Temporal Bacterial Community Diversity in the Nicotiana tabacum Rhizosphere Over Years of Continuous Monocropping

Abstract

Long-term continuous monocropping negatively influences the physicochemical and biological characteristics of cultivated soil, especially for the economically important crop of flue-cured tobacco that is intolerant to continuous monocropping. The underlying mechanism of soil sickness under continuous monoculture and the temporal dynamic changes over the tobacco life cycle among different monoculture time spans remain poorly characterized. In this study, high-throughput sequencing targeting the 16S rRNA gene phylogenetic marker was performed on 60 soil samples of rhizosphere soil from flue-cured tobacco in the replanting, growth and harvest period across 5, 10, and 20 years of a continuous monocropping system. Bacterial community diversity decreased with the increase in duration of continuous monocropping, and the rhizosphere microbiota was highly dynamic in the harvest period. The random forests algorithm identified 17 taxa as biomarkers and a model was established to correlate root microbiota with continuous monocropping time of flue-cured tobacco. Molecular ecological network analysis elaborated the differences and interactions in bacterial co-occurrence patterns under different monocropping systems. The co-occurrence microbial network was larger in size but there were fewer interactions among microbial communities with the increase in continuous monocropping duration. These results provide insights into the changes of flue-cured tobacco root microbiome diversity in response to continuous monocropping and suggest a model for successional dynamics of the root-associated microbiota over continuous monocropping time and development stage. This study may help elucidate the theoretical basis underlying obstacles to continuous monocropping and could contribute to improving guidance for tobacco production.

Keywords: continuous monocropping; flue-cured tobacco; microbial community; monoculture problems; rhizosphere soil.

FIGURE 1

Patterns of relative abundance of bacterial communities among different continuous monoculture time spans. (A) Distribution of the 10 most abundant bacterial phyla of soils after different periods of continuous monocropping. The bar length on the outer ring represents the percentage of each phylum in each sample. (B) Venn diagram of the number of core- and pan-OTUs among different continuous monoculture time spans. (C) Ratio of the log2 fold change in the relative abundance of bacterial taxa in soils from different continuous monocropping time periods and a non-monocropping control. The quantity of OTUs was normalized to unity.

FIGURE 2

Diversity of bacterial communities at sites after various years of continuous monocropping. (A) Rarefaction curves for alpha diversity measures of OTUs comparing microbiota from the continuously cropped soils. Error bars correspond to one standard deviation out from the average (n = 6 biological replicates. (B) Unconstrained PCoA (for principal coordinates PCo1 and PCo2) with weighted unifrac distance showing that root microbiota in different periods separate in the first axis (P < 0.001, PERMANOVA). (C) RDA plots of the bacterial communities with respect to environmental variables in the root zone of tobacco. Blue text indicates the bacterial phyla with top 10 abundance, and gray arrows represent different soil properties. (D) Heatmap of correlations between bacterial families with ecological functions in rhizosphere soil. Heatmap values ranged from +1 to −0.5. Values above/below zero represent positive/negative correlations between bacterial families and parameters analyzed. *P < 0.05 for the indicated comparisons.

FIGURE 3

Random-forest model detects bacterial taxa representing the biomarkers across corresponding continuous monocropping time periods of flue-cured tobacco. (A) The top 17 bacterial families were identified by applying random-forest classification of the relative abundance of the root microbiota in different monocropping years. Biomarker taxa are ranked in descending order of importance to the accuracy of the model. The inset represents 10-fold cross-validation error as a function of the number of input families used to differentiate biomarker taxa in order of variable importance. (B) Heatmap showing the relative abundances of the top 13 predictive biomarker bacterial families against continuous monocropping time periods.

FIGURE 4

Overview of continuous cropping network in rhizosphere soil samples after continuous monocropping for 5 years (5 yrCC), 10 years (10 yrCC), and 20 years (20 yrCC). The nodes represent OTUs and edges stands for a strong (Spearman’s ρ > 0.6) and significant (P < 0.01) correlation. There were 899 positive and 820 negative interaction among 1,719 nodes in 5 yrCC while 1,093 and 1,088 positive interactions and only 2 and 74 negative interactions in 10 and 20 yrCC, respectively. Nodes in different colors belong to the top 10 bacterial phyla in the networks.

FIGURE 5

Zi-Pi plot showing the distribution of OTUs based on their topological roles. Each symbol represents an OTU in short-term (5 years) and long-term (20 years) continuous monocropping networks. The threshold values of Zi and Pi for categorizing OTUs were 2.5 and 0.62, respectively, as previously reported (Olesen et al., 2007). All OTUs identified as generalists and co-existing in both groups are labeled.