Understanding processes that have driven the extraordinary high level of biodiversity in the tropics is a long-standing question in biology. Here we try to assess whether the large lineage richness found in a New Guinean clade of mayflies (Ephemeroptera), namely the Thraulus group (Leptophlebiidae) could be associated with the recent orogenic processes, by applying a combination of phylogenetic, biogeographic and ecological shift analyses. New Guinean representatives of the Thraulus group appear monophyletic, with the possible exception of a weakly-supported early-diverging clade from the Sunda Islands. Dating analyses suggest an Eocene origin of the Thraulus group, predating by several million years current knowledge on the origin of other New Guinean aquatic organisms. Biogeographic inferences indicate that 27 of the 28 inferred dispersals (96.4%) occurred during the Eocene, Oligocene and Miocene, while only one dispersal (3.6%) took place during the Pliocene-Pleistocene. This result contrasts with the higher number of altitudinal shifts (15 of 22; 68.2%) inferred during the Pliocene-Pleistocene. Our study illustrates the role played by—potentially ecological—diversification along the elevation gradient in a time period concomitant with the establishment of high-altitude ecological niches, i.e., during orogenesis of the central New Guinean mountain range. This process might have taken over the previous main mode of diversification at work, characterized by dispersal and vicariance, by driving lineage divergence of New Guinean Leptophlebiidae across a wide array of habitats along the elevation gradient. Additional studies on organisms spanning the same elevation range as Thraulus mayflies in the tropics are needed to evaluate the potential role of the ecological opportunity or taxon cycles hypotheses in partly explaining the latitudinal diversity gradient.

For the last 2 decades, supertree reconstruction has been an active field of research and has seen the development of a large number of major algorithms. Because of the growing popularity of the supertree methods, it has become necessary to evaluate the performance of these algorithms to determine which are the best options (especially with regard to the supermatrix approach that is widely used). In this study, seven of the most commonly used supertree methods are investigated by using a large empirical data set (in terms of number of taxa and molecular markers) from the worldwide flowering plant family Sapindaceae. Supertree methods were evaluated using several criteria: similarity of the supertrees with the input trees, similarity between the supertrees and the total evidence tree, level of resolution of the supertree and computational time required by the algorithm. Additional analyses were also conducted on a reduced data set to test if the performance levels were affected by the heuristic searches rather than the algorithms themselves. Based on our results, two main groups of supertree methods were identified: on one hand, the matrix representation with parsimony (MRP), MinFlip, and MinCut methods performed well according to our criteria, whereas the average consensus, split fit, and most similar supertree methods showed a poorer performance or at least did not behave the same way as the total evidence tree. Results for the super distance matrix, that is, the most recent approach tested here, were promising with at least one derived method performing as well as MRP, MinFlip, and MinCut. The output of each method was only slightly improved when applied to the reduced data set, suggesting a correct behavior of the heuristic searches and a relatively low sensitivity of the algorithms to data set sizes and missing data. Results also showed that the MRP analyses could reach a high level of quality even when using a simple heuristic search strategy, with the exception of MRP with Purvis coding scheme and reversible parsimony. The future of supertrees lies in the implementation of a standardized heuristic search for all methods and the increase in computing power to handle large data sets. The latter would prove to be particularly useful for promising approaches such as the maximum quartet fit method that yet requires substantial computing power.