Microbial communities in the diagnostic horizons of agricultural Isohumosols in northeast China reflect their soil classification

• Soil pH play a crucial role regulating microbial communities along with diagnostic horizons. • C horizons had greater variations in microbial communities compared to Ah horizons. • Soil microbiomes can be served as quantitative index for soil classification. Soil depth greatly affects soil microorganisms due to the intensive changes in soil physical and chemical properties along soil profile. However, little is known about microbial communities in diagnostic horizons of soil profiles among different soil types, which is the key to understanding the biogeochemical cycling in deep soils. Herein, we collected soil samples from eight agricultural fields across Heilongjiang Province of China, which belong to two suborders of Isohumosols (Ustic and Udic), based on the diagnostic horizons. The microbial community abundances, diversities and structures were comparatively investigated using qPCR and high-throughput sequencing methods. Results showed that the abundances of bacteria, archaea, and fungi consistently decreased more than by 90% in C horizon (parent material horizons) compared with those in Ah horizons (humus horizons). In items of alpha diversity, the fungal diversity decreased by>50%, while archaeal diversity increased by more than two folds. The bacterial diversity varied along soil depths at different sites. In addition, all soil microbial community structures were obviously divided into Ustic and Udic groups, and a distinct succession of microbial communities was detected from Ah horizons to C horizons at individual sites. Moreover, compared to Ah horizons, the difference of microbial community structure between Ustic and Udic Isohumosols was greater in C horizons. Canonical correspondence analysis (CCA) and random forest (RF) analysis revealed that pH was the most important soil factor regulating the microbial communities among all tested edaphic variables. More importantly, using machine-learning methods, we found that soil microbial communities can be used to accurately predict two suborders of Isohumosols and diagnostic horizons. Overall, the findings of this study highlight that the microbial data of diagnostic horizons can be served as quantitative indices for the soil classification, and this conclusion needs to be verified in the future research using more soil types.