Modern Evolutionary Classification
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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 Trees and Cladograms Are Graphical Representations (Models) of Evolutionary History That Can Be Tested
AP Biology Lab/Cladograms and Phylogenetic Trees Name _______________________________ Relationship to the AP Biology Curriculum Framework Big Idea 1: The process of evolution drives the diversity and unity of life. Essential knowledge 1.B.2: Phylogenetic trees and cladograms are graphical representations (models) of evolutionary history that can be tested. Learning Objectives: LO 1.17 The student is able to pose scientific questions about a group of organisms whose relatedness is described by a phylogenetic tree or cladogram in order to (1) identify shared characteristics, (2) make inferences about the evolutionary history of the group, and (3) identify character data that could extend or improve the phylogenetic tree. LO 1.18 The student is able to evaluate evidence provided by a data set in conjunction with a phylogenetic tree or a simple cladogram to determine evolutionary history and speciation. LO 1.19 The student is able create a phylogenetic tree or simple cladogram that correctly represents evolutionary history and speciation from a provided data set. [Introduction] Cladistics is the study of evolutionary classification. Cladograms show evolutionary relationships among organisms. Comparative morphology investigates characteristics for homology and analogy to determine which organisms share a recent common ancestor. A cladogram will begin by grouping organisms based on a characteristics displayed by ALL the members of the group. Subsequently, the larger group will contain increasingly smaller groups that share the traits of the groups before them. However, they also exhibit distinct changes as the new species evolve. Further, molecular evidence from genes which rarely mutate can provide molecular clocks that tell us how long ago organisms diverged, unlocking the secrets of organisms that may have similar convergent morphology but do not share a recent common ancestor. -
An Introduction to Phylogenetic Analysis
This article reprinted from: Kosinski, R.J. 2006. An introduction to phylogenetic analysis. Pages 57-106, in Tested Studies for Laboratory Teaching, Volume 27 (M.A. O'Donnell, Editor). Proceedings of the 27th Workshop/Conference of the Association for Biology Laboratory Education (ABLE), 383 pages. Compilation copyright © 2006 by the Association for Biology Laboratory Education (ABLE) ISBN 1-890444-09-X All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the copyright owner. Use solely at one’s own institution with no intent for profit is excluded from the preceding copyright restriction, unless otherwise noted on the copyright notice of the individual chapter in this volume. Proper credit to this publication must be included in your laboratory outline for each use; a sample citation is given above. Upon obtaining permission or with the “sole use at one’s own institution” exclusion, ABLE strongly encourages individuals to use the exercises in this proceedings volume in their teaching program. Although the laboratory exercises in this proceedings volume have been tested and due consideration has been given to safety, individuals performing these exercises must assume all responsibilities for risk. The Association for Biology Laboratory Education (ABLE) disclaims any liability with regards to safety in connection with the use of the exercises in this volume. The focus of ABLE is to improve the undergraduate biology laboratory experience by promoting the development and dissemination of interesting, innovative, and reliable laboratory exercises. -
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. -
Interpreting Cladograms Notes
Interpreting Cladograms Notes INTERPRETING CLADOGRAMS BIG IDEA: PHYLOGENIES DEPICT ANCESTOR AND DESCENDENT RELATIONSHIPS AMONG ORGANISMS BASED ON HOMOLOGY THESE EVOLUTIONARY RELATIONSHIPS ARE REPRESENTED BY DIAGRAMS CALLED CLADOGRAMS (BRANCHING DIAGRAMS THAT ORGANIZE RELATIONSHIPS) What is a Cladogram ● A diagram which shows ● Is not an evolutionary ___________ among tree, ___________show organisms. how ancestors are related ● Lines branching off other to descendants or how lines. The lines can be much they have changed traced back to where they branch off. These branching off points represent a hypothetical ancestor. Parts of a Cladogram Reading Cladograms ● Read like a family tree: show ________of shared ancestry between lineages. • When an ancestral lineage______: speciation is indicated due to the “arrival” of some new trait. Each lineage has unique ____to itself alone and traits that are shared with other lineages. each lineage has _______that are unique to that lineage and ancestors that are shared with other lineages — common ancestors. Quick Question #1 ●What is a_________? ● A group that includes a common ancestor and all the descendants (living and extinct) of that ancestor. Reading Cladogram: Identifying Clades ● Using a cladogram, it is easy to tell if a group of lineages forms a clade. ● Imagine clipping a single branch off the phylogeny ● all of the organisms on that pruned branch make up a clade Quick Question #2 ● Looking at the image to the right: ● Is the green box a clade? ● The blue? ● The pink? ● The orange? Reading Cladograms: Clades ● Clades are nested within one another ● they form a nested hierarchy. ● A clade may include many thousands of species or just a few. -
Conceptual Issues in Phylogeny, Taxonomy, and Nomenclature
Contributions to Zoology, 66 (1) 3-41 (1996) SPB Academic Publishing bv, Amsterdam Conceptual issues in phylogeny, taxonomy, and nomenclature Alexandr P. Rasnitsyn Paleontological Institute, Russian Academy ofSciences, Profsoyuznaya Street 123, J17647 Moscow, Russia Keywords: Phylogeny, taxonomy, phenetics, cladistics, phylistics, principles of nomenclature, type concept, paleoentomology, Xyelidae (Vespida) Abstract On compare les trois approches taxonomiques principales développées jusqu’à présent, à savoir la phénétique, la cladis- tique et la phylistique (= systématique évolutionnaire). Ce Phylogenetic hypotheses are designed and tested (usually in dernier terme s’applique à une approche qui essaie de manière implicit form) on the basis ofa set ofpresumptions, that is, of à les traits fondamentaux de la taxonomic statements explicite représenter describing a certain order of things in nature. These traditionnelle en de leur et particulier son usage preuves ayant statements are to be accepted as such, no matter whatever source en même temps dans la similitude et dans les relations de evidence for them exists, but only in the absence ofreasonably parenté des taxons en question. L’approche phylistique pré- sound evidence pleading against them. A set ofthe most current sente certains avantages dans la recherche de réponses aux phylogenetic presumptions is discussed, and a factual example problèmes fondamentaux de la taxonomie. ofa practical realization of the approach is presented. L’auteur considère la nomenclature A is made of the three -
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. -
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.