We observed a strong correlation between the relative abundance of microbial functional genes and the activity of NAG (R 2 =0.947), AG (R 2 =0.888), XYL (R 2 =0.966) and CB (R 2 =0.956), which were highly significant in all cases (P<0.001; Figure 1). Similarly, we observed a strong correlation (R 2 =0.896 and 0.949 for bacteria and fungi, respectively; P<0.005 in both cases) between taxonomic and functional diversity in our studied sites (Supplementary Figure S5).
Dating between GeoChip study and their relevant enzyme points (n=51). Good lines portray the suitable linear regressions and dashed outlines depict 95% count on periods.
The abundance of functional genes was the single most important variable for the activity of all four studied enzymes (P<0.01; Figure 2). Among bacteria, ?-Proteobacteria was an important variable for predicting NAG (P<0.05), XYL (P<0.01) and CB (P<0.01) (Figure 2). Actinobacteria (for NAG; P<0.05), Firmicutes (for AG, P<0.05) and Acidobacteria (for XYL and CB; both P<0.01) were other important phyla predicting the activities of different enzymes. Among fungal families, Eurotiomycetes (for NAG and XYL; both P<0.01), Leotiomycetes (for NAG (P<0.01), CB (P<0.01), and XYL (P<0.05)), Classiculomycetes (for NAG and XYL (both P<0.01) and AG (P<0.05)) and Tremellomucetes (for XYL (P<0.05) and CB (P<0.01)) were also important variables for predicting activities of different enzymes.
RF mean predictor importance menchats (percentage of increase of mean square error) of bacterial and fungal relative abundances and GeoChip data as drivers of the different enzyme activities (a: NAG; b: AG; c: XYL; and d: CB). This accuracy importance measure was computed for each tree and averaged over the forest (5000 trees). Significance levels are as follows: *P<0.05 and **P<0.01.
Architectural formula modeling
SEM explained 91.0–97.0% of the variation in enzyme activities and provided a good fit using ?2 test, RMSEA and Bollen–Stine bootstrap metrics (Schermelleh-Engel et al., 2003; Grace, 2006) (Figures 3a–d). Most importantly, our SEM analysis provided evidence that the direct effect of functional genes on enzyme activities was maintained even when considering key abiotic and biotic factors, such as total C, pH and microbial community composition (Figures 3a–d). Interestingly, our SEM analysis further suggested that the effects of soil properties on enzyme activities were indirectly driven via microbial community composition and functional gene abundance (P<0.01; Figures 3a–d). Further, the abundance of the genes involved in driving the enzymatic activity of the four studied enzymes was directly linked with microbial community composition (P<0.05 for AG; P<0.01 for NAG, XYL and CB, respectively). However, the structure of the soil microbial community had no direct effect on the enzymatic activity of NAG and XYL and a very low direct effect on AG and CB. This interesting result further indicated that the structure of the soil microbial community indirectly regulated the activity of extracellular enzymes via functional genes.
Structural equation models based on the effects of soil properties (total C and pH), bacterial and fungal relative abundances and Geochip data on enzyme activities. Numbers adjacent to arrows are standardized path coefficients, analogous to partial regression weights and indicative of the effect size of the relationship (panels a 1–d 1). The sign of the the microbial community composition (microbial comm.) composite is not interpretable; thus absolute values are presented. Arrow width is proportional to the strength of path coefficients. As in other linear models, R 2 indicates the proportion of variance explained and appears above every response variable in the model. Model fitness details (? 2 vs RMSEA and non-parametric Bootstrap parameters are close by each figure) are close to each figure. Significance levels are as follows: *P<0.05 and **P<0.01. Panels (a 2–d 2) represent standardized total effects (direct plus indirect effects) derived from the structural equation model used.