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E-grāmata: From Observations to Optimal Phylogenetic Trees: Phylogenetic Analysis of Morphological Data: Volume 1 [Taylor & Francis e-book]

  • Formāts: 278 pages, 4 Tables, black and white; 29 Line drawings, black and white; 29 Illustrations, black and white
  • Sērija : Species and Systematics
  • Izdošanas datums: 22-Jul-2022
  • Izdevniecība: CRC Press
  • ISBN-13: 9781003220084
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  • Taylor & Francis e-book
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  • Standarta cena: 228,69 €
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From Observations to Optimal Phylogenetic Trees: Phylogenetic Analysis of Morphological Data: Volume 1Palielināt
  • Formāts: 278 pages, 4 Tables, black and white; 29 Line drawings, black and white; 29 Illustrations, black and white
  • Sērija : Species and Systematics
  • Izdošanas datums: 22-Jul-2022
  • Izdevniecība: CRC Press
  • ISBN-13: 9781003220084
Citas grāmatas par šo tēmu:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Rokasgrāmatas mājas lapa: https://www.taylorfrancis.com/books/9781003220084
Taxonomists specializing in different groups once based phylogenetic analysis only on morphological data; molecular data was used more rarely. Although molecular systematics is routine today, the use of morphological data continues to be important, especially for phylogenetic placement of many taxa known only from fossils and rare or difficult to collect species. In addition, morphological analyses help identify potential biases in molecular analyses. And finally, scenarios with respect to morphology continue to motivate biologists: the beauty of a cheetah or a baobab does not lie in their DNA sequence, but instead on what they are and do! This book is an up-to-date revision of methods and principles of phylogenetic analysis of morphological data. It is also a general guide for using the computer program TNT in the analysis of such data. The book covers the main aspects of phylogenetic analysis and general methods to compare classifications derived from molecules and morphology. The basic aspects of molecular analysis are covered only as needed to highlight the differences with methods and assumptions for analysis of morphological datasets.
Preface xiii
Author Biography xvii
Abbreviations xix
Chapter 1 Introduction to Phylogenetics
1(62)
1.1 Logical and Conceptual Aspects of Phylogenetic Analysis
2(6)
1.1.1 Explanatory Power
3(3)
1.1.2 Ad Hoc Hypotheses
6(1)
1.1.3 Logical Asymmetry
7(1)
1.2 Trees and Monophyly
8(3)
1.2.1 Tree Terminology
9(2)
1.3 Parsimony, Synapomorphies, and Rooting
11(4)
1.4 Maximum Likelihood and Assumptions Implicit in Parsimony
15(3)
1.5 Distances, Phenetics, and Information Content
18(9)
1.5.1 Distances and Their Properties
19(3)
1.5.2 Phenetics vs. Cladistics
22(1)
1.5.3 Retrieving Distances
23(2)
1.5.4 Transmitting Character Information
25(2)
1.6 Pattern Cladistics and Three-Taxon Statements
27(4)
1.6.1 Three-Taxon Statements
28(3)
1.7 Phylogeny as an Assumption; Limits of Phylogeny
31(3)
1.8 Optimality Criterion and Foundations of Phylogenetic Analysis
34(2)
1.9 On the Need for Computer Programs
36(1)
1.10 Implementation in TNT: Tree Analysis Using New Technology
37(3)
1.11 Input, Commands, Format of Data Matrices
40(16)
1.11.1 Commands and Truncation
40(2)
1.11.2 Input Files
42(1)
1.11.3 Getting Help
43(1)
1.11.4 Lists and Ranges; Numbering of Elements
43(2)
1.11.5 Reading Basic Data
45(1)
1.11.6 Different Data Formats and Sizes
46(2)
1.11.7 Wiping the Dataset from Memory
48(1)
1.11.8 Multiple Blocks and Data Formats
48(2)
1.11.9 Combining Existing Datasets
50(1)
1.11.10 Datasets in Multiple Files
50(2)
1.11.11 Redefining Data Blocks
52(1)
1.11.12 Other Data Formats
52(1)
1.11.13 Changing the Data
53(2)
1.11.14 Saving Edited Dataset in Different Formats
55(1)
1.12 Output
56(4)
1.12.1 Text Buffer
56(1)
1.12.2 GUI Screens (Windows Only)
56(1)
1.12.3 Outputting Numbers or Names
57(1)
1.12.4 Table Formats
57(1)
1.12.5 Output Files
58(1)
1.12.6 Quote Command
58(1)
1.12.7 Silencing Output
59(1)
1.12.8 Progress Reports, Warnings
59(1)
1.12.9 Graphics Trees
59(1)
1.12.10 Saving Trees to Files
60(1)
1.13 Outline of the Remaining
Chapters
60(3)
Chapter 2 Characters, Homology, and Datasets
63(56)
2.1 The Great Chain of Characters
63(1)
2.2 Homology
64(4)
2.2.1 Two Main Meanings of Homology
65(2)
2.2.2 Types of Homology
67(1)
2.3 Criteria for Homology
68(4)
