Soil fertility impact on recruitment and diversity of the soil microbiome in sub-humid tropical pastures in Northeastern Brazil

Abstract

Soil fertility is key point to pastures systems and drives the microbial communities and their functionality. Therefore, an understanding of the interaction between soil fertility and microbial communities can increase our ability to manage pasturelands and maintain their soil functioning and productivity. This study probed the influence of soil fertility on microbial communities in tropical pastures in Brazil. Soil samples, gathered from the top 20 cm of twelve distinct areas with diverse fertility levels, were analyzed via 16S rRNA sequencing. The soils were subsequently classified into two categories, namely high fertility (HF) and low fertility (LF), using the K-Means clustering. The random forest analysis revealed that high fertility (HF) soils had more bacterial diversity, predominantly Proteobacteria, Nitrospira, Chloroflexi, and Bacteroidetes, while Acidobacteria increased in low fertility (LF) soils. High fertility (HF) soils exhibited more complex network interactions and an enrichment of nitrogen-cycling bacterial groups. Additionally, functional annotation based on 16S rRNA varied between clusters. Microbial groups in HF soil demonstrated enhanced functions such as nitrate reduction, aerobic ammonia oxidation, and aromatic compound degradation. In contrast, in the LF soil, the predominant processes were ureolysis, cellulolysis, methanol oxidation, and methanotrophy. Our findings expand our knowledge about how soil fertility drives bacterial communities in pastures.

Figure 1

Grouping of pastures through principal components and K-Means clustering algorithm based on soil chemical attributes and leaf nitrogen content. (a) Biplot of the principal component analysis (PCA) highlighting the three most important clusters according to the K-Means grouping; (b) Contribution of the main soil variables to the variance explained by the two main axes of the PCA; (c) New PCA biplot highlighting the difference in soil fertility levels between the two most contrasting clusters. CEC cation exchange capacity, N_grass leaf-N, TOC total organic carbon, V% base saturation. Created in the R environment (v.4.3.1).

Figure 2

Alpha-diversity metrics and their associations with the chemical attributes of HF and HL pasture soils. (a) Comparisons of diversity indices by Wilcoxon signed-rank statistics; (b) Monotonic associations between alpha-diversity and chemical variables through Pearson correlation coefficients. Associations marked with one asterisk (*) or more were considered significant. CEC cation exchange capacity, Effec. Effective number of ASVs based on the Shannon and Simpson indices, N_grass leaf nitrogen in the aerial part of the pastures, TOC total organic carbon, V% base saturation in the soil. Created in the R environment (v.4.3.1).

Figure 3

Beta-Diversity analysis of microbial communities in pastures with high (HF) and low fertility (LF) soils. Biplot with canonical correlation analysis (CCA) based on generalized UniFrac distance highlighting significant environmental variables (a), according to the Mantel test (p < 0.05), and the main responsive phyla. The average similarity between samples, calculated by Bray–Curtis dissimilarity, was associated with geographic distances (b) and edaphic distances (c). The phyla with significant correlations with one or more variables were also analyzed (d). CEC cation exchange capacity, N_grass leaf-N, TOC total organic carbon, V% base saturation. Created in the R environment (v.4.3.1).

Figure 4

Relative composition and differential abundance of the main bacterial taxonomic ranks found in rich (HF) and poor (LF) pastures in fertilization. (a) Venn diagram showing the percentage of ASVs unique to each niche and shared between both; (b) relative abundance of the ten most abundant bacterial phyla; (c) relative abundance of the 12 most abundant classes; (d) Differential abundance analysis based on the taxon importance estimator (phyla and classes) in the decision tree branched by the Random-Forest algorithm (Mean Decrease Gini). Created in the R environment (v.4.3.1).

Figure 5

Microbial co-occurrence analyses in pastures on fertile (HF) and poor (LF) soil highlighting highly correlated groups (ASVs) through the SPIEC-EASI association measure (SparCC > 0.6, p < 0.01). (a) Networks, where modules were differentiated by colors and the degrees (number of connections) were directly proportional to the node diameter; (b) Abundance of connections at the bacterial phylum level. Values around circles represented the number of edges connected between phyla or within the same phylum. (c) Autogenic analysis of modules that showed significant association with at least one environmental variable. CEC cation exchange capacity, N_grass leaf-N, TOC total organic carbon, V% base saturation. Created in the R environment (v.4.3.1).

Figure 6

Metagenomic prediction analysis based on the abundance of 16S rRNA genes associated with functional profiles from the FAProTax database. (a) correlations between environmental variables and predicted functional profiles; (b) relative frequency of the most abundant functional processes among clusters; (c) differential abundance of processes significantly distinct between HF and LF according to Mean Decrease Gini (MDG); (d) Richness of predicted functions depending on sample size. CEC cation exchange capacity, N_grass leaf-N, TOC total organic carbon, V% base saturation. Created in the R environment (v.4.3.1).