Systematics: A Course of Lectures

Systematics: A Course of Lectures is designed for use in an advanced undergraduate or introductory graduate level course in systematics and is meant to present core systematic concepts and literature. The book covers topics such as the history of systematic thinking and fundamental concepts in the field including species concepts, homology, and hypothesis testing. Analytical methods are covered in detail with chapters devoted to sequence alignment, optimality criteria, and methods such as distance, parsimony, maximum likelihood and Bayesian approaches. Trees and tree searching, consensus and super-tree methods, support measures, and other relevant topics are each covered in their own sections. The work is not a bleeding-edge statement or in-depth review of the entirety of systematics, but covers the basics as broadly as could be handled in a one semester course. Most chapters are designed to be a single 1.5 hour class, with those on parsimony, likelihood, posterior probability, and tree searching two classes (2 x 1.5 hours).

Table of Contents:
Preface xv Using these notes xv Acknowledgments  xvi List of algorithms xix I Fundamentals 1 1 History 2 1.1 Aristotle  2 1.2 Theophrastus 3 1.3 Pierre Belon 4 1.4 Carolus Linnaeus 4 1.5 Georges Louis Leclerc, Comte de Buffon  6 1.6 Jean-Baptiste Lamarck 7 1.7 Georges Cuvier  8 1.8 ´Etienne Geoffroy Saint-Hilaire  8 1.9 JohannWolfgang von Goethe 8 1.10 Lorenz Oken 9 1.11 Richard Owen 9 1.12 Charles Darwin  9 1.13 Stammb¨aume  12 1.14 Evolutionary Taxonomy 14 1.15 Phenetics 15 1.16 Phylogenetic Systematics  16 1.16.1 Hennig’s Three Questions 16 1.17 Molecules and Morphology  18 1.18 We are all Cladists 18 1.19 Exercises 19 2 Fundamental Concepts 20 2.1 Characters 20 2.1.1 Classes of Characters and Total Evidence  22 2.1.2 Ontogeny, Tokogeny, and Phylogeny  23 2.1.3 Characters and Character States 23 2.2 Taxa 26 2.3 Graphs, Trees, and Networks 28 2.3.1 Graphs and Trees 30 2.3.2 Enumeration 31 2.3.3 Networks  33 2.3.4 Mono-, Para-, and Polyphyly 33 2.3.5 Splits and Convexity  38 2.3.6 Apomorphy, Plesiomorphy, and Homoplasy  39 2.3.7 Gene Trees and Species Trees 41 2.4 Polarity and Rooting 43 2.4.1 Stratigraphy  43 2.4.2 Ontogeny  43 2.4.3 Outgroups  45 2.5 Optimality 49 2.6 Homology  49 2.7 Exercises  50 3 Species Concepts, Definitions, and Issues 53 3.1 Typological or Taxonomic Species Concept  54 3.2 Biological Species Concept  54 3.2.1 Criticisms of the BSC 55 3.3 Phylogenetic Species Concept(s) 56 3.3.1 Autapomorphic/Monophyletic Species Concept 56 3.3.2 Diagnostic/Phylogenetic Species Concept  58 3.4 Lineage Species Concepts  59 3.4.1 Hennigian Species  59 3.4.2 Evolutionary Species  60 3.4.3 Criticisms of Lineage-Based Species  61 3.5 Species as Individuals or Classes  62 3.6 Monoism and Pluralism  63 3.7 Pattern and Process  63 3.8 Species Nominalism  64 3.9 Do Species Concepts Matter?  