Some Remarks on the Theoretical Aspects of Bacterial Taxonomy

Floodgate, G. D.

doi:10.1128/mmbr.26.3.277-291.1962

Cited by 3 publications

(1 citation statement)

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“…Differences in size, dispersal, dormancy regimes, and growth and reproduction may not prohibit application of the same quantitative techniques to both single‐celled and multicellular organisms. However, genome‐scale data have given an evolutionary context to the phylogeny function disconnect in microorganisms, particularly bacteria, which has been known for decades (Cowan, ; Floodgate, ), in the process spawning or reigniting debates about microbial evolution, taxonomy, and the microbial species (Gevers et al ., ; Konstantinidis & Tiedje, ; Bapteste & Boucher, ; Doolittle & Zhaxybayeva, ). Although a unifying species concept is not needed for ecological analysis, a sound rationale and clear approach (or set of approaches) to define ‘units’ is.…”

Section: Introductionmentioning

confidence: 99%

Interactions in the microbiome: communities of organisms and communities of genes

et al. 2014

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A central challenge in microbial community ecology is the delineation of appropriate units of biodiversity, which can be taxonomic, phylogenetic, or functional in nature. The term ‘community’ is applied ambiguously; in some cases, the term refers simply to a set of observed entities, while in other cases, it requires that these entities interact with one another. Microorganisms can rapidly gain and lose genes, potentially decoupling community roles from taxonomic and phylogenetic groupings. Trait-based approaches offer a useful alternative, but many traits can be defined based on gene functions, metabolic modules, and genomic properties, and the optimal set of traits to choose is often not obvious. An analysis that considers taxon assignment and traits in concert may be ideal, with the strengths of each approach offsetting the weaknesses of the other. Individual genes also merit consideration as entities in an ecological analysis, with characteristics such as diversity, turnover, and interactions modeled using genes rather than organisms as entities. We identify some promising avenues of research that are likely to yield a deeper understanding of microbial communities that shift from observation-based questions of ‘Who is there?’ and ‘What are they doing?’ to the mechanistically driven question of ‘How will they respond?’

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