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Systematics: A Course of Lectures

ISBN: 978-0-470-67170-2
446 pages
May 2012, Wiley-Blackwell
Systematics: A Course of Lectures (047067170X) cover image
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).

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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 Oken9

1.11 Richard Owen 9

1.12 Charles Darwin  9

1.13 Stammbäume  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, ?)  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 ?

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Ward Wheeler is Professor 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.
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“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)

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