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Determining people out of hobby regarding surface nutrients related to crushed C destruction
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).
Relationship between GeoChip study as well as their related chemical situations (n=51). Strong contours represent the latest fitting linear regressions and dashed lines show 95% trust intervals.
RF study
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 (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.
Structural picture modelling
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.
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