Deforestation impacts network co-occurrence patterns of microbial communities in Amazon soils

Abstract

Co-occurrence networks allow for the identification of potential associations among species, which may be important for understanding community assembly and ecosystem functions. We employed this strategy to examine prokaryotic co-occurrence patterns in the Amazon soils and the response of these patterns to land use change to pasture, with the hypothesis that altered microbial composition due to deforestation will mirror the co-occurrence patterns across prokaryotic taxa. In this study, we calculated Spearman correlations between operational taxonomic units (OTUs) as determined by 16S rRNA gene sequencing, and only robust correlations were considered for network construction (-0.80 ≥ P ≥ 0.80, adjusted P < 0.01). The constructed network represents distinct forest and pasture components, with altered compositional and topological features. A comparative analysis between two representative modules of these contrasting ecosystems revealed novel information regarding changes to metabolic pathways related to nitrogen cycling. Our results showed that soil physicochemical properties such as temperature, C/N and H++Al3+ had a significant impact on prokaryotic communities, with alterations to network topologies. Taken together, changes in co-occurrence patterns and physicochemical properties may contribute to ecosystem processes including nitrification and denitrification, two important biogeochemical processes occurring in tropical forest systems.

Figure 1.

Network of co-occurring OTUs pairs based on a Spearman correlation (−0.80 ≥ Ρ ≥ 0.80) with significance of adjusted P ≤ 0.01. An interaction (edge) between nodes (OTUs) implies a significant correlation. Red edges represent negative correlations, while the grey edges represent positive correlations. The network is visualized using group attributes layout in Cytoscape 3.2.1, where every module is given an arbitrary number. Among 14 modules, 6 modules are forest associated (M3, M5, M8, M10, M12 and M14; green labeling), 5 modules are pasture associated (M1, M4, M6, M7 and M11; red labeling), and 3 modules comprise both types of nodes (M2, M9 and M13; blue labeling).

Figure 2.

Mean of topological features of the forest and pasture components of the network; (A), average betweenness centralities, (B), average clustering coefficients. Statistical significance was calculated using Mann–Whitney test. Error bars represent standard error. Symbols (*) and (***) indicate significance values of P < 0.05 and P < 0.001, respectively.

Figure 3.

Abundances of major microbial taxa in forest and pasture soils. (A)Relative abundance of major microbial phyla and classes in forest (black) and pasture (grey) networks. These microbial taxa comprise more than 98% of 16S rRNA gene reads, (B) Copy numbers of seven major bacterial phyla and classes per gram of dry soil determined by quantitative PCR in two different land uses in the Amazon. Forest, black; pasture, grey. Error bars represent the standard error. Statistical significance was calculated using Mann–Whitney test. Symbols (*), (**) and (***) indicate significance values of P < 0.05, P < 0.01 and P < 0.001, respectively.

Figure 4.

A representative module was obtained from (A), forest and (B), pasture components of the network for comparative analysis. The criteria used in comparing these modules were that they were most connected within their members and have high node size. Forest module (M8) is comprised of 55 nodes with average connectivity of 7.98, while the pasture module is comprised of 61 nodes with average connectivity of 10.62. The size of the nodes in each network is scaled according to their betweenness centrality values to indicate their relative importance in their networks.

Figure 5.

Relationship between the microbial community structures and soil physicochemical parameters. (A), Pearson correlations between the relative abundances of major microbial taxa and soil variables. (B), Pearson correlations of the species richness (alpha diversity) and community similarity, the ordination score of the first axis of principal coordinate analysis (PCoA) (which explains 55.78% of the variance in the original data), with the soil variables. C, carbon; N, nitrogen; Fe, iron; V, base saturation; m, aluminum saturation; Ca, calcium; Mg, magnesium; Mn, manganese; B, boron; Cu, copper; T, cation exchange capacity; SB, sum of bases; H⁺+Al³⁺, exchangeable acidity; OM, organic matter; P, phosphorus; S, sulfur. The color gradient represents the correlation strength of the relationships. Symbols (*), (**) and (***) indicate significance values of P < 0.05, P < 0.01 and P < 0.001, respectively.