What Is a Lineage?
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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. -
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. -
Dieter Thomas Tietze Editor How They Arise, Modify and Vanish
Fascinating Life Sciences Dieter Thomas Tietze Editor Bird Species How They Arise, Modify and Vanish Fascinating Life Sciences This interdisciplinary series brings together the most essential and captivating topics in the life sciences. They range from the plant sciences to zoology, from the microbiome to macrobiome, and from basic biology to biotechnology. The series not only highlights fascinating research; it also discusses major challenges associated with the life sciences and related disciplines and outlines future research directions. Individual volumes provide in-depth information, are richly illustrated with photographs, illustrations, and maps, and feature suggestions for further reading or glossaries where appropriate. Interested researchers in all areas of the life sciences, as well as biology enthusiasts, will find the series’ interdisciplinary focus and highly readable volumes especially appealing. More information about this series at http://www.springer.com/series/15408 Dieter Thomas Tietze Editor Bird Species How They Arise, Modify and Vanish Editor Dieter Thomas Tietze Natural History Museum Basel Basel, Switzerland ISSN 2509-6745 ISSN 2509-6753 (electronic) Fascinating Life Sciences ISBN 978-3-319-91688-0 ISBN 978-3-319-91689-7 (eBook) https://doi.org/10.1007/978-3-319-91689-7 Library of Congress Control Number: 2018948152 © The Editor(s) (if applicable) and The Author(s) 2018. This book is an open access publication. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. -
Species Names in Phylogenetic Nomenclature
Syst. Biol. 48(4):790–807, 1999 Species Names in Phylogenetic Nomenclature PHILIP D. CANTINO,1* HAROLD N. BRYANT,2 KEVIN DE QUEIROZ,3 MICHAEL J. DONOGHUE,4 TORSTEN ERIKSSON,5 DAVID M. HILLIS,6 AND MICHAEL S. Y. LEE7 1Department of Environmental and Plant Biology, Ohio University, Athens, Ohio 45701, USA; E-mail: [email protected] 2Royal Saskatchewan Museum, 2340 Albert Street, Regina, Saskatchewan S4P 3V7, Canada; E-mail: [email protected] 3Department of Vertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, DC 20560, USA; E-mail: [email protected] 4Harvard University Herbaria, 22 Divinity Ave., Cambridge, Massachusetts 02138, USA; E-mail: [email protected] 5Bergius Foundation, Royal Swedish Academy of Sciences, Box 50017, 104 05 Stockholm, Sweden; E-mail: [email protected] 6Section of Integrative Biology, School of Biological Sciences, University of Texas, Austin, Texas 78712, USA; E-mail: [email protected] 7Department of Zoology, University of Queensland, Brisbane, Queensland 4072, Australia; E-mail: [email protected] Abstract.—Linnaean binomial nomenclature is logically incompatible with the phylogenetic nomenclature of de Queiroz and Gauthier (1992, Annu. Rev. Ecol. Syst. 23:449–480): The former is based on the concept of genus, thus making this rank mandatory, while the latter is based on phylo- genetic definitions and requires the abandonment of mandatory ranks. Thus, if species are to re- ceive names under phylogenetic nomenclature, a different method must be devised to name them. Here, 13 methods for naming species in the context of phylogenetic nomenclature are contrasted with each other and with Linnaean binomials. -
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. -
BIOLOGICAL SYSTEMATICS and EVOLUTIONARY THEORY By
BIOLOGICAL SYSTEMATICS AND EVOLUTIONARY THEORY by Aleta Quinn BA, University of Maryland, 2005 BS, University of Maryland, 2005 Submitted to the Graduate Faculty of The Kenneth P. Dietrich School of Arts and Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2015 UNIVERSITY OF PITTSBURGH KENNETH P. DIETRICH SCHOOL OF ARTS AND SCIENCES This dissertation was presented by Aleta Quinn It was defended on July 1, 2015 and approved by James Lennox, PhD, History & Philosophy of Science Sandra Mitchell, PhD, History & Philosophy of Science Kenneth Schaffner, PhD, History & Philosophy of Science Jeffrey Schwartz, PhD, Anthropology Dissertation Director: James Lennox, PhD, History & Philosophy of Science ii Copyright © by Aleta Quinn 2015 iii BIOLOGICAL SYSTEMATICS AND EVOLUTIONARY THEORY Aleta Quinn, PhD University of Pittsburgh, 2015 In this dissertation I examine the role of evolutionary theory in systematics (the science that discovers biodiversity). Following Darwin’s revolution, systematists have aimed to reconstruct the past. My dissertation analyzes common but mistaken assumptions about sciences that reconstruct the past by tracing the assumptions to J.S. Mill. Drawing on Mill’s contemporary, William Whewell, I critique Mill’s assumptions and develop an alternative and more complete account of systematic inference as inference to the best explanation. First, I analyze the inadequate view: that scientists use causal theories to hypothesize what past chains of events must have been, and then form hypotheses that identify segments of a network of events and causal transactions between events. This model assumes that scientists can identify events in the world by reference to neatly delineated properties, and that discovering causal laws is simply a matter of testing what regularities hold between events so delineated. -
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. -
Phylocode: a Phylogenetic Code of Biological Nomenclature Version 2B
PhyloCode: A Phylogenetic Code of Biological Nomenclature Version 2b Philip D. Cantino and Kevin de Queiroz (equal contributors; names listed alphabetically) Advisory Group: William S. Alverson, David A. Baum, Christopher A. Brochu, Harold N. Bryant, David C. Cannatella, Peter R. Crane, Michael J. Donoghue, Torsten Eriksson, Jacques Gauthier, Kenneth Halanych, David S. Hibbett, Kathleen A. Kron, Michel Laurin, Michael S. Y. Lee, Alessandro Minelli, Brent D. Mishler, Gerry Moore, Richard G. Olmstead, Fredrik Pleijel, J. Mark Porter, Greg W. Rouse, Timothy Rowe, Christoffer Schander, Per Sundberg, Mikael Thollesson, and André R. Wyss. Most recent revision: June 17, 2004* *The only change since the previous version (December 21, 2003) in a rewording of Recommendation 10A. 1 Table of Contents Preface 3 Preamble 15 Division I. Principles 16 Division II. Rules 17 Chapter I. Taxa 17 Article 1. The Nature of Taxa 17 Article 2. Clades 17 Article 3. Hierarchy and Rank 19 Chapter II. Publication 19 Article 4. Publication Requirements 19 Article 5. Publication Date 20 Chapter III. Names 20 Section 1. Status 20 Article 6 20 Section 2. Establishment 21 Article 7. General Requirements 21 Article 8. Registration 22 Chapter IV. Clade Names 23 Article 9. General Requirements for Establishment of Clade Names 23 Article 10. Selection of Clade Names for Establishment 26 Article 11. Specifiers and Qualifying Clauses 27 Chapter V. Selection of Accepted Names 34 Article 12. Precedence 34 Article 13. Homonymy 34 Article 14. Synonymy 37 Article 15. Conservation 37 Chapter VI. Provisions for Hybrids 38 Article 16. 38 Chapter VII. Orthography 39 Article 17. Orthographic Requirements for Establishment 39 Article 18.