Synapomorphy, Parsimony, and Evidence
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Species Concepts Should Not Conflict with Evolutionary History, but Often Do
ARTICLE IN PRESS Stud. Hist. Phil. Biol. & Biomed. Sci. xxx (2008) xxx–xxx Contents lists available at ScienceDirect Stud. Hist. Phil. Biol. & Biomed. Sci. journal homepage: www.elsevier.com/locate/shpsc Species concepts should not conflict with evolutionary history, but often do Joel D. Velasco Department of Philosophy, University of Wisconsin-Madison, 5185 White Hall, 600 North Park St., Madison, WI 53719, USA Department of Philosophy, Building 90, Stanford University, Stanford, CA 94305, USA article info abstract Keywords: Many phylogenetic systematists have criticized the Biological Species Concept (BSC) because it distorts Biological Species Concept evolutionary history. While defences against this particular criticism have been attempted, I argue that Phylogenetic Species Concept these responses are unsuccessful. In addition, I argue that the source of this problem leads to previously Phylogenetic Trees unappreciated, and deeper, fatal objections. These objections to the BSC also straightforwardly apply to Taxonomy other species concepts that are not defined by genealogical history. What is missing from many previous discussions is the fact that the Tree of Life, which represents phylogenetic history, is independent of our choice of species concept. Some species concepts are consistent with species having unique positions on the Tree while others, including the BSC, are not. Since representing history is of primary importance in evolutionary biology, these problems lead to the conclusion that the BSC, along with many other species concepts, are unacceptable. If species are to be taxa used in phylogenetic inferences, we need a history- based species concept. Ó 2008 Elsevier Ltd. All rights reserved. When citing this paper, please use the full journal title Studies in History and Philosophy of Biological and Biomedical Sciences 1. -
A Phylogenomic Analysis of Turtles ⇑ Nicholas G
Molecular Phylogenetics and Evolution 83 (2015) 250–257 Contents lists available at ScienceDirect Molecular Phylogenetics and Evolution journal homepage: www.elsevier.com/locate/ympev A phylogenomic analysis of turtles ⇑ Nicholas G. Crawford a,b,1, James F. Parham c, ,1, Anna B. Sellas a, Brant C. Faircloth d, Travis C. Glenn e, Theodore J. Papenfuss f, James B. Henderson a, Madison H. Hansen a,g, W. Brian Simison a a Center for Comparative Genomics, California Academy of Sciences, 55 Music Concourse Drive, San Francisco, CA 94118, USA b Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA c John D. Cooper Archaeological and Paleontological Center, Department of Geological Sciences, California State University, Fullerton, CA 92834, USA d Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA e Department of Environmental Health Science, University of Georgia, Athens, GA 30602, USA f Museum of Vertebrate Zoology, University of California, Berkeley, CA 94720, USA g Mathematical and Computational Biology Department, Harvey Mudd College, 301 Platt Boulevard, Claremont, CA 9171, USA article info abstract Article history: Molecular analyses of turtle relationships have overturned prevailing morphological hypotheses and Received 11 July 2014 prompted the development of a new taxonomy. Here we provide the first genome-scale analysis of turtle Revised 16 October 2014 phylogeny. We sequenced 2381 ultraconserved element (UCE) loci representing a total of 1,718,154 bp of Accepted 28 October 2014 aligned sequence. Our sampling includes 32 turtle taxa representing all 14 recognized turtle families and Available online 4 November 2014 an additional six outgroups. Maximum likelihood, Bayesian, and species tree methods produce a single resolved phylogeny. -
Phylogenetic Comparative Methods: a User's Guide for Paleontologists
