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Merging taxonomy with ecological population prediction in a case study of Vibrionaceae.

ABSTRACT: We synthesized population structure data from three studies that assessed the fine-scale distribution of Vibrionaceae among temporally and spatially distinct environmental categories in coastal seawater and animals. All studies used a dynamic model (AdaptML) to identify phylogenetically cohesive and ecologically distinct bacterial populations and their predicted habitats without relying on a predefined genetic cutoff or relationships to previously named species. Across the three studies, populations were highly overlapping, displaying similar phylogenetic characteristics (identity and diversity), and were predominantly congruent with taxonomic Vibrio species previously characterized as genotypic clusters by multilocus sequence analysis (MLSA). The environmental fidelity of these populations appears high, with 9 out of 12 reproducibly associating with the same predicted (micro)habitats when similar environmental categories were sampled. Overall, this meta-analysis provides information on the habitat predictability and structure of previously described species, demonstrating that MLSA-based taxonomy can, at least in some cases, serve to approximate ecologically cohesive populations.

SUBMITTER: Preheim SP

PROVIDER: S-EPMC3194886 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Merging taxonomy with ecological population prediction in a case study of Vibrionaceae.

Preheim Sarah P SP Timberlake Sonia S Polz Martin F MF Polz Martin F MF

Applied and environmental microbiology 20110826 20

We synthesized population structure data from three studies that assessed the fine-scale distribution of Vibrionaceae among temporally and spatially distinct environmental categories in coastal seawater and animals. All studies used a dynamic model (AdaptML) to identify phylogenetically cohesive and ecologically distinct bacterial populations and their predicted habitats without relying on a predefined genetic cutoff or relationships to previously named species. Across the three studies, populat ...[more]

PMID: 21873482

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Project description:BackgroundInappropriate taxon definitions may have severe consequences in many areas. For instance, biologically sensible species delimitation of plant pathogens is crucial for measures such as plant protection or biological control and for comparative studies involving model organisms. However, delimiting species is challenging in the case of organisms for which often only molecular data are available, such as prokaryotes, fungi, and many unicellular eukaryotes. Even in the case of organisms with well-established morphological characteristics, molecular taxonomy is often necessary to emend current taxonomic concepts and to analyze DNA sequences directly sampled from the environment. Typically, for this purpose clustering approaches to delineate molecular operational taxonomic units have been applied using arbitrary choices regarding the distance threshold values, and the clustering algorithms.MethodologyHere, we report on a clustering optimization method to establish a molecular taxonomy of Peronospora based on ITS nrDNA sequences. Peronospora is the largest genus within the downy mildews, which are obligate parasites of higher plants, and includes various economically important pathogens. The method determines the distance function and clustering setting that result in an optimal agreement with selected reference data. Optimization was based on both taxonomy-based and host-based reference information, yielding the same outcome. Resampling and permutation methods indicate that the method is robust regarding taxon sampling and errors in the reference data. Tests with newly obtained ITS sequences demonstrate the use of the re-classified dataset in molecular identification of downy mildews.ConclusionsA corrected taxonomy is provided for all Peronospora ITS sequences contained in public databases. Clustering optimization appears to be broadly applicable in automated, sequence-based taxonomy. The method connects traditional and modern taxonomic disciplines by specifically addressing the issue of how to optimally account for both traditional species concepts and genetic divergence.

Dataset Information

Merging taxonomy with ecological population prediction in a case study of Vibrionaceae.

Publications

Merging taxonomy with ecological population prediction in a case study of Vibrionaceae.

Similar Datasets

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