This topic sequence is transcribed from the supplied screenshots. Add dates and confirm the remaining weeks before using it for a new offering.

Week 0 · Computing environment

Before the first meeting, students install software, conduct basic exercises, and read introductory material.

Week 1 · Introduction to quantitative biodiversity

What is biodiversity and why do we study it? Introduction to GitHub and R, followed by exercises in data manipulation, plotting, and simple statistics.

Week 2 · Diversity within a sample

The site-by-species matrix; richness, evenness, diversity metrics, and species abundance distributions.

Week 3 · Control flow

Basic programming methods, including conditional statements, applied to sampling exercises and resampling procedures.

Week 4 · Diversity among samples

Beta diversity, heatmaps, hierarchical clustering, and multivariate ordination.

Week 5 · Diversity among samples

Continue beta diversity and introduce statistical approaches to testing hypotheses about differences among samples.

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

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.