Phylogenetic Comparative Methods: A User’s Guide for Paleontologists Laura C. Soul - Department of Paleobiology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA David F. Wright - Division of Paleontology, American Museum of Natural History, Central Park West at 79th Street, New York, New York 10024, USA and Department of Paleobiology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA Abstract. Recent advances in statistical approaches called Phylogenetic Comparative Methods (PCMs) have provided paleontologists with a powerful set of analytical tools for investigating evolutionary tempo and mode in fossil lineages. However, attempts to integrate PCMs with fossil data often present workers with practical challenges or unfamiliar literature. In this paper, we present guides to the theory behind, and application of, PCMs with fossil taxa. Based on an empirical dataset of Paleozoic crinoids, we present example analyses to illustrate common applications of PCMs to fossil data, including investigating patterns of correlated trait evolution, and macroevolutionary models of morphological change. We emphasize the importance of accounting for sources of uncertainty, and discuss how to evaluate model fit and adequacy. Finally, we discuss several promising methods for modelling heterogenous evolutionary dynamics with fossil phylogenies. Integrating phylogeny-based approaches with the fossil record provides a rigorous, quantitative perspective to understanding key patterns in the history of life. 1. Introduction A fundamental prediction of biological evolution is that a species will most commonly share many characteristics with lineages from which it has recently diverged, and fewer characteristics with lineages from which it diverged further in the past. This principle, which results from descent with modification, is one of the most basic in biology (Darwin 1859). -
Lineages, Splits and Divergence Challenge Whether the Terms Anagenesis and Cladogenesis Are Necessary
Biological Journal of the Linnean Society, 2015, , – . With 2 figures. Lineages, splits and divergence challenge whether the terms anagenesis and cladogenesis are necessary FELIX VAUX*, STEVEN A. TREWICK and MARY MORGAN-RICHARDS Ecology Group, Institute of Agriculture and Environment, Massey University, Palmerston North, New Zealand Received 3 June 2015; revised 22 July 2015; accepted for publication 22 July 2015 Using the framework of evolutionary lineages to separate the process of evolution and classification of species, we observe that ‘anagenesis’ and ‘cladogenesis’ are unnecessary terms. The terms have changed significantly in meaning over time, and current usage is inconsistent and vague across many different disciplines. The most popular definition of cladogenesis is the splitting of evolutionary lineages (cessation of gene flow), whereas anagenesis is evolutionary change between splits. Cladogenesis (and lineage-splitting) is also regularly made synonymous with speciation. This definition is misleading as lineage-splitting is prolific during evolution and because palaeontological studies provide no direct estimate of gene flow. The terms also fail to incorporate speciation without being arbitrary or relative, and the focus upon lineage-splitting ignores the importance of divergence, hybridization, extinction and informative value (i.e. what is helpful to describe as a taxon) for species classification. We conclude and demonstrate that evolution and species diversity can be considered with greater clarity using simpler, more transparent terms than anagenesis and cladogenesis. Describing evolution and taxonomic classification can be straightforward, and there is no need to ‘make words mean so many different things’. © 2015 The Linnean Society of London, Biological Journal of the Linnean Society, 2015, 00, 000–000. -
Statistical Inference for the Linguistic and Non-Linguistic Past
Statistical inference for the linguistic and non-linguistic past Igor Yanovich DFG Center for Advanced Study “Words, Bones, Genes and Tools” Universität Tübingen July 6, 2017 Igor Yanovich 1 / 46 Overview of the course Overview of the course 1 Today: trees, as a description and as a process 2 Classes 2-3: simple inference of language-family trees 3 Classes 4-5: computational statistical inference of trees and evolutionary parameters 4 Class 6: histories of languages and of genes 5 Class 7: simple spatial statistics 6 Class 8: regression taking into account linguistic relationships; synthesis of the course Igor Yanovich 2 / 46 Overview of the course Learning outcomes By the end of the course, you should be able to: 1 read and engage with the current literature in linguistic phylogenetics and in spatial statistics for linguistics 2 run phylogenetic and basic spatial analyses on linguistic data 3 proceed further in the subject matter on your own, towards further advances in the field Igor Yanovich 3 / 46 Overview of the course Today’s class 1 Overview of the course 2 Language families and their structures 3 Trees as classifications and as process depictions 4 Linguistic phylogenetics 5 