Refining numerical approaches for analyzing soil microbial community catabolic profiles based on carbon source utilization patterns

Despite several limitations, community level catabolic profiles (CLCPs) based on carbon source utilization patterns are extensively used in ecological surveys to study the functional diversity of soil microbial communities. Our paper discusses two ways by which numerical approaches currently applied...

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Bibliographic Details
Published in:Soil biology & biochemistry 2006-03, Vol.38 (3), p.629-632
Main Authors: Bradley, Robert L., Shipley, Bill, Beaulieu, Carole
Format: Article
Language:English
Subjects:
Agronomy. Soil science and plant productions
Biochemistry and biology
Biolog microtiter plates
Biological and medical sciences
Carbon source utilization pattern
Chemical, physicochemical, biochemical and biological properties
Community level catabolic profile
computer software
equations
functional diversity
Functional diversity index
Functional subgroups
Fundamental and applied biological sciences. Psychology
Microbiology
PCA
Physics, chemistry, biochemistry and biology of agricultural and forest soils
PMANOVA
principal component analysis
soil microorganisms
Soil science
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Summary:Despite several limitations, community level catabolic profiles (CLCPs) based on carbon source utilization patterns are extensively used in ecological surveys to study the functional diversity of soil microbial communities. Our paper discusses two ways by which numerical approaches currently applied to CLCP data may be refined. First, we discuss the negative relationship between the sensitivity of a functional diversity index (D f ) and the number of catabolic reactions used to calculate this index. Consequently, we propose that D f be based on specific subsets of substrates to describe the diversity among functional subgroups of the soil microbial community. Secondly, we discuss the current lack of a proper inferential test of significance for testing treatment effects on CLCPs. Permutation multivariate analysis of variance (PMANOVA) is thus proposed as a robust statistical alternative for comparing treatment effects on multiple response variables that are not independent. For illustrative purposes, we have compared the sensitivity of principal component analysis (PCA) with that of PMANOVA, performed on the same CLCP data set.
ISSN:0038-0717
1879-3428
DOI:10.1016/j.soilbio.2005.07.002