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      Integrating phylogenomic and population genomic patterns in avian lice provides a more complete picture of parasite evolution : PARASITE EVOLUTION AT MULTIPLE TIME SCALES

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          Abstract

          Parasite diversity accounts for most of the biodiversity on earth, and is shaped by many processes (e.g., cospeciation, host switching). To identify the effects of the processes that shape parasite diversity, it is ideal to incorporate both deep (phylogenetic) and shallow (population) perspectives. To this end, we developed a novel workflow to obtain phylogenetic and population genetic data from whole genome sequences of body lice parasitizing New World ground-doves. Phylogenies from these data showed consistent, highly resolved species-level relationships for the lice. By comparing the louse and ground-dove phylogenies, we found that over long-term evolutionary scales their phylogenies were largely congruent. Many louse lineages (both species and populations) also demonstrated high host-specificity, suggesting ground-dove divergence is a primary driver of their parasites' diversity. However, the few louse taxa that are generalists are structured according to biogeography at the population level. This suggests dispersal among sympatric hosts has some effect on body louse diversity, but over deeper time scales the parasites eventually sort according to host species. Overall, our results demonstrate that multiple factors explain the patterns of diversity in this group of parasites, and that the effects of these factors can vary over different evolutionary scales. The integrative approach we employed was crucial for uncovering these patterns, and should be broadly applicable to other studies.

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          Most cited references 81

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          Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs

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            Bayesian species delimitation using multilocus sequence data.

            In the absence of recent admixture between species, bipartitions of individuals in gene trees that are shared across loci can potentially be used to infer the presence of two or more species. This approach to species delimitation via molecular sequence data has been constrained by the fact that genealogies for individual loci are often poorly resolved and that ancestral lineage sorting, hybridization, and other population genetic processes can lead to discordant gene trees. Here we use a Bayesian modeling approach to generate the posterior probabilities of species assignments taking account of uncertainties due to unknown gene trees and the ancestral coalescent process. For tractability, we rely on a user-specified guide tree to avoid integrating over all possible species delimitations. The statistical performance of the method is examined using simulations, and the method is illustrated by analyzing sequence data from rotifers, fence lizards, and human populations.
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              Is Open Access

              ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes

              Motivation: The estimation of species phylogenies requires multiple loci, since different loci can have different trees due to incomplete lineage sorting, modeled by the multi-species coalescent model. We recently developed a coalescent-based method, ASTRAL, which is statistically consistent under the multi-species coalescent model and which is more accurate than other coalescent-based methods on the datasets we examined. ASTRAL runs in polynomial time, by constraining the search space using a set of allowed ‘bipartitions’. Despite the limitation to allowed bipartitions, ASTRAL is statistically consistent. Results: We present a new version of ASTRAL, which we call ASTRAL-II. We show that ASTRAL-II has substantial advantages over ASTRAL: it is faster, can analyze much larger datasets (up to 1000 species and 1000 genes) and has substantially better accuracy under some conditions. ASTRAL’s running time is O ( n 2 k | X | 2 ) , and ASTRAL-II’s running time is O ( n k | X | 2 ) , where n is the number of species, k is the number of loci and X is the set of allowed bipartitions for the search space. Availability and implementation: ASTRAL-II is available in open source at https://github.com/smirarab/ASTRAL and datasets used are available at http://www.cs.utexas.edu/~phylo/datasets/astral2/. Contact: smirarab@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                Evolution
                Evolution
                Wiley
                00143820
                January 2018
                January 2018
                November 24 2017
                : 72
                : 1
                : 95-112
                Affiliations
                [1 ]Illinois Natural History Survey; Prairie Research Institute, University of Illinois at Urbana-Champaign; Illinois 61820
                [2 ]Program in Ecology, Evolution, and Conservation Biology, School of Integrative Biology; University of Illinois at Urbana-Champaign; Champaign Illinois 61820
                [3 ]Department of Entomology; University of Georgia; Athens Georgia 30602
                [4 ]Florida Museum of Natural History; University of Florida; Gainesville Florida 32611
                [5 ]Department of Biology; University of Utah; Salt Lake City Utah 84112
                [6 ]Biotério da Universidade Iguaçu; Av. Abílio Augusto Távora, 2134 RJ 26275 Brazil
                [7 ]Departamento de Petroleos, Facultad de Geologia y Petroleos; Escuela Politecnica Nacional; Quito Ecuador
                [8 ]Institute of Arctic Biology; University of Alaska; Fairbanks Alaska 99775
                Article
                10.1111/evo.13386
                29094340
                © 2017

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