2.4 Homology by Special Knowledge?
72(2)
2.5 No Special Knowledge of Homology Is Possible or Necessary
74(3)
2.6 Life Stages, Comparability, Ontogeny
77(3)
2.7 Gathering Morphological Data
80(5)
2.8 Character Independence
85(3)
2.9 Character "Choice"
88(2)
2.10 Character Coding and Character Types
90(7)
2.10.1 Discrete Characters
90(7)
2.11 Transformation Series Analysis
97(1)
2.12 Continuous and Landmark Data
98(2)
2.13 Implementation
100(1)
2.14 Character Settings
101(18)
2.14.1 Basic Character Settings: ccode Command
101(2)
2.14.2 Step-Matrix Characters (and Ancestral States)
103(6)
2.14.3 Deactivating Blocks of Data
109(1)
2.14.4 Character Names
109(2)
2.14.5 Taxon Settings and Taxonomic Information
111(4)
2.14.6 Comparing and Merging Datasets
115(4)
Chapter 3 Character Optimization: Evaluation of Trees and Inference of Ancestral States
119(42)
3.1 Finding Optimal Ancestral Reconstructions
119(2)
3.2 Generalized Optimization: Simple Cases
121(3)
3.3 Optimization for Nonadditive Characters: Fitch's (1971) Method
124(3)
3.4 Optimization for Additive Characters: Farris's (1970) Method
127(5)
3.5 Step-Matrix Optimization
132(3)
3.6 Other Types of Optimization
135(1)
3.7 Ambiguity, Polymorphisms, Missing Entries
135(3)
3.7.1 Polymorphisms
137(1)
3.7.2 Missing Entries
138(1)
3.8 Mapping, Synapomorphies, and Reconstructed Ancestors
138(3)
3.8.1 Reconstructed Ancestors
140(1)
3.8.2 Branch Lengths
141(1)
3.9 The Myth of Polarity
141(2)
3.10 Polytomies, Multiple MPTs, and Consensus
143(4)
3.10.1 Length of Polytomies and Their Resolutions
143(1)
3.10.2 Polytomies as "Soft"
144(1)
3.10.3 Informative Characters
145(1)
3.10.4 Mapping Multiple Trees
145(2)
3.11 Inapplicables
147(4)
3.12 Readability of Ancestors
151(2)
3.13 Implementation in TNT
153(8)
3.13.1 Options for Scoring Trees
153(2)
3.13.2 Diagnosis and Mapping
155(1)
3.13.3 Diagrams for Publication
156(1)
3.13.4 Reconstructions and Specific Changes
157(1)
3.13.5 Selecting and Preparing the Trees to Be Optimized
158(3)
Chapter 4 Models and Assumptions in Morphology
161(46)
4.1 Maximum Likelihood (ML)
162(1)
4.2 Assumptions of Models of Molecular Evolution
163(4)
4.3 Likelihood Calculation
167(4)
4.3.1 Basic Ideas
167(2)
4.3.2 Pruning Algorithm
169(1)
4.3.3 Pulley Principle
170(1)
4.4 Among Site Rate Variation
171(1)
4.5 Linked and Unlinked Partitions
172(1)
4.6 Bayesian Inference
173(2)
4.7 Some Difficulties with Bayesian Phylogenetics
175(5)
4.7.1 No Optimality Criterion
176(1)
4.7.2 Priors
176(1)
4.7.3 Summarizing Results
176(4)
4.7.4 Sample Size and Frequency
180(1)
4.7.5 Proposals
180(1)
4.8 Model Choice
180(2)
4.9 Adapting Models for Molecular evolution to Morphology
182(4)
4.9.1 Mk Model
183(1)
4.9.2 Mkv Variant
