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Refining Phylogenetic Analyses Phylogenetic Analysis of Morphological Data Volume 2 1st Edition by Pablo A Goloboff ISBN 9780367420277 2021061922

Name: Refining Phylogenetic Analyses Phylogenetic Analysis of Morphological Data Volume 2 1st Edition by Pablo A Goloboff ISBN 9780367420277 2021061922
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Refining Phylogenetic Analyses Phylogenetic Analysis of Morphological Data: Volume 2 1st Edition Pablo A. Goloboff Digital Instant Download

Author(s): Pablo A. Goloboff

ISBN(s): 9780367420277, 9781032274676, 9782021061925, 2021061922, 0367420279, 1032274670, 2021061923

Edition: 1

File Details: PDF, 21.01 MB

Year: 2022

Language: English

SKU: EB-44606112 Category: Uncategorized Tag: Pablo A. Goloboff

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Refining Phylogenetic Analyses Phylogenetic Analysis of Morphological Data Volume 2 1st Edition by Pablo A Goloboff – Ebook PDF Instant Download/Delivery: 9780367420277 ,2021061922
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ISBN 10: 2021061922
ISBN 13: 9780367420277
Author: Pablo A Goloboff

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

Refining Phylogenetic Analyses Phylogenetic Analysis of Morphological Data Volume 2 1st Edition Table of contents:

