From Observations to Optimal Phylogenetic Trees Phylogenetic Analysis of Morphological Data Volume 1 1st Edition by Pablo A Goloboff ISBN 9781032114859 1032114851

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ISBN 10: 1032114851
ISBN 13: 9781032114859
Author: Pablo A Goloboff

From Observations to Optimal Phylogenetic Trees Phylogenetic Analysis of Morphological Data Volume 1 1st Edition Table of contents:

Chapter 1 Introduction to Phylogenetics

1.1 Logical and Conceptual Aspects of Phylogenetic Analysis

1.1.1 Explanatory Power

1.1.2 Ad Hoc Hypotheses

1.1.3 Logical Asymmetry

1.2 Trees and Monophyly

1.2.1 Tree Terminology

1.3 Parsimony, Synapomorphies, and Rooting

1.4 Maximum Likelihood and Assumptions Implicit in Parsimony

1.5 Distances, Phenetics, and Information Content

1.5.1 Distances and Their Properties

1.5.2 Phenetics vs. Cladistics

1.5.3 Retrieving Distances

1.5.4 Transmitting Character Information

1.6 Pattern Cladistics and Three-Taxon Statements

1.6.1 Three-Taxon Statements

1.7 Phylogeny as an Assumption; Limits of Phylogeny

1.8 Optimality Criterion and Foundations of Phylogenetic Analysis

1.9 On the Need for Computer Programs

1.10 Implementation in TNT: Tree Analysis Using New Technology

1.11 Input, Commands, Format of Data Matrices

1.11.1 Commands and Truncation

1.11.2 Input Files

1.11.3 Getting Help

1.11.4 Lists and Ranges; Numbering of Elements

1.11.5 Reading Basic Data

1.11.6 Different Data Formats and Sizes

1.11.7 Wiping the Dataset from Memory

1.11.8 Multiple Blocks and Data Formats

1.11.9 Combining Existing Datasets

1.11.10 Datasets in Multiple Files

1.11.11 Redefining Data Blocks

1.11.12 Other Data Formats

1.11.13 Changing the Data

1.11.14 Saving Edited Dataset in Different Formats

1.12 Output

1.12.1 Text Buffer

1.12.2 GUI Screens (Windows Only)

1.12.3 Outputting Numbers or Names

1.12.4 Table Formats

1.12.5 Output Files

1.12.6 Quote Command

1.12.7 Silencing Output

1.12.8 Progress Reports, Warnings

1.12.9 Graphics Trees

1.12.10 Saving Trees to Files

1.13 Outline of the Remaining Chapters

Chapter 2 Characters, Homology, and Datasets

2.1 The Great Chain of Characters

2.2 Homology

2.2.1 Two Main Meanings of Homology

2.2.2 Types of Homology

2.3 Criteria for Homology

2.4 Homology by Special Knowledge?

2.5 No Special Knowledge of Homology Is Possible … or Necessary

2.6 Life Stages, Comparability, Ontogeny

2.7 Gathering Morphological Data

2.8 Character Independence

2.9 Character “Choice”

2.10 Character Coding and Character Types

2.10.1 Discrete Characters

2.11 Transformation Series Analysis

2.12 Continuous and Landmark Data

2.13 Implementation

2.14 Character Settings

2.14.1 Basic Character Settings: ccode Command

2.14.2 Step-Matrix Characters (and Ancestral States)

2.14.3 Deactivating Blocks of Data

2.14.4 Character Names

2.14.5 Taxon Settings and Taxonomic Information

2.14.6 Comparing and Merging Datasets

Chapter 3 Character Optimization: Evaluation of Trees and Inference of Ancestral States

