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      Reconstructing Squamate Biogeography in Afro-Arabia Reveals the Influence of a Complex and Dynamic Geologic Past

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          Abstract

          The geographic distribution of biodiversity is central to understanding evolutionary biology. Paleogeographic and paleoclimatic histories often help to explain how biogeographic patterns unfold through time. However, such patterns are also influenced by a variety of other factors, such as lineage diversification, that may affect the probability of certain types of biogeographic events. The complex and well-known geologic and climatic history of Afro-Arabia, together with the extensive research on reptile systematics in the region, makes Afro-Arabian squamate communities an ideal system to investigate biogeographic patterns and their drivers. Here, we reconstruct the phylogenetic relationships and the ancestral geographic distributions of several Afro-Arabian reptile clades (totaling 430 species) to estimate the number of dispersal, vicariance and range contraction events. We then compare the observed biogeographic history to a distribution of simulated biogeographic events based on the empirical phylogeny and the best-fit model. This allows us to identify periods in the past where the observed biogeographic history was likely shaped by forces beyond the ones included in the model. We find an increase in vicariance following the Oligocene, most likely caused by the fragmentation of the Afro-Arabian plate. In contrast, we did not find differences between observed and expected dispersal and range contraction levels. This is consistent with diversification enhanced by environmental processes and with the establishment of a dispersal corridor connecting Africa, Arabia and Eurasia since the middle Miocene. Finally, here we show that our novel approach is useful to pinpoint events in the evolutionary history of lineages that might reflect external forces not predicted by the underlying biogeographic model. [Dispersal; diversification; model adequacy; paleogeography; reptiles; simulations; vicariance.]

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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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              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
                Role: Associate Editor
                Journal
                Syst Biol
                Syst Biol
                sysbio
                Systematic Biology
                Oxford University Press
                1063-5157
                1076-836X
                March 2022
                13 March 2021
                13 March 2021
                : 71
                : 2
                : 261-272
                Affiliations
                [1 ] Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra) , Barcelona, Spain
                [2 ] Department of Integrative Biology and Museum of Vertebrate Zoology , University of California, Berkeley, CA 94720, USA
                [3 ] Department of Ecology, University of São Paulo , São Paulo, Brazil
                [4 ] Department of Biological Sciences, Towson University , Towson, MD 21252, USA
                [5 ] Department of Zoology, National Museum , Cirkusová 1740, 19300 Prague, Czech Republic
                [6 ] Department of Zoology, Faculty of Science, Charles University , Viničná 7, 12844 Prague, Czech Republic
                [7 ] Department of Biological Sciences, University of Idaho , Moscow, ID 83844, USA
                Author notes
                Correspondence to be sent to: Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), Passeig Marítim de la Barceloneta 37-49, Barcelona, Spain; E-mail: hector.tejero@ 123456ibe.upf-csic.es .
                Article
                syab025
                10.1093/sysbio/syab025
                8830062
                33787928
                45ce89e1-2184-4450-a55e-611bb1fda9ff
                © The authors 2021. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 07 December 2020
                : 23 March 2021
                : 29 March 2021
                : 14 May 2021
                Page count
                Pages: 12
                Categories
                Spotlight
                AcademicSubjects/SCI01130

                Animal science & Zoology
                Animal science & Zoology

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