Using a combination of PLFA and DNA-based sequencing analyses to detect shifts in the soil microbial community composition after a simulated spring precipitation in a semi-arid grassland in China.

Increased spring precipitation in semi-arid grasslands could improve annual primary productivity. However, little is known about the responses of soil microbes to individual spring precipitation. In this study, we combined phospholipid fatty acid (PLFA) and DNA-based high-throughput sequencing analyses to investigate short-term (days) shifts in the soil microbial community composition after a simulated spring precipitation. Under field conditions, the soils (approx. -0.3 MPa) were exposed to either a watering of 20 cm or natural drought, and soil samples were collected at days 1, 3, 5, 8, and 12 after watering. Soil labile organic carbon (C) and nitrogen (N) as well as microbial biomass C (MBC) were positively correlated with soil water content (SWC). Spring watering significantly increased plant phosphorus (P) uptake, but had no impact on soil available P (AP). Watering increased the PLFA biomarkers indicative for Gram-negative (G- ) bacteria and fungi. Two phyla of G- bacteria, Proteobacteria and Bacteroidetes, as well as the fungal phylum Ascomycota were more abundant when SWC increased. In addition to SWC and its related environmental factors such as C and N availabilities, AP appeared to be an important factor in shaping the soil microbial community composition. The study highlights the combination use of the methods based on different microbial biomarkers (PLFA vs. DNA), and the results were in line with each other. While the PLFA-based method was more sensitive to short-term shifts in soil microbial community composition in response to a precipitation event, DNA-based method could provide more information on the microbial taxa at a finer taxonomic resolution. Our results provide methodological insights for future research on short-term response of soil microbial community to changing environmental conditions.