65 3.10 Exercises  65 4 Hypothesis Testing and the Philosophy of Science 67 4.1 Forms of Scientific Reasoning 67 4.1.1 The Ancients  67 4.1.2 Ockham’s Razor  68 4.1.3 Modes of Scientific Inference  69 4.1.4 Induction 69 4.1.5 Deduction 69 4.1.6 Abduction 70 4.1.7 Hypothetico-Deduction  71 4.2 Other Philosophical Issues 75 4.2.1 Minimization, Transformation, and Weighting 75 4.3 Quotidian Importance  76 4.4 Exercises  76 5 Computational Concepts 77 5.1 Problems, Algorithms, and Complexity 77 5.1.1 Computer Science Basics  77 5.1.2 Algorithms  79 5.1.3 Asymptotic Notation 79 5.1.4 Complexity  80 5.1.5 Non-Deterministic Complexity  82 5.1.6 Complexity Classes: P and NP  82 5.2 An Example: The Traveling Salesman Problem  84 5.3 Heuristic Solutions  85 5.4 Metricity, and Untrametricity  86 5.5 NP–Complete Problems in Systematics  87 5.6 Exercises 88 6 Statistical and Mathematical Basics 89 6.1 Theory of Statistics  89 6.1.1 Probability  89 6.1.2 Conditional Probability  91 6.1.3 Distributions 92 6.1.4 Statistical Inference  98 6.1.5 Prior and Posterior Distributions  99 6.1.6 Bayes Estimators 100 6.1.7 Maximum Likelihood Estimators  101 6.1.8 Properties of Estimators 101 6.2 Matrix Algebra, Differential Equations, and Markov Models 102 6.2.1 Basics  102 6.2.2 Gaussian Elimination 102 6.2.3 Differential Equations  104 6.2.4 Determining Eigenvalues  105 6.2.5 MarkovMatrices  106 6.3 Exercises  107 II Homology 109 7 Homology 110 7.1 Pre-Evolutionary Concepts110 7.1.1 Aristotle  110 7.1.2 Pierre Belon  110 7.1.3 ´Etienne Geoffroy Saint-Hilaire  111 7.1.4 Richard Owen 112 7.2 Charles Darwin  113 7.3 E. Ray Lankester  114 7.4 Adolf Remane  114 7.5 Four Types of Homology  115 7.5.1 Classical View  115 7.5.2 Evolutionary Taxonomy  115 7.5.3 Phenetic Homology  116 7.5.4 Cladistic Homology  116 7.5.5 Types of Homology  117 7.6 Dynamic and Static Homology  118 7.7 Exercises  120 8 Sequence Alignment 121 8.1 Background  121 8.2 “Informal” Alignment  121 8.3 Sequences  121 8.3.1 Alphabets  122 8.3.2 Transformations  123 8.3.3 Distances  123 8.4 Pairwise StringMatching 123 8.4.1 An Example  127 8.4.2 Reducing Complexity  129 8.4.3 Other Indel Weights  130 8.5 Multiple Sequence Alignment  131 8.5.1 The Tree Alignment Problem  133 8.5.2 Trees and Alignment  133 8.5.3 Exact Solutions 134 8.5.4 Polynomial Time Approximate Schemes  134 8.5.5 Heuristic Multiple Sequence Alignment  134 8.5.6 Implementations  135 8.5.7 Structural Alignment  139 8.6 Exercises 145 III Optimality Criteria 147 9 Optimality Criteria−Distance 148 9.1 Why Distance? 148 9.1.1 Benefits  149 9.1.2 Drawbacks 149 9.2 Distance Functions  150 9.2.1 Metricity  150 9.3 Ultrametric Trees  150 9.4 Additive Trees  152 9.4.1 Farris Transform  153 9.4.2 Buneman Trees  154 9.5 General Distances  156 9.5.1 Phenetic Clustering 157 9.5.2 Percent Standard Deviation 160 9.5.3 Minimizing Length  163 9.6 Comparisons 170 9.7 Exercises  171 10 Optimality Criteria−Parsimony 173 10.1 Perfect Phylogeny  174 10.2 Static Homology Characters  174 10.2.1 Additive Characters  175 10.2.2 Non-Additive Characters  179 10.2.3 Matrix Characters  182 10.3 Missing Data  184 10.4 Edge Transformation Assignments  187 10.5 Collapsing Branches  188 10.6 Dynamic Homology  188 