Schedule
Week 0
Introduction to the computing environment Prior to our first class meeting, students will install software, conduct basic exercises, and read introductory material.
Week 1
Introduction to Quantitative Biodiversity
What is biodiversity and why do we study it? After introducing the course, we will provide an overview of GitHub and R. Laboratory exercises will include an exercise in GitHub followed by exercises in R such as data manipulations, plotting, and simple statistics.
Week 2
Diversity within a sample (i.e., alpha-Diversity) We will begin by introducing one the primary ecological data structures: the site-by-species matrix. From this, we will derive the core components of diversity: richness and evenness. We will integrate richness and evenness components by covering diversity metrics and the species abundance distribution (SAD).
Week 3
Control flow We will introduce basic programming methods that are useful for writing code (e.g., if-then statements), which are useful for a range of applications. We will apply these tools to lab-sampling exercises, and introduce resampling procdures.
Week 4
Diversity among samples (i.e., beta-diversity) We will learn how to quantify diversity among samples (beta-diversity). We will then focus on the visualization of beta-diversity, which will include heatmaps, hierarchical clustering, and multivariate ordination.
Week 5
Diversity among samples (i.e., beta-diversity) We will continue with topics from previous week while also highlighting statistical approaches that allow one to test hypotheses about beta-diversity.
Week 6
Phylogenetic diversity: traits We will learn how to make phylogenetic trees in R. After this, we will use contributed R packages to map traits onto trees, peform phylogenetically independent contrasts, and test for phylogenetic signal.
Week 7
Phylogenetic diversity: communities We will continue to learn about the integration of phylogenetic for questions related to biodiversity. Specifically, we will think about phylogenetic community ecology, by introducing tools such as unifrac, net-relatedness index (NRI), and nearest taxon index (NTI).
Week 8
Projects and synthesis: GitOn We will use this class period for student presentations of their independent projects while recapping major concepts and tools, in addition to “big data” approaches to addressing global biodiversity issues.