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      SELECTION FOR MECHANICAL ADVANTAGE UNDERLIES MULTIPLE CRANIAL OPTIMA IN NEW WORLD LEAF-NOSED BATS : CRANIAL OPTIMA IN NEW WORLD LEAF-NOSED BATS

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          Abstract

          Selection for divergent performance optima has been proposed as a central mechanism underlying adaptive radiation. Uncovering multiple optima requires identifying forms associated with different adaptive zones and linking those forms to performance. However, testing and modeling the performance of complex morphologies like the cranium is challenging. We introduce a three-dimensional finite-element (FE) model of the cranium that can be morphed into different shapes by varying simple parameters to investigate the relationship between two engineering-based measures of performance, mechanical advantage and von Mises stress, and four divergent adaptive zones occupied by New World Leaf-nosed bats. To investigate these relationships, we tested the fit of Brownian motion and Ornstein-Uhlenbeck models of evolution in mechanical advantage and von Mises stress using dated multilocus phylogenies. The analyses revealed three performance optima for mechanical advantage among species from three adaptive zones: bats that eat nectar; generalized insectivores, omnivores and some frugivores; and bats that specialize on hard canopy fruits. Only two optima, one corresponding to nectar feeding, were consistently uncovered for von Mises stress. These results suggest that mechanical advantage played a larger role than von Mises stress in the radiation of New World Leaf-nosed bats into divergent adaptive zones.

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          RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models.

          RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively. icwww.epfl.ch/~stamatak
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            APE: Analyses of Phylogenetics and Evolution in R language.

            Analysis of Phylogenetics and Evolution (APE) is a package written in the R language for use in molecular evolution and phylogenetics. APE provides both utility functions for reading and writing data and manipulating phylogenetic trees, as well as several advanced methods for phylogenetic and evolutionary analysis (e.g. comparative and population genetic methods). APE takes advantage of the many R functions for statistics and graphics, and also provides a flexible framework for developing and implementing further statistical methods for the analysis of evolutionary processes. The program is free and available from the official R package archive at http://cran.r-project.org/src/contrib/PACKAGES.html#ape. APE is licensed under the GNU General Public License.
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              Is Open Access

              Bayesian Phylogenetics with BEAUti and the BEAST 1.7

              Computational evolutionary biology, statistical phylogenetics and coalescent-based population genetics are becoming increasingly central to the analysis and understanding of molecular sequence data. We present the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7, which implements a family of Markov chain Monte Carlo (MCMC) algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses. This package includes an enhanced graphical user interface program called Bayesian Evolutionary Analysis Utility (BEAUti) that enables access to advanced models for molecular sequence and phenotypic trait evolution that were previously available to developers only. The package also provides new tools for visualizing and summarizing multispecies coalescent and phylogeographic analyses. BEAUti and BEAST 1.7 are open source under the GNU lesser general public license and available at http://beast-mcmc.googlecode.com and http://beast.bio.ed.ac.uk
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                Author and article information

                Journal
                Evolution
                Evolution
                Wiley
                00143820
                May 2014
                May 2014
                March 20 2014
                : 68
                : 5
                : 1436-1449
                Affiliations
                [1 ]Department of Biology; University of Massachusetts Amherst; 221 Morrill Science Center; Amherst Massachusetts 01003
                [2 ]Department of Mechanical and Industrial Engineering; University of Massachusetts Amherst; 160 Governor's Drive Amherst Massachusetts 01003
                [3 ]Department of Ecology and Evolution; Stony Brook University; 650 Life Sciences Building Stony Brook New York 11794
                [4 ]Consortium for Inter-Disciplinary Environmental Research; School of Marine and Atmospheric Sciences; Stony Brook University; 129 Dana Hall Stony Brook New York 11794
                Article
                10.1111/evo.12358
                24433457
                20caedd8-59f7-4e1a-bfda-648999157ee4
                © 2014

                http://doi.wiley.com/10.1002/tdm_license_1.1

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