3 RESULTS
3.1 Resistance of ecosystem multifunctionality and major microbial taxa
After conversion from primary forest to the other three land use, we
found similar multifunctional resistance among SF, AL and CL (Fig. 1).
C-cycling functional resistance decreased by 77%, nitrogen and
phosphorus functional resistance increased by 17% and 19% in abandoned
land, compared to secondary forest. Cultivated land had the lowest N
functional resistance, while SF had lowest P functional resistance (Fig.
1).
At the level of the major bacterial phylum, in addition to
Verrucomicrobia, the groups with relatively high abundance had higher
resistance than the groups with low relative abundance, and the
proteobacteria (p<0.001) had the highest resistance (Fig. 2).
Land-use intensity levels affected the resistance of the major bacterial
phylum (Fig. 3a). Compared with SF, the resistance of Actinobacteria (p
< 0 .001), Acidobacteria (p = 0.001) and Chloroflexi (p =
0.049) were 40%, 11% and 78% lower, respectively, in AL, and 41%,
8.0% and 53% lower in CL. In contrast, Verrucomicrobia
(p<0.001) was 49% more resistant in AL than in SF.
Significant effects of land-use intensity levels were also found for
bacterial multifunctionality resistance at the class level (Fig. 3b).
Deltaproteobacteria, Actinobacteria and Alphaproteobacteria had the
highest resistance (Fig. 2). Alphaproteobacteria, Betaproteobacteria,
Gammaproteobacteria and Thermoleophilia showed a pattern of lesser
resistance in AL and CL than in SF (p < 0.05, Fig. 3b). In
contrast, Actinobacteria was more resistant in AL (42%, p <
0.05) as well as in CL (10%, p < 0.05) compared to SF.
3.2 Linkage of bacteria communities with resistance of ecosystem
multifunctionality
NMDS ordination of the bacterial Bray-Curtis distance matrices clearly
separates the PF and SF from the AL and CL (Fig. 4a). Soil
physicochemical properties and soil enzyme activities were also clearly
separated by land-use in the PCA plot (Fig. 4b) but, in contrast to the
NMDS plot, the two forest sites (PF and SF) were not grouped close
together. Structural equation modelling was constructed to explore the
direct and indirect effects of land-use and bacterial communities on
multifunctional resistance (Fig. 4c). The bacterial communities (NMDS1,
NMDS2, p<0.05) showed the largest direct positive effect on
multifunctional and N-related functional resistance (Fig. 4e, Fig. S1).
The positive direct effect of soil Mg content (path coefficient = 1.029)
on C-functional resistance was the greatest, followed by bacterial
community structure and Fe content (Fig. 4d, Fig. S1). Besides bacterial
community, land use presented as the second main predictor influencing
the N functional resistance via direct (path coefficient = 0.676) and
indirect effects (path coefficient = -0.237) on soil pH, minerals and
bacterial community (Fig. 4d, Fig.S1). Among soil variables, the
positive direct effect of bacterial community on P functional resistance
was the greatest, while Mg was related to the largest negative
indirecteffect (Fig. 4f, Fig. S1).
3.3 Bacterial groups predicting multifunctional resistance of ecosystem
and soil single function
To further elucidate the role of bacterial community structure in
driving multifunctional resistance, we used Random Forest modelling to
quantify the influence of bacterial abundance at both phylum and class
level on multifunctional resistance of the different ecosystems in the
land-use gradient (Fig. 5). The model explained 46.4% variance on
multifunctional resistance (Fig. 5). The minimum cross-validation error
was obtained when using 20 important classes, And the number of classes
against the cross-validation error curve stabilized (Fig. 5a). Thus, we
defined these 20 classes as biomarker taxa in the model. The list of the
top 20 bacterial taxa at the class level across multifunctional
resistance, in order of multifunctional resistance importance, is shown
in Fig.5a. Verrucomicrobia showed the highest contribution to
multifunctional resistance (Fig. 5a), and was mainly present in
secondary forest and abandoned land (Fig. 5b). This was followed in
importance by Chloroflexia, Anaerolineae and Chloroplast (Fig. 5a),
which started to accumulate in the AL and CL stages (Fig. 5b). We also
found Thermoleophilia and Alphaproteobacteria showed a contribution to
multifunctional resistance (Fig. 5a), and started to accumulate in PF
and SF (Fig. 5b). These indicates that some biomarker taxa showed high
relative abundance in the corresponding land-use gradient (Fig. 5b).
Based on 20 bacterial groups as important predictors identified by
random forests(Fig. 5a), we further predicted the soil single function
(e.g. TOC, TN, and TP contents) and listed the corresponding significant
microbial species (Fig. 6, p<0.05).Our Random Forest models
indicate that the relative abundance of Chthonomonadetes and
OPB35_soil_group were the best predictors of TOC and DOC,
respectively. The relative abundance of Chloroflexia,
Alphaproteobacteria and OPB35_soil_group were the best predictors of
TN, NH4+ and NO3-, respectively. OPB35_soil_group was also the best
predictor for TP.