Joseph Felsenstein
（2004年刊行［実際には2003年夏に出た］，Sinauer Associates，Sunderland, xx+664 pp., ISBN:0878931775 [pbk]）
前に紹介したものの，目次は概略だけだったので，あらためて詳細目次をば．
【目次】
Preface xix1. Parsimony methods 1
A simple example 1
Evaluating a particular tree 1
Rootedness and unrootedness 4
Methods of rooting the tree 6
Branch lengths 8
Unresolved questions 92. Counting evolutionary changes 11
The Fitch algorithm 11
The Sankoff algorithm 13
Connection between the two algorithms 16
Using the algorithms when modifying trees 16
Views 16
Using views when a tree is altered 17
Further economies 183. How many trees are there? 19
Rooted bifurcating trees 20
Unrooted bifurcating trees 24
Multifurcating trees 25
Unrooted trees with multifurcations 28
Tree shapes 28
Rooted bifurcating tree shapes 29
Rooted multifurcating tree shapes 30
Unrooted Shapes 32
Labeled histories 35
Perspective 364. Finding the best tree by heuristic search 37
Nearest-neighbor interchanges 38
Subtree pruning and regrafting 41
Tree bisection and reconnection 44
Other tree rearrangement methods 44
Tree-fusing 44
Genetic algorithms 44
Tree windows and sectorial search 46
Speeding up rearrangements 46
Sequential addition 47
Star decomposition 48
Tree space 48
Search by reweighting of characters 51
Simulated annealing 52
History 535. Finding the best tree by branch and bound 54
A nonbiological example 54
Finding the optimal solution 57
NP-hardness 57
Branch and bound methods 60
Phylogenies: Despair and hope 60
Branch and bound for parsimony 61
Improving the bound 64
Using still-absent states 64
Using compatibility 64
Rules limiting the search 656. Ancestral states and branch lengths 67
Reconstructing ancestral states 67
Accelerated and delayed transformation 70
Branch lengths 707. Variants of parsimony 73
Camin-Sokal parsimony 73
Parsimony on an ordinal scale 74
Dollo parsimony 75
Polymorphism parsimony 76
Unknown ancestral states 78
Multiple states and binary coding 78
Dollo parsimony and multiple states 80
Polymorphism parsimony and multiple states 81
Transformation series analysis 81
Weighting characters 82
Successive weighting and nonlinear weighting 83
Successive weighting 83
Nonsuccessive algorithms 848. Compatibility 87
Testing compatibility 88
The Pairwise Compatibility Theorem 89
Cliques of compatible characters 91
Finding the tree from the clique 92
Other cases where cliques can be used 94
Where cliques cannot be used 94
Perfect phylogeny 95
Using compatibility on molecules anyway 959. Statistical properties of parsimony 97
Likelihood and parsimony 97
The weights 100
Unweighted parsimony 100
Limitations of this justification of parsimony 101
Farris’s proofs 102
No common mechanism 103
Likelihood and compatibility 105
Parsimony versus compatibility 107
Consistency and parsimony 107
Character patterns and parsimony 107
Observed numbers of the patterns 110
Observed fractions of the patterns 110
Expected fractions of the patterns 111
Inconsistency 113
When inconsistency is not a problem 114
The nucleotide sequence case 115
Other situations where consistency is guaranteed 117
Does a molecular clock guarantee consistency? 118
The Farris zone 120
Some perspective 12110. A digression on history and philosophy 123
How phylogeny algorithms developed 123
Sokal and Sneath 123
Edwards and Cavalli-Sforza 125
Camin and Sokal and parsimony 128
Eck and Dayhoff and molecular parsimony 130
Fitch and Margoliash popularize distance matrix methods 131
Wilson and Le Quesne introduce compatibility 133
Jukes and Cantor and molecular distances 134
Farris and Kluge and unordered parsimony 134
Fitch and molecular parsimony 136
Further work 136
What about Willi Hennig and Walter Zimmerman? 136
Different philosophical frameworks 138
Hypothetico-deductive 138
Logical parsimony 140
Logical probability? 142
Criticisms of statistical inference 143
The irrelevance of classification 14511. Distance matrix methods 147
Branch lengths and times 147
The least squares methods 148
Least squares branch lengths 148
Finding the least squares tree topology 153
