Literature DB >> 27821703

Do Macrophylogenies Yield Stable Macroevolutionary Inferences? An Example from Squamate Reptiles.

Pascal O Title1, Daniel L Rabosky1.   

Abstract

Advances in the generation, retrieval, and analysis of phylogenetic data have enabled researchers to create phylogenies that contain many thousands of taxa. These "macrophylogenies"-large trees that typically derive from megaphylogeny, supermatrix, or supertree approaches-provide researchers with an unprecedented ability to conduct evolutionary analyses across broad phylogenetic scales. Many studies have now used these phylogenies to explore the dynamics of speciation, extinction, and phenotypic evolution across large swaths of the tree of life. These trees are characterized by substantial phylogenetic uncertainty on multiple levels, and the stability of macroevolutionary inferences from these data sets has not been rigorously explored. As a case study, we tested whether five recently published phylogenies for squamate reptiles-each consisting of more than 4000 species-yield congruent inferences about the processes that underlie variation in species richness across replicate evolutionary radiations of Australian snakes and lizards. We find discordance across the five focal phylogenies with respect to clade age and several diversification rate metrics, and in the effects of clade age on species richness. We also find that crown clade ages reported in the literature on these Australian groups are in conflict with all of the large phylogenies examined. Macrophylogenies offer an unprecedented opportunity to address evolutionary and ecological questions at broad phylogenetic scales, but accurately representing the uncertainty that is inherent to such analyses remains a critical challenge to our field. [Australia; macroevolution; macrophylogeny; squamates; time calibration.].
© The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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Year:  2017        PMID: 27821703     DOI: 10.1093/sysbio/syw102

Source DB:  PubMed          Journal:  Syst Biol        ISSN: 1063-5157            Impact factor:   15.683


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