Soil pH and Organic Carbon Properties Drive Soil Bacterial Communities in Surface and Deep Layers Along an Elevational Gradient

Elevational gradients strongly affect the spatial distribution and structure of soil bacterial communities. However, our understanding of the effects and determining factors is still limited, especially in the deep soil layer. Here, we investigated the diversity and composition of soil bacterial communities in different soil layers along a 1,500-m elevational gradient in the Taibai Mountain. The variables associated with climate conditions, plant communities, and soil properties were analyzed to assess their contributions to the variations in bacterial communities. Soil bacterial richness and α-diversity showed a hump-shaped trend with elevation in both surface and deep layers. In the surface layer, pH was the main factor driving the elevational pattern in bacterial diversity, while in the deep layer, pH and soil carbon (C) availability were the two main predictors. Bacterial community composition differed significantly along the elevational gradient in all soil layers. In the surface layer, Acidobacteria, Delta-proteobacteria, and Planctomycetes were significantly more abundant in the lower elevation sites than in the higher elevation sites; and Gemmatimonadetes, Chloroflexi, and Beta-proteobacteria were more abundant in the higher elevation sites. In the deep layer, AD3 was most abundant in the highest elevation site. The elevational pattern of community composition co-varied with mean annual temperature, mean annual precipitation, diversity and basal area of trees, pH, soil C availability, and soil C fractions. Statistical results showed that pH was the main driver of the elevational pattern of the bacterial community composition in the surface soil layer, while soil C fractions contributed more to the variance of the bacterial composition in the deep soil layer. These results indicated that changes in soil bacterial communities along the elevational gradient were driven by soil properties in both surface and deep soil layers, which are critical for predicting ecosystem functions under future climate change scenarios.