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Springer Nature [academic journals on nature.com], The ISME Journal: Multidisciplinary Journal of Microbial Ecology, 4(10), p. 959-967, 2015

DOI: 10.1038/ismej.2015.171

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Predicting microbial traits with phylogenies

Journal article published in 2015 by Marta Goberna, Miguel Verdú ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Phylogeny reflects genetic and phenotypic traits in Bacteria and Archaea. The phylogenetic conservatism of microbial traits has prompted the application of phylogeny-based algorithms to predict unknown trait values of extant taxa based on the traits of their evolutionary relatives to estimate, for instance, rRNA gene copy numbers, gene contents or tolerance to abiotic conditions. Unlike the 'macrobial' world, microbial ecologists face scenarios potentially compromising the accuracy of trait reconstruction methods, as, for example, extremely large phylogenies and limited information on the traits of interest. We review 990 bacterial and archaeal traits from the literature and support that phylogenetic trait conservatism is widespread through the tree of life, while revealing that it is generally weak for ecologically relevant phenotypic traits and high for genetically complex traits. We then perform a simulation exercise to assess the accuracy of phylogeny-based trait predictions in common scenarios faced by microbial ecologists. Our simulations show that ca. 60% of the variation in phylogeny-based trait predictions depends on the magnitude of the trait conservatism, the number of species in the tree, the proportion of species with unknown trait values and the mean distance in the tree to the nearest neighbour with a known trait value. Results are similar for both binary and continuous traits. We discuss these results under the light of the reviewed traits and provide recommendations for the use of phylogeny-based trait predictions for microbial ecologists. ; Financial support was provided by the Spanish Ministry of Science and Innovation (R+D Projects CGL2011-29585-C02-01 and CGL2014-58333-P). MG acknowledges support by the Ramón y Cajal Programme (Spanish Ministry of Economy and Competitiveness). ; Peer Reviewed