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El. knyga: Refining Phylogenetic Analyses: Phylogenetic Analysis of Morphological Data: Volume 2 [Taylor & Francis e-book]

  • Formatas: 292 pages, 17 Tables, black and white; 36 Line drawings, black and white; 36 Illustrations, black and white
  • Serija: Species and Systematics
  • Išleidimo metai: 22-Jul-2022
  • Leidėjas: CRC Press
  • ISBN-13: 9780367823412
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  • Taylor & Francis e-book
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Refining Phylogenetic Analyses: Phylogenetic Analysis of Morphological Data: Volume 2Didesnis paveikslėlis
  • Formatas: 292 pages, 17 Tables, black and white; 36 Line drawings, black and white; 36 Illustrations, black and white
  • Serija: Species and Systematics
  • Išleidimo metai: 22-Jul-2022
  • Leidėjas: CRC Press
  • ISBN-13: 9780367823412
Kitos knygos pagal šią temą:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Partnerių interneto svetainė: https://www.taylorfrancis.com/books/9780367823412
This volume discusses the aspects of a phylogenetic analysis that go beyond basic calculation of most parsimonious trees. Practical application of all principles discussed is illustrated by reference to TNT, a freely available software package that can perform all the steps needed in a phylogenetic analysis. The first problem considered is how to summarize and compare multiple trees (including identification and handling wildcard taxa). Evaluation of the strength of support for groups, another critical component of any phylogenetic analysis, is given careful consideration. The different interpretations of measures of support are discussed and connected with alternative implementations. The book reviews rationales for estimating character reliability on the basis of homoplasy, with particular attention to morphological characters. The main methods for character weighting and their practical implementation, several of them unique to TNT, are discussed ad libitum. Also unique to TNT is the ability to directly analyze morphometric data (including landmarks), on the same footing as discrete characters. Finally, the scripting language of TNT is introduced. With scripting, it is possible to "program" TNT to create personalized routines and automate complex calculations, taking analyses to the next level and allowing exploration of new methods and ideas.

Key Features





Discusses the treatment of ambiguity in phylogenetic analyses in depth, for summarizing results or comparing trees Reviews literature on arguments and methods for weighting morphological characters and their practical application Describes theory and application of methods for evaluating strength of group support, based on either resampling or comparisons with suboptimal trees Discusses the use of morphometric characters in phylogenetic analysis Presents extensive information on commands and options of the TNT computer program, including the use and creation of scripts
Preface xiii
Author Biography xvii
Abbreviations xix
Chapter 6 Summarizing and comparing phylogenetic trees
1(52)
6.1 Consensus methods
2(8)
6.1.1 Cluster-based methods
3(1)
6.1.1.1 Strict consensus trees
3(1)
6.1.1.2 Majority rule consensus trees
4(2)
6.1.1.3 Combinable components consensus
6(2)
6.1.1.4 Frequency difference consensus
8(1)
6.1.2 Methods not based on clusters
8(1)
6.1.2.1 Adams consensus
8(1)
6.1.2.2 Rough recovery consensus
8(1)
6.1.2.3 Median trees
9(1)
6.2 Taxonomic congruence vs. total evidence
10(3)
6.3 Pruned (=reduced) consensus and identification of unstable taxa
13(8)
6.3.1 Maximum agreement subtrees (MAST)
15(1)
6.3.2 Brute-force methods
15(1)
6.3.3 Triplet-based methods
16(1)
6.3.4 Improving majority rule or frequency difference consensus
17(2)
6.3.5 Swap and record moves
19(1)
6.3.6 Improving prune sets with an optimality criterion
20(1)
6.4 Zero-length branches and ambiguity
21(5)
6.4.1 Identification of zero-length branches and collapsing rules
21(3)
6.4.2 Consensus under different collapsing rules
24(1)
6.4.3 Numbers of trees, search effort
24(2)
6.4.4 Temporary collapsing
26(1)
6.5 Supertrees
26(6)
6.5.1 Semi-strict supertrees
29(1)
6.5.2 Matrix representation with parsimony (MRP)
30(1)
6.5.3 Other methods based on matrix representation
31(1)
6.5.4 Majority rule supertrees
31(1)
6.6 Anticonsensus
32(1)
6.7 Tree distances
32(7)
6.7.1 Robinson-Foulds distances (RF), and derivatives
34(2)
6.7.2 Group similarity (rough recovery)
36(1)
6.7.3 Rearrangement distances
37(1)
