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Bacterial taxa abundance pattern in an industrial wastewater treatment system determined by the full rRNA cycle approach
Author(s) -
Figuerola Eva L. M.,
Erijman Leonardo
Publication year - 2007
Publication title -
environmental microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.954
H-Index - 188
eISSN - 1462-2920
pISSN - 1462-2912
DOI - 10.1111/j.1462-2920.2007.01298.x
Subject(s) - biology , operational taxonomic unit , abundance (ecology) , 16s ribosomal rna , relative abundance distribution , taxon , ribosomal rna , biodiversity , ecology , community structure , relative species abundance , evolutionary biology , gene , genetics
Summary The description of the diversity and structure of microbial communities through quantification of the constituent populations is one of the major objectives in environmental microbiology. The implications of models for community assembly are practical as well as theoretical, because the extent of biodiversity is thought to influence the function of ecosystems. Current attempts to predict species diversity in different environments derive the numbers of individuals for each operational taxonomic unit (OTU) from the frequency of clones in 16S rDNA gene libraries, which are subjected to a number of inherent biases and artefacts. We show that diversity of the bacterial community present in a complex microbial ensemble can be estimated by fitting the data of the full‐cycle rRNA approach to a model of species abundance distribution. Sequences from a 16S rDNA gene library from activated sludge were reliably assigned to OTUs at a genetic distance of 0.04. A group of 17 newly designed rRNA‐targeted oligonucleotide probes were used to quantify by fluorescence in situ hybridization, OTUs represented with more than three clones in the 16S rDNA clone library. Cell abundance distribution was best described by a geometric series, after the goodness of fit was evaluated by the Kolmogorov‐Smirnov test. Although a complete mechanistic understanding of all the ecological processes involved is still not feasible, describing the distribution pattern of a complex bacterial assemblage model can shed light on the way bacterial communities operate.

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