The statistical rationale 153
Generalized least squares 154
Distances 155
The Jukes-Cantor model―-an example 156
Why correct for multiple changes? 158
Minimum evolution 159
Clustering algorithms 161
UPGMA and least squares 161
A clustering algorithm 162
An example 162
UPGMA on nonclocklike trees 165
Neighbor-joining 166
Performance 168
Using neighbor-joining with other methods 169
Relation of neighbor-joining to least squares 169
Weighted versions of neighbor-joining 170
Other approximate distance methods 171
Distance Wagner method 171
A related family 171
Minimizing the maximum discrepancy 172
Two approaches to error in trees 172
A puzzling formula 174
Consistency and distance methods 174
A limitation of distance methods 17512. Quartets of species 176
The four point metric 177
The split decomposition 178
Related methods 182
Short quartets methods 182
The disk-covering method 183
Challenges for the short quartets and DCM methods 185
Three-taxon statement methods 186
Other uses of quartets with parsimony 188
Consensus supertrees 189
Neighborliness 191
De Soete’s search method 192
Quartet puzzling and searching tree space 193
Perspective 19413. Models of DNA evolution 196
Kimura’s two-parameter model 196
Calculation of the distance 198
The Tamura-Nei model, F84, and HKY 200
The general time-reversible model 204
Distances from the GTR model 206
The general 12-parameter model 210
LogDet distances 211
Other distances 213
Variance of distance 214
Rate variation between sites or loci 215
Different rates at different sites 215
Distances with known rates 216
Distribution of rates 216
Gamma- and lognormally distributed rates 217
Distances from gamma-distributed rates 217
Models with nonindependence of sites 22114. Models of protein evolution 222
Amino acid models 222
The Dayhoff model 222
Other empirically-based models 223
Models depending on secondary structure 225
Codon-based models 225
Inequality of synonymous and nonsynonymous substitutions 227
Protein structure and correlated change 22815. Restriction sites, RAPDs, AFLPs, and microsatellites 230
Restriction sites 230
Nei and Tajima’s model 230
Distances based on restriction sites 233
Issues of ascertainment 234
Parsimony for restriction sites 235
Modeling restriction fragments 236
Parsimony with restriction fragments 239
RAPDs and AFLPs 239
The issue of dominance 240
Unresolved problems 240
Microsatellite models 241
The one-step model 241
Microsatellite distances 242
A Brownian motion approximation 244
Models with constraints on array size 246
Multi-step and heterogeneous models 246
Snakes and Ladders 246
Complications 24716. Likelihood methods 248
Maximum likelihood 248
An example 249
Computing the likelihood of a tree 251
Economizing on the computation 253
Handling ambiguity and error 255
Unrootedness 256
Finding the maximum likelihood tree 256
Inferring ancestral sequences 259
Rates varying among sites 260
Hidden Markov models 262
Autocorrelation of rates 264
HMMs for other aspects of models 265
Estimating the states 265
Models with clocks 266
Relaxing molecular clocks 266
Models for relaxed clocks 267
Covarions 268
Empirical approaches to change of rates 269
Are ML estimates consistent? 269
Comparability of likelihoods 270
A nonexistent proof? 270
A simple proof 271
Misbehavior with the wrong model 272
Better behavior with the wrong model 27417. Hadamard methods 275
The edge length spectrum and conjugate spectrum 279
The closest tree criterion 281
DNA models 284
Computational effort 285
Extensions of Hadamard methods 28618. Bayesian inference of phylogenies 288
Bayes’ theorem 288
Bayesian methods for phylogenies 289
Markov chain Monte Carlo methods 292
The Metropolis algorithm 292
Its equilibrium distribution 293
Bayesian MCMC 294
Bayesian MCMC for phylogenies 295
Priors 295
Proposal distributions 296
Computing the likelihoods 298
Summarizing the posterior 299
Priors on trees 300
Controversies over Bayesian inference 301
Universality of the prior 301
Flat priors and doubts about them 301
