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      Spatial genetic diversity in the Cape mole-rat, Georychus capensis: Extreme isolation of populations in a subterranean environment

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          Abstract

          The subterranean niche harbours animals with extreme adaptations. These adaptations decrease the vagility of taxa and, along with other behavioural adaptations, often result in isolated populations characterized by small effective population sizes, high inbreeding, population bottlenecks, genetic drift and consequently, high spatial genetic structure. Although information is available for some species, estimates of genetic diversity and whether this variation is spatially structured, is lacking for the Cape mole-rat ( Georychus capensis). By adopting a range-wide sampling regime and employing two variable mitochondrial markers (cytochrome b and control region), we report on the effects that life-history, population demography and geographic barriers had in shaping genetic variation and population genetic patterns in G. capensis. We also compare our results to information available for the sister taxon of the study species, Bathyergus suillus. Our results show that Georychus capensis exhibits low genetic diversity relative to the concomitantly distributed B. suillus, most likely due to differences in habitat specificity, habitat fragmentation and historical population declines. In addition, the isolated nature of G. capensis populations and low levels of population connectivity has led to small effective population sizes and genetic differentiation, possibly aided by genetic drift. Not surprisingly therefore, G. capensis exhibits pronounced spatial structure across its range in South Africa. Along with geographic distance and demography, other factors shaping the genetic structure of G. capensis include the historical and contemporary impacts of mountains, rivers, sea-level fluctuations and elevation. Given the isolation and differentiation among G. capensis populations, the monotypic genus Georychus may represent a species complex.

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          Dynamics of mitochondrial DNA evolution in animals: amplification and sequencing with conserved primers.

          With a standard set of primers directed toward conserved regions, we have used the polymerase chain reaction to amplify homologous segments of mtDNA from more than 100 animal species, including mammals, birds, amphibians, fishes, and some invertebrates. Amplification and direct sequencing were possible using unpurified mtDNA from nanogram samples of fresh specimens and microgram amounts of tissues preserved for months in alcohol or decades in the dry state. The bird and fish sequences evolve with the same strong bias toward transitions that holds for mammals. However, because the light strand of birds is deficient in thymine, thymine to cytosine transitions are less common than in other taxa. Amino acid replacement in a segment of the cytochrome b gene is faster in mammals and birds than in fishes and the pattern of replacements fits the structural hypothesis for cytochrome b. The unexpectedly wide taxonomic utility of these primers offers opportunities for phylogenetic and population research.
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            African climate change and faunal evolution during the Pliocene–Pleistocene

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              Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations

              Background During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions. Results We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem. In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software. Conclusion The Bayesian modelling methods introduced in this article represent an array of enhanced tools for learning the genetic structure of populations. Their implementations in the BAPS software are designed to meet the increasing need for analyzing large-scale population genetics data. The software is freely downloadable for Windows, Linux and Mac OS X systems at .
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Funding acquisitionRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                15 March 2018
                2018
                : 13
                : 3
                : e0194165
                Affiliations
                [1 ] Centre for Ecological Genomics and Wildlife Conservation, Department of Zoology, University of Johannesburg, Auckland Park, South Africa
                [2 ] Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Pretoria, South Africa
                National Cheng Kung University, TAIWAN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-5004-9900
                Article
                PONE-D-17-40430
                10.1371/journal.pone.0194165
                5854370
                29543917
                624001db-6287-4e36-8277-4a9ce74f2d0c
                © 2018 Visser et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 November 2017
                : 26 February 2018
                Page count
                Figures: 4, Tables: 4, Pages: 18
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001321, National Research Foundation;
                Award ID: 109498
                Award Recipient :
                The work was funded through a NRF research grant to BJVV.
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                Custom metadata
                Relevant data are in the paper and its Supporting Information files. All genetic sequence data files are available from the Genbank database (cytochrome b accession numbers: MG496663 - MG496927; control region accession numbers: MG496398 - MG496662).

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