Functional and Phylogenetic Ecology in R (eBook)

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.

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 Investigator's 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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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.