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      A phylogenomic framework for pelagiarian fishes (Acanthomorpha: Percomorpha) highlights mosaic radiation in the open ocean

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          Abstract

          The fish clade Pelagiaria, which includes tunas as its most famous members, evolved remarkable morphological and ecological variety in a setting not generally considered conducive to diversification: the open ocean. Relationships within Pelagiaria have proven elusive due to short internodes subtending major lineages suggestive of rapid early divergences. Using a novel sequence dataset of over 1000 ultraconserved DNA elements (UCEs) for 94 of the 286 species of Pelagiaria (more than 70% of genera), we provide a time-calibrated phylogeny for this widely distributed clade. Some inferred relationships have clear precedents (e.g. the monophyly of ‘core’ Stromateoidei, and a clade comprising ‘Gempylidae’ and Trichiuridae), but others are unexpected despite strong support (e.g. Chiasmodontidae + Tetragonurus ). Relaxed molecular clock analysis using node-based fossil calibrations estimates a latest Cretaceous origin for Pelagiaria, with crown-group families restricted to the Cenozoic. Estimated mean speciation rates decline from the origin of the group in the latest Cretaceous, although credible intervals for root and tip rates are broad and overlap in most cases, and there is higher-than-expected partitioning of body shape diversity (measured as fineness ratio) between clades concentrated during the Palaeocene–Eocene. By contrast, more direct measures of ecology show either no substantial deviation from a null model of diversification (diet) or patterns consistent with evolutionary constraint or high rates of recent change (depth habitat). Collectively, these results indicate a mosaic model of diversification. Pelagiarians show high morphological disparity and modest species richness compared to better-studied fish radiations in contrasting environments. However, this pattern is also apparent in other clades in open-ocean or deep-sea habitats, and suggests that comparative study of such groups might provide a more inclusive model of the evolution of diversity in fishes.

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          The Paradox of the Plankton

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            Is Open Access

            ASTRAL: genome-scale coalescent-based species tree estimation

            Motivation: Species trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions. Results: We present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy—improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees. Availability and implementation: ASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral. Contact: warnow@illinois.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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              geiger v2.0: an expanded suite of methods for fitting macroevolutionary models to phylogenetic trees.

              Phylogenetic comparative methods are essential for addressing evolutionary hypotheses with interspecific data. The scale and scope of such data have increased dramatically in the past few years. Many existing approaches are either computationally infeasible or inappropriate for data of this size. To address both of these problems, we present geiger v2.0, a complete overhaul of the popular R package geiger. We have reimplemented existing methods with more efficient algorithms and have developed several new approaches for accomodating heterogeneous models and data types.
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                Author and article information

                Journal
                Proceedings of the Royal Society B: Biological Sciences
                Proc. R. Soc. B
                The Royal Society
                0962-8452
                1471-2954
                September 11 2019
                September 11 2019
                September 11 2019
                September 11 2019
                : 286
                : 1910
                : 20191502
                Affiliations
                [1 ]Museum of Paleontology, University of Michigan, Ann Arbor, MI, USA
                [2 ]Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor, MI, USA
                [3 ]Department of Earth Sciences, University of Oxford, Oxford, UK
                [4 ]Department of Ecology and Evolutionary Biology, University of California at Los Angeles, Los Angeles, CA, USA
                [5 ]Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
                [6 ]Museum of Natural Science, Louisiana State University, Baton Rouge, LA, USA
                [7 ]Natural History Museum and Institute, Chiba, Aoba-cho, Chuo-ku, Chiba, Japan
                [8 ]Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
                [9 ]Peabody Museum, Yale University, New Haven, CT, USA
                Article
                10.1098/rspb.2019.1502
                6742994
                31506051
                66e82e5f-da3a-4658-ae8b-fbbe3923574d
                © 2019
                History

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