Phylogenomics and a posteriori data partitioning resolve the Cretaceous angiosperm radiation Malpighiales

Abstract

The angiosperm order Malpighiales includes ~16,000 species and constitutes up to 40% of the understory tree diversity in tropical rain forests. Despite remarkable progress in angiosperm systematics during the last 20 y, relationships within Malpighiales remain poorly resolved, possibly owing to its rapid rise during the mid-Cretaceous. Using phylogenomic approaches, including analyses of 82 plastid genes from 58 species, we identified 12 additional clades in Malpighiales and substantially increased resolution along the backbone. This greatly improved phylogeny revealed a dynamic history of shifts in net diversification rates across Malpighiales, with bursts of diversification noted in the Barbados cherries (Malpighiaceae), cocas (Erythroxylaceae), and passion flowers (Passifloraceae). We found that commonly used a priori approaches for partitioning concatenated data in maximum likelihood analyses, by gene or by codon position, performed poorly relative to the use of partitions identified a posteriori using a Bayesian mixture model. We also found better branch support in trees inferred from a taxon-rich, data-sparse matrix, which deeply sampled only the phylogenetically critical placeholders, than in trees inferred from a taxon-sparse matrix with little missing data. Although this matrix has more missing data, our a posteriori partitioning strategy reduced the possibility of producing multiple distinct but equally optimal topologies and increased phylogenetic decisiveness, compared with the strategy of partitioning by gene. These approaches are likely to help improve phylogenetic resolution in other poorly resolved major clades of angiosperms and to be more broadly useful in studies across the Tree of Life.

Fig. 1.

ML 50% majority-rule bootstrap consensus tree of Malpighiales inferred from the combined-incomplete matrix using the MixtPart partitioning strategy. ML BPs/Bayesian PPs are indicated above each branch; a hyphen indicates that the node is not present in the Bayesian 50% majority-rule consensus tree. The 12 additional clades we identified are designated using the numbers corresponding to those in Table 2. Taxa included in the 82-gene matrix are highlighted in green and marked with asterisks; spp. = composite terminals compiled from multiple closely related species; the approximate number of accepted species for each family is given in parentheses to the right; lettered photographs depicting representative species from all included families are shown to the right. Clades exhibiting a shift in net species diversification are highlighted in red (acceleration) and blue (deceleration). More detailed results of diversification analyses are provided in the text and SI Appendix. Outgroup relationships are shown in SI Appendix, Fig. S1.

Fig. 2.

Mean ML BPs of the bipartition trees inferred using ML for each of the four matrices and four partitioning strategies. SEs around the means are indicated, and the MixtPart partitioning strategy is highlighted in gray. The bipartition trees inferred from the combined-incomplete and 13-gene matrices were pruned to match the taxon sampling of the 82-gene and combined-complete matrices.

Fig. 3.

ML BPs of the 12 additional clades we identified in Malpighiales (Fig. 1) inferred from three matrices and four partitioning strategies. The MixtPart partitioning strategy is highlighted in gray.