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El. knyga: Functional and Phylogenetic Ecology in R

4.50/5 (12 ratings by Goodreads)
  • Formatas: PDF+DRM
  • Serija: Use R!
  • Išleidimo metai: 26-Mar-2014
  • Leidėjas: Springer-Verlag New York Inc.
  • Kalba: eng
  • ISBN-13: 9781461495420
Kitos knygos pagal šią temą:
Functional and Phylogenetic Ecology in RDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Serija: Use R!
  • Išleidimo metai: 26-Mar-2014
  • Leidėjas: Springer-Verlag New York Inc.
  • Kalba: eng
  • ISBN-13: 9781461495420
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Functional and Phylogenetic Ecology in R is designed to teach readers to use R for phylogenetic and functional trait analyses. Over the past decade, a dizzying array of tools and methods were generated to incorporate phylogenetic and functional information into traditional ecological analyses. Increasingly these tools are implemented in R, thus greatly expanding their impact. Researchers getting started in R can use this volume as a step-by-step entryway into phylogenetic and functional analyses for ecology in R. More advanced users will be able to use this volume as a quick reference to understand particular analyses. The volume begins with an introduction to the R environment and handling relevant data in R. Chapters then cover phylogenetic and functional metrics of biodiversity; null modeling and randomizations for phylogenetic and functional trait analyses; integrating phylogenetic and functional trait information; and interfacing the R environment with a popular C-based program. This book presents a unique approach through its focus on ecological analyses and not macroevolutionary analyses. The author provides his own code, so that the reader is guided through the computational steps to calculate the desired metrics. This guided approach simplifies the work of determining which package to use for any given analysis. Example datasets are shared to help readers practice, and readers can then quickly turn to their own datasets.



This book shows how to use R for phylogenetic and functional trait analyses, covering biodiversity metrics, null modeling and randomizations for phylogenetic and functional trait analyses, and interfacing the R environment with C and Python-based programs.

Recenzijos

From the book reviews:

This book is structured in nine interlinked chapters . Each chapter is built in a lecture-style incremental manner and does not assume an extensive previous knowledge of R. All chapters conclude with a series of exercises that consolidate the presented notions. This approach makes the book suitable for undergraduates and postgraduates, as well as researchers with an interest in the field. Its structure and detailed examples supported with exercises make it a timely addition for the scientific community. (Irina Ioana Mohorianu, zbMATH, Vol. 1300, 2015)

This book is based on a course taught by the author and has therefore gone through rigorous user testing, which shows in the clear layout and detailed step-by-step guidance through sophisticated statistical analyses. Anyone embarking on related research will benefit from this. (Markus Eichhorn, Frontiers of Biogeography, Vol. 6 (2), 2014)

