Network construction
To understand whether and how the co-occurrence networks including
fungal and bacterial community varies across the Tibetan Plateau in
relation to environmental and plant richness gradients, two different
kinds of networks were constructed basing on the Spearman correlation
matrix by “WGCNA” R package (Langfelder & Horvath 2012), viz.,
molecular ecological network (MEN)
including fungi-only and bacteria-only network, and
plant–microbiota interkingdom
ecological networks (IDEN) including plant-fungi network and
plant-bacteria networks.
Given the generally observed relationships between broad habitat zones
and microbiota and floristic composition, we also divided the 60 samples
according to the three main types of vegetation in our samples (desert
steppe, alpine meadow, alpine steppe), and constructed plant-fungi and
plant-bacteria networks for each vegetation type.
To avoid the bias of the correlation matrix causing by rare taxa, only
OTUs with average relative abundances > 0.01% of each
subgroup were retained. Since the number of plant species amongst these
60 sites is low, we kept all the plant species for the plant-microbiota
network construction. The Spearman correlations between OTUs and plants
were filtered by the thresholds r > 0.6 and false discovery
rate adjusted p < 0.05(Huang et al. 2019). The
OTUs and plants presented at each site were retained and generated
subnetworks for each soil sample from the combined interkingdom
ecological networks by the “igraph” R package. Only the correlations
between plants and fungi (or bacteria) in each site were kept by the
“startswith” function in python, and were chosen as the adjacent
matrix of the bipartite graph. The obtained adjacent matrix associated
with the bipartite graph consisted of 1 or 0, showing the
presence/absence of corresponding plant–microbiota associations (Fenget al. 2019). The plant–fungi and plant-bacteria network
architecture of each group was visualized based on the “ForceAtlas2”
layout algorithm (Jacomy et al. 2014) using the program Gephi
(Bastian et al. 2009). We then examined the number of edges,
plant and microbial species richness in the observed IDEN in 60 sites.
The observed IDEN topological features (Table S1) was evaluated at both
network and group (plants or microbiota) levels using “bipartite”
v.2.08 package of R v.3.1.1 (Dormann et al. 2009). Note that low
Nestedness values indicate nestedness, while high Nestedness values (0
means cold, i.e. high nestedness, 100 means hot, i.e. chaos) indicate
antinestedness. In a nested network,
specialists (that is, species with narrow partner ranges) interact with
subsets of the partners of generalists (that is, species with broad
partner ranges) (Toju et al. 2015). To further determine the
compartmentalization of the observed IDEN, modularity was calculated by
module detection algorithm for example simulated annealing (Guimeraet al. 2005), and high value indicates modular structure.
Modularity is a measure of the extent
to which the network is structured as cohesive subgroups of nodes
(modules), in which the density of interactions is higher within
subgroups than among subgroups (Olesen et al. 2007).
We then conducted the microbial intrakingdom ecological networks
analysis using the same thresholds for OTU and correlations mentioned
above. To caculated the network-level topological features (Table S2),
60 subnetworks were generated by retaining the OTUs and associated edges
for each site using the “subgraph” function in “igraph” R package.
Network-level topological features with a high value (such as edge
density, degree centralization and betweenness centralization) indicate
closer connections within the network, whereas those with lower values
(such as average path length and modularity) suggest a more aggregated
network (Barberán et al. 2012; Ma et al. 2016). We then
calculated the absolute value of negative/positive cohesion to explore
the stability of microbial networks along gradient(Yuan et al.2021).