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      Disease associations between honeybees and bumblebees as a threat to wild pollinators

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          Emerging infectious diseases (EIDs) pose a risk to human welfare, both directly 1 and indirectly, by affecting managed livestock and wildlife that provide valuable resources and ecosystem services, such as the pollination of crops 2 . Honey bees ( Apis mellifera), the prevailing managed insect crop pollinator, suffer from a range of emerging and exotic high impact pathogens 3, 4 and population maintenance requires active management by beekeepers to control them. Wild pollinators such as bumble bees ( Bombus spp.) are in global decline 5, 6 , one cause of which may be pathogen spillover from managed pollinators like honey bees 7, 8 or commercial colonies of bumble bees 9 . In our study, a combination of infection experiments with landscape scale field data indicates that honey bee EIDs are indeed widespread infectious agents within the pollinator assemblage. The prevalence of deformed wing virus (DWV) and the exotic Nosema ceranae is linked between honey bees and bumble bees, with honey bees having higher DWV prevalence, and sympatric bumble bees and honey bees sharing DWV strains; Apis is therefore the likely source of at least one major EID in wild pollinators. Lessons learned from vertebrates 10, 11 highlight the need for increased pathogen control in managed bee species to maintain wild pollinators, as declines in native pollinators may be caused by interspecies pathogen transmission originating from managed pollinators.

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          Most cited references 64

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          New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

          PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from
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            APE: Analyses of Phylogenetics and Evolution in R language.

            Analysis of Phylogenetics and Evolution (APE) is a package written in the R language for use in molecular evolution and phylogenetics. APE provides both utility functions for reading and writing data and manipulating phylogenetic trees, as well as several advanced methods for phylogenetic and evolutionary analysis (e.g. comparative and population genetic methods). APE takes advantage of the many R functions for statistics and graphics, and also provides a flexible framework for developing and implementing further statistical methods for the analysis of evolutionary processes. The program is free and available from the official R package archive at APE is licensed under the GNU General Public License.
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              MRBAYES: Bayesian inference of phylogenetic trees.

              The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo. MRBAYES, including the source code, documentation, sample data files, and an executable, is available at

                Author and article information

                24 March 2014
                20 February 2014
                20 August 2014
                : 506
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                : 364-366
                [1 ]Royal Holloway University of London, School of Biological Sciences, Bourne Building, Egham TW20 0EX, UK
                [2 ]IST Austria (Institute of Science and Technology Austria), 3400 Klosterneuburg, Austria
                [3 ]Queen’s University Belfast, School of Biological Sciences, 97 Lisburn Road, Belfast BT9 7BL, UK
                [4 ]Rothamsted Research, Department of Agro-Ecology, Harpenden AL5 2JQ, UK
                [5 ]University of Exeter, Environment & Sustainability Institute, Penryn TR10 9EZ, UK
                [6 ]Martin-Luther-Universität Halle-Wittenberg, Institute for Biology/General Zoology, Hoher Weg 8, 06120 Halle (Saale), Germany
                [7 ]German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany
                Author notes

                Author Contributions: The study was jointly conceived by R.J.P., J.O. and M.J.F.B.. Experiments were designed by M.A.F. and M.J.F.B.; M.A.F prepared the manuscript; M.J.F.B., D.P.M., R.J.P. and J.O. edited the manuscript. M.A.F. carried out the experimental work, molecular work and analyses apart from the phylogenetic analysis carried out by D.P.M..

                Author Information: Viral RNA sequences have been deposited in GeneBank under accession numbers KF929216 - KF929290. The authors declare no competing financial interests. Readers are welcome to comment on the online version of the paper. Correspondence and requests for materials should be addressed to M.A.F ( Matthias.Fuerst@ or Apocrite@ ).


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