6.7.4 Distortion coefficient (DC)
37(1)
6.7.5 Triplets and quartets
38(1)
6.8 Implementation in TNT
39(14)
6.8.1 Consensus trees
39(2)
6.8.2 Temporary collapsing of zero-length branches, unshared taxa
41(2)
6.8.3 Tree comparisons and manipulations
43(2)
6.8.4 Identifying unstable taxa
45(4)
6.8.5 Supertrees
49(1)
6.8.6 Measures of tree distance
50(3)
Chapter 7 Character weighting
53(66)
7.1 Generalities
55(3)
7.2 General arguments for weighting
58(2)
7.2.1 Homoplasy and reliability
60(1)
7.3 Successive approximations weighting (SAW)
60(8)
7.3.1 Weighting and functions of homoplasy
62(3)
7.3.2 Problems with SAW
65(3)
7.3.3 Potential solutions
68(1)
7.4 Implied weighting (IW)
68(10)
7.4.1 Weighting functions
70(1)
7.4.1.1 Weighting strength
71(3)
7.4.1.2 Maximization of weights and self-consistency
74(1)
7.4.2 Binary recoding, step-matrix characters
75(1)
7.4.3 Tree searches
76(1)
7.4.4 Prior weights
76(1)
7.4.5 IW and compatibility
76(2)
7.5 Weighting strength, sensitivity, and conservativeness
78(2)
7.6 Practical consequences of application of IW
80(2)
7.7 Problematic methods for evaluating data quality
82(2)
7.7.1 Tree-independent
82(1)
7.7.2 Probability-based
82(2)
7.8 Improvements to IW
84(9)
7.8.1 Influence of missing entries
84(3)
7.8.2 Uniform and average weighting of molecular partitions
87(1)
7.8.3 Self-weighted optimization and state transformations
87(5)
7.8.4 Weights changing in different branches
92(1)
7.9 Implied weights and likelihood
93(5)
7.10 To weight or not to weight, that is the question
98(13)
7.10.1 Criticisms of IW based on simulations
98(5)
7.10.2 Support and character reliability
103(3)
7.10.3 Weighting, predictivity, and stability
106(1)
7.10.4 Convergence between results of IW and equal weights
107(2)
7.10.5 Weighting in morphology vs molecules
109(2)
7.11 Implementation in TNT
111(8)
7.11.1 Self-weighted optimization
112(1)
7.11.2 Extended implied weighting
113(1)
7.11.2.1 Missing entries
114(1)
7.11.2.2 Uniform weighting of characters or sets
115(4)
Chapter 8 Measuring degree of group support
119(66)
8.1 The difficulty of measuring group supports
119(3)
8.2 Bremer supports: definitions
122(12)
8.2.1 Variants of Bremer supports
123(1)
8.2.1.1 Relative Bremer supports (RBS)
123(2)
8.2.1.2 Combined Bremer supports
125(2)
8.2.1.3 Relative explanatory power
127(2)
8.2.1.4 Site concordance factors (sCF) and group supports
129(2)
8.2.1.5 Partitioned Bremer supports
131(3)
8.3 Bremer supports in practice
134(6)
8.3.1 Performing searches under reverse constraints
135(1)
8.3.2 Searching suboptimal trees
135(2)
8.3.3 Recording score differences during TBR branch-swapping
137(1)
8.3.3.1 The ALRT and aBayes methods
138(1)
8.3.4 Calculating average differences in length
139(1)
8.4 Resampling methods
140(30)
8.4.1 Plotting group supports
143(1)
8.4.2 Different resampling methods
144(1)
8.4.2.1 Bootstrapping
144(1)
8.4.2.2 Jackknifing
145(1)
8.4.2.3 Symmetric resampling
146(1)
8.4.2.4 No-zero-weight resampling
147(2)
8.4.2.5 Influence of number of pseudoreplicates
149(2)
8.4.3 Final summary of results
151(1)
8.4.3.1 Frequency-within-replicates (FWR) or strict consensus
151(1)
8.4.3.2 Frequency differences (GQ track support better than absolute frequencies
152(3)
8.4.3.3 A death blow to measuring support with resampling
155(2)
8.4.3.4 Frequency slopes
157(1)
8.4.3.5 Rough recovery of groups
158(1)
8.4.4 Search algorithms and group supports
158(3)
8.4.4.1 Search bias worsens the problems of saving a single tree
161(3)
8.4.4.2 Approximations for further speedups
164(2)
8.4.4.3 Worse search methods cannot produce better results
166(4)
8.5 Confidence and stability are related to support, but not the same thing
170(2)
8.6 Implementation in TNT
172(13)
8.6.1 Calculation of Bremer supports
173(1)
8.6.1.1 Searching suboptimal trees
174(2)
8.6.1.2 Searching with reverse constraints
176(1)
8.6.1.3 Estimation of Bremer supports via TBR
176(1)
8.6.1.4 Variants of Bremer support
177(1)
8.6.2 Resampling
178(1)
8.6.2.1 Options to determine how resampling is done
178(1)
8.6.2.2 Options to determine how results are summarized
178(1)
8.6.2.3 Tree searches
179(1)
8.6.3 Superposing labels on tree branches
180(1)
8.6.4 Wildcard taxa and supports
181(4)
Chapter 9 Morphometric characters
185(44)
9.1 Continuous characters
186(11)
9.1.1 Ancestral states, explanation, and homology
187(2)
9.1.2 Heritability and the phylogenetic meaning of descriptive statistics
189(1)
9.1.3 Significant differences and methods for discretization
190(1)
9.1.4 Scaling and ratios
191(1)
9.1.4.1 Shifting scale using logarithms
