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 Stammbaume 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, theta) 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)