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Data from: Can we build it? Yes we can, but should we use it? Assessing the quality and value of a very large phylogeny of campanulid angiosperms

Cite this dataset

Beaulieu, Jeremy M.; O'Meara, Brian C. (2019). Data from: Can we build it? Yes we can, but should we use it? Assessing the quality and value of a very large phylogeny of campanulid angiosperms [Dataset]. Dryad. https://doi.org/10.5061/dryad.bv1g6

Abstract

The study of very large and very old clades holds the promise of greater insights into evolution across the tree of life. However, there has been a fair amount of criticism regarding the interpretations and quality of studies to date, with some suggesting that detailed studies carried out on smaller, tractable scales should be preferred over the increasingly grand syntheses of these data. Methods - We provided in detail our trials and tribulations of compiling a large, sparsely sampled matrix from GenBank data and inferring a well-supported, time-calibrated phylogeny of Campanulidae. We also used a simulation approach to assess tree quality, and to study the value of using very large, comprehensive phylogenies in a comparative context. Key results - A robust and well-supported phylogeny can be produced as long as automated procedures are supplemented with some human intervention. In the case of campanulids, the overall topology may be driven not only by particular genes, but also particular sequences for a gene. We also determined that estimates of divergence times should be fairly robust to issues related to clade-specific heterogeneity. Finally, we demonstrated how relying on results from smaller, younger clades are prone to produce biased interpretations of tropical to temperate evolution across campanulids as a whole. Conclusions - While we were both surprised and encouraged by the robust and fairly well-resolved, comprehensive phylogeny of campanulids, challenges still remain. Nevertheless, large phylogenies are inherently valuable in a comparative context if only to attenuate the issue of ascertainment bias.

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