Ecological Modeling
PBIO 294 (3 credits)

24 May to 18 June 2021; Tuesday, Wednesday, Thursday 1:00-4:45 pm (Eastern US) (remote via Microsoft Teams)
Instructor: Brian Beckage (
Office Hours (remote by appointment)

Course Description

Course Goals


Books & Media



Course Description

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This course provides an introduction to data modeling using the R statistical computing language and likelihood, information theoretic, and Bayesian approaches to inference. The course includes a focus on the R language as a tool for data modeling and emphasizes examples and case studies from ecological and environmental sciences.

Class Structure. This class will meet three times a week for 3.75 hours (yes, 3 hours and 45 minutes!). But we will take frequent breaks and will engage in a variety of activities, including discussions, watching videos, and small group work, to break up the meetings. Each meeting will be divided into four ~45 minute components with a ~10 minute break between components.

  1. Readings. We will discuss the assigned readings for that class periods in small groups and in the class as a whole. This will also present an opportunity to ask questions or seek clarification about the readings. These discussions will provide a review prior to taking a short quiz on the readings that will be posted in Blackboard.

  2. Supporting videos. In each class period, we will a pre-recorded lecture, by the author (Richard McElreath) of our main text, where he presents each chapter. (I choose this book in part because of these video lectures). This will reinforce our chapter readings and provide further explanation of assigned material. As you are watching these video lectures, please ask questions, make comments, and respond to your classmates' questions using Yellowdig. Following the lecture, we will discuss the chapters first in small breakout rooms and then in the main group. We will repeat this for a series of R videos. These videos do not correspond to our readings in R but provide another perspective on programming in R.

  3. Exercises. Each class period, students will receive a set of problems that incorporate the statistical and/or R programming components of the class. Students will work in small groups during class on these problems and will complete them outside of class if not completed in class. Although sharing of information and ideas is encouraged as a means of learning the material, each student should produce their own solutions (i.e., should write their own R code).

  4. Chapter presentations. Beginning in the second week of class, the assigned readings on statistics (not R) from McElreath and Hobbs and Hooten will be presented by pairs of students. These presentations should summarize and outline the material presented in the readings, including the most important and key points, and should highlight components that were confusing or unclear. These presentations should be roughly 20 to 25 mins.

Course Goals

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    Students will

  1. gain proficiency in the R statistical programming language
  2. learn to construct and use likelihood functions.
  3. learn to use information theoretic approaches to statistical inference.
  4. become familiar with Bayesian approaches to statistical inference.

Achieving these goals will require:


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Student grades will be based on the following components:

Grading for Undergraduate students

Grading for Graduate students

  1. Class presentation of assigned chapters: 20% of course grade. PRESENTER SCHEDULE
  2. Participation in discussions through Yellowdig: 20% of course grade.
  3. Quizzes on assigned readings: 10% of course grade.
  4. Homework assignments as Complete/Incomplete: 35% of course grade.
  5. Final exam (take home): 15% of course grade.
  1. Class presentation of assigned chapters: 20% of course grade. PRESENTER SCHEDULE
  2. Participation in discussions through Yellowdig: 20% of course grade.
  3. Quizzes on assigned readings: 10% of course grade.
  4. Homework assignments as Complete/Incomplete: 30% of course grade.
  5. Data analysis project: 10% of course grade.
  6. Final exam (take home): 10% of course grade.

Books and Media

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Students will also be assigned readings from the following set of books (below). These books will be required for this class and are available in the university bookstore or as otherwise noted below.

  1. PRIMARY text for the statistics portion of this class: McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and STAN (2nd Edition). ISBN-13: 978-0367139919 Link to book.
  2. Supplementary/optional readings for further background: Hobbs, N. Thompson and Mevin B. Hooten. 2015. Bayesian Models. A statistical primer for ecologists. ISBN: 9780691159287. Link to book.
  3. Supplemental reference: Introduction to Statistical Thought by Michael Lavine. (free pdf)
  4. PRIMARY text for the R programming portion of this class: Introduction to data science. Data Analysis and Prediction Algorithms with R. by Rafael A. Irizarry. (pdf available for an optional donation)

We will also watch a variety of videos inside of class on both statistics and R.


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We will build data models in the free, open source programming language R. Below are some additional resources for installing and using R.

R project for statistical computing

RStudio IDE

R for data science (book)

Installation of RStan/Stan & Rethinking package

Information on installing R and R studio.

Install R: Windows

Install R: Mac

Install RStudio

Setting Working Dir: Windows

Setting Working Dir: Mac