192(1)
9.1.4.2 Ratios
193(1)
9.1.5 Squared changes "parsimony" and other models for continuous characters
194(3)
9.2 Geometric morphometries
197(21)
9.2.1 Geometric morphometries in a nutshell
197(1)
9.2.1.1 Superimposition and criteria for measuring shape differences
198(1)
9.2.1.2 Symmetries
199(1)
9.2.2 Problematic proposals to extract characters from landmarks
200(1)
9.2.3 Application of the parsimony criterion: phylogenetic morphometries
201(2)
9.2.4 Shape optimization in more detail
203(1)
9.2.4.1 Fermat points and iterative refinement of point positions
204(1)
9.2.4.2 Using grid templates for better point estimates
204(2)
9.2.4.3 Missing entries and inapplicable characters
206(1)
9.2.5 Landmark dependencies, scaling
207(2)
9.2.6 Implied weighting and minimum possible scores
209(1)
9.2.6.1 Weighting landmarks or configurations
209(1)
9.2.6.2 The minimum (ISmin) may not be achievable on any tree
209(1)
9.2.7 Ambiguity in landmark positions
210(1)
9.2.7.1 Coherence in reconstructions of different landmarks
211(1)
9.2.8 Dynamic alignment of landmarks
212(3)
9.2.9 Other criteria for aligning or inferring ancestral positions
215(1)
9.2.9.1 Least squares or linear changes
215(3)
9.3 Choice of method and correctness of results
218(2)
9.4 Implementation in TNT
220(9)
9.4.1 Continuous (and meristic) characters
220(1)
9.4.2 Phylogenetic morphometries
221(1)
9.4.2.1 Reading and exporting data
221(2)
9.4.2.2 Alignment
223(1)
9.4.2.3 Scoring trees, displaying, and saving mapped configurations
223(1)
9.4.2.4 Settings for estimating coordinates of landmark points
224(1)
9.4.2.5 Weights, factors, minima
225(1)
9.4.2.6 Group supports
226(1)
9.4.2.7 Ambiguity
226(3)
Chapter 10 Scripting: The next level of TNT mastery
229(40)
10.1 Basic description of TNT language
230(3)
10.2 The elements of TNT language in depth
233(14)
10.2.1 Getting help
233(1)
10.2.2 Expressions and operators
233(1)
10.2.3 Flow control
234(1)
10.2.3.1 Decisions
234(1)
10.2.3.2 Loops
234(1)
10.2.4 Arguments
235(1)
10.2.5 Internal variables
236(4)
10.2.6 User variables
240(1)
10.2.6.1 Declaration
240(1)
10.2.6.2 Assignment
241(4)
10.2.6.3 Access
245(1)
10.2.7 Efficiency and memory management
246(1)
10.3 Other facilities of the TNT language
247(11)
10.3.1 Goto
247(1)
10.3.1.1 Handling errors and interruptions
248(1)
10.3.2 Progress reports
248(1)
10.3.3 Handling input files
248(2)
10.3.4 Formatted output
250(1)
10.3.4.1 Handling strings
251(1)
10.3.5 Arrays into and from tables
252(1)
10.3.6 Automatic input redirection
253(1)
10.3.7 Dialogs
253(1)
10.3.8 Editing trees and branch labels
254(1)
10.3.9 Tree searching and traversals
254(2)
10.3.10 Most parsimonious reconstructions (MPRs)
256(1)
10.3.11 Random numbers and lists, combinations, permutations
256(2)
10.4 Graphics and correlation
258(5)
10.4.1 Plotting graphic trees
258(1)
10.4.2 Bar plots
259(1)
10.4.2.1 Heat maps
260(1)
10.4.3 Correlation
260(2)
10.4.4 Scatter plots
262(1)
10.5 Simulating and modifying data
263(1)
10.6 A digression: the C interpreter of TNT
264(2)
10.7 Some general advice on how to write scripts
266(3)
References 269(18)
Index 287
Born in Buenos Aires, Pablo A. Goloboff became interested in spider biology and systematics in the late 70s, in the Museo Argentino de Ciencias Naturales. His first papers (published during the 80s) were on spider systematics, but he soon became more interested in systematic theory and phylogenetic methods. He graduated with a Licenciatura in Biology in 1989, from Universidad de Buenos Aires, and then pursued doctoral studies in Cornell University and the American Museum of Natural History, in New York, between 1989 and 1994. He published his first methodological papers in the early 90s, gradually switching his research from spider systematics to systematic theory. During his stay at Cornell University, he became more involved with quantitative methods for parsimony analysis, and wrote his first computer programs. He moved to Tucumįn in 1994, to work for the CONICET, and continued working on theory and methods for systematics and historical biogeography. He has published over a hundred scientific papers and about a dozen computer programs, the best known of which are Nona, Piwe, TNT (for phylogenetics), and VNDM (for biogeography). He is a Fellow Honoris Causa of the Willi Hennig Society, and served as President of the society from 2004 to 2006. Since 1995, he has been regularly teaching courses on phylogenetics in Argentina and about a dozen countries.
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