Submitted to: Soil Biology and Biochemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 30, 2002
Publication Date: December 31, 2002
Citation: DIERKSEN, K.P., WHITTAKER, G.W., BANOWETZ, G.M., AZEVEDO, M.D., KENNEDY, A.C., STEINER, J.J., GRIFFITH, S.M. HIGH RESOLUTION CHARACTERIZATION OF SOIL MICROBIAL DIVERSITY BY NUCLEIC ACID AND FATTY ACID ANALYSES. SOIL BIOLOGY AND BIOCHEMISTRY. 2002. v.34. p. 1853-1860. Interpretive Summary: Two analytical methods were used to create unique "fingerprints" representing soils that had been subject to tillage or no-till farming. The methods were able to distinguish the soils and to distinguish soil samples collected between the crop rows from samples collected immediately underneath the grass crop. We developed statistical procedures to combine the data from both methods to determine whether the combination would improve our ability to identify the source of unknown soil samples. One of the methods, fatty acid methyl ester analysis (FAME) was better able to distinguish soils from different origins and combining the data from both methods did not improve the resolution. We used new statistical procedures that were highly accurate in identifying the source of soil and these procedures also selected fatty acids that are specifically associated with the presence of certain soil microbes. This work will improve our capacity to determine the source of soils and sediments transported to surface waters.
Technical Abstract: Fatty acid methyl ester (FAME) and length-heterogeneity PCR (LH-PCR) analyses were used to generate "fingerprints" of microbial FAMEs and 16S rDNA sequences of soil microbial communities. We hypothesized that a procedure that pooled data from both methods would improve the resolution of fingerprints characterizing the impact of contrasting tillage and ground cover practices on soil microbial communities. 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 the assumptions about data distribution improved the capacity of FAME analysis to resolve differences in soil types.