Worries about phylogenetics in linguistics vs. biology 6 Quick overview of the homework 7 Summary of Class 1 Igor Yanovich 4 / 46 Language families and their structures Language families and their structures Igor Yanovich 5 / 46 Language families and their structures Dravidian A modification of [Krishnamurti, 2003, Map 1.1], from Wikipedia Igor Yanovich 6 / 46 Language families and their structures Dravidian [Krishnamurti, 2003]’s classification: Dravidian family South Dravidian Central Dravidian North Dravidian SD I SD II Tamil Malay¯al.am Kannad.a Telugu Kolami Brahui (70M) (38M) (40M) (75M) (0.1M) (4M) (Numbers of speakers from Wikipedia) Igor Yanovich 7 / 46 Language families and their structures Dravidian: similarities and differences Proto-Dr. -
1 Integrative Biology 200 "PRINCIPLES OF
Integrative Biology 200 "PRINCIPLES OF PHYLOGENETICS" Spring 2018 University of California, Berkeley B.D. Mishler March 14, 2018. Classification II: Phylogenetic taxonomy including incorporation of fossils; PhyloCode I. Phylogenetic Taxonomy - the argument for rank-free classification A number of recent calls have been made for the reformation of the Linnaean hierarchy (e.g., De Queiroz & Gauthier, 1992). These authors have emphasized that the existing system is based in a non-evolutionary world-view; the roots of the Linnaean hierarchy are in a specially- created world-view. Perhaps the idea of fixed, comparable ranks made some sense under that view, but under an evolutionary world view they don't make sense. There are several problems with the current nomenclatorial system: 1. The current system, with its single type for a name, cannot be used to precisely name a clade. E.g., you may name a family based on a certain type specimen, and even if you were clear about what node you meant to name in your original publication, the exact phylogenetic application of your name would not be clear subsequently, after new clades are added. 2. There are not nearly enough ranks to name the thousands of levels of monophyletic groups in the tree of life. Therefore people are increasingly using informal rank-free names for higher- level nodes, but without any clear, formal specification of what clade is meant. 3. Most aspects of the current code, including priority, revolve around the ranks, which leads to instability of usage. For example, when a change in relationships is discovered, several names often need to be changed to adjust, including those of groups whose circumscription has not changed. -
Learning the “Game” of Life: Reconstruction of Cell Lineages Through Crispr-Induced Dna Mutations
LEARNING THE “GAME” OF LIFE: RECONSTRUCTION OF CELL LINEAGES THROUGH CRISPR-INDUCED DNA MUTATIONS JIAXIAO CAI, DA KUANG, ARUN KIRUBARAJAN, MUKUND VENKATESWARAN ABSTRACT. Multi-cellular organisms are composed of many billions of individuals cells that exist by the mutation of a single stem cell. The CRISPR-based molecular-tools induce DNA mutations, which allows us to study the mutation lineages of various multi-ceulluar organisms. However, to date no lineage reconstruction algorithms have been examined for their performance/robustness across various molecular tools, datasets and sizes of lineage trees. In addition, it is unclear whether classical machine learning algorithms, deep neural networks or some combination of the two are the best approach for this task. In this paper, we introduce 1) a simulation framework for the zygote development process to achieve a dataset size required by deep learning models, and 2) various supervised and unsupervised approaches for cell mutation tree reconstruction. In particular, we show how deep generative models (Autoencoders and Variational Autoencoders), unsupervised clustering methods (K-means), and classical tree reconstruction algorithms (UPGMA) can all be used to trace cell mutation lineages. 1. INTRODUCTION Multi-cellular organisms are composed of billions or trillions of different interconnected cells that derive from a single cell through repeated rounds of cell division — knowing the cell lineage that produces a fully developed organism from a single cell provides the framework for understanding when, where, and how cell fate decisions are made. The recent advent of new CRISPR-based molecular tools synthetically induced DNA mutations and made it possible to solve lineage of complex model organisms at single-cell resolution.[1] Starting in early embryogenesis, CRISPR-induced mutations occur stochastically at introduced target sites, and these mutations are stably inherited by the offspring of these cells and immune to further change. -
