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AbstractEvolutionary rate heterogeneity and rapid radiations are common phenomena in organismal evolution and represent major challenges for reconstructing deep‐level phylogenies. Here we detected substantial conflicts in and among data sets as well as uncertainty concerning relationships among lineages of Vitaceae from individual gene trees, supernetworks and tree certainty values. Congruent deep‐level relationships of Vitaceae were retrieved by comprehensive comparisons of results from optimal partitioning analyses, multispecies coalescent approaches and the Bayesian concordance method. We found that partitioning schemes selected by PartitionFinder were preferred over those by gene or by codon position, and the unpartitioned model usually performed the worst. For a data set with conflicting signals, however, the unpartitioned model outperformed models that included more partitions, demonstrating some limitations to the effectiveness of concatenation for these data. For a transcriptome data set, fast coalescent methods (STAR and MP‐EST) and a Bayesian concordance approach yielded congruent topologies with trees from the concatenated analyses and previous studies. Our results highlight that well‐resolved gene trees are critical for the effectiveness of coalescent‐based methods. Future efforts to improve the accuracy of phylogenomic analyses should emphasize the development of new methods that can accommodate multiple biological processes and tolerate missing data while remaining computationally tractable.
Molecular phylogenetic analysis, Estimating species trees, Parthenocissus vitaceae, Chloroplast phylogenomics, Early land plants, Compositional heterogeneity, Nuclear gene data, Long-branch attraction, Character evolution, Seed plants
Molecular phylogenetic analysis, Estimating species trees, Parthenocissus vitaceae, Chloroplast phylogenomics, Early land plants, Compositional heterogeneity, Nuclear gene data, Long-branch attraction, Character evolution, Seed plants
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 44 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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