6 Summarizing and comparing phylogenetic trees

6.1 Consensus methods

6.1.1 Cluster-based methods

6.1.1.1 Strict consensus trees

6.1.1.2 Majority rule consensus trees

6.1.1.3 Combinable components consensus

6.1.1.4 Frequency difference consensus

6.1.2 Methods not based on clusters

6.1.2.1 Adams consensus

6.1.2.2 Rough recovery consensus

6.1.2.3 Median trees

6.2 Taxonomic congruence vs. total evidence

6.3 Pruned (=reduced) consensus and identification of unstable taxa

6.3.1 Maximum agreement subtrees (MAST)

6.3.2 Brute-force methods

6.3.3 Triplet-based methods

6.3.4 Improving majority rule or frequency difference consensus

6.3.5 Swap and record moves

6.3.6 Improving prune sets with an optimality criterion

6.4 Zero-length branches and ambiguity

6.4.1 Identification of zero-length branches and collapsing rules

6.4.2 Consensus under different collapsing rules

6.4.3 Numbers of trees, search effort

6.4.4 Temporary collapsing

6.5 Supertrees

6.5.1 Semi-strict supertrees

6.5.2 Matrix representation with parsimony (MRP)

6.5.3 Other methods based on matrix representation

6.5.4 Majority rule supertrees

6.6 Anticonsensus

6.7 Tree distances

6.7.1 Robinson-Foulds distances (RF), and derivatives

6.7.2 Group similarity (rough recovery)

6.7.3 Rearrangement distances

6.7.4 Distortion coefficient (DC)

6.7.5 Triplets and quartets

6.8 Implementation in TNT

6.8.1 Consensus trees

6.8.2 Temporary collapsing of zero-length branches, unshared taxa

6.8.3 Tree comparisons and manipulations

6.8.4 Identifying unstable taxa

6.8.5 Supertrees

6.8.6 Measures of tree distance

7 Character weighting

7.1 Generalities

7.2 General arguments for weighting

7.2.1 Homoplasy and reliability

7.3 Successive approximations weighting (SAW)

7.3.1 Weighting and functions of homoplasy

7.3.2 Problems with SAW

7.3.3 Potential solutions

7.4 Implied weighting (IW)

7.4.1 Weighting functions

7.4.1.1 Weighting strength

7.4.1.2 Maximization of weights and self-consistency

7.4.2 Binary recoding, step-matrix characters

7.4.3 Tree searches

7.4.4 Prior weights

7.4.5 IW and compatibility

7.5 Weighting strength, sensitivity, and conservativeness

7.6 Practical consequences of application of IW

7.7 Problematic methods for evaluating data quality

7.7.1 Tree-independent

7.7.2 Probability-based

7.8 Improvements to IW

7.8.1 Influence of missing entries

7.8.2 Uniform and average weighting of molecular partitions

7.8.3 Self-weighted optimization and state transformations

7.8.4 Weights changing in different branches

7.9 Implied weights and likelihood

7.10 To weight or not to weight, that is the question…

7.10.1 Criticisms of IW based on simulations

7.10.2 Support and character reliability

7.10.3 Weighting, predictivity, and stability

7.10.4 Convergence between results of IW and equal weights

7.10.5 Weighting in morphology vs molecules

7.11 Implementation in TNT

7.11.1 Self-weighted optimization

7.11.2 Extended implied weighting

7.11.2.1 Missing entries

7.11.2.2 Uniform weighting of characters or sets

8 Measuring degree of group support

8.1 The difficulty of measuring group supports

8.2 Bremer supports: definitions

8.2.1 Variants of Bremer supports

8.2.1.1 Relative Bremer supports (RBS)

8.2.1.2 Combined Bremer supports

8.2.1.3 Relative explanatory power

8.2.1.4 Site concordance factors (sCF) and group supports

8.2.1.5 Partitioned Bremer supports

8.3 Bremer supports in practice

8.3.1 Performing searches under reverse constraints

8.3.2 Searching suboptimal trees

8.3.3 Recording score differences during TBR branch-swapping

8.3.3.1 The ALRT and aBayes methods

8.3.4 Calculating average differences in length

8.4 Resampling methods

8.4.1 Plotting group supports

8.4.2 Different resampling methods

8.4.2.1 Bootstrapping

8.4.2.2 Jackknifing

8.4.2.3 Symmetric resampling

8.4.2.4 No-zero-weight resampling

8.4.2.5 Influence of number of pseudoreplicates

8.4.3 Final summary of results

8.4.3.1 Frequency-within-replicates (FWR) or strict consensus

8.4.3.2 Frequency differences (GC) track support better than absolute frequencies

8.4.3.3 A death blow to measuring support with resampling

8.4.3.4 Frequency slopes

8.4.3.5 Rough recovery of groups

8.4.4 Search algorithms and group supports

8.4.4.1 Search bias worsens the problems of saving a single tree

8.4.4.2 Approximations for further speedups

8.4.4.3 Worse search methods cannot produce better results

8.5 Confidence and stability are related to support, but not the same thing

8.6 Implementation in TNT

8.6.1 Calculation of Bremer supports

8.6.1.1 Searching suboptimal trees

8.6.1.2 Searching with reverse constraints

8.6.1.3 Estimation of Bremer supports via TBR

8.6.1.4 Variants of Bremer support

8.6.2 Resampling

8.6.2.1 Options to determine how resampling is done

8.6.2.2 Options to determine how results are summarized

8.6.2.3 Tree searches

8.6.3 Superposing labels on tree branches

8.6.4 Wildcard taxa and supports

9 Morphometric characters

9.1 Continuous characters

9.1.1 Ancestral states, explanation, and homology

9.1.2 Heritability and the phylogenetic meaning of descriptive statistics

9.1.3 Significant differences and methods for discretization

9.1.4 Scaling and ratios

9.1.4.1 Shifting scale using logarithms

9.1.4.2 Ratios

9.1.5 Squared changes “parsimony” and other models for continuous characters

9.2 Geometric morphometrics

9.2.1 Geometric morphometrics in a nutshell

9.2.1.1 Superimposition and criteria for measuring shape differences

9.2.1.2 Symmetries

9.2.2 Problematic proposals to extract characters from landmarks

9.2.3 Application of the parsimony criterion: phylogenetic morphometrics

9.2.4 Shape optimization in more detail

9.2.4.1 Fermat points and iterative refinement of point positions

9.2.4.2 Using grid templates for better point estimates

9.2.4.3 Missing entries and inapplicable characters

9.2.5 Landmark dependencies, scaling

9.2.6 Implied weighting and minimum possible scores

9.2.6.1 Weighting landmarks or configurations

9.2.6.2 The minimum (∑Smin) may not be achievable on any tree

9.2.7 Ambiguity in landmark positions

9.2.7.1 Coherence in reconstructions of different landmarks

9.2.8 Dynamic alignment of landmarks

9.2.9 Other criteria for aligning or inferring ancestral positions

9.2.9.1 Least squares or linear changes

9.3 Choice of method and correctness of results

9.4 Implementation in TNT

9.4.1 Continuous (and meristic) characters

9.4.2 Phylogenetic morphometrics

9.4.2.1 Reading and exporting data

9.4.2.2 Alignment

9.4.2.3 Scoring trees, displaying, and saving mapped configurations

9.4.2.4 Settings for estimating coordinates of landmark points

9.4.2.5 Weights, factors, minima

9.4.2.6 Group supports

9.4.2.7 Ambiguity

10 Scripting: The next level of TNT mastery

10.1 Basic description of TNT language

10.2 The elements of TNT language in depth

10.2.1 Getting help

10.2.2 Expressions and operators

10.2.3 Flow control

10.2.3.1 Decisions

10.2.3.2 Loops

10.2.4 Arguments

10.2.5 Internal variables

10.2.6 User variables

10.2.6.1 Declaration

10.2.6.2 Assignment

10.2.6.3 Access

10.2.7 Efficiency and memory management

10.3 Other facilities of the TNT language

10.3.1 Goto

10.3.1.1 Handling errors and interruptions

10.3.2 Progress reports

10.3.3 Handling input files

10.3.4 Formatted output

10.3.4.1 Handling strings

10.3.5 Arrays into and from tables

10.3.6 Automatic input redirection

10.3.7 Dialogs

10.3.8 Editing trees and branch labels

10.3.9 Tree searching and traversals

10.3.10 Most parsimonious reconstructions (MPRs)

10.3.11 Random numbers and lists, combinations, permutations

10.4 Graphics and correlation

10.4.1 Plotting graphic trees

10.4.2 Bar plots

10.4.2.1 Heat maps

10.4.3 Correlation

10.4.4 Scatter plots

10.5 Simulating and modifying data

10.6 A digression: the C interpreter of TNT

10.7 Some general advice on how to write scripts

References

Index

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Tags: Pablo A Goloboff, Refining Phylogenetic, Analyses Phylogenetic, Morphological Data