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      Fossils reveal the complex evolutionary history of the mammalian regionalized spine

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      Science
      American Association for the Advancement of Science (AAAS)

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          Abstract

          A unique characteristic of mammals is a vertebral column with anatomically distinct regions, but when and how this trait evolved remains unknown. We reconstructed vertebral regions and their morphological disparity in the extinct forerunners of mammals, the nonmammalian synapsids, to elucidate the evolution of mammalian axial differentiation. Mapping patterns of regionalization and disparity (heterogeneity) across amniotes reveals that both traits increased during synapsid evolution. However, the onset of regionalization predates increased heterogeneity. On the basis of inferred homology patterns, we propose a “pectoral-first” hypothesis for region acquisition, whereby evolutionary shifts in forelimb function in nonmammalian therapsids drove increasing vertebral modularity prior to differentiation of the vertebral column for specialized functions in mammals.

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          Most cited references28

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          TimeTree: a public knowledge-base of divergence times among organisms.

          Biologists and other scientists routinely need to know times of divergence between species and to construct phylogenies calibrated to time (timetrees). Published studies reporting time estimates from molecular data have been increasing rapidly, but the data have been largely inaccessible to the greater community of scientists because of their complexity. TimeTree brings these data together in a consistent format and uses a hierarchical structure, corresponding to the tree of life, to maximize their utility. Results are presented and summarized, allowing users to quickly determine the range and robustness of time estimates and the degree of consensus from the published literature. TimeTree is available at http://www.timetree.net
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            SURFACE: detecting convergent evolution from comparative data by fitting Ornstein-Uhlenbeck models with stepwise Akaike Information Criterion

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              A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data.

              Our understanding of macroevolutionary patterns of adaptive evolution has greatly increased with the advent of large-scale phylogenetic comparative methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an adaptive process of divergence and selection. However, inference of the dynamics of adaptive landscapes from comparative data is complicated by interpretational difficulties, lack of identifiability among parameter values and the common requirement that adaptive hypotheses must be assigned a priori. Here, we develop a reversible-jump Bayesian method of fitting multi-optima OU models to phylogenetic comparative data that estimates the placement and magnitude of adaptive shifts directly from the data. We show how biologically informed hypotheses can be tested against this inferred posterior of shift locations using Bayes Factors to establish whether our a priori models adequately describe the dynamics of adaptive peak shifts. Furthermore, we show how the inclusion of informative priors can be used to restrict models to biologically realistic parameter space and test particular biological interpretations of evolutionary models. We argue that Bayesian model fitting of OU models to comparative data provides a framework for integrating of multiple sources of biological data-such as microevolutionary estimates of selection parameters and paleontological timeseries-allowing inference of adaptive landscape dynamics with explicit, process-based biological interpretations. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
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                Author and article information

                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                September 20 2018
                September 21 2018
                September 20 2018
                September 21 2018
                : 361
                : 6408
                : 1249-1252
                Article
                10.1126/science.aar3126
                30237356
                9b5ab2ef-8255-4a92-ac4e-c98669bd4dc8
                © 2018

                http://www.sciencemag.org/about/science-licenses-journal-article-reuse

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