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      Phylogeny of the Turkic Languages Inferred from Basic Vocabulary: Limitations of the Lexicostatistical Methods in an Intensive Contact Situation

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      Journal of Language Evolution
      Oxford University Press (OUP)

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          Abstract

          This article provides an attempt to revise the phylogenetic structure of the Turkic family using a computational lexicostatistical approach. The methodological framework of the present research is characterized by the following features: (1) wordlists with strictly controlled semantics; (2) step-by-step reconstruction using Swadesh wordlists for proto-languages; (3) three stages of post-processing of the input data (analysis of root cognacy, elimination of derivational drift, and optimization of homoplasy); (4) application of several computational algorithms (Starling neighbor-joining, Bayesian MCMC, and maximum parsimony). The analysis provided confirms the status of Chuvash as the first outlier and suggests a subsequent multifurcation of Proto-Nuclear-Turkic into eight branches. The Siberian Turkic group is a purely areal unity, that is, Yakut-Dolgan, Tofa-Tuvinian, Khakas-Mrassu, Sarygh Yugur and Altai do not form a clade. Altai is grouped together with the Kipchak languages as a separate taxon; it does not show a particularly close relationship with Kirghiz, which belongs to another Kipchak subgroup. Karluk is a low-level taxon inside the Kipchak clade.

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          Is Open Access

          MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

          Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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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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              Towards Greater Accuracy in Lexicostatistic Dating

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                Author and article information

                Contributors
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                Journal
                Journal of Language Evolution
                Oxford University Press (OUP)
                2058-458X
                January 01 2022
                July 23 2022
                July 06 2022
                January 01 2022
                July 23 2022
                July 06 2022
                : 7
                : 1
                : 16-39
                Article
                10.1093/jole/lzac006
                92315ea1-4891-4f5d-b27f-34d8d2d395e2
                © 2022

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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