Description
Book SynopsisSystematics: 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 probabili
Trade 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)
Table of ContentsPreface 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 ?