Phylocode: a Phylogenetic Code of Biological Nomenclature
PhyloCode: A Phylogenetic Code of Biological Nomenclature Philip D. Cantino and Kevin de Queiroz (equal contributors; names listed alphabetically) Advisory Group: William S. Alverson, David A. Baum, Harold N. Bryant, David C. Cannatella, Peter R. Crane, Michael J. Donoghue, Torsten Eriksson*, Jacques Gauthier, Kenneth Halanych, David S. Hibbett, David M. Hillis, Kathleen A. Kron, Michael S. Y. Lee, Alessandro Minelli, Richard G. Olmstead, Fredrik Pleijel*, J. Mark Porter, Heidi E. Robeck, Greg W. Rouse, Timothy Rowe*, Christoffer Schander, Per Sundberg, Mikael Thollesson, and Andre R. Wyss. *Chaired a committee that authored a portion of the current draft. Most recent revision: April 8, 2000 1 Table of Contents Preface Preamble Division I. Principles Division II. Rules Chapter I. Taxa Article 1. The Nature of Taxa Article 2. Clades Article 3. Hierarchy and Rank Chapter II. Publication Article 4. Publication Requirements Article 5. Publication Date Chapter III. Names Section 1. Status Article 6 Section 2. Establishment Article 7. General Requirements Article 8. Registration Chapter IV. Clade Names Article 9. General Requirements for Establishment of Clade Names Article 10. Selection of Clade Names for Establishment Article 11. Specifiers and Qualifying Clauses Chapter V. Selection of Accepted Names Article 12. Precedence Article 13. Homonymy Article 14. Synonymy Article 15. Conservation Chapter VI. Provisions for Hybrids Article 16. Chapter VII. Orthography Article 17. Orthographic Requirements for Establishment Article 18. Subsequent Use and Correction of Established Names Chapter VIII. Authorship of Names Article 19. Chapter IX. Citation of Authors and Registration Numbers Article 20. Chapter X. Governance Article 21. Glossary Table 1. Equivalence of Nomenclatural Terms Appendix A. -
Phylogenetics
Phylogenetics What is phylogenetics? • Study of branching patterns of descent among lineages • Lineages – Populations – Species – Molecules • Shift between population genetics and phylogenetics is often the species boundary – Distantly related populations also show patterning – Patterning across geography What is phylogenetics? • Goal: Determine and describe the evolutionary relationships among lineages – Order of events – Timing of events • Visualization: Phylogenetic trees – Graph – No cycles Phylogenetic trees • Nodes – Terminal – Internal – Degree • Branches • Topology Phylogenetic trees • Rooted or unrooted – Rooted: Precisely 1 internal node of degree 2 • Node that represents the common ancestor of all taxa – Unrooted: All internal nodes with degree 3+ Stephan Steigele Phylogenetic trees • Rooted or unrooted – Rooted: Precisely 1 internal node of degree 2 • Node that represents the common ancestor of all taxa – Unrooted: All internal nodes with degree 3+ Phylogenetic trees • Rooted or unrooted – Rooted: Precisely 1 internal node of degree 2 • Node that represents the common ancestor of all taxa – Unrooted: All internal nodes with degree 3+ • Binary: all speciation events produce two lineages from one • Cladogram: Topology only • Phylogram: Topology with edge lengths representing time or distance • Ultrametric: Rooted tree with time-based edge lengths (all leaves equidistant from root) Phylogenetic trees • Clade: Group of ancestral and descendant lineages • Monophyly: All of the descendants of a unique common ancestor • Polyphyly: -
Advances in Computational Methods for Phylogenetic Networks in the Presence of Hybridization
Advances in Computational Methods for Phylogenetic Networks in the Presence of Hybridization R. A. Leo Elworth, Huw A. Ogilvie, Jiafan Zhu, and Luay Nakhleh Abstract Phylogenetic networks extend phylogenetic trees to allow for modeling reticulate evolutionary processes such as hybridization. They take the shape of a rooted, directed, acyclic graph, and when parameterized with evolutionary parame- ters, such as divergence times and population sizes, they form a generative process of molecular sequence evolution. Early work on computational methods for phylo- genetic network inference focused exclusively on reticulations and sought networks with the fewest number of reticulations to fit the data. As processes such as incom- plete lineage sorting (ILS) could be at play concurrently with hybridization, work in the last decade has shifted to computational approaches