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 organic carbon and nitrogen as well as microbial biomass carbon (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). A sharp decrease of AP in both treatments indicated a great P demand at the early stage of the growth cycle. Watering improved 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. Our results highlight the comparison of the methods based on different microbial biomarkers (PLFA vs. DNA). The PLFA-based method was more sensitive to short-term shifts in the soil microbial community composition than the DNA-based method. However, DNA sequencing could provide more detailed information on the microbial taxa at a finer taxonomic resolution.