1 Introduction
1(8)
1.1 Why Phylogenetics and Functional Traits in Ecology?
1(1)
1.2 Why R?
2(1)
1.3 Structure and How to Use This Book?
3(2)
1.4 Setting Working Directories and Package Installation
5(4)
2 Phylogenetic Data in R
9(18)
2.1 Objectives
9(1)
2.2 Loading Phylogenies into R and the Structure of the "Phylo" Class
9(3)
2.3 Plotting Phylogenetic Trees in R
12(3)
2.4 Manipulating and Calculating Additional Information from Phylogenetic Trees in R
15(7)
2.5 Simulating Phylogenies in R
22(3)
2.6 Conclusions
25(1)
2.7 Exercises
25(2)
3 Phylogenetic Diversity
27(30)
3.1 Objectives
27(1)
3.2 Background
27(2)
3.3 "Community" Datasets
29(3)
3.4 Tree-Based Measures of Phylogenetic Diversity
32(9)
3.5 Distance-Based Measures of Phylogenetic Diversity
41(11)
3.5.1 Pairwise Measures
41(7)
3.5.2 Nearest Neighbor Measures
48(4)
3.6 Comparing Metrics
52(2)
3.7 Conclusions
54(1)
3.8 Exercises
55(2)
4 Functional Diversity
57(28)
4.1 Objectives
57(1)
4.2 Background
57(1)
4.3 Quantifying the Functional Composition of Communities Using the Moments of Trait Distributions
58(6)
4.4 Dendrogram-Based Versus Euclidean Distance-Based Measures of Functional Diversity
64(16)
4.4.1 Generating Trait Distance Matrices
65(3)
4.4.2 Generating Trait Dendrograms
68(2)
4.4.3 Pairwise and Nearest Neighbor Measures
70(6)
4.4.4 Ranges and Convex Hulls
76(4)
4.4.5 Other Measures
80(1)
4.5 Comparing Metrics of Functional Diversity
80(1)
4.6 Conclusions
81(2)
4.7 Exercises
83(2)
5 Phylogenetic and Functional Beta Diversity
85(24)
5.1 Objectives
85(1)
5.2 Background
85(2)
5.3 Tree-Based Measures of Phylogenetic Beta Diversity
87(8)
5.3.1 UniFrac
87(7)
5.3.2 Phylogenetic Sorenson's Index
94(1)
5.4 Distance-Based Measures of Phylogenetic and Functional Beta Diversity
95(9)
5.4.1 Pairwise Measures
95(5)
5.4.2 Nearest Neighbor Measures
100(4)
5.5 Other Metrics
104(1)
5.6 Comparing Metrics
105(3)
5.7 Conclusions
108(1)
5.8 Exercises
108(1)
6 Null Models
109(38)
6.1 Objectives
109(1)
6.2 Background
109(7)
6.2.1 Why Use Null Models for Phylogenetic and Functional Analyses?
110(4)
6.2.2 Calculating Standardized Effect Sizes, Quantiles, and P-Values
114(2)
6.3 Classes of Null Models in Phylogenetic and Functional Analyses of Species Assemblages?
116(1)
6.4 Randomizing Community Data Matrices in R
116(4)
6.4.1 Unconstrained Randomizations
117(1)
6.4.2 Constrained Randomizations
118(2)
6.5 Randomizing Phylogenetic Data
120(12)
6.5.1 Unconstrained Randomizations
120(8)
6.5.2 Constrained Randomizations
128(4)
6.6 Randomizing Functional Trait Data
132(4)
6.6.1 Unconstrained Randomizations
133(1)
6.6.2 Constrained Randomizations
134(2)
6.7 Null Models for Phylogenetic and Functional Alpha Diversity
136(5)
6.8 Null Models for Phylogenetic and Functional Beta Diversity
141(4)
6.9 Conclusions
145(1)
6.10 Exercises
146(1)
7 Comparative Methods and Phylogenetic Signal
147(26)
7.1 Objectives
147(1)
7.2 Trait Correlations
147(7)
7.2.1 Independent Contrasts
148(2)
7.2.2 Phylogenetic Generalized Least Squares
150(1)
7.2.3 Phylogenetic Eigenvector Regression
151(3)
7.3 Quantifying Phylogenetic Signal
154(11)
7.3.1 Mantel Test
155(1)
7.3.2 Blomberg's K and Significance Tests
156(3)
7.3.3 Pagel's Lambda
159(3)
7.3.4 Standardized Contrast Variance, Unstandardized Contrast Means, and Randomization Tests
162(3)
7.3.5 Phylogenetic Eigenvectors
165(1)
7.4 Quantifying the Timing and Magnitude of Trait Divergences
165(6)
7.5 Conclusions
171(1)
7.6 Exercises
171(2)
8 Partitioning the Phylogenetic, Functional, Environmental, and Spatial Components of Community Diversity
173(16)
8.1 Objectives
173(1)
8.2 Background
173(1)
8.3 Partitioning Variation in Community Functional Alpha Diversity by the Environment, Space, and the Community Phylogenetic Alpha Diversity
174(5)
8.3.1 Partitioning FD Using Multiple Regression on Distance Matrices
175(3)
8.3.2 Partitioning FD Using Principal Coordinates of Neighbor Matrices (PCNM) and Forward Selection
178(1)
8.4 Variance Partitioning of Phylogenetic or Functional Beta Diversity Along Environmental and Spatial Gradients
179(3)
8.4.1 Beta Diversity and Multiple Regression on Distance Matrices
180(1)
8.4.2 Partitioning Beta Diversity Using Principal Coordinates of Neighbor Matrices (PCNM) and Forward Selection
181(1)
8.5 Integrating Phylogenetic, Trait, Environmental and Spatial Information to Quantify the Role of Abiotic Filtering During Community Assembly
182(3)
8.6 Conclusions
185(2)
8.7 Exercises
187(2)
9 Integrating R with Other Phylogenetic and Functional Trait Analytical Software
189(14)
9.1 Objectives
189(1)
9.2 Background: The Development of Eco-Informatics Tools for Phylogenetic- and Functional Trait-Based Ecology
189(1)
9.3 Phylocom
190(10)
9.3.1 Quantifying Phylogenetic and Functional Diversity and Dispersion in Phylocom
191(6)
9.3.2 Comparative Analyses in Phylocom
197(1)
9.3.3 Interfacing R and Phylocom for Null Modeling
197(3)
9.4 Conclusions
200(1)
9.5 Exercises
201(2)
References 203(8)
Index 211
The author is a tropical plant field biologist by training with a research focus on using phylogenetic and functional information to understand the distribution and dynamics of biodiversity through space and time. He received his Ph.D. in Ecology and Evolutionary Biology in 2008 at the University of Arizona and was a NSF postdoctoral fellow in bioinformatics with the Center for Tropical Forest Science then located at the Arnold Arboretum, Harvard University. His career over the past decade has coincided with the rapid boom in phylogenetic- and functional trait-based analyses of ecological datasets. In recognition of his work integrating phylogenetic and functional trait information across scales, he has received the 2011 Jasper Loftus-Hills Young Investigators Award from the American Society of Naturalists and the 2012 Ebbe Nielsen Prize from the Global Biodiversity Information Facility. Being recognized as a leader in his field has lead to the author providing R analytical workshops in places such China, Costa Rica and Taiwan. Designing and implementing these workshops naturally lead to the development and refinement of this volume.
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