Highly diverse exoenzymes mediate the energy flow from substrates to the multitrophic microbiota within the soil decomposer micro-food web. Here, we used a \"soil enzyme profile analysis\" approach to establish a series of enzyme profile indices; those indices were hypothesized to reflect micro-food web features. We systematically evaluated the shifts in enzyme profile indices in relation to the micro-food web features in the restoration of an abandoned cropland to a natural area. We found that enzymatic C:N stoichiometry and decomposability index were significantly associated with substrate availability. Furthermore, the higher Shannon diversity index in the exoenzyme profile, especially for the C-degrading hydrolase, corresponded to a greater microbiota community diversity. The increased complexity and stability of the exoenzyme network reflected similar changes with the micro-food web networks. In addition, the gross activity of the enzyme profile as a parameter for soil multifunctionality, effectively predicted the substrate content, microbiota community size, diversity, and network complexity. Ultimately, the proposed enzymic channel index was closely associated with the traditional decomposition channel indices derived from microorganisms and nematodes. Our results showed that soil enzyme profile analysis reflected very well the decomposer food web features. Our study has important implications for projecting future climate change or anthropogenic disturbance impacts on soil decomposer micro-food web features by using soil enzyme profile analysis.

It is essential to remediate or amend soils contaminated with various heavy metals or pollutants so that the soils may be used again safely. Verifying that the remediated or amended soils meet soil quality standards is an important part of the process. We estimated the activity levels of eight soil exoenzymes (acid phosphatase, arylsulfatase, catalase, dehydrogenase, fluorescein diacetate hydrolase, protease, urease, and ß-glucosidase) in contaminated and remediated soils from two sites near a non-ferrous metal smelter, using colorimetric and titrimetric determination methods. Our results provided the levels of activity of soil exoenzymes that indicate soil health. Most enzymes showed lower activity levels in remediated soils than in contaminated soils, with the exception of protease and urease, which showed higher activity after remediation in some soils, perhaps due to the limited nutrients available in remediated soils. Soil exoenzymes showed significantly higher activity in soils from one of the sites than from the other, due to improper conditions at the second site, including high pH, poor nutrient levels, and a high proportion of sand in the latter soil. Principal component analysis revealed that ß-glucosidase was the best indicator of soil ecosystem health, among the enzymes evaluated. We recommend using ß-glucosidase enzyme activity as a prior indicator in estimating soil ecosystem health.