Effects of Different Soils on the Biomass and Photosynthesis of Rumex nepalensis in Subalpine Region of Southwestern China

Abstract

The performance of Rumex nepalensis, an important medicinal herb, varies significantly among subalpine grasslands, shrublands and forest ecosystems in southwestern China. Plant–soil feedback is receiving increasing interest as an important driver influencing plant growth and population dynamics. However, the feedback effects of soils from different ecosystems on R. nepalensis remain poorly understood. A greenhouse experiment was carried out to identify the effects of different soil sources on the photosynthesis and biomass of R. nepalensis. R. nepalensis was grown in soils collected from the rooting zones of R. nepalensis (a grassland soil, RS treatment), Hippophae rhamnoides (a shrub soil, HS treatment), and Picea asperata (a forest soil, PS treatment). The chlorophyll contents, net photosynthetic rates, and biomasses of R. nepalensis differed significantly among the three soils and followed the order of RS > HS > PS. After soil sterilization, these plant parameters followed the order of RS > PS > HS. The total biomass was 16.5 times higher in sterilized PS than in unsterilized PS, indicating that the existence of soil microbes in P. asperata forest ecosystems could strongly inhibit R. nepalensis growth. The root to shoot biomass ratio of R. nepalensis was the highest in the sterilized PS but the lowest in the unsterilized PS, which showed that soil microbes in PS could change the biomass allocation. Constrained redundancy analysis and path analysis suggested that soil microbes could impact the growth of R. nepalensis via the activities of soil extracellular enzymes (e.g., β-1,4-N-acetylglucosaminidase (NAG)) in live soils. The soil total soluble nitrogen concentration might be the main soil factor regulating R. nepalensis performance in sterilized soils. Our findings underline the importance of the soil microbes and nitrogen to R. nepalensis performance in natural ecosystems and will help to better predict plant population dynamics.