A statistical procedure for the analysis of microbial communities based on phenotypic properties of isolates

A novel statistical procedure for the analysis of microbial communities based on phenotypic properties of randomly collected isolates is presented and discussed. The procedure allows the representation of the microbial communities as a set of ellipses in a bidimensional graph. This representation is obtained by the following steps: (a) measurement of a set of binary phenotypic properties for n isolates belonging to k samples, each representing a different community; (b) repeated sampling by bootstrapping of the m samples, thus obtaining, for each community, i subsamples of j isolates; (c) calculation of the frequency of positive results for each test for each subsample; (d) calculation of the matrix of Euclidean distances between the k x i frequency vectors; (e) use of multidimensional scaling (MDS) to obtain a representation in two dimensions of the distance relationships between the frequency vectors; (f) plotting of the 95% confidence ellipses for the i frequency vectors of each of the k communities. By using both simple, synthetic microbial communities, and samples of lactic acid bacteria isolated from natural microbial communities (sourdoughs, compressed yeast, fermented sausages), it was demonstrated that the position and shape of the ellipses are clearly related to the composition of the community, while the relationship between the size of the ellipses and the phenotypical diversity of the community is less straightforward: while communities with very different diversity (measured with the Functional Evenness index and the mean taxonomic distance) had ellipses that were very different in size, there was no strict proportionality between the size of the ellipse and the diversity of the community. Nevertheless, the representation of microbial communities obtained by bootstrapping and multidimensional scaling appears to be superior to the more usual representation based on tabulation of the frequencies of isolates belonging to different clusters. (C) 2002 Elsevier Science B.V All rights reserved.