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      Genetic and paleomodelling evidence of the population expansion of the cattle egretBubulcus ibisin Africa during the climatic oscillations of the Late Pleistocene

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          A simulated annealing approach to define the genetic structure of populations

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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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              Phylogenetic endemism: a new approach for identifying geographical concentrations of evolutionary history.

              We present a new, broadly applicable measure of the spatial restriction of phylogenetic diversity, termed phylogenetic endemism (PE). PE combines the widely used phylogenetic diversity and weighted endemism measures to identify areas where substantial components of phylogenetic diversity are restricted. Such areas are likely to be of considerable importance for conservation. PE has a number of desirable properties not combined in previous approaches. It assesses endemism consistently, independent of taxonomic status or level, and independent of previously defined political or biological regions. The results can be directly compared between areas because they are based on equivalent spatial units. PE builds on previous phylogenetic analyses of endemism, but provides a more general solution for mapping endemism of lineages. We illustrate the broad applicability of PE using examples of Australian organisms having contrasting life histories: pea-flowered shrubs of the genus Daviesia (Fabaceae) and the Australian species of the Australo-Papuan tree frog radiation within the family Hylidae.
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                Author and article information

                Journal
                Journal of Avian Biology
                J Avian Biol
                Wiley-Blackwell
                09088857
                December 2016
                December 20 2016
                : 47
                : 6
                : 846-857
                Article
                10.1111/jav.00972
                c4030961-becf-482b-93d7-6b73463558f4
                © 2016

                http://doi.wiley.com/10.1002/tdm_license_1.1

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