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      The evolution of paleo- and neo-endemic species of Cactaceae in the isolated Valley of Tehuacán-Cuicatlán, Mexico

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      Plant Ecology and Evolution
      Agentschap Plantentuin Meise

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          Abstract

          Background and aims – Endemism may be defined according to the time of origin of taxa. Neo-endemics refer to relatively recent species that have not dispersed outside their ancestral areas. In contrast, paleo-endemics refer to species of ancient origins, which are currently geographically restricted but probably were more widespread in the past. Geographically, endemism areas may also be based on the co-occurrence of more than one species. We aimed to qualitatively identify the neo-endemism and paleo-endemism of endemic Cactaceae of the Tehuacán-Cuicatlán Valley, as well as to quantitatively assess paleo- and neo-endemics areas. Material and methods – Using a dated molecular phylogeny of endemic Cactaceae, we defined paleo- and neo-endemics using an arbitrary boundary of 2.6 million years ago; we also assessed the significance of concentrations of these species using a categorical analysis of paleo- and neo-endemism. Key results – Our results showed that most endemic Cactaceae in the Tehuacán-Cuicatlán Valley arose throughout the Pleistocene, while categorical analysis indicated localised mixed- and super-endemism (including both paleo- and neo-endemics) areas. Conclusion – We suggest that paleo- and neo-endemics, as well as localised mixed-endemism areas, may have originated due to a probable high climatic stability in the Tehuacán-Cuicatlán Valley, which in addition to topographically rugged and ecologically complex zones (e.g. ecotones, isolated habitat patches) may have allowed it to function as a refuge throughout Pleistocene climatic changes, mainly promoting the speciation of neo-endemics, as well as the persistence of relatively few paleo-endemics.

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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            MUSCLE: multiple sequence alignment with high accuracy and high throughput.

            We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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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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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Plant Ecology and Evolution
                plecevo
                Agentschap Plantentuin Meise
                2032-3921
                2032-3913
                January 30 2024
                January 30 2024
                : 157
                : 1
                : 42-54
                Article
                10.5091/plecevo.110352
                ada572fd-3733-4e42-8e4d-4f721c83fa4d
                © 2024

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

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