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      Macroevolutionary Dynamics and Historical Biogeography of Primate Diversification Inferred from a Species Supermatrix

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          Abstract

          Phylogenetic relationships, divergence times, and patterns of biogeographic descent among primate species are both complex and contentious. Here, we generate a robust molecular phylogeny for 70 primate genera and 367 primate species based on a concatenation of 69 nuclear gene segments and ten mitochondrial gene sequences, most of which were extracted from GenBank. Relaxed clock analyses of divergence times with 14 fossil-calibrated nodes suggest that living Primates last shared a common ancestor 71–63 Ma, and that divergences within both Strepsirrhini and Haplorhini are entirely post-Cretaceous. These results are consistent with the hypothesis that the Cretaceous-Paleogene mass extinction of non-avian dinosaurs played an important role in the diversification of placental mammals. Previous queries into primate historical biogeography have suggested Africa, Asia, Europe, or North America as the ancestral area of crown primates, but were based on methods that were coopted from phylogeny reconstruction. By contrast, we analyzed our molecular phylogeny with two methods that were developed explicitly for ancestral area reconstruction, and find support for the hypothesis that the most recent common ancestor of living Primates resided in Asia. Analyses of primate macroevolutionary dynamics provide support for a diversification rate increase in the late Miocene, possibly in response to elevated global mean temperatures, and are consistent with the fossil record. By contrast, diversification analyses failed to detect evidence for rate-shift changes near the Eocene-Oligocene boundary even though the fossil record provides clear evidence for a major turnover event (“Grande Coupure”) at this time. Our results highlight the power and limitations of inferring diversification dynamics from molecular phylogenies, as well as the sensitivity of diversification analyses to different species concepts.

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          Most cited references 88

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          A likelihood framework for inferring the evolution of geographic range on phylogenetic trees.

          At a time when historical biogeography appears to be again expanding its scope after a period of focusing primarily on discerning area relationships using cladograms, new inference methods are needed to bring more kinds of data to bear on questions about the geographic history of lineages. Here we describe a likelihood framework for inferring the evolution of geographic range on phylogenies that models lineage dispersal and local extinction in a set of discrete areas as stochastic events in continuous time. Unlike existing methods for estimating ancestral areas, such as dispersal-vicariance analysis, this approach incorporates information on the timing of both lineage divergences and the availability of connections between areas (dispersal routes). Monte Carlo methods are used to estimate branch-specific transition probabilities for geographic ranges, enabling the likelihood of the data (observed species distributions) to be evaluated for a given phylogeny and parameterized paleogeographic model. We demonstrate how the method can be used to address two biogeographic questions: What were the ancestral geographic ranges on a phylogenetic tree? How were those ancestral ranges affected by speciation and inherited by the daughter lineages at cladogenesis events? For illustration we use hypothetical examples and an analysis of a Northern Hemisphere plant clade (Cercis), comparing and contrasting inferences to those obtained from dispersal-vicariance analysis. Although the particular model we implement is somewhat simplistic, the framework itself is flexible and could readily be modified to incorporate additional sources of information and also be extended to address other aspects of historical biogeography.
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            Divergence time and evolutionary rate estimation with multilocus data.

            Bayesian methods for estimating evolutionary divergence times are extended to multigene data sets, and a technique is described for detecting correlated changes in evolutionary rates among genes. Simulations are employed to explore the effect of multigene data on divergence time estimation, and the methodology is illustrated with a previously published data set representing diverse plant taxa. The fact that evolutionary rates and times are confounded when sequence data are compared is emphasized and the importance of fossil information for disentangling rates and times is stressed.
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              The reconstructed evolutionary process.

              Phylogenies reconstructed from contemporary taxa do not contain information about lineages that have gone extinct. We derive probability models for such phylogenies, allowing real data to be compared with specified null models of evolution, and lineage birth and death rates to be estimated.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                16 November 2012
                : 7
                : 11
                Affiliations
                [1 ]Department of Biology, University of California Riverside, Riverside, California, United States of America
                [2 ]Department of Biology and Molecular Biology, Montclair State University, Montclair, New Jersey, United States of America
                [3 ]Department of Biology, University of Washington, Seattle, Washington, United States of America
                [4 ]Department of Integrative Biology, University of California, Berkeley, California, United States of America
                [5 ]Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America
                [6 ]Institut für Integrative Biologie, Eidgenössiche Technische Hochschule Zurich, Zurich, Switzerland
                [7 ]San Diego Zoo Institute for Conservation Research, San Diego Zoo Global, San Diego, California, United States of America
                [8 ]Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, United States of America
                University of Florence, Italy
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: MSS RWM JG DLR TS OAR JEJ WJM. Performed the experiments: MSS RWM CAE JP CS JEJ CAF WJM. Analyzed the data: MSS RWM CAE JP WJM. Contributed reagents/materials/analysis tools: MSS JG DLR TS OAR WJM. Wrote the paper: MSS RWM WJM. Provided comments on manuscript: JG CAE JP DLR TS CS OAR CAF JEJ.

                Article
                PONE-D-12-14229
                10.1371/journal.pone.0049521
                3500307
                23166696

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 23
                Funding
                This work was supported by NSF (EF0629860 to MSS and JG; EF0629849 to WJM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Computational Biology
                Sequence Analysis
                Evolutionary Biology
                Evolutionary Systematics
                Phylogenetics
                Genetics
                Genomics
                Paleontology
                Zoology
                Animal Phylogenetics
                Earth Sciences
                Geography
                Biogeography
                Geology
                Geologic Time

                Uncategorized

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