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El. knyga: Phylogenetic Comparative Methods in R

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  • Formatas: 440 pages
  • Išleidimo metai: 12-Jul-2022
  • Leidėjas: Princeton University Press
  • Kalba: eng
  • ISBN-13: 9780691219042
Kitos knygos pagal šią temą:
Phylogenetic Comparative Methods in RDidesnis paveikslėlis
  • Formatas: 440 pages
  • Išleidimo metai: 12-Jul-2022
  • Leidėjas: Princeton University Press
  • Kalba: eng
  • ISBN-13: 9780691219042
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An authoritative introduction to the latest comparative methods in evolutionary biology

Phylogenetic comparative methods are a suite of statistical approaches that enable biologists to analyze and better understand the evolutionary tree of life, and shed vital new light on patterns of divergence and common ancestry among all species on Earth. This textbook shows how to carry out phylogenetic comparative analyses in the R statistical computing environment. Liam Revell and Luke Harmon provide an incisive conceptual overview of each method along with worked examples using real data and challenge problems that encourage students to learn by doing. By working through this book, students will gain a solid foundation in these methods and develop the skills they need to interpret patterns in the tree of life.

  • Covers every major method of modern phylogenetic comparative analysis in R
  • Explains the basics of R and discusses topics such as trait evolution, diversification, trait-dependent diversification, biogeography, and visualization
  • Features a wealth of exercises and challenge problems
  • Serves as an invaluable resource for students and researchers, with applications in ecology, evolution, anthropology, disease transmission, conservation biology, and a host of other areas
  • Written by two of today’s leading developers of phylogenetic comparative methods
1 A brief introduction to phylogenetics in R
1(34)
1.1 Introduction
1(2)
1.2 Preliminaries
3(2)
1.3 R phylogenetics
5(2)
1.4 Ape and the "phylo" object in R
7(4)
1.5 The internal structure of a tree in R
11(4)
1.6 Reading and writing phylogenetic trees
15(1)
1.7 Plotting and manipulating trees
16(8)
1.8 Multiple trees in a single object
24(1)
1.9 Managing trees and comparative data
25(4)
1.10 A simple comparative analysis: Phylogenetic principal components analysis
29(3)
1.11 Practice problems
32(3)
2 Phylogenetically independent contrasts
35(24)
2.1 Introduction
35(1)
2.2 Phylogenetic nonindependence
36(2)
2.3 Phylogenetically independent contrasts
38(9)
2.4 What happens if we ignore the tree?
47(11)
2.5 Practice problems
58(1)
3 Phylogenetic generalized least squares
59(16)
3.1 Introduction
59(1)
3.2 Statistical nonindependence of phylogenetic data
60(1)
3.3 Equivalence of contrasts regression and PGLS
61(6)
3.4 Assumptions of PGLS
67(4)
3.5 Phylogenetic ANOVA and ANCOVA
71(3)
3.6 Practice problems
74(1)
4 Modeling continuous character evolution on a phylogeny
75(32)
4.1 Introduction
75(1)
4.2 The Brownian motion model
75(4)
4.3 Brownian motion on a phylogeny
79(1)
4.4 Properties of Brownian motion
80(2)
4.5 Fitting a Brownian model to data
82(8)
4.6 Phylogenetic signal
90(8)
4.7 Other models of continuous character evolution on phylogenies
98(2)
4.8 Fitting and comparing alternative continuous character models
100(5)
4.9 Practice problems
105(2)
5 Multi-rate, multi-regime, and multivariate models for continuous traits
107(38)
5.1 Multi-rate Brownian evolution
108(4)
5.2 Multi-optimum Ornstein-Uhlenbeck evolution
112(10)
5.3 Multivariate Brownian evolution
122(7)
5.4 Exploring evolutionary heterogeneity
129(15)
5.5 Practice problems
144(1)
6 Modeling discrete character evolution on a phylogeny
145(22)
6.1 Introduction
145(1)
6.2 The Mk model
145(4)
6.3 Fitting the Mk model to data
149(6)
6.4 Comparing alternative discrete character models
161(4)
6.5 Practice problems
165(2)
7 Other models of discrete character evolution
167(60)
7.1 Introduction
167(1)
7.2 Correlated binary traits
167(10)
7.3 Modeling heterogeneity in the evolutionary rate for a discrete trait
177(8)
7.4 Modeling rate variation using the hidden-rates model
185(22)
7.5 A polymorphic trait model
207(2)
7.6 The threshold model for studying discrete and continuous character traits
209(10)
7.7 Practice problems
219(2)
8 Reconstructing ancestral states
221(1)
8.1 Introduction
221(1)
8.2 Ancestral states for continuous characters
222(6)
8.3 Properties of ancestral state estimation for continuous traits
228(7)
8.4 Discrete characters
235(3)
8.5 Joint ancestral state reconstruction
238(3)
8.6 Marginal ancestral state reconstruction
241(2)
8.7 Stochastic character mapping
243(8)
8.8 What about parsimony?
251(3)
8.9 Practice problems
254(1)
9 Analysis of diversification with phylogenies
255(1)
9.1 Introduction
255(1)
9.2 Lineage-through-time plots and the y statistic
256(6)
9.3 Estimating speciation and extinction rates from a reconstructed phylogeny
262(7)
9.4 The effect of incomplete sampling on diversification rates
269(3)
9.5 Likelihood surface of a birth-death model
272(1)
9.6 Analyzing diversification using diversitree
273(7)
9.7 Practice problems
280(1)
10 Time-and density-dependent diversification
281(1)
10.1 Introduction
281(1)
10.2 Time-varying diversification
282(2)
10.3 Fitting time-variable diversification models to data
284(16)
10.4 Diversity-dependent diversification
300(5)
10.5 Testing for variation in diversification rates among clades
305(3)
10.6 Practice problems
308(1)
11 Character-dependent diversification
309(48)
11.1 Introduction
309(1)
11.2 Binary-state speciation and extinction (BiSSE) model
310(11)
11.3 Multi-state speciation and extinction (MuSSE) model
321(7)
11.4 Hidden-state speciation and extinction (HiSSE) model
328(16)
11.5 Quantitative-trait speciation and extinction (QuaSSE) model
344(10)
11.6 Practice problems
354(3)
12 Biogeography and phylogenetic community ecology
357(26)
12.1 Introduction
357(1)
12.2 Ancestral area reconstruction
358(12)
12.3 Phylogenetic community ecology
370(12)
12.4 Practice problems
382(1)
13 Plotting phylogenies and comparative data
383(90)
13.1 Introduction
383(1)
13.2 Phylogenies in the R plotting environment
384(15)
13.3 Plotting phylogenies without actually plotting them
399(8)
13.4 Algorithms for drawing trees
407(5)
13.5 Practice problems
412(1)
References 413(8)
Index 421(4)
Index of R functions 425
Liam J. Revell is associate professor of biology at the University of Massachusetts, Boston, and an adjunct researcher at the Universidad Católica de la Santķsima Concepción in Chile. Luke J. Harmon is professor of biological sciences at the University of Idaho and the author of Phylogenetic Comparative Methods: Learning from Trees.
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