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Amblyomma cajennense (Fabricius, 1787) (Acari: Ixodidae), the Cayenne tick: phylogeography and evidence for allopatric speciation

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      Abstract

      Background

      Amblyomma cajennense F. is one of the best known and studied ticks in the New World because of its very wide distribution, its economical importance as pest of domestic ungulates, and its association with a variety of animal and human pathogens. Recent observations, however, have challenged the taxonomic status of this tick and indicated that intraspecific cryptic speciation might be occurring. In the present study, we investigate the evolutionary and demographic history of this tick and examine its genetic structure based on the analyses of three mitochondrial (12SrDNA, d-loop, and COII) and one nuclear (ITS2) genes. Because A. cajennense is characterized by a typical trans-Amazonian distribution, lineage divergence dating is also performed to establish whether genetic diversity can be linked to dated vicariant events which shaped the topology of the Neotropics.

      Results

      Total evidence analyses of the concatenated mtDNA and nuclear + mtDNA datasets resulted in well-resolved and fully congruent reconstructions of the relationships within A. cajennense. The phylogenetic analyses consistently found A. cajennense to be monophyletic and to be separated into six genetic units defined by mutually exclusive haplotype compositions and habitat associations. Also, genetic divergence values showed that these lineages are as distinct from each other as recognized separate species of the same genus. The six clades are deeply split and node dating indicates that they started diverging in the middle-late Miocene.

      Conclusions

      Behavioral differences and the results of laboratory cross-breeding experiments had already indicated that A. cajennense might be a complex of distinct taxonomic units. The combined and congruent mitochondrial and nuclear genetic evidence from this study reveals that A. cajennense is an assembly of six distinct species which have evolved separately from each other since at least 13.2 million years ago (Mya) in the earliest and 3.3 Mya in the latest lineages. The temporal and spatial diversification modes of the six lineages overlap the phylogeographical history of other organisms with similar extant trans-Amazonian distributions and are consistent with the present prevailing hypothesis that Neotropical diversity often finds its origins in the Miocene, after the Andean uplift changed the topology and consequently the climate and ecology of the Neotropics.

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      Most cited references 62

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      A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.

      The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximum- likelihood principle, which clearly satisfies these requirements. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximum-likelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distance-based and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page: http://www.lirmm.fr/w3ifa/MAAS/.
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        DnaSP v5: a software for comprehensive analysis of DNA polymorphism data.

         P Librado,  J Rozas (2009)
        DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser. Freely available to academic users from: (http://www.ub.edu/dnasp).
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          BEAST: Bayesian evolutionary analysis by sampling trees

          Background The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented. Results BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at under the GNU LGPL license. Conclusion BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.
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            Author and article information

            Affiliations
            [1 ]Institute for Coastal Plain Sciences and Biology Department, Georgia Southern University, P.O. Box 8056, Statesboro, GA 30460, USA
            [2 ]Instituto Nacional de Tecnología Agropecuaria, Estación Experimental Agropecuaria Rafaela, CC 22, CP 2300 Rafaela, Santa Fe, Argentina
            [3 ]Department of Infectious Diseases, University of Georgia College of Veterinary Medicine, 501 D.W. Brooks Drive Athens, GA 30602, USA
            [4 ]Laboratório de Parasitologia, Instituto Butantan, Av. Vital Brasil 1500, 05503-900 São Paulo, SP, Brazil
            [5 ]Departamento de Medicina Veterinária Preventiva e Saúde Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, São Paulo, SP, 05508-270, Brazil
            [6 ]Departamento Académico de Microbiologia Médica, Facultad de Medicina, Universidad Nacional Mayor de San Marcos, Lima, Perú
            [7 ]Laboratorio de Entomología, Instituto Nacional de Salud, Lima, Perú
            [8 ]Laboratorio de Acarología, Departamento de Biología Comparada, Facultad de Ciencias, Universidad Nacional Autónoma de México, Coyoacán 04510, Distrito Federal, México
            [9 ]Laboratorio de Entomología Médica y Medicina Tropical (LEMMT), Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito, Cumbayá, Quito, Ecuador
            [10 ]Biology Department, Georgia Southern University, P.O. Box 8042, Statesboro, GA 30460, USA
            [11 ]Departamento de Parasitologia Animal, Instituto de Veterinária, Universidade Federal Rural do Rio de Janeiro, 23890-000 Seropédica, RJ, Brazil
            Contributors
            Journal
            BMC Evol Biol
            BMC Evol. Biol
            BMC Evolutionary Biology
            BioMed Central
            1471-2148
            2013
            9 December 2013
            : 13
            : 267
            24320199
            3890524
            1471-2148-13-267
            10.1186/1471-2148-13-267
            Copyright © 2013 Beati et al.; licensee BioMed Central Ltd.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            Categories
            Research Article

            Evolutionary Biology

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