High resolution characterization of soil biological communities by nucleic acid and fatty acid analyses

Fatty acid methyl ester (FAME) and length heterogeneity-polymerase chain reaction (LH-PCR) analyses were used to generate ‘fingerprints’ of FAMEs and eubacterial 16S rDNA sequences characteristic of agricultural soil communities. We hypothesized that pooling data from two methods that characterized different components of soil biological communities would improve the resolution of fingerprints characterizing the effects of contrasting tillage and ground cover practices. By using supervised classifications of FAME and LH-PCR, a discriminant analysis procedure distinguished soils from contrasting tillage and ground cover management and predicted the origin of soil samples. Used independently, FAME provided higher resolution of tillage, ground cover, and field location than LH-PCR, but LH-PCR was effective at identifying field location. Pooling data from both methods did not enhance the predictive power. A comparison of linear discriminant analysis, quadratic discriminant analysis, and nonparametric density estimation demonstrated that minimizing assumptions about data distribution improved the capacity of FAME analysis to resolve differences in soil types. Use of a purely statistical Bayesian method to select a subset of fatty acids (FAʹs) as variables in discriminant analyses identified FAʹs that represented signature FAʹs for specific groups of organisms.