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. 2021 Mar 1;87(4):e02673-20.
doi: 10.1128/AEM.02673-20. Epub 2020 Dec 11.

The Effects of Soil Depth on the Structure of Microbial Communities in Agricultural Soils in Iowa, USA

Affiliations

The Effects of Soil Depth on the Structure of Microbial Communities in Agricultural Soils in Iowa, USA

Jingjie Hao et al. Appl Environ Microbiol. .

Abstract

This study investigated the differences in microbial community abundance, composition and diversity throughout the depth profiles in soils collected from corn and soybean fields in lowa, USA using 16S rRNA amplicon sequencing. The results revealed decreased richness and diversity in microbial communities at increasing soil depth. Soil microbial community composition differed due to crop type only in the top 60 cm and due to location only in the top 90 cm. While the relative abundance of most phyla decreased in deep soils, the relative abundance of the phylum Proteobacteria increased and dominated agricultural soils below the depth of 90 cm. Although soil depth was the most important factor shaping microbial communities, edaphic factors including soil organic matter, soil bulk density and the length of time that deep soils were saturated with water were all significant factors explaining the variation in soil microbial community composition. Soil organic matter showed the highest correlation with the exponential decrease in bacterial abundance with depth. A greater understanding of how soil depth influences the diversity and composition of soil microbial communities is vital for guiding sampling approaches in agricultural soils where plant roots extend beyond the upper soil profile. In the long term a greater knowledge of the influence of depth on microbial communities should contribute to new strategies that enhance the sustainability of soil which is a precious resource for food security.IMPORTANCE Determining how microbial properties change across different soils and within the soil depth profile, will be potentially beneficial to understanding the long-term processes that are involved in the health of agricultural ecosystems. Most literature on soil microbes has been restricted to the easily accessible surface soils. However, deep soils are important in soil formation, carbon sequestration, and in providing nutrients and water for plants. In the most productive agricultural systems in the USA where soybean and corn are grown, crop plant roots extend into the deeper regions of soils (> 100 cm), but little is known about the taxonomic diversity or the factors that shape deep soil microbial communities. The findings reported here highlight the importance of soil depth in shaping microbial communities, provide new information about edaphic factors that influence the deep soil communities and reveal more detailed information on taxa that exist in deep agricultural soils.

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Figures

FIG 1
FIG 1
Changes in alpha diversity levels with soil depth. (A) Average number of observed ASVs at different soil depths. (B) Shannon index at different soil depths. (C) Simpson index at different soil depths. (D) Faith’s phylogenetic diversity index at different soil depths. Differences in alpha diversity were compared using Wilcoxon test adjusted for false-discovery rate. A P value of <0.05 was considered statistically significant. Different letters above the bars indicate significant differences between soil depths. Lines in boxes represent medians. The top and bottom of each box represent the first and the third quartiles, respectively. Whiskers indicate data ranges, with outliers shown as open circles.
FIG 2
FIG 2
Beta diversity showing changes in microbial community composition with depth, site, and crop type. Canonical analysis of principal coordinates (CAP) using Bray-Curtis dissimilarity for all samples was conducted. The Bray-Curtis dissimilarity matrix was generated using QIIME. CAP was conducted by constraining soil depth, crop type, and sampling site using the ‘capscale’ function in the vegan R package. PERMANOVA was performed to determine whether the shifts in microbial community due to soil depth, crop type, and sampling site and their interactions were significant. Each color indicates different soil depth as shown in the key.
FIG 3
FIG 3
Bacterial abundance as determined by 16S rRNA gene copy number at different soil depths. Quantitative PCR results show the average 16S rRNA gene copies per gram of soil at each soil depth. The open symbols indicate that at specific depths many samples were below the detection level for the standard curve and were not included in the averages. Three and 4 out of 18 samples were used for calculating the average copy number at 120 to 150 cm and 150 to 180 cm, respectively.
FIG 4
FIG 4
Distribution of pairwise Bray-Curtis dissimilarities between crop type and site at different soil depths. Bray-Curtis distances between soils from corn and soybean fields (A) and between soils (B) from each of three locations along a soil depth gradient were computed using the “make_distance_comparison_plots.py” function in QIIME 1. Significance tests were performed using two-sided Student’s two sample t test. Asterisks indicate significant differences (**, 0.01; ***, 0.001). NS, not significant.
FIG 5
FIG 5
Canonical correspondence analysis (CCA) and correlations of microbial abundance with additional factors influencing soil microbial community composition. (A) CCA1 is the constrained ordination of the data with 22.41% (P < 0.001) of the variation and CCA2 with 14.57% (P < 0.001) of the total variation. The significance for each soil property is presented in Table 1. (B) Linear correlation analyses between 16S rRNA gene copies and single soil attributes. The Pearson correlation coefficient and P value are shown for each graph.
FIG 6
FIG 6
Relative abundances of the dominant microbial phyla in all samples separated by soil depth. (A) Phylum-level relative abundance of the top 20 most abundant taxa. (B and C) Statistical comparison of each domain (B) and phylum (C) relative abundance at each soil depth using Welch’s t test and the Bonferroni P value correction.

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