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      Rivers, not refugia, drove diversification in arboreal, sub‐Saharan African snakes

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          Abstract

          The relative roles of rivers versus refugia in shaping the high levels of species diversity in tropical rainforests have been widely debated for decades. Only recently has it become possible to take an integrative approach to test predictions derived from these hypotheses using genomic sequencing and paleo‐species distribution modeling. Herein, we tested the predictions of the classic river, refuge, and river‐refuge hypotheses on diversification in the arboreal sub‐Saharan African snake genus Toxicodryas. We used dated phylogeographic inferences, population clustering analyses, demographic model selection, and paleo‐distribution modeling to conduct a phylogenomic and historical demographic analysis of this genus. Our results revealed significant population genetic structure within both Toxicodryas species, corresponding geographically to river barriers and divergence times from the mid‐Miocene to Pliocene. Our demographic analyses supported the interpretation that rivers are indications of strong barriers to gene flow among populations since their divergence. Additionally, we found no support for a major contraction of suitable habitat during the last glacial maximum, allowing us to reject both the refuge and river‐refuge hypotheses in favor of the river‐barrier hypothesis. Based on conservative interpretations of our species delimitation analyses with the Sanger and ddRAD data sets, two new cryptic species are identified from east‐central Africa. This study highlights the complexity of diversification dynamics in the African tropics and the advantages of integrative approaches to studying speciation in tropical regions.

          Abstract

          We tested the predictions of the classic river, refuge, and river‐refuge hypotheses of tropical rainforest evolution on diversification in the arboreal Sub‐Saharan African snake genus Toxicodryas. We used dated phylogeographic inferences, population clustering analyses, demographic model selection, and paleo‐distribution modeling to conduct a phylogenomic and historical demographic analysis of this genus. Our demographic analyses supported the interpretation that rivers have represented strong barriers to gene flow among populations since their divergence, and we found no support for a major contraction of suitable habitat during the last glacial maximum, allowing us to reject both the refuge and river‐refuge hypotheses in favor of the river barrier hypothesis.

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          Most cited references 212

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data

            Summary: The two main functions of bioinformatics are the organization and analysis of biological data using computational resources. Geneious Basic has been designed to be an easy-to-use and flexible desktop software application framework for the organization and analysis of biological data, with a focus on molecular sequences and related data types. It integrates numerous industry-standard discovery analysis tools, with interactive visualizations to generate publication-ready images. One key contribution to researchers in the life sciences is the Geneious public application programming interface (API) that affords the ability to leverage the existing framework of the Geneious Basic software platform for virtually unlimited extension and customization. The result is an increase in the speed and quality of development of computation tools for the life sciences, due to the functionality and graphical user interface available to the developer through the public API. Geneious Basic represents an ideal platform for the bioinformatics community to leverage existing components and to integrate their own specific requirements for the discovery, analysis and visualization of biological data. Availability and implementation: Binaries and public API freely available for download at http://www.geneious.com/basic, implemented in Java and supported on Linux, Apple OSX and MS Windows. The software is also available from the Bio-Linux package repository at http://nebc.nerc.ac.uk/news/geneiousonbl. Contact: peter@biomatters.com
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              ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates

              Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates. The improvement is achieved by incorporating a model of rate-heterogeneity across sites not previously considered in this context, and by allowing concurrent searches of model-space and tree-space.
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                Author and article information

                Contributors
                kallen9@ku.edu
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                01 May 2021
                June 2021
                : 11
                : 11 ( doiID: 10.1002/ece3.v11.11 )
                : 6133-6152
                Affiliations
                [ 1 ] Department of Ecology and Evolutionary Biology University of Kansas Lawrence KS USA
                [ 2 ] Biodiversity Institute University of Kansas Lawrence KS USA
                [ 3 ] Department of Biological Sciences University of Texas at El Paso El Paso TX USA
                [ 4 ] Laboratoire d’Hérpétologie, Département de Biologie Centre de Recherche en Sciences Naturelles Lwiro Democratic Republic of Congo
                [ 5 ] Museum für Naturkunde – Leibniz Institute for Evolution and Biodiversity Science Berlin Germany
                [ 6 ] Chair of Wildlife Ecology and Management University of Freiburg Freiburg Germany
                Author notes
                [* ] Correspondence

                Kaitlin E. Allen, Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS 66045, USA.

                Email: kallen9@ 123456ku.edu

                Article
                ECE37429
                10.1002/ece3.7429
                8207163
                34141208
                © 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 7, Tables: 0, Pages: 20, Words: 16511
                Product
                Funding
                Funded by: University of Kansas College of Liberal Arts and Sciences
                Funded by: Deutsche Forschungsgemeinschaft , open-funder-registry 10.13039/501100001659;
                Award ID: DFG VE 183/4‐1
                Award ID: RO 3064/1‐2
                Funded by: Division of Environmental Biology , open-funder-registry 10.13039/100000155;
                Award ID: 1145459
                Award ID: 1557053
                Award ID: 1654388
                Funded by: Bundesministerium für Bildung und Forschung , open-funder-registry 10.13039/501100002347;
                Award ID: 01LC0017
                Funded by: University of Kanasas Office of Graduate Studies
                Funded by: Villanova University Department of Biology
                Funded by: The Linnean Society of London
                Funded by: National Geographic Society , open-funder-registry 10.13039/100006363;
                Award ID: 8556‐08
                Award ID: WW‐R018‐17
                Funded by: IUCN/SSC Amphibian Specialist Group Seed Grant
                Funded by: University of Texas El Paso , open-funder-registry 10.13039/100011349;
                Funded by: University of Kansas Biodiversity Institute
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                June 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.2 mode:remove_FC converted:16.06.2021

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