3.3 Factors driving rhizosphere microbial community structure
and assembly
We analyzed the correlations between rhizosphere microbial community
structure and soil environmental properties using the Mantel test and
Spearman correlation analysis. Bacterial community structure was
significantly correlated with MBC, AP, TP,
NH4+-N,
ON3--N, and SOC contents (P< 0.01), whereas fungal community structure was significantly
correlated with NH4+-N, TN, TP, and
SOC contents (P < 0.01; Supplemental information Fig.
S4). Furthermore, we used random forest analysis to predict the
contribution of soil environmental properties to β-diversity in
rhizosphere bacterial and fungal communities. TP and
NH4+-N were the major factors driving
bacterial β-diversity (Fig. 2E), whereas
NH4+-N and pH were the major factors
driving fungal β-diversity (Fig. 2F).
3.4 Co-occurrence patterns
of rhizosphere microbial communities across different forest ages
We constructed co-occurrence networks for rhizosphere bacterial and
fungal communities separately, and determined network topographical
properties to explore the effects of different forest ages. Consistent
with the results of β-diversity analysis, RS15 and RS25 samples
clustered in a module, while RS35 and RS45 samples clustered in another
module for both bacteria and fungi (Fig. 3). We also mapped the fsOTUs
(Fig. S5) into the rhizosphere microbial networks, and observed their
clustering according to forest age (Fig. 3A–B).
We then explored the distribution patterns of fsOTUs in the
co-occurrence networks of rhizosphere bacterial and fungal communities
(Fig. 3, Table1). The abundances of inter-kingdom microbial associations
also varied based on forest age, and there are difference among
different modules (Fig. 3C–D). The module members were sensitive to
forest age and their distribution in the network partially reflected the
drivers of community dissimilarity illustrated in the PCoA plot (Fig.
2). Module 1 mainly contained fsOTUs specific to RS15 and RS25, which
were not fully separated from other modules (Module 3; Fig. 3A–B).
Numerous fsOTUs assigned to RS35 and RS45 were predominantly located in
Module 2 (Fig. 3A–B). Our results suggested that the species with
similar community phylogeny had more similar community structure, closer
symbiotic relationship and easier aggregation in the network.
All the forest age-responsive modules comprised a taxonomically broad
set of bacteria (Supplemental information Fig. S6A), revealing that the
different forest ages did not target specific bacterial lineages.
However, in the case of fungi, Olpidiomycota andKickxellomycota only existed in Module 1, andAphelidiomycota only existed in Module 2, whileZoopagomycota mainly existed in both Module 1 and Module 2
(Supplemental information Fig. S6B).
To reveal the effects of soil multinutrient cycling on the structures of
sensitive bacteria and fungi, we used rhizosphere soil multinutrient
index for regression analysis with normalized abundances of fsOTUs in
different modules. The abundances of
fsOTUs
exhibited positive correlations with rhizosphere soil multinutrient
index in Module 1 (bacteria: R 2 = 31, fungi:R 2 = 30); however, in Module 2 there were
negative correlations (bacteria: R 2 = 24,
fungi: R 2 = 38, Fig. 3E−F).
3.5 Identification of core
microbiota and their potential roles in soil nutrient cycling
We identified 37 bacterial and 16 fungal classes as core microbiota
(Fig. 4). Nitrososphaeria , Nitrospiria , Verrucom-
icrobiae , Alphaproteobacteria, Gemmatimonadetes ,Bacteriodia, Bacillus , and Acidimicrobiia were
considered core bacterial microbiota. Agaricomycetes ,Tremellomycetes , Motrierellomycetes ,Glomeromycetes , and Sordariomycetes were considered core
fungal microbiota. Subsequently, we explored the role of core microbiota
in rhizosphere soil nutrient cycling through co-occurrence network and
random forest analyses. The co-occurrence networks of core bacteria and
fungi were established based on correlation analysis (Fig. 4A). Core
bacterial microbiota comprised 272 nodes, and other bacterial microbiota
had 2,967 nodes. Core fungal microbiota contained 64 nodes, and other
fungal microbiota had 1532 nodes. The degrees of different
sub-communities were significantly higher (P < 0.001; Wilcoxon
rank sum tests) in core microbiota than in other microbiota in both
fungi and bacteria (Fig. 4B-C).
To disentangle the linkages between core microbiota and soil nutrient
cycling, we quantified the contributions of core and other
sub-communities to the rhizosphere soil multinutrient cycling index
(Fig. 4C). In the course of forest restoration, the bacterial and fungal
diversities of the core microbiota were more important than those of
non-core microbiota. Furthermore, multiple regression modeling was used
to evaluate the associations of core microbiota and rhizosphere soil
nutrient contents. Different bacterial and fungal classes contributed to
the variation in rhizosphere soil nutrient contents (Fig. 5).
Particularly, TN, SOM, SOC, and NH4+-N
contents were major factors associated with fungal core microbiota.
NH4+-N content was a major factor
associated with most bacterial core microbiota, and was positively
correlated with Acidobacteriae, Alphaproteobacteria , andVerrucomicrobiae abundance, and negatively correlated with the
abundance of most core bacteria.
Discussion