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      Systematics of Amphineurus (Rhamphoneurus Alexander) (Diptera: Tipuloidea: Limoniidae)

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      Arthropod Systematics & Phylogeny
      Pensoft Publishers

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          Abstract

          The subgenus Amphineurus (Ramphoneurus Alexander) (Diptera: Limoniidae), a group of craneflies endemic to southern South America, is revised. The previously described species are redescribed and illustrated, along with fifteen new species: A. (R.) alexanderi sp. nov., A. (R.) amorimi sp. nov., A. (R.) anchoralis sp. nov., A. (R.) anfractus sp. nov., A. (R.) billinghami sp. nov., A. (R.) caleuchus sp. nov., A. (R.) deceptus sp. nov., A. (R.) falcatus sp. nov., A. (R.) immaculatus sp. nov., A. (R.) morphyi sp. nov., A. (R.) oosterbroeki sp. nov., A. (R.) podenasi sp. nov., A. (R.) theischingeri sp. nov., A. (R.) triangularis sp. nov., and A. (R.) stigmaticus sp. nov. In addition, A. (R.) fuscifusus Alexander is considered a nomen dubium. A phylogenetic analysis with a broad taxonomic sampling with all valid species of the subgenus A. (Rhamphoneurus Alexander) and species from all the subgenera of Amphineurus Skuse is performed. The broad outgroup sampling used in the study (including taxa from the entire range of distribution of Amphineurus) suggests that the subgenus A. (Rhamphoneurus) is a monophyletic taxon, that its closest relatives are taxa currently distributed in New Zealand, and that Australasian taxa are paraphyletic with respect to the Neotropical Clade.

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          Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7

          Abstract Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.
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            TNT version 1.5, including a full implementation of phylogenetic morphometrics

            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 Likelihood Approach to Estimating Phylogeny from Discrete Morphological Character Data

              Paul Lewis (2001)
              Evolutionary biologists have adopted simple likelihood models for purposes of estimating ancestral states and evaluating character independence on specified phylogenies; however, for purposes of estimating phylogenies by using discrete morphological data, maximum parsimony remains the only option. This paper explores the possibility of using standard, well-behaved Markov models for estimating morphological phylogenies (including branch lengths) under the likelihood criterion. An important modification of standard Markov models involves making the likelihood conditional on characters being variable, because constant characters are absent in morphological data sets. Without this modification, branch lengths are often overestimated, resulting in potentially serious biases in tree topology selection. Several new avenues of research are opened by an explicitly model-based approach to phylogenetic analysis of discrete morphological data, including combined-data likelihood analyses (morphology + sequence data), likelihood ratio tests, and Bayesian analyses.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Arthropod Systematics & Phylogeny
                ASP
                Pensoft Publishers
                1864-8312
                1863-7221
                September 12 2022
                September 12 2022
                : 80
                : 439-494
                Article
                10.3897/asp.80.e83035
                817e74e5-bc8c-44d0-82c0-b3dfd4ca8468
                © 2022

                http://creativecommons.org/licenses/by/4.0/

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