Importance of soil labile organic carbon fractions in shaping microbial community after vegetation restoration

Abstract

Studies have been increasingly focusing on soil carbon (C) pools to facilitate the sequestration and stabilisation of soil organic carbon (SOC). However, interactions between different SOC fractions and microbial communities have remained largely unexplored after vegetation restoration on the Loess Plateau. To address this gap, we analysed 16S and ITS rRNA gene sequencing, active SOC (Ca), slow SOC (Cs), passive SOC (Cp) and edaphic factors to decipher the importance of soil C fractions in shaping microbial communities after vegetation restoration. Compared to CK (Control, cropland), the average of SOC contents improved 70.52 % (p < 0.05), and the Ca, Cs and Cp improved 83.18 % (p < 0.05), 128.74 % (p < 0.05) and 18.69 % (p > 0.05). Under different land use types, the relative abundance of four bacterial phyla and two fungal phylum were > 80 % and 75 %, respectively. The Actinobacteria relative abundance in natural grassland and natural shrubland was higher than that in CK. The Shannon index, Chao index and OTUs of soil fungal and bacterial communities under CK were significantly lower than that of other land use types, except for artificial grassland (p < 0.05). The microbial communities transited from oligotrophic to copiotrophic groups according to the results of redundancy analysis (RDA), and the cumulative loads of two axes of RDA in bacterial and fungal communities approached 89.90 % and 88.19 %. The variation partitioning analysis showed that Ca and Cs exhibited large path coefficients in explaining the changes in soil microbial communities. And the microbial communities were mediated through change in labile organic C according to structural equation models, indicating that the structures and compositions of microbial communities were mainly under the control of labile SOC content after vegetation restoration. These results are referentially valuable for comprehending how to apply labile SOC in predicting variations of microbial communities and improving predictive ecosystem models for early-warning indicators of soil degradation.