasing growing time of rice, with H showing the largest gain (82.73%) after harvest relative to pre-planting. However, rice grains in H treatment had significantly lower TN and TOC content than L (p < 0.05). Soil salinity-alkalinity was negatively correlated with nutrient (i.e., N and C) content and microbial diversity, with declines in Acidobacteriota and Actinomycetes, and increases in Chloroflexi, Planctomycetes, and Firmicutes. Based on a Random Forest model, biomarker taxa (Parcubacteria_genera_incertae_sedis, Deltaproteobacteria, Gemmatimonas, and Flavobacterium) that were sensitive to fluctuations in soil pH and EC was identified. The assembly of bacterial community was driven by stochastic drift (DR) and deterministic processes (HoS), with the latter being pronounced in H treatment. Key dominant species (Sphingomonadaceae, Anaerolineaceae, and Chitinophagaceae) adapted well to high salinity, while less resilient species were outcompeted, potentially reducing community diversity. This study underscores the complex responses of microorganisms to salinity-alkalinity gradients, affecting soil processes, nutrient cycling, and community structure. Specific taxa serve as indicators of environmental stress, offering insights into ecosystem resilience under varying stress scenarios.