Applications of Bayesian methods 30419. Testing models, trees, and clocks 307
Likelihood and tests 307
Likelihood ratios near asymptopia 308
Multiple parameters 309
Some parameters constrained, some not 310
Conditions 310
Curvature or height? 311
Interval estimates 311
Testing assertions about parameters 311
Coins in a barrel 313
Evolutionary rates instead of coins 314
Choosing among nonnested hypotheses: AIC and BIC 315
An example using the AIC criterion 317
The problem of multiple topologies 318
LRTs and single branches 319
Interior branch tests 320
Interior branch tests using parsimony 321
A multiple-branch counterpart of interior branch tests 322
Testing the molecular clock 322
Parsimony-based methods 322
Distance-based methods 323
Likelihood-based methods 323
The relative rate test 324
Simulation tests based on likelihood 328
Further literature 329
More exact tests and confidence intervals 329
Tests for three species with a clock 329
Bremer support 330
Zander’s conditional probability of reconstruction 331
More generalized confidence sets 33220. Bootstrap, jackknife, and permutation tests 335
The bootstrap and the jackknife 335
Bootstrapping and phylogenies 337
The delete-half jackknife 339
The bootstrap and jackknife for phylogenies 340
The multiple-tests problem 342
Independence of characters 342
Identical distribution —— a problem? 343
Invariant characters and resampling methods 344
Biases in bootstrap and jackknife probabilities 346
P values in a simple normal case 349
Methods of reducing the bias 352
The drug testing analogy 355
Alternatives to P values 356
Probabilities of trees 357
Using tree distances 357
Jackknifing species 358
Parametric bootstrapping 358
Advantages and disadvantages of the parametric bootstrap 358
Permutation tests 358
Permuting species within characters 359
Permuting characters 361
Skewness of tree length distribution 36221. Paired-sites tests 364
An example 365
Multiple trees 369
The SH test 369
Other multiple-comparison tests 371
Testing other parameters 372
Perspective 37222. Invariants 373
Symmetry invariants 374
Three-species invariants 376
Lake’s linear invariants 378
Cavender’s quadratic invariants 380
The K invariants 380
The L invariants 381
Generalization of Cavender’s L invariants 382
Drolet and Sankoff’s k-state quadratic invariants 385
Clock invariants 385
General methods for finding invariants 386
Fourier transform methods 386
Gröbner bases and other general methods 387
Expressions for all the 3ST invariants 387
Finding all invariants empirically 387
All linear invariants 388
Special cases and extensions 389
Invariants and evolutionary rates 389
Testing invariants 389
What use are invariants? 39023. Brownian motion and gene frequencies 391
Brownian motion 391
Likelihood for a phylogeny 392
What likelihood to compute? 395
Assuming a clock 399
The REML approach 400
Multiple characters and Kronecker products 402
Pruning the likelihood 404
Maximizing the likelihood 406
Inferring ancestral states 408
Squared-change parsimony 409
Gene frequencies and Brownian motion 410
Using approximate Brownian motion 411
Distances from gene frequencies 412
A more exact likelihood method 413
Gene frequency parsimony 41324. Quantitative characters 415
Neutral models of quantitative characters 416
Changes due to natural selection 419
Selective correlation 419
Covariances of multiple characters in multiple lineages 420
Selection for an optimum 420
Brownian motion and selection 422
Correcting for correlations 422
Punctuational models 424
Inferring phylogenies and correlations 425
Chasing a common optimum 426
The character-coding “problem” 426
Continuous-character parsimony methods 428
Manhattan metric parsimony 428
Other parsimony methods 429
Threshold models 42925. Comparative methods 432
An example with discrete states 432
An example with continuous characters 433
The contrasts method 435
Correlations between characters 436
When the tree is not completely known 437
Inferring change in a branch 438
Sampling error 439
The standard regression and other variations 442