10.7 Dynamic and Static Homology  189 10.8 Sequences as Characters 190 10.9 The Tree Alignment Problem on Trees  191 10.9.1 Exact Solutions  191 10.9.2 Heuristic Solutions 191 10.9.3 Lifted Alignments, Fixed-States, and Search-Based Heuristics  193 10.9.4 Iterative Improvement  197 10.10 Performance of Heuristic Solutions 198 10.11 Parameter Sensitivity  198 10.11.1 Sensitivity Analysis  199 10.12 Implied Alignment  199 10.13 Rearrangement  204 10.13.1 Sequence Characters with Moves  204 10.13.2Gene Order Rearrangement 205 10.13.3Median Evaluation  207 10.13.4Combination ofMethods 207 10.14 Horizontal Gene Transfer, Hybridization, and Phylogenetic Networks  209 10.15 Exercises  210 11 Optimality Criteria−Likelihood 213 11.1 Motivation  213 11.1.1 Felsenstein’s Example  213 11.2 Maximum Likelihood and Trees  216 11.2.1 Nuisance Parameters  216 11.3 Types of Likelihood  217 11.3.1 Flavors ofMaximum Relative Likelihood 217 11.4 Static-Homology Characters  218 11.4.1 Models  218 11.4.2 Rate Variation  219 11.4.3 Calculating p(D|T, θ)  221 11.4.4 Links Between Likelihood and Parsimony  222 11.4.5 A Note onMissing Data 224 11.5 Dynamic-Homology Characters  224 11.5.1 Sequence Characters  225 11.5.2 CalculatingML Pairwise Alignment  227 11.5.3 MLMultiple Alignment  230 11.5.4 Maximum Likelihood Tree Alignment Problem 230 11.5.5 Genomic Rearrangement  232 11.5.6 Phylogenetic Networks  234 11.6 Hypothesis Testing  234 11.6.1 Likelihood Ratios  234 11.6.2 Parameters and Fit  236 11.7 Exercises  238 12 Optimality Criteria−Posterior Probability 240 12.1 Bayes in Systematics  240 12.2 Priors  241 12.2.1 Trees  241 12.2.2 Nuisance Parameters  242 12.3 Techniques 246 12.3.1 Markov ChainMonte Carlo  246 12.3.2 Metropolis–Hastings Algorithm 246 12.3.3 Single Component 248 12.3.4 Gibbs Sampler  249 12.3.5 Bayesian MC3 249 12.3.6 Summary of Posterior  250 12.4 Topologies and Clades  252 12.5 Optimality versus Support  254 12.6 Dynamic Homology  254 12.6.1 Hidden Markov Models  255 12.6.2 An Example 256 12.6.3 Three Questions—Three Algorithms  258 12.6.4 HMMAlignment  262 12.6.5 Bayesian Tree Alignment  264 12.6.6 Implementations  264 12.7 Rearrangement  266 12.8 Criticisms of BayesianMethods  267 12.9 Exercises  267 13 Comparison of Optimality Criteria 269 13.1 Distance and CharacterMethods  269 13.2 Epistemology 270 13.2.1 Ockham’s Razor and Popperian Argumentation  271 13.2.2 Parsimony and the Evolutionary Process  272 13.2.3 Induction and Statistical Estimation  272 13.2.4 Hypothesis Testing and Optimality Criteria  272 13.3 Statistical Behavior  273 13.3.1 Probability  273 13.3.2 Consistency  274 13.3.3 Efficiency  281 13.3.4 Robustness  282 13.4 Performance 282 13.4.1 Long-Branch Attraction 283 13.4.2 Congruence  285 13.5 Convergence  285 13.6 CanWe Argue Optimality Criteria? 