One of the lasting controversies in phylogenetic inference is the degree to which specific evolutionary models should influence the choice of methods. Model‐based approaches to phylogenetic inference (likelihood, Bayesian) are defended on the premise that without explicit statistical models there is no science, and parsimony is defended on the grounds that it provides the best rationalization of the data, while refraining from assigning specific probabilities to trees or character‐state reconstructions. Authors who favour model‐based approaches often focus on the statistical properties of the methods and models themselves, but this is of only limited use in deciding the best method for phylogenetic inference—such decision also requires considering the conditions of evolution that prevail in nature. Another approach is to compare the performance of parsimony and model‐based methods in simulations, which traditionally have been used to defend the use of models of evolution for DNA sequences. Some recent papers, however, have promoted the use of model‐based approaches to phylogenetic inference for discrete morphological data as well. These papers simulated data under models already known to be unfavourable to parsimony, and modelled morphological evolution as if it evolved just like DNA, with probabilities of change for all characters changing in concert along tree branches. The present paper discusses these issues, showing that under reasonable and less restrictive models of evolution for discrete characters, equally weighted parsimony performs as well or better than model‐based methods, and that parsimony under implied weights clearly outperforms all other methods.

The South American and West Indian Casque‐headed Treefrogs (Hylidae: Hylinae: Lophyohylini) include 85 species. These are notably diverse in morphology (e.g. disparate levels of cranial hyperossification) and life history (e.g. different reproductive modes, chemical defences), have a wide distribution, and occupy habitats from the tropical rainforests to semiarid scrubland. In this paper, we present a phylogenetic analysis of this hylid tribe based on sequence fragments of up to five mitochondrial (12S, 16S, ND1, COI, Cytb) and six nuclear genes (POMC, RAG‐1, RHOD, SIAH, TNS3, TYR). We included most of its species (> 96%), in addition to a number of new species. Our results indicate: (i) the paraphyly of Trachycephalus with respect to Aparasphenodon venezolanus; (ii) the nonmonophyly of Aparasphenodon, with Argenteohyla siemersi, Corythomantis galeata and Nyctimantis rugiceps nested within it, and Ap. venezolanus nested within Trachycephalus; (iii) the polyphyly of Corythomantis; (iv) the nonmonophyly of the recognized species groups of Phyllodytes; and (v) a pervasive low support for the deep relationships among the major clades of Lophyohylini, including C. greeningi and the monotypic genera Itapotihyla and Phytotriades. To remedy the nonmonophyly of Aparasphenodon, Corythomantis, and Trachycephalus, we redefined Nyctimantis to include Aparasphenodon (with the exception of Ap. venezolanus, which we transferred to Trachycephalus), Argenteohyla, and C. galeata. Additionally, our results indicate the need for taxonomic work in the following clades: (i) Trachycephalus dibernardoi and Tr. imitatrix; (ii) Tr. atlas, Tr. mambaiensis and Tr. nigromaculatus; and (iii) Phyllodytes. On the basis of our phylogenetic results, we analyzed the evolution of skull hyperossification and reproductive biology, with emphasis on the multiple independent origins of phytotelm breeding, in the context of Anura. We also analyzed the inter‐related aspects of chemical defences, venom delivery, phragmotic behaviour, co‐ossification, and prevention of evaporative water loss.

based on landmark data in 41 empirical data sets. -Zoologica Scripta, 46: 1-11. The inference of phylogenetic hypotheses from landmark data has been questioned during the last two decades. Besides theoretical concerns, one of the limitations pointed out for the use of landmark data in phylogenetics is its (supposed) lack of information relevant to the inference of phylogenetic relationships. However, empirical analyses are scarce; there exists no previous study that systematically evaluates the phylogenetic performance of landmark data in a series of data sets. In the present study, we analysed 41 published data sets in order to assess the correspondence between the phylogenetic trees derived from landmark data and those obtained with alternative and independent sources of evidence, and determined the main factors that might affect this inference. The data sets presented a variable number of terminals (5-200) and configurations (1-14), belonging to different taxonomic groups. The results showed that for most of the data sets analysed, the trees derived from landmark data presented a low correspondence with the reference phylogenies. The results were similar irrespective of the phylogenetic method considered. Complementary analyses strongly suggested that the limited amount of evidence included in each data set (one or a few landmark configurations) is the main cause for that low correspondence: the phylogenetic analysis of eight data sets that presented three or more configurations clearly showed that the inclusion of several landmark configurations improves the results. In addition, the analyses indicated that the inclusion of landmark data from different configurations is more important than the inclusion of more landmarks from the same configuration. Based on the results presented here, we consider that the poor results previously obtained in phylogenetic analyses based on landmark data were not caused by methodological limitations, but rather due to the limited amount of evidence included in the data sets.

