Bacterial and fungal networks
Our results showed most nodes of bacterial networks (Figure 1) and fungal networks (Figure 2) varied with forest type in both the dry season and rainy season. For bacterial networks, there were 2559 and 2501 edges in tropical rainforest and rubber plantation in dry season respectively, but these two networks only shared 262 edges, accounting only about 10% the total edges (Figure 3A-B). Similarly, these networks only shared 519 edges during the rainy season. For fungal networks, there were only 4 and 5 shared edges (i.e., no more than 5% of the total edges) in dry season and rainy season, respectively (Figure 3C-D).
The number of edges of bacterial and fungal networks were almost equivalent during the dry season. However, in rainy season, there were more edges in the bacterial network in tropical rainforest than in the rubber plantation (Table 1 & Figure 3B). For the network structure of the fungal community, more edges were observed in rubber plantations in rainy season (Table 1 & Figure 3D). Similarly, there were no significant differences in both bacterial and fungal network degree between tropical rainforest sites and rubber plantations in the dry season (Figure 4A, C). In the rainy season, rainforest sites had higher bacterial network degree, while rubber plantations showed higher fungal network degree (Figure 4B, D). For bacterial networks, more nodes (OTUs) with high degree (rubber plantation had 2 nodes with degree greater than 75, rainforest had 8 such nodes) of rainforest were observed during the rainy season (Figure S2B). For fungal networks, 15 nodes of higher degree (degree greater than 25) were observed in rubber plantations, however, only 7 such nodes existed for rainforest sites (Figure S2D). These results indicate rubber plantation fungal network structure was more complex than tropical rainforest during the rainy season, but that the reverse was true for bacteria.
When considering the ratio of positive to negative correlation coefficients, more correlations (greater than 0.3, P < 0.05) were calculated, and the results showed that the negative correlations between bacterial and fungal OTUs of rubber plantations were consistently stronger than for tropical rainforest in both dry season and rainy season (Figure 5).
For both the bacterial and fungal communities, neither tropical rainforest nor rubber plantation networks possessed module hubs and network hubs (Figure S3-S4). For bacterial network, the majority of nodes in both the rubber plantation and tropical rainforest networks were connectors (Figure S3). However, for fungal networks, the majority of the nodes in both rubber plantation and tropical rainforest networks were peripherals and connectors (Figure S4). The ratio of peripherals and connectors of these two forest types was not different indicating the network structures of rainforest and rubber plantation were very similar as visualized in Figure 1 and Figure 2. However, the bacterial networks had more connectors than fungal networks, which suggests bacterial networks may contain more generalists than fungal networks do. This indicated bacterial network were more complex than fungal network, which can further confirmed by visualization of the network (Figure 1 and Figure 2).
For bacterial groups, members of the phyla Acidobateria, Planctomycetes and Verrucomicrobia showed higher degree in the tropical rainforest sites than in rubber plantations, suggesting that these taxa are strongly associated with the other members of the community in tropical rainforest (Figure S5A). Members of the phyla Actinobacteria showed higher degree in rubber plantations. Seasonal change also had effects on network degree for soil bacterial networks. For instance, Chloroflex had higher degree in rubber plantation in the dry season, but the opposite is true in the rainy season. For fungal networks, members of Basidiomycota showed higher degree in tropical rainforest sites duringin the dry season, however, Ascomycota showed higher degree in rubber plantations (Figure S5C-D) during the rainy season.
We used total degree of each phylum to reveal the influence of each phylum on network structure (Figure 6). For bacteria, Proteobacteria, Actinobacteria and Acidobacteria had a large influence on network structure (Figure 6). Acidobacteria and Planctomycetes contributed more to rainforest networks than rubber plantation networks. However, Actinobacteria and Chloroflex showed the opposite. For fungi, Ascomycota and Basidiomycota had large influence of network structure. Both Ascomycota and Basidiomycota had stronger influence on rainforest networks than rubber plantation networks. The influence of Ascomycota was stronger during the rainy season than in the dry season, indicating seasonal change also had impact on fungal community networks.
Keystone OTUs of the bacterial and fungal communities were selected on the basis of high degree, high closeness centrality, and low betweenness centrality. The results showed that forest conversion altered the keystone taxa of bacteria and fungi. The keystone taxa of bacteria were very different between rubber plantations and tropical rainforest sites in both the dry season and rainy season. For bacteria, there were more keystone taxa in tropical rainforest sites than in rubber plantations in both the dry season and rainy season indicating that the tropical rainforest networks had higher complexity. We found that some groups of Acidobacteria are keystone taxa in tropical rainforest sites but disappeared after forest conversion. There were more Actinobacteria bacteria in rubber plantations than in tropical rainforest sites (Table S3).
For fungi, more keystone taxa were observed in rubber plantations than in tropical rainforest sites during both the dry season and rainy season, indicating the rubber plantation networks were more complex. Most keystone taxa belong to Ascomycota suggesting member of this group are very import for network structure. In addition to forest conversion, seasonal changes also affect the keystone taxa of the fungal community network. There were more Basidiomycota OTUs in the dry season, but more Ascomycota in rainy season (Table S4).
Two-ways correlation networks
Two-way network analysis of the 50 most abundant species (metageomic data, the 50 most abundant species all belong to bacteria groups) and the 50 most abundant KEGG functions revealed that soil microbial community structure in at rainforests sites was more complex than rubber plantations (Figure 7). Both rubber plantations and rainforest networks were more complex in the rainy season than in dry season. We also found that metabolism was the most important function in soil microbial network. Surprisingly, species of Actinobacteria negatively correlated with other species and function in rubber plantations (Figure 7).
Two-ways correlation network analysis revealed the interaction between microbial composition and environmental variables. This analysis includes different environmental factors as nodes in the network, and the number of connections these nodes have indicates the number of OTUs that are impacted by that environmental factor (Figure 8). For bacteria, elevation had the highest network degree at 106, and was followed by AK (104), soil pH (86) and TK (9). In other words, elevations, AK, soil pH are all drivers of bacterial community composition. Soil pH negatively correlated with most bacterial Acidobcteria OTUs. For fungi, elevation had the highest network degree (61), followed by AK (51), longitude (15), and NN (11). AK positively correlated with most OTUs of Basidiomycota. Relationship between OTU abundance and soil pH revealed the soil pH negatively correlated with members of Acidobacteria, but positively correlated with members of Chloroflexi and members of Ascomycota (Figure 9). AK positively correlated with members of Planctomycetes Verrucomicrobia and Basidiomycota, however negatively correlated with Chloroflexi and Ascomycota.