Alleviated environmental constraints and restructured fungal microbiome facilitate aggregate formation and stabilization in coastal mudflat saline soil amended by sewage sludge

Soil aggregates are important drivers of soil productivity. However, the relative importance of soil abiotic and biotic agents in driving aggregate formation and stabilization remain largely unexplored, especially in coastal mudflat saline soils. We amended saline soil with sewage sludge at rates of 0, 30, 75, and 150 t ha⁻¹ to investigate the effects of sewage sludge on the particle size distribution and stability of soil aggregate and the underlying mechanisms induced by soil environmental factors and fungal community. Results revealed that the sewage sludge amendment significantly (p < 0.05) increased the proportion of macroaggregates with sizes larger than 0.25 mm (R0.25) and enhanced aggregate stability. Moreover, alleviation of saline-alkali stress and nutrient (C, N, and P) deficiency conditions were significantly (p < 0.05) observed in sewage sludge-amended soils. Additionally, restructured fungal communities in amended soils harbored significantly (p < 0.05) distinguishable structures and core and unique microbiomes. Particularly, fungal species belonging to Moterella significantly (p < 0.05) enriched in sludge reclaimed soils. Results derived from the random forest (RF) model accompanied by linear regression analysis revealed that soil pH, soil organic carbon, and fungal structural diversity were significantly (p < 0.05) related to aggregate composition (R0.25) and stability (geometric average diameter, GMD). Furthermore, fungal consortia composed of 20 closely interconnected operational taxonomic units (OTUs) affiliated with Ascomycota, Basidiomycota, and Zygomycota explained 28.36% and 49.88% variance of R0.25 and GMD, respectively. Overall, our results revealed the effect of sewage sludge on soil aggregation improvement in coastal areas and highlighted the respective importance of soil chemical properties and fungal microbiome in predicting aggregation status.

Duke Scholars

Author Chuanhui Gu DKU Faculty