Vargas, O. M. | bioRxiv Science

bioRxiv2017

Comparative analysis of 24 chloroplast genomes yields highly informative genetic markers for the Brazil nut family (Lecythidaceae)

O_LIPremise of the study: The tropical tree family Lecythidaceae has enormous ecological and economic importance in the Amazon basin. Lecythidaceae species can be difficult to identify without molecular data, however, and phylogenetic relationships within and among the most diverse genera are poorly resolved.\nC_LIO_LIMethods: To develop informative genetic markers for Lecythidaceae, we used genome skimming to assemble de novo the full plastome of the Brazil nut tree (Bertholletia excelsa) and 23 other Lecythidaceae species. Indices of nucleotide diversity and phylogenetic signal were used to identify regions suitable for genetic marker development.\nC_LIO_LIResults: The B. excelsa plastome contained 160,472 bp and was arranged in a quadripartite structure. Using the 24 plastome alignments, we developed primers for 10 coding and non-coding DNA regions containing exceptional nucleotide diversity and phylogenetic signal. We additionally developed 19 chloroplast simple sequence repeats (cpSSRs) for population-level studies.\nC_LIO_LIDiscussion: The coding region ycf1 and the spacer rpl16-rps3 outperformed plastid DNA markers previously used for barcoding and phylogenetics. Used in a phylogenetic analysis, the matrix of 24 plastomes showed with 100% bootstrap support that Lecythis and Eschweilera are polyphyletic. The plastomes and primers presented in this study will facilitate a broad array of ecological and evolutionary studies in Lecythidaceae.\nC_LI

evolutionary biology↗

bioRxiv2017

Missing the point (estimate): Bayesian and likelihood phylogenetic reconstructions of morphological characters produce generally concordant inferences. A comment on Puttick et al.

Puttick et al. [1] performed a simulation study to compare accuracy among methods of inferring phylogeny from discrete morphological characters. They report that a Bayesian implementation of the Mk model [2] was most accurate (but with low resolution), while a maximum likelihood (ML) implementation of the same model was least accurate. They conclude by strongly advocating that Bayesian implementations of the Mk model should be the default method of analysis for such data. While we appreciate the authors attempt to investigate the accuracy of alternative methods of analysis, their conclusion is based on an inappropriate comparison of the ML point estimate, which does not consider confidence, with the Bayesian consensus, which incorporates estimation credibility into the summary tree. Using simulation, we demonstrate that ML and Bayesian estimates are concordant when confidence and credibility are comparably reflected in summary trees, a result expected from statistical theory. We therefore disagree with the conclusions of PEA and consider their prescription of any default method to be poorly founded. Instead, we recommend caution and thoughtful consideration of the model or method being applied to a morphological dataset.

evolutionary biology↗