Figure 3 The linear regression of stability and standard richness under
different proportions of network positive interactions. The shaded area
indicates the 0.95 confidence interval. Fig. 3A and 3B depict the linear
relationship of standard richness to resistance and resilience under
higher proportions of positive interactions, respectively, while 3C and
3D were under a balanced proportion of positive interactions. P%
indicate the positive proportion for all interactions. The dark dots
indicate the means of bacterial or fungal stability for different
ecosystems.
In order to investigate the effect of interactions on RSRs, we chose the
richness and stability components (resistance and resilience) for
regression analysis. However, we did not find any direct linear
relationship between standard richness and stability (Fig. S5), and the
R2 of regression to resistance and resilience were
0.03 and 0.05, respectively, (P > 0.05). This indicated
potential drivers were governing their relationship. Next, we adopted
network analysis to obtain a detailed overview of the microbial
community interactions. Interestingly, the results showed that when the
proportion of positive interactions was high and unbalanced (P%
> 0.52), richness decreased resistance (Fig. 3A,
slope=-0.22, R2=0.52), but increased resilience (Fig.
3B, slope=0.32, R2=0.76). When the proportion of
positive interactions were balanced (0.48 < P% <
0.52), richness increased resistance (Fig. 3C, slope=0.92,
R2=0.95) and decreased resilience (Fig. 3D,
slope=-0.97, R2=0.65). These results showed that the
relationship between richness and stability was governed by the balance
between positive and negative interactions, which highlighted that the
organizational pattern of community members plays an important role in
influencing the relationship between the size of species pool and system
stability.