Determinants of soil carbon- and nitrogen-hydrolyzing enzymes within different afforested lands in central China

Li, Qianxi; Dong, Yiran; Zhang, Qian; Jia, Wei; Cheng, Xiaoli

Determinants of soil carbon- and nitrogen-hydrolyzing enzymes within different afforested lands in central China

2022

Li, Qianxi | Dong, Yiran | Zhang, Qian | Jia, Wei | Cheng, Xiaoli

Soil organic matter (SOM) decomposition is regulated by a complex set of enzymes. However, the influences of biotic and abiotic factors on spatial variations of soil enzyme activity (EA) within ecosystems remain unresolved. Here, we measured EA at different locations within two afforested lands (coniferous woodland and leguminous shrubland), and simultaneously collected data on soil physico-chemical, vegetation-related, and microbial properties to identify the determinants of EA spatial patterns. The results showed that soil organic C and total N contents were the predominant abiotic factors in regulating absolute EA (EA per unit of oven-dry soil mass) in both afforested lands, while soil pH was the predominant factor in regulating specific EA (EA per unit of microbial biomass (MB)). However, the predominant biotic factors varied with the afforested type: the root biomass and MB were the determinants of EA in the shrubland, whereas the tree distribution, litter and root biomass, and bacterial biomass were the determinants in the woodland. Vegetation-related factors (i.e., litter and root biomass) indirectly influenced soil EA by regulating the soil abiotic factors. Compared with the MB, microbial community composition had a minor impact on EA. The variance of specific EA (EA per unit of MB or SOM) explained by selected factors was much lower than that of absolute EA. In addition, the enzymatic C/N ratio within ecosystems did not follow a general pattern (1:1) observed at a global scale. Our results provide novel experimental insight into ecosystem-level spatial variability of C and N cycling via enzymes, suggesting that soil abiotic factors are more reliable than biotic factors to reflect EA spatial patterns across afforested systems.

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