Ecology encompasses a range of interactions between the biotic and abiotic world and thus one needs a broad range of tools to understand ecological process and patterns. This course will begin with a general overview of ecology and an introduction to evolutionary processes. We start with evolutionary processes since they are central to understanding fundamental aspects of ecological interactions. From there, we will examine populations and move on to two-species interactions
such as interspecific competition, plant-herbivore interactions, and disease dynamics. Afterwards, we will begin considering more complex interactions involving entire ecological communities such as trophic dynamics and food-webs. The course will conclude with a review of ecosystem-level processes. Throughout the course, I use examples from the ecological literature to emphasize each of main concepts covered.
In this course, we will explore the use of mathematical and probability-based tools to quantitatively describe patterns in nature. We will start by examining single-species dynamics and work our way up to trophic interactions. The course will cover how to analyze model dynamics from a mathematical perspective and use probability/likelihood-based approaches to understand those dynamics by combining models with data. By the end of the course, you will be able to develop and analyze mathematical models on your own.
As ecologists, we spend a lot of time collecting data and a lot more time analyzing it. This course will introduce students to a set of tools to analyze their data. The course will be divided into three sections: 1) Likelihood-based analysis; 2) Comparing multiple models using Information Theory; and, 3) Bayesian approaches. The class will have two meetings per week. The first meeting will consist of an introduction to a topic. The second meeting will involve a hands-on computer exercise. The course will make extensive use of the R programming language.