Generalized least squares 442
Phylogenetic autocorrelation 442
Transformations of time 442
Should we use the phylogeny at all? 443
Paired-lineage tests 443
Discrete characters 444
Ridley’s method 444
Concentrated-changes tests 445
A paired-lineages test 446
Methods using likelihood 446
Advantages of the likelihood approach 448
Molecular applications 44826. Coalescent trees 450
Kingman’s coalescent 454
Bugs in a box―an analogy 460
Effect of varying population size 460
Migration 461
Effect of recombination 464
Coalescents and natural selection 467
Neuhauser and Krone’s method 46827. Likelihood calculations on coalescents 470
The basic equation 470
Using accurate genealogies―a reverie 471
Two random sampling methods 473
A Metropolis-Hastings method 473
Griffiths and Tavaré’s method 476
Bayesian methods 482
MCMC for a variety of coalescent models 482
Single-tree methods 484
Slatkin and Maddison’s method 484
Fu’s method 484
Summary-statistic methods 485
Watterson’s method 485
Other summary-statistic methods 486
Testing for recombination 48628. Coalescents and species trees 488
Methods of inferring the species phylogeny 490
Reconciled tree parsimony approaches 492
Likelihood 49329. Alignment, gene families, and genomics 496
Alignment 497
Why phylogenies are important 497
Parsimony method 497
Approximations and progressive alignment 500
Probabilistic models 502
Bishop and Thompson’s method 502
The minimum message length method 502
The TKF model 503
Multibase insertions and deletions 506
Tree HMMs 507
Trees 507
Inferring the alignment 509
Gene families 509
Reconciled trees 509
Reconstructing duplications 511
Rooting unrooted trees 512
A likelihood analysis 514
Comparative genomics 515
Tandemly repeated genes 515
Inversions 516
Inversions in trees 516
Inversions, transpositions, and translocations 516
Breakpoint and neighbor-coding approximations 517
Synteny 517
Probabilistic models 518
Genome signature methods 51930. Consensus trees and distances between trees 521
Consensus trees 521
Strict consensus 521
Majority-rule consensus 523
Adams consensus tree 524
A dismaying result 525
Consensus using branch lengths 526
Other consensus tree methods 526
Consensus subtrees 528
Distances between trees 528
The symmetric difference 528
The quartets distance 530
The nearest-neighbor interchange distance 530
The path-length-difference metric 531
Distances using branch lengths 531
Are these distances truly distances? 533
Consensus trees and distances 534
Trees significantly the same? different? 534
What do consensus trees and tree distances tell us? 535
The total evidence debate 536
A modest proposal 53731. Biogeography, hosts, and parasites 539
Component compatibility 540
Brooks parsimony 541
Event-based parsimony methods 543
Relation to tree reconciliation 545
Randomization tests 545
Statistical inference 54632. Phylogenies and paleontology 547
Stratigraphic indices 548
Stratophenetics 549
Stratocladistics 549
Controversies 552
A not-quite-likelihood method 553
Stratolikelihood 553
Making a full likelihood method 554
More realistic fossilization models 554
Fossils within species: Sequential sampling 555
Between species 55533. Tests based on tree shape 559
Using the topology only 559
Imbalance at the root 560
Harding’s probabilities of tree shapes 561
Tests from shapes 562
Measures of overall asymmetry 563
Choosing a powerful test 564
Tests using times 564
Lineage plots 565
Likelihood formulas 567
Other likelihood approaches 569
Other statistical approaches 569
A time transformation 570
Characters and key innovations 571
Work remaining 57134. Drawing trees 573
Issues in drawing rooted trees 574
Placement of interior nodes 574
Shapes of lineages 576
Unrooted trees 578
The equal-angle algorithm 578
n-Body algorithms 580
The equal-daylight algorithm 582
Challenges 58435. Phylogeny software 585
Trees, records, and pointers 585
Declaring records 586
Traversing the tree 587
Unrooted tree data structures 589
Tree file formats 590
Widely used phylogeny programs and packages 591
References 595
Index 644
