Resolving Recent Plant Radiations: Power and Robustness of Genotyping-by-Sequencing

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Abstract

Disentangling species boundaries and phylogenetic relationships within recent evolutionary radiations is a challenge due to the poor morphological differentiation and low genetic divergence between species, frequently accompanied by phenotypic convergence, interspecific gene flow and incomplete lineage sorting. Here we employed a genotyping-by-sequencing (GBS) approach, in combination with morphometric analyses, to investigate a small western Mediterranean clade in the flowering plant genus Linaria that radiated in the Quaternary. After confirming the morphological and genetic distinctness of eight species, we evaluated the relative performances of concatenation and coalescent methods to resolve phylogenetic relationships. Specifically, we focused on assessing the robustness of both approaches to variations in the parameter used to estimate sequence homology (clustering threshold). Concatenation analyses suffered from strong systematic bias, as revealed by the high statistical support for multiple alternative topologies depending on clustering threshold values. By contrast, topologies produced by two coalescent-based methods (NJst, SVDquartets) were robust to variations in the clustering threshold. Reticulate evolution may partly explain incongruences between NJst, SVDquartets and concatenated trees. Integration of morphometric and coalescent-based phylogenetic results revealed (i) extensive morphological divergence associated with recent splits between geographically close or sympatric sister species and (ii) morphological convergence in geographically disjunct species. These patterns are particularly true for floral traits related to pollinator specialization, including nectar spur length, tube width and corolla color, suggesting pollinator-driven diversification. Given its relatively simple and inexpensive implementation, GBS is a promising technique for the phylogenetic and systematic study of recent radiations, but care must be taken to evaluate the robustness of results to variation of data assembly parameters.

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