183(1)
4.9.3 Assumptions of Mk/Mkv Models
184(2)
4.10 Parsimony, Models, and Consistency
186(9)
4.10.1 Low Rates of Change in the MDG Make Parsimony Consistent
187(1)
4.10.2 Inferring Trees by Fixing Branch Lengths and Using Best Individual Reconstruction Amounts to Parsimony
187(1)
4.10.3 For Data Generated with All Branches of the Same Length, Parsimony Produces Good Results
188(3)
4.10.4 If All Characters and Branches Can Have Different Lengths, MP Is Identical to ML
191(2)
4.10.5 Invariant Characters and a Large Number of States
193(2)
4.10.6 Missing Data and Likelihood
195(1)
4.11 Standard Poisson Models in Morphology
195(7)
4.11.1 Simulations
200(2)
4.12 Conclusions
202(1)
4.13 Implementation
203(4)
Chapter 5 Tree Searches: Finding Most Parsimonious Trees
207(48)
5.1 Optimization
208(3)
5.2 Small Datasets: Exact Solutions
211(1)
5.3 Datasets of Medium Difficulty: Basic Methods
212(9)
5.3.1 Wagner Trees
212(2)
5.3.2 Branch-Swapping
214(2)
5.3.3 Multiple Trees
216(1)
5.3.4 Local Optima and Islands
216(2)
5.3.5 Escaping Local Optima
218(1)
5.3.6 Comparing Efficiency of Search Algorithms
219(2)
5.4 Datasets of Medium Difficulty: Multiple Starting Points in Depth
221(6)
5.4.1 Wagner Trees vs. Random Trees
221(1)
5.4.2 Saving Reduced Numbers of Trees per Replication
221(1)
5.4.3 Effect of Full Tree Buffer
222(1)
5.4.4 Convergence and Choice of Search Settings
223(1)
5.4.5 Collapsing of Zero-Length Branches and Search Efficiency
224(2)
5.4.6 Many Hits or Many Trees?
226(1)
5.5 Searches under Constraints; Backbone Topologies
227(2)
5.6 Difficult Datasets: Basic Ideas and Methods
229(11)
5.6.1 Composite Optima
229(1)
5.6.2 Sectorial Searches (55)
230(1)
5.6.2.1 Types of Sector Selection
231(2)
5.6.2.2 Analysis of Reduced Datasets
233(1)
5.6.2.3 Performance
233(2)
5.6.3 Ratchet
235(1)
5.6.3.1 Performance
236(1)
5.6.4 Tree Drifting
236(2)
5.6.4.1 Performance
238(1)
5.6.5 Tree Fusing (TF)
238(2)
5.6.5.1 Performance
240(1)
5.7 Difficult Datasets: Combined Methods and Driven Searches
240(3)
5.7.1 Searching for a Stable Consensus
241(1)
5.7.2 Strengths and Weaknesses of the Different Algorithms
241(1)
5.7.2.1 Alternatives to the Algorithms Described
242(1)
5.7.3 Challenges Posed by Morphological Datasets
243(1)
5.8 Approximate Searches and Quick Consensus Estimation
243(3)
5.9 Implementation in TNT
246(9)
5.9.1 General Settings
247(2)
5.9.2 Exact Searches
249(1)
5.9.3 Heuristic Searches--Basic Algorithms
249(1)
5.9.4 Heuristic Searches--Special Algorithms
250(5)
References 255(18)
Index 273
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.
Permanent link: https://www.kriso.lv/db/9781003220084_pe.html
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