Version 1.5 of the computer program TNT completely integrates landmark data into phylogenetic analysis. Landmark data consist of coordinates (in two or three dimensions) for the terminal taxa; TNT reconstructs shapes for the internal nodes such that the difference between ancestor and descendant shapes for all tree branches sums up to a minimum; this sum is used as tree score. Landmark data can be analysed alone or in combination with standard characters; all the applicable commands and options in TNT can be used transparently after reading a landmark data set. The program continues implementing all the types of analyses in former versions, including discrete and continuous characters (which can now be read at any scale, and automatically rescaled by TNT). Using algorithms described in this paper, searches for landmark data can be made tens to hundreds of times faster than it was possible before (from T to 3T times faster, where T is the number of taxa), thus making phylogenetic analysis of landmarks feasible even on standard personal computers.

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A method for the direct use of aligned landmark data (2D or 3D coordinates of comparable points) in phylogenetic analysis is described. The approach is based on finding, for each of the landmark points, the ancestral positions that minimize the distance between the ancestor ⁄ descendant points along the tree. Doing so amounts to maximizing the degree to which similar positions of the landmarks in different taxa can be accounted for by common ancestry, i.e. parsimony. This method requires no transformation of the aligned data or the results: the data themselves are the x, y, z coordinates of the landmarks, and the output of mapping a character onto a given tree is the x, y, z coordinates for the hypothetical ancestors. In the special case of collinear points, the results are identical to those of optimization of (continuous) additive characters.

Premise of research. Phylogenetic relationships of Araucariaceae (Coniferophyta, Araucariales) are revised on the basis of the first combined data matrix for the family.Methodology. Taxon sampling includes 39 ingroup species (31 extant, 8 fossils) and outgroup species of all the remaining conifer families. Five fossil Araucaria species, one species of the genus Araucarites, and two species of the extinct genera Wairarapaia and Emwadea were included in the analyses. Character sampling includes 23 genomic regions (19 plastid, 2 nuclear, and 2 mitochondrial) and 62 morphological characters (52 discrete and 10 continuous). The phylogenetic analyses were conducted with equally weighted parsimony. Additionally, several analyses under different taxon-and gene-sampling regimes were analyzed for identifying the causes of the long-lasting controversies in the interrelationships of the three extant genera of Araucariaceeae.Pivotal results. Monophyletic Araucariaceae is the sister group of Podocarpaceae, forming the order Araucariales. Monophyly of Araucaria and Agathis is also strongly supported by the data. The results of both molecular and combined analyses indicate that Wollemia and Agathis form a clade (pagathioid clade) sister to Araucaria. Within Araucaria, the analyses support the monophyly of the four currently recognized sections: Araucaria, Bunya, Intermedia, and Eutacta. Results support the monophyly of living and fossil Araucaria (including Araucarites), whereas the remaining extinct genera are placed as the stem of the agathioid clade. In terms of the sensitivity analyses performed, results suggest that inconsistencies among previous results would be related to ingroup sampling.Conclusions. By means of a combined phylogenetic analysis, we have been able to obtain a strongly supported and well-resolved phylogeny of Araucariaceae that includes both living species and fossil species for the group. This study shows the feasibility and usefulness of phylogenetic analyses that incorporate multiple sources of evidence (molecules/morphology, living/fossil species, discrete/continuous characters).

Obtaining a well supported schema of phylogenetic relationships among the major groups of living organisms requires considering as much taxonomic diversity as possible, but the computational cost of calculating large phylogenies has so far been a major obstacle. We show here that the parsimony algorithms implemented in TNT can successfully process the largest phylogenetic data set ever analysed, consisting of molecular sequences and morphology for 73 060 eukaryotic taxa. The trees resulting from molecules alone display a high degree of congruence with the major taxonomic groups, with a small proportion of misplaced species; the combined data set retrieves these groups with even higher congruence. This shows that tree-calculation algorithms effectively retrieve phylogenetic history for very large data sets, and at the same time provides strong corroboration for the major eukaryotic lineages long recognized by taxonomists.

The most extensive combined phylogenetic analyses of the subclass Marchantiidae yet undertaken was conducted on the basis of morphological and molecular data. The morphological data comprised 126 characters and 56 species. Taxonomic sampling included 35 ingroup species with all genera and orders of Marchantiidae sampled, and 21 outgroup species with two genera of Blasiidae (Marchantiopsida), 15 species of Jungermanniopsida (the three subclasses represented) and the three genera of Haplomitriopsida. Takakia ceratophylla (Bryophyta) was employed to root the trees. Character sampling involved 92 gametophytic and 34 sporophytic traits, supplemented with ten continuous characters. Molecular data included 11 molecular markers: one nuclear ribosomal (26S), three mitochondrial genes (nad1, nad5, rps3) and seven chloroplast regions (atpB, psbT‐psbH, rbcL, ITS, rpoC1, rps4, psbA). Searches were performed under extended implied weighting, weighting the character blocks against the average homoplasy. Clade stability was assessed across three additional weighting schemes (implied weighting corrected for missing entries, standard implied weighting and equal weighting) in three datasets (molecular, morphological and combined). The contribution from different biological phases regarding node recovery and diagnosis was evaluated. Our results agree with many of the previous studies but cast doubt on some relationships, mainly at the family and interfamily level. The combined analyses underlined the fact that, by combining data, taxonomic enhancements could be achieved regarding taxon delimitation and quality of diagnosis. Support values for many clades of previous molecular studies were improved by the addition of morphological data. The long‐held assumption that morphology may render spurious or low‐quality results in this taxonomic group is challenged. The morphological trends previously proposed are re‐evaluated in light of the new phylogenetic scheme.

The genus Prosopis is an important member of arid and semiarid environments around the world. To study Prosopis diversification and evolution, a combined approach including molecular phylogeny, molecular dating, and character optimization analysis was applied. Phylogenetic relationships were inferred from five different molecular markers (matK-trnK, trnL-trnF, trnS-psbC, G3pdh, NIA). Taxon sampling involved a total of 30 Prosopis species that represented all Sections and Series and the complete geographical range of the genus. The results suggest that Prosopis is not a natural group. Molecular dating analysis indicates that the divergence between Section Strombocarpa and Section Algarobia plus Section Monilicarpa occurred in the Oligocene, contrasting with a much recent diversification (Late Miocene) within each of these groups. The diversification of the group formed by species of Series Chilenses, Pallidae, and Ruscifoliae is inferred to have started in the Pliocene, showing a high diversification rate. The moment of diversification within the major lineages of American species of Prosopis is coincident with the spreading of arid areas in the Americas, suggesting a climatic control for diversification of the group. Optimization of habitat parameters suggests an ancient occupation of arid environments by Prosopis species.

This paper describes algorithms for optimizing two-or three-dimensional landmark data onto trees directly. The method is based on a first approximation using grids, and subsequent iterative refinement of the initial point estimates. Details of the implementation are discussed, as well as an empirical example.