Functional and Phylogenetic Ecology in R

The distribution of biodiversity is a, if not the, major focus of ecologists. Specifically, ecologists often investigate the spatial or temporal trends in biodiversity levels within a particular study region or across the planet. The study of biodiversity has traditionally focused on quantifying patterns of species diversity or species richness across some type of gradient and determining the potential processes that have produced the observed pattern. This approach is a cornerstone of ecological investigations and thinking regarding biodiversity. However, there are two clear limitations to this species-centric approach. First, biodiversity is not simply species diversity. Biodiversity also includes the phylogenetic, genetic, and functional diversity in an assemblage [1]. Indeed, species diversity may even be the least informative of all of these dimensions of biodiversity. For example, regions could have the same exact species diversity, but very different levels of phylogenetic and functional diversity and therefore very different levels of biodiversity. Or they could have very similar levels of functional and phylogenetic diversity despite large differences in their species richness [2–5]. Thus, attempting to determine the processes that produce biodiversity cannot be obtained by examining only one component of biodiversity. A second challenge for the species-centric approach to studying biodiversity that is perhaps more important than the first one is that species names are relatively information poor. While they are fundamental to biology, they convey little information regarding the function or evolutionary history of species, and such information is critical for determining the processes that have combined to produce the observed levels of biodiversity. These inherent limitations of a species-centric approach suggest that a more pluralistic approach to studying biodiversity is needed in order to obtain a mechanistic understanding of how patterns of biodiversity are formed [6–13]. In particular, a biodiversity synthesis will necessarily require the consideration of the interrelationships between the three primary components of biodiversity—species diversity, functional trait diversity, and phylogenetic diversity [1]. Ecologists are now embracing this reality and have altered their research programs accordingly. The number of phylogenetic- and functional trait-based analyses in ecology has skyrocketed in recent years resulting in hundreds of publications. Indeed, entirely new fields in ecology have formed such as community phylogenetics, and new grant programs have sprung up such as the United States National Science Foundation’s Dimensions of Biodiversity program.

The objectives of this chapter are to introduce the user to how phylogenetic information is stored, presented, and manipulated in R. We will cover primarily the class “phylo” in this chapter since that is the class that is most frequently utilized in phylogenetic diversity and comparative analyses in R. By the end of this chapter the user should have a basic command of how to plot phylogenies and extract information from the data files. The chapter is designed for beginners, and users familiar with using phylogenies in R may quickly scan this chapter to refresh.

The objectives of this chapter are to first build a background understanding of why ecologists are interested in quantifying phylogenetic diversity (PD) and then to utilize R to quantify a variety of PD metrics that are the most frequently used. As in other chapters, we will focus on breaking down each analysis into its constituent parts to deepen our understanding of what exactly is being calculated and to facilitate your ability to write modifications of the code or novel code to generate PD analyses suited to your particular research objective.

The objectives of this chapter are to explore the variety of metrics and approaches for analyzing the functional composition and diversity of species assemblages. Important topics will include the consideration of how uni- and multivariate trait data are utilized in functional diversity analyses, the use of raw trait distance matrices versus trait dendrograms, and the degree of similarity between functional diversity metrics.

The first objective of this chapter is to introduce the conceptual and empirical background for why phylogenetic and functional analyses of beta diversity are of interest to ecologists. This is followed by detailed instructions on how to calculate in R the major metrics of phylogenetic and functional beta diversity that are most commonly employed in the literature. The ultimate goal is to obtain a robust conceptual and practical knowledge of phylogenetic and functional beta diversity.

The ultimate goal of this chapter is to understand when, where, and why null models should be used in the analysis of phylogenetic and functional diversity. The specific objectives of this chapter are to first discuss the philosophy behind null models, what they seek to accomplish, and how they work. Second is to establish why null models are necessary for most analyses of phylogenetic and functional diversity. Lastly, we will implement several classes of null models for phylogenetic and functional alpha and beta diversity.

The objectives of this chapter are to consider trait data in the context of phylogenetic information. We will begin by discussing how to quantify the relationships between the traits of species while accounting for the nonindependence of species. We will then explore how to quantify the degree to which variation in traits reflects phylogenetic nonindependence. Throughout we will discuss multiple approaches, but keep in mind that not all approaches are equal. Some approaches that will be presented are now rarely used due to their documented weaknesses. Nonetheless we will cover these approaches to provide some breadth, background, and context.

The objectives of this chapter are to understand and implement methods for partitioning the functional and phylogenetic dimensions of diversity within and between communities and to quantify the relationships between traits, phylogenetic relatedness, space, and the environment simultaneously.

The objectives of this chapter are to quickly cover how to use R to interface with analytical software that is written in other programming languages. The chapter will focus primarily on integrating R commands with the software Phylocom written in C [14]. I have chosen to focus on this program because it carries out many of the types of analyses covered in this book. Although I have focused on this particular program, the general principles of how to call other programs from R and integrate them into your R-based analyses remain the same.