Landscape Composition and Soil Physical-Chemical Properties Drive the Assemblages of Bacteria and Fungi in Conventional Vegetable Fields

Abstract

The soil microbiome is crucial for improving the services and functioning of agroecosystems. Numerous studies have demonstrated the potential of soil physical-chemical properties in driving the belowground microbial assemblages in different agroecosystems. However, not much is known about the assemblage of bacteria and fungi in response to soil physical-chemical properties and the surrounding landscape composition in different vegetable fields of a highly intensive agricultural system. Here, we investigated the effects of soil physical-chemical properties and landscape composition on the community trends of bacteria and fungi in two different soil compartments (bulk and rhizospheric soils) of two different brassica crop types (Chinese cabbage and flower cabbage). The results revealed that bulk soil had a higher alpha diversity of both bacteria and fungi than rhizospheric soil. Each of the soil physical-chemical properties and landscape compositions contributed differently to driving the community structure of distinct bacterial and fungal taxa in both soil compartments and crop types. The higher proportions of forest, grassland, and cultivated land, along with the higher amount of soil calcium in flower cabbage fields, promote the assemblage of Gammaproteobacteria, Actinobacteria, Oxyophotobacteria, Agaricomycetes, and Eurotiomycetes. On the other hand, in Chinese cabbage fields, the increased amounts of iron, zinc, and manganese in the soil together with higher proportions of non-brassica crops in the surrounding landscape strongly support the assemblage of Deltaproteobacteria, Gemmatimonadetes, Bacilli, Clostridia, Alphaproteobacteria, an unknown bacterial species Subgroup-6, Mortierellomycetes, Rhizophlyctidomycetes, and Chytridiomycetes. The findings of this study provide the most comprehensive, comparative, and novel insights related to the bacterial and fungal responses in a highly intensive vegetable growing system for the improvement of the soil fertility and structure. These are important clues for the identification of key bacteria and fungi contributing to the plant-environment interactions and are of a practical significance for landscape-based ecological pest management.

Keywords: agroecosystem; high throughput sequencing; microbiome; rhizosphere; soil-microbe interactions.

Figure 1

Geographical mapping of sampling sites in the region of Fuzhou city using QGIS. China map (white background in the upper right corner) was drawn using R software.

Figure 2

Relative abundance (%) of different bacterial and fungal communities in bulk and rhizospheric soils collected from Chinese cabbage and flower cabbage fields. (a) Relative abundance of bacteria at order level and (b) relative abundance of fungi at order level.

Figure 3

Alpha diversity indices of soil bacteria and fungi. The bar graph depicts alpha diversity (ACE, Chao1, and Shannon indices) of soil bacteria (a–c) and fungi (d–f). Error bars with “*” represent significant difference and “ns” represents non-significant differences between different bulk “BuS” and rhizospheric “RiS” soils of Chinese cabbage “CC” and flower cabbage “FC”.

Figure 4

NMDS plots indicating the beta-diversity for (a) soil bacteria and (b) fungi. Different crop types along with the soil fraction have been designated with different colors. “CC−BuS”: Chinese cabbage and bulk soil, “CC−RiS”: Chinese cabbage and rhizospheric soil, “FC−BuS”: flower cabbage and bulk soil, “FC−RiS”: flower cabbage and rhizospheric soil. Stress values, p-values and ANOSIM and Adonis test confirmatory values are indicated on the top of each figure.

Figure 5

Redundancy analysis (RDA), illustrating the effects of landscape factors, soil physical–chemical properties, soil fraction, and crop type (arrows) on (a) soil bacterial orders and (b) soil fungal orders. The length and orientation of the arrows show the amount of variance that the explanatory and response variables can explain. The correlations between soil bacterial and fungal classes and explanatory variables are represented by the perpendicular distance between them (<90° = positive correlation, >90° = negative correlation). The strong association is represented by a smaller perpendicular distance.