for phylogenetic net- work inference in the presence of ILS. In such a short period, significant advances have been made on developing and implementing such computational approaches. In particular, parsimony, likelihood, and Bayesian methods have been devised for estimating phylogenetic networks and associated parameters using estimated gene trees as data. Use of those inference methods has been augmented with statistical tests for specific hypotheses of hybridization, like the D-statistic. Most recently, Bayesian approaches for inferring phylogenetic networks directly from sequence data were developed and implemented. In this chapter, we survey such advances and discuss model assumptions as well as methods’ strengths and limitations. We also discuss parallel efforts in the population genetics community aimed at inferring similar structures. Finally, we highlight major directions for future research in this area. R. A. Leo Elworth Rice University e-mail: [email protected] Huw A. -
Reading Phylogenetic Trees: a Quick Review (Adapted from Evolution.Berkeley.Edu)
Biological Trees Gloria Rendon SC11 Education June, 2011 Biological trees • Biological trees are used for the purpose of classification, i.e. grouping and categorization of organisms by biological type such as genus or species. Types of Biological trees • Taxonomy trees, like the one hosted at NCBI, are hierarchies; thus classification is determined by position or rank within the hierarchy. It goes from kingdom to species. • Phylogenetic trees represent evolutionary relationships, or genealogy, among species. Nowadays, these trees are usually constructed by comparing 16s/18s ribosomal RNA. • Gene trees represent evolutionary relationships of a particular biological molecule (gene or protein product) among species. They may or may not match the species genealogy. Examples: hemoglobin tree, kinase tree, etc. TAXONOMY TREES Exercise 1: Exploring the Species Tree at NCBI •There exist many taxonomies. •In this exercise, we will examine the taxonomy at NCBI. •NCBI has a taxonomy database where each category in the tree (from the root to the species level) has a unique identifier called taxid. •The lineage of a species is the full path you take in that tree from the root to the point where that species is located. •The (NCBI) taxonomy common tree is therefore the tree that results from adding together the full lineages of each species in a particular list of your choice. Exercise 1: Exploring the Species Tree at NCBI • Open a web browser on NCBI’s Taxonomy page http://www.ncbi.nlm.n ih.gov/Taxonomy/ • Click on each one of the names here to look up the taxonomy id (taxid) of each one of the five categories of the taxonomy browser: Archaea, bacteria, Eukaryotes, Viroids and Viruses. -
Reconsidering Relationships Among Stem and Crown Group Pinaceae: Oldest Record of the Genus Pinus from the Early Cretaceous of Yorkshire, United Kingdom
Int. J. Plant Sci. 173(8):917–932. 2012. Ó 2012 by The University of Chicago. All rights reserved. 1058-5893/2012/17308-0006$15.00 DOI: 10.1086/667228 RECONSIDERING RELATIONSHIPS AMONG STEM AND CROWN GROUP PINACEAE: OLDEST RECORD OF THE GENUS PINUS FROM THE EARLY CRETACEOUS OF YORKSHIRE, UNITED KINGDOM Patricia E. Ryberg,* Gar W. Rothwell,1,y,z Ruth A. Stockey,y,§ Jason Hilton,k Gene Mapes,z and James B. Riding# *Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, Kansas 66045, U.S.A.; yDepartment of Botany and Plant Pathology, 2082 Cordley Hall, Oregon State University, Corvallis, Oregon 97331, U.S.A.; zDepartment of Environmental and Plant Biology, Ohio University, Athens, Ohio 45701, U.S.A.; §Department of Biological Sciences, University of Alberta, Edmonton AB T6G 2E9, Canada; kSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom; and #British Geological Survey, Kingsley Dunham Centre, Keyworth, Nottingham NG12 5GG, United Kingdom This study describes a specimen that extends the oldest fossil evidence of Pinus L. to the Early Cretaceous Wealden Formation of Yorkshire, UK (131–129 million years ago), and prompts a critical reevaluation of criteria that are employed to identify crown group genera of Pinaceae from anatomically preserved seed cones. The specimen, described as Pinus yorkshirensis sp. nov., is conical, 5 cm long, and 3.1 cm in maximum diameter. Bract/scale complexes are helically arranged and spreading. Vasculature of the axis forms a complete cylinder with few resin canals in the wood, and the inner cortex is dominated by large resin canals.