Effect of land use and soil organic matter quality on the structure and function of microbial communities in pastoral soils: Implications for disease suppression

Abstract

Cropping soils vary in extent of natural suppression of soil-borne plant diseases. However, it is unknown whether similar variation occurs across pastoral agricultural systems. We examined soil microbial community properties known to be associated with disease suppression across 50 pastoral fields varying in management intensity. The composition and abundance of the disease-suppressive community were assessed from both taxonomic and functional perspectives. Pseudomonas bacteria were selected as a general taxonomic indicator of disease suppressive potential, while genes associated with the biosynthesis of a suite of secondary metabolites provided functional markers (GeoChip 5.0 microarray analysis). The composition of both the Pseudomonas communities and disease suppressive functional genes were responsive to land use. Underlying soil properties explained 37% of the variation in Pseudomonas community structure and up to 61% of the variation in the abundance of disease suppressive functional genes. Notably, measures of soil organic matter quality, C:P ratio, and aromaticity of the dissolved organic matter content (carbon recalcitrance), influenced both the taxonomic and functional disease suppressive potential of the pasture soils. Our results suggest that key components of the soil microbial community may be managed on-farm to enhance disease suppression and plant productivity.

Fig 1

Influence of soil type and land use on microbial community structure: metric MDS ordination plots of mean total bacterial (A and B) and Pseudomonas (C and D) communities. The structures of the total bacterial and Pseudomonas communities were assessed based on the relative abundance of terminal restriction fragments (TRFs) and denaturing gradient gel electrophoresis (DGGE) banding patterns, respectively. Mean communities (individual points) for each land use and soil type were derived from 150 bootstrap averages. For land uses and soil types with sufficient replication, 95% region estimates for the mean communities (clouds) represent the spread of the bootstrap averages. Points and/or 95% region estimates in closer proximity represent groups that share increasing similarity in microbial community structure. Observations are statistically supported by PERMANOVA testing of Bray-Curtis dissimilarity data (S4 Table). Underlying OTU data for T-RFLP and DGGE analysis is available in S5 and S6 Tables, respectively.

Fig 2

Influence of land use and soil type on (A) the abundance of the total bacterial community and (B) the relative abundance of the Pseudomonas community (mean ± SEM). The size of the total bacterial and Pseudomonas communities were determined by quantitative PCR. The effects of land use and soil type were formally tested by REML analysis (Genstat). Samples were characterized by land use as either high intensity ‘dairy’ systems or ‘other’, relatively lower intensity pasture systems, e.g. sheep and beef grazing systems.

Fig 3

Network plots of disease suppressive functional gene abundance in (A) dairy (high intensity) and (B) other (low intensity) pasture soil. Nodes represent each individual gene, rather than gene categories, with a putative role in disease suppression. Edges (blue lines) correspond to associations between genes; bold lines reflect Pearson correlations ≥ 0.9, and fine lines correlations between 0.8 and 0.9. Sid_fun, Sid_arc and Sid_bac represent fungal, archaeal and bacterial siderophore production genes, respectively. (C) Measures of average connectivity and density were higher for other (low intensity) systems than for dairy (high intensity) systems.