Combining molecular and geographical data to infer the phylogeny of Lamiales and its dispersal patterns in and out of the tropics

Lamiales is one of the most intractable orders of flowering plants, with several changes in family composition, and circumscription throughout history. The order is worldwide distributed, occurring in tropical forests and frozen habitats. In this study, a comprehensive phylogeny of Lamiales was reconstructed using DNA sequences. The tree was used to infer dispersal patterns, focusing on the tropics and extratropics. Molecular and species geographic data available from public repositories were combined to address both objectives. A total of 6,910 species, and 842 genera of Lamiales were sampled using the Python tool PyPHLAWD. The tree was inferred using RAxML, and recovered a monophyletic Lamiales. All 26 families were recovered as monophyletic with high support. The families Bignoniaceae, and Plantaginaceae are remarkable examples. The first emerged as monophyletic and included tribe Jacarandeae, while the later emerged as monophyletic in its sensu lato and included both the tribes Angelonieae, and Gratioleae. Distribution points for all species were retrieved from GBIF. After filtering, 1,136,425 records were retained. Species were coded as present in extratropical or tropical environments. The in and out of the tropics dispersal patterns were inferred using a maximum likelihood approach that identifies hidden rate changes. The model recovered higher rates of transition from extratropics to tropics, estimating two rates of state transitions. When ancestral states are considered, more discrete transitions from extratropics to tropics were observed. The extratropical state was also inferred for the crown node of Lamiales and old nested nodes, revealing a rare pattern of transitions to the tropics throughout the upper Cretaceous and Tertiary. A significant phylogenetic signal was recovered for the in and out of the tropics dispersal patterns, showing that state transitions are not frequent enough to erase the effect of tree structure on the data.