3.1 Finding Optimal Ancestral Reconstructions

3.2 Generalized Optimization: Simple Cases

3.3 Optimization for Nonadditive Characters: Fitch’s (1971) Method

3.4 Optimization for Additive Characters: Farris’s (1970) Method

3.5 Step-Matrix Optimization

3.6 Other Types of Optimization

3.7 Ambiguity, Polymorphisms, Missing Entries

3.7.1 Polymorphisms

3.7.2 Missing Entries

3.8 Mapping, Synapomorphies, and Reconstructed Ancestors

3.8.1 Reconstructed Ancestors

3.8.2 Branch Lengths

3.9 The Myth of Polarity

3.10 Polytomies, Multiple MPTs, and Consensus

3.10.1 Length of Polytomies and Their Resolutions

3.10.2 Polytomies as “Soft”

3.10.3 Informative Characters

3.10.4 Mapping Multiple Trees

3.11 Inapplicables

3.12 Realizability of Ancestors

3.13 Implementation in TNT

3.13.1 Options for Scoring Trees

3.13.2 Diagnosis and Mapping

3.13.3 Diagrams for Publication

3.13.4 Reconstructions and Specific Changes

3.13.5 Selecting and Preparing the Trees to Be Optimized

Chapter 4 Models and Assumptions in Morphology

4.1 Maximum Likelihood (ML)

4.2 Assumptions of Models of Molecular Evolution

4.3 Likelihood Calculation

4.3.1 Basic Ideas

4.3.2 Pruning Algorithm

4.3.3 Pulley Principle

4.4 Among Site Rate Variation

4.5 Linked and Unlinked Partitions

4.6 Bayesian Inference

4.7 Some Difficulties with Bayesian Phylogenetics

4.7.1 No Optimality Criterion

4.7.2 Priors

4.7.3 Summarizing Results

4.7.4 Sample Size and Frequency

4.7.5 Proposals

4.8 Model Choice

4.9 Adapting Models for Molecular evolution to Morphology

4.9.1 Mk Model

4.9.2 Mkv Variant

4.9.3 Assumptions of Mk/Mkv Models

4.10 Parsimony, Models, and Consistency

4.10.1 Low Rates of Change in the MDG Make Parsimony Consistent

4.10.2 Inferring Trees by Fixing Branch Lengths and Using Best Individual Reconstruction Amounts to Parsimony

4.10.3 For Data Generated with All Branches of the Same Length, Parsimony Produces Good Results

4.10.4 If All Characters and Branches Can Have Different Lengths, MP Is Identical to ML

4.10.5 Invariant Characters and a Large Number of States

4.10.6 Missing Data and Likelihood

4.11 Standard Poisson Models in Morphology

4.11.1 Simulations

4.12 Conclusions

4.13 Implementation

Chapter 5 Tree Searches: Finding Most Parsimonious Trees

5.1 Optimization

5.2 Small Datasets: Exact Solutions

5.3 Datasets of Medium Difficulty: Basic Methods

5.3.1 Wagner Trees

5.3.2 Branch-Swapping

5.3.3 Multiple Trees

5.3.4 Local Optima and Islands

5.3.5 Escaping Local Optima

5.3.6 Comparing Efficiency of Search Algorithms

5.4 Datasets of Medium Difficulty: Multiple Starting Points in Depth

5.4.1 Wagner Trees vs. Random Trees

5.4.2 Saving Reduced Numbers of Trees per Replication

5.4.3 Effect of Full Tree Buffer

5.4.4 Convergence and Choice of Search Settings

5.4.5 Collapsing of Zero-Length Branches and Search Efficiency

5.4.6 Many Hits or Many Trees?

5.5 Searches under Constraints; Backbone Topologies

5.6 Difficult Datasets: Basic Ideas and Methods

5.6.1 Composite Optima

5.6.2 Sectorial Searches (SS)

5.6.2.1 Types of Sector Selection

5.6.2.2 Analysis of Reduced Datasets

5.6.2.3 Performance

5.6.3 Ratchet

5.6.3.1 Performance

5.6.4 Tree Drifting

5.6.4.1 Performance

5.6.5 Tree Fusing (TF)

5.6.5.1 Performance

5.7 Difficult Datasets: Combined Methods and Driven Searches

5.7.1 Searching for a Stable Consensus

5.7.2 Strengths and Weaknesses of the Different Algorithms

5.7.2.1 Alternatives to the Algorithms Described

5.7.3 Challenges Posed by Morphological Datasets

5.8 Approximate Searches and Quick Consensus Estimation

5.9 Implementation in TNT

5.9.1 General Settings

5.9.2 Exact Searches

5.9.3 Heuristic Searches—Basic Algorithms

5.9.4 Heuristic Searches—Special Algorithms

References

Index

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Tags: Pablo A Goloboff, Observations, Optimal Phylogenetic, Morphological Data