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      Symbioses shape feeding niches and diversification across insects

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          Abstract

          For over 300 million years, insects have relied on symbiotic microbes for nutrition and defence. However, it is unclear whether specific ecological conditions have repeatedly favoured the evolution of symbioses, and how this has influenced insect diversification. Here, using data on 1,850 microbe–insect symbioses across 402 insect families, we found that symbionts have allowed insects to specialize on a range of nutrient-imbalanced diets, including phloem, blood and wood. Across diets, the only limiting nutrient consistently associated with the evolution of obligate symbiosis was B vitamins. The shift to new diets, facilitated by symbionts, had mixed consequences for insect diversification. In some cases, such as herbivory, it resulted in spectacular species proliferation. In other niches, such as strict blood feeding, diversification has been severely constrained. Symbioses therefore appear to solve widespread nutrient deficiencies for insects, but the consequences for insect diversification depend on the feeding niche that is invaded.

          Abstract

          Insects rely on symbiotic microbes for nutrition and defence. Analysing a large dataset of microbe–insect symbioses, the authors show that symbiosis evolved in response to nutrient deficiencies but its impacts on insect diversification depend on their feeding niche.

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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 http://www.atgc-montpellier.fr/phyml/.
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            phytools: an R package for phylogenetic comparative biology (and other things)

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              MCMC Methods for Multi-Response Generalized Linear Mixed Models: TheMCMCglmmRPackage

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                Author and article information

                Contributors
                charlie.cornwallis@biol.lu.se
                l.henry@qmul.ac.uk
                Journal
                Nat Ecol Evol
                Nat Ecol Evol
                Nature Ecology & Evolution
                Nature Publishing Group UK (London )
                2397-334X
                18 May 2023
                18 May 2023
                2023
                : 7
                : 7
                : 1022-1044
                Affiliations
                [1 ]GRID grid.4514.4, ISNI 0000 0001 0930 2361, Department of Biology, , Lund University, ; Lund, Sweden
                [2 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Amsterdam Institute for Life and Environment, section Ecology and Evolution, , Vrije Universiteit, ; Amsterdam, the Netherlands
                [3 ]GRID grid.4818.5, ISNI 0000 0001 0791 5666, Laboratory of Genetics, , Wageningen University and Research, ; Wageningen, the Netherlands
                [4 ]GRID grid.4868.2, ISNI 0000 0001 2171 1133, School of Biological and Behavioural Sciences, , Queen Mary University of London, ; London, UK
                [5 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Department of Biology, , University of Oxford, ; Oxford, UK
                Author information
                http://orcid.org/0000-0003-1308-3995
                http://orcid.org/0000-0003-2633-2153
                http://orcid.org/0000-0003-2665-1971
                http://orcid.org/0000-0002-6500-047X
                http://orcid.org/0000-0003-2152-3153
                http://orcid.org/0000-0002-5706-1328
                Article
                2058
                10.1038/s41559-023-02058-0
                10333129
                37202501
                c7c3b92a-f5e2-454e-84fc-769bf77623a1
                © The Author(s) 2023, corrected publication 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 June 2022
                : 15 March 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100004063, Knut och Alice Wallenbergs Stiftelse (Knut and Alice Wallenberg Foundation);
                Award ID: 2018.0138
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100004359, Vetenskapsrådet (Swedish Research Council);
                Award ID: 2017-03880
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 335542
                Award ID: 834164
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000270, RCUK | Natural Environment Research Council (NERC);
                Award ID: NE/M018016/1
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                social evolution,coevolution,evolutionary ecology
                social evolution, coevolution, evolutionary ecology

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