We discuss here the use of TNT (Tree Analysis using New Technology) for phylogenomic analysis. For such data, parsimony is a useful alternative to model-based analyses, which frequently utilize models that make unrealistic assumptions (e.g. low heterotachy), struggle with high levels of missing data, etc. Parsimony and model-based methods often yield trees with few topological differences, which can then be analyzed further in order to investigate whether these few topological differences are due to undesirable analysis artefacts. This is facilitated by the greater speed and computational efficiency of parsimony, which allow for a more in-depth analysis of datasets. We here briefly describe the computationally most efficient and versatile parsimony software, TNT, which can be used for phylogenetic and phylogenomic analyses. In particular, we describe and provide a series of scripts that are specifically designed for the analysis of phylogenomic datasets. This includes scripts for concatenation of gene data files in different formats, generation of plots and datasets with different levels of gene/taxon occupancy, calculation of different support measures and phylogenetic reconstruction based on concatenated matrices and single genes. The execution of the scripts is also demonstrated with video clips (https://www.youtube.com/channel/UCpIgK8sVH-yK0Bo3fK62IxA). Lastly, we describe the main commands and functions that enable efficient phylogenomic analyses in TNT.

This paper examines the implementation of parsimony methods in the programs PAUP*, MEGA and MPBoot, and compares them with TNT. PAUP* implements standard, well-tested algorithms, and flexible search strategies and options for handling trees; its main drawback is the lack of advanced search algorithms, which makes it difficult to find most parsimonious trees for large and complex datasets. In addition, branch-swapping can be much slower than in TNT for datasets with large numbers of taxa, although this is only occasionally a problem for phylogenomic datasets given that they typically have small numbers of taxa. The parsimony implementation of MEGA has major drawbacks. MEGA often fails to find parsimonious trees because it does not perform all possible branch swapping subtree pruning regrafting (SPR)/tree bisection-reconnection (TBR) rearrangements. It furthermore fails to properly handle ambiguity or multiple equally parsimonious trees, and it uses the same addition sequence for all bootstrap replicates. The latter yields values of group support that depend on the order in which taxa are listed in the dataset. In addition, tree searches are very slow and do not facilitate the exploration of different starting points (as random seed is fixed). MPBoot searches for optimal trees using the ratchet, but it is based on SPR instead of TBR (and only evaluates by default a subset of the SPR rearrangements). MPBoot approximates bootstrap frequencies by first finding a sample of trees and then selecting from those trees for every replicate, without performing a tree-search. The approximation is too rough in many cases, producing serious under-or overestimations of the correct support values and, for most kinds of datasets, slower estimations than can be obtained with TNT. In addition, bootstrapping with PAUP*, MEGA or MPBoot can attribute strong supports to groups that have no support at all under any meaningful concept of support, such as likelihood ratios or Bremer supports. In TNT, this problem is decreased by using the strict consensus tree to represent each replicate, or eliminated entirely by using different approximations of the Bremer support.

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We evaluated the directional asymmetry between right and left wings and quantified the intraspecific and interspecific variation of the wing shape of 601 specimens of the genus Diachlorus to determine to what extent the geometrical variation discriminates six species distributed in six protected areas of Colombia. Geometric analyses were performed, integrating Procrustes methods, principal component analyses, cluster analyses, linear and quadratic discriminant analyses, and evaluations of shape changes. In Diachlorus, left and right wings did not present significant asymmetry but a geometrical analysis was allowed for species identification and, in some cases, the origin of the specimens using the variation of wing shape; the best-assigned species was Diachlorus leticia Wilkerson & Fairchild, while the worst was Diachlorus jobbinsi Fairchild, which also had the highest intraspecific variation, while Diachlorus fuscistigma Lutz had the lowest variation. Diachlorus fuscistigma and Diachlorus leucotibialis Wilkerson & Fairchild were the most similar species, while D. leucotibialis and Diachlorus nuneztovari Fairchild & Ortiz were the most disimilar. The specimens with the most different wing shape belonged to Chocó (especially those of D. jobbinsi), the geographically farthest area from the others in the study; however, no correlation was observed between geometric and geographical distances. Linear discriminants were better than nonlinear (quadratic) discriminant analyses in predicting species membership, but the opposite was true for predicting area membership. Based on our data, we hypothesized that other species of Diachlorus could also be discriminated using geometric morphometry of the wing shape.

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