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      The phylogeny of early amniotes and the affinities of Parareptilia and Varanopidae

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      Nature Ecology & Evolution
      Springer Science and Business Media LLC

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          Abstract

          <p class="first" id="d3257492e69">Amniotes include mammals, reptiles and birds, representing 75% of extant vertebrate species on land. They originated around 318 million years ago in the early Late Carboniferous and their early fossil record is central to understanding the expansion of vertebrates in terrestrial ecosystems. We present a phylogenetic hypothesis that challenges the widely accepted consensus about early amniote evolution, based on parsimony analysis and Bayesian inference of a new morphological dataset. We find a reduced membership of the mammalian stem lineage, which excludes varanopids. This implies that evolutionary turnover of the mammalian stem lineage during the Early-Middle Permian transition (273 million years ago) was more abrupt than has previously been recognized. We also find that Parareptilia are nested within Diapsida. This suggests that temporal fenestration, a key structural innovation with important functional implications, evolved fewer times than generally thought, but showed highly variable morphology among early reptiles after its initial origin. Our phylogeny also addresses controversies over the affinities of mesosaurids, the earliest known aquatic amniotes, which we recover as early diverging parareptiles. </p>

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          The fossilized birth-death process for coherent calibration of divergence-time estimates.

          Time-calibrated species phylogenies are critical for addressing a wide range of questions in evolutionary biology, such as those that elucidate historical biogeography or uncover patterns of coevolution and diversification. Because molecular sequence data are not informative on absolute time, external data--most commonly, fossil age estimates--are required to calibrate estimates of species divergence dates. For Bayesian divergence time methods, the common practice for calibration using fossil information involves placing arbitrarily chosen parametric distributions on internal nodes, often disregarding most of the information in the fossil record. We introduce the "fossilized birth-death" (FBD) process--a model for calibrating divergence time estimates in a Bayesian framework, explicitly acknowledging that extant species and fossils are part of the same macroevolutionary process. Under this model, absolute node age estimates are calibrated by a single diversification model and arbitrary calibration densities are not necessary. Moreover, the FBD model allows for inclusion of all available fossils. We performed analyses of simulated data and show that node age estimation under the FBD model results in robust and accurate estimates of species divergence times with realistic measures of statistical uncertainty, overcoming major limitations of standard divergence time estimation methods. We used this model to estimate the speciation times for a dataset composed of all living bears, indicating that the genus Ursus diversified in the Late Miocene to Middle Pliocene.
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            AMNIOTE PHYLOGENY AND THE IMPORTANCE OF FOSSILS

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              Sampling-through-time in birth-death trees.

              I consider the constant rate birth-death process with incomplete sampling. I calculate the density of a given tree with sampled extant and extinct individuals. This density is essential for analyzing datasets which are sampled through time. Such datasets are common in virus epidemiology as viruses in infected individuals are sampled through time. Further, such datasets appear in phylogenetics when extant and extinct species data is available. I show how the derived tree density can be used (i) as a tree prior in a Bayesian method to reconstruct the evolutionary past of the sequence data on a calender-timescale, (ii) to infer the birth- and death-rates for a reconstructed evolutionary tree, and (iii) for simulating trees with a given number of sampled extant and extinct individuals which is essential for testing evolutionary hypotheses for the considered datasets. Copyright © 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Nature Ecology & Evolution
                Nat Ecol Evol
                Springer Science and Business Media LLC
                2397-334X
                January 2020
                December 23 2019
                January 2020
                : 4
                : 1
                : 57-65
                Article
                10.1038/s41559-019-1047-3
                31900445
                30ca83aa-fab7-4574-a699-77f05cabfe3e
                © 2020

                http://www.springer.com/tdm

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