286 13.7 Exercises 287 IV Trees 289 14 Tree Searching 290 14.1 Exact Solutions  290 14.1.1 Explicit Enumeration 290 14.1.2 Implicit Enumeration—Branch-and-Bound  292 14.2 Heuristic Solutions 294 14.2.1 Local versus Global Optima 294 14.3 Trajectory Search 296 14.3.1 Wagner Algorithm 296 14.3.2 Branch-Swapping Refinement  298 14.3.3 Swapping as Distance 301 14.3.4 Depth-First versus Breadth-First Searching  302 14.4 Randomization  304 14.5 Perturbation  305 14.6 Sectorial Searches and Disc-Covering Methods  309 14.6.1 Sectorial Searches  309 14.6.2 Disc-CoveringMethods  310 14.7 Simulated Annealing  312 14.8 Genetic Algorithm  316 14.9 Synthesis and Stopping 318 14.10 Empirical Examples  319 14.11 Exercises 323 15 Support 324 15.1 ResamplingMeasures 324 15.1.1 Bootstrap  325 15.1.2 Criticisms of the Bootstrap  326 15.1.3 Jackknife  328 15.1.4 Resampling and Dynamic Homology Characters  329 15.2 Optimality-BasedMeasures  329 15.2.1 Parsimony  330 15.2.2 Likelihood 332 15.2.3 Bayesian Posterior Probability  334 15.2.4 Strengths of Optimality-Based Support  335 15.3 Parameter-BasedMeasures 336 15.4 Comparison of Support Measures—Optimal and Average  336 15.5 Which to Choose?  339 15.6 Exercises  339 16 Consensus, Congruence, and Supertrees 341 16.1 Consensus TreeMethods  341 16.1.1 Motivations  341 16.1.2 Adams I and II  341 16.1.3 Gareth Nelson  344 16.1.4 Majority Rule  347 16.1.5 Strict  347 16.1.6 Semi-Strict/Combinable Components  348 16.1.7 Minimally Pruned 348 16.1.8 When to UseWhat?  350 16.2 Supertrees 350 16.2.1 Overview  350 16.2.2 The Impossibility of the Reasonable  350 16.2.3 Graph-BasedMethods 353 16.2.4 Strict Consensus Supertree  355 16.2.5 MR-Based  355 16.2.6 Distance-Based Method  358 16.2.7 Supertrees or Supermatrices?  360 16.3 Exercises  361 V Applications 363 17 Clocks and Rates 364 17.1 The Molecular Clock  364 17.2 Dating  365 17.3 Testing Clocks  365 17.3.1 Langley–Fitch  365 17.3.2 Farris  366 17.3.3 Felsenstein  367 17.4 Relaxed ClockModels  368 17.4.1 Local Clocks  368 17.4.2 Rate Smoothing  368 17.4.3 Bayesian Clock  369 17.5 Implementations  369 17.5.1 r8s  369 17.5.2 MULTIDIVTIME 370 17.5.3 BEAST  370 17.6 Criticisms  370 17.7 Molecular Dates?  373 17.8 Exercises  373 A Mathematical Notation 374 Bibliography 376 Index 415 Color plate section between pp. 76 and 77

About the Author :
Ward Wheeler is Professor in the Richard Gilder Graduate School and Curator of Invertebrate Zoology at the American Museum of Natural History. He is the author of several books, software packages, and over 100 technical papers in empirical and theoretical systematics.

Review :
“Viewed as a series of lectures, this is clearly aimed at graduate level courses in systematics, although some elements would prove useful at undergraduate level.”  (British Ecological Society Bulletin, 1 August 2013) “If you want to teach yourself systematics, this book is for you. It’s just a series of lectures and exercises compiled by Wheeler, one of the top systematic biologists.”  (Teaching Biology, 20 December 2012) “All things considered, I strongly recommend this work as a textbook for those teaching in systematics, biologists and palaeontologists alike . . . I would advise this book to graduate students – MSc and above.”  (Journal of Zoological Systematics and Evolutionary Research, 1 February 2013)