APPLICATION OF MULTIVARIATE-ANALYSIS OF VARIANCE AND RELATED TECHNIQUES IN SOIL STUDIES WITH SUBSTRATE UTILIZATION TESTS

Substrate utilization tests are increasingly used for characterizing m icrobial communities. The applied statistical methods like principal c omponent analysis or detrended correspondence analysis are one-sample methods unsuited for specifying the substrates contributing for separa tion among groups. Further, these methods demand a high number of repl ications, a prerequisite that is usually not met. In this paper, a met hod is proposed that reduces the amount of replicates needed but still allows statistically sound data evaluation. In a first step, in a scr eening assay with a high number of substrates (31) in three replicates , those substrates are identified that most likely discriminate among the sample types under investigation. In a second step, multivariate a nalysis of variance and tests based on simultaneous confidence interva ls are applied in an assay using this smaller set of substrates (8), b ut in sixteen replicates. Our approach emphasizes the need of a high r atio of numbers of replicates to the numbers of variables. The substra tes contributing most to the separation among groups are determined wi th a multivariate separation measure, taking the combined effect of se veral substrates into account. The Mahalanobis distance is calculated to measure distances between the various sample types. The advantage o f the approach is that it allows more advanced statistical techniques, like factor analysis and canonical correlation analysis to reduce the variables of different substrate groups, followed by resampling techn iques like jackknife and bootstrap algorithms (calculated with Monte C arlo approximation) and Bayes statistics to improve statistical infere nces. The approach was tested with a set of three sample types (compos t, pasture soil and a mixture of both) and proved suitable for this ap plication. (C) 1997 Elsevier Science B.V.