Assessment of soil microbial functional diversity: land use and soil properties affect CLPP-MicroResp and enzymes responses

• Published in: Pedobiologia Vol. 66; pp. 36 - 42
• Main Authors: Moscatelli, M.C., Secondi, L., Marabottini, R., Papp, R., Stazi, S.R., Mania, E., Marinari, S.
• Format: Journal Article
• Language: English
• Published: Elsevier GmbH 01.01.2018
• Subjects: Microbial processes; Shannon diversity index; Soil properties; Microbial processes; Shannon diversity index; Soil properties
• ISSN: 0031-4056; 1873-1511
• DOI: 10.1016/j.pedobi.2018.01.001

•Enzymes and MicroResp as reliable indicators to assess microbial functional diversity.•No correlation was found between the enzyme and MicroResp diversity indexes.•The two methods target complementary components of microbial functional diversity.•Both methodswere effective to show differences among various land use categories.•Quantile regression model allowed analysis along the distribution diversity indexes. The assessment of microbial functional diversity is an important indicator of soil quality. Different methodological approaches are currently used; among them are enzyme activities (EA) and CLPP (community level physiological profile) techniques (e.g. MicroResp™, MR). The aims of the study were: i) to assess the efficacy of both methods in capturing differences among various land use categories when different levels of selected explanatory variables such as, total organic carbon (TOC) and pH are considered, and ii) to explore, through a quantile regression approach, the possible relationships between each of the two methods with land use category, TOC and pH. The Shannon diversity index (H’), calculated from EA and MR data, was chosen as a synthetic index deriving from the same mathematical model. The quantile regression model (QRM), the Kruskal-Wallis and Spearman rank correlation tests were performed. Enzyme activities and MicroResp were reliable ecological indicators to assess soil microbial functional diversity. No correlation was found between the diversity indexes, H’EA and H’MR; it was therefore supposed that the two methods may target complementary components of microbial functional diversity. Both methods were effective in capturing differences among various land use categories, in particular H’MR in soils with low TOC content (<1.5%). Moreover, the QRM approach allowed a more detailed analysis along the distribution of the diversity indexes (H’EA and H’MR) indicating that H’EA was more dependent on the selected variables.