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      A subterranean adaptive radiation of amphipods in Europe

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          Abstract

          Adaptive radiations are bursts of evolutionary species diversification that have contributed to much of the species diversity on Earth. An exception is modern Europe, where descendants of ancient adaptive radiations went extinct, and extant adaptive radiations are small, recent and narrowly confined. However, not all legacy of old radiations has been lost. Subterranean environments, which are dark and food-deprived, yet buffered from climate change, have preserved ancient lineages. Here we provide evidence of an entirely subterranean adaptive radiation of the amphipod genus Niphargus, counting hundreds of species. Our modelling of lineage diversification and evolution of morphological and ecological traits using a time-calibrated multilocus phylogeny suggests a major adaptive radiation, comprised of multiple subordinate adaptive radiations. Their spatio-temporal origin coincides with the uplift of carbonate massifs in South-Eastern Europe 15 million years ago. Emerging subterranean environments likely provided unoccupied, predator-free space, constituting ecological opportunity, a key trigger of adaptive radiation. This discovery sheds new light on the biodiversity of Europe.

          Abstract

          There are relatively few known extant adaptive radiations in Europe that predate the Pleistocene. Here, Borko et al. characterize the diversity and diversification of the subterranean amphipod genus Niphargus, showing evidence for a large adaptive radiation associated with massif uplift 15 million years ago.

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

              Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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                Author and article information

                Contributors
                spela.borko@bf.uni-lj.si
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                17 June 2021
                17 June 2021
                2021
                : 12
                : 3688
                Affiliations
                [1 ]GRID grid.8954.0, ISNI 0000 0001 0721 6013, SubBio Lab, Biotechnical Faculty, , University of Ljubljana, ; Ljubljana, Slovenia
                [2 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Aquatic Ecology and Evolution, Institute of Ecology and Evolution, , University of Bern, ; Bern, Switzerland
                [3 ]GRID grid.418656.8, ISNI 0000 0001 1551 0562, Department of Fish Ecology and Evolution, , Centre for Ecology, Evolution, and Biogeochemistry, Swiss Federal Institute of Aquatic Science and Technology (EAWAG), ; Kastanienbaum, Switzerland
                [4 ]GRID grid.425614.0, ISNI 0000 0001 0721 8609, Agricultural institute of Slovenia, ; Ljubljana, Slovenia
                Author information
                http://orcid.org/0000-0002-8383-8778
                http://orcid.org/0000-0003-4057-6912
                http://orcid.org/0000-0001-6598-1434
                http://orcid.org/0000-0001-7517-293X
                Article
                24023
                10.1038/s41467-021-24023-w
                8211712
                34140494
                1e412219-0f4a-4c26-83c5-78d08a548af4
                © The Author(s) 2021

                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
                : 11 September 2020
                : 1 June 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100004329, Javna Agencija za Raziskovalno Dejavnost RS (Slovenian Research Agency);
                Award ID: J1-2464
                Award ID: P1-0184
                Award ID: 1000-08-310028
                Award ID: J1-2464
                Award ID: P1-0184
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s) 2021

                Uncategorized
                biodiversity,biogeography,evolutionary ecology,phylogenetics,adaptive radiation
                Uncategorized
                biodiversity, biogeography, evolutionary ecology, phylogenetics, adaptive radiation

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