Ecological and Environmental Modeling
PBIO 295 (3 credits)
crosslisted as CSYS 295

Spring 2012 Tuesday / Thursday 1:00-2:15 PM

Instructor: Brian Beckage (Brian.Beckage@uvm.edu)

Location: Discussions & Lectures in Jeffords 227; Labs in Morrill 005

Office Hours: Tuesdays 12-1 pm; Thursdays 2:15-3:15 pm; Jeffords 345

Course Description

This seminar course will provide an introduction to data visualization and modeling. This course will emphasize likelihood, information theoretic, and Bayesian approaches to data modeling in an interactive class environment that is based primarily on discussions and applications rather than lectures. This class will stress applications in the environmental sciences. Class time will be divided between discussions (with some lectures) and computer labs. Students will learn the open source R statistical computing language (R project website). This course is designed to provide students with the analytic tools required for independent research in the environmental sciences.

Course Prerequisites

Intoductory calculus or permission of the instructor.  The course is intended for advanced undergraduates and graduate students.

Course Texts and Readings

• We will use two books for the class:
  • Ecological Models and Data in R by Dr. Ben Bolker.
  • Introduction to Statistical Thought by Dr. Michael Lavine.
  
  Lavine's book can be downloaded for free as a pdf from the link above. Purchase Bolker's book on your own--try to get it in time for the start of the semester. The ISBN number for Bolker's book is available at the link to his book above.
• Please read the assigned chapter prior to coming to class.

Computing

We will be using the statistical programming language R in the class. We will have frequent labs where students will work with R. Computers will be provided for the labs but students are welcome to bring their laptops if preferred. R can be freely downloaded and installed on a wide variety of computing platforms. To obtain R for your own machine, go to the R project website, follow the link beneath 'Download' on the left, choose a site near your geographic location, download the pre-compiled binary distribution for your operating system, and then follow the instructions for installation. We may also use openBugs software for fitting Bayesian models.

Preliminary Outline of Grading. This may be modified until the beginning of the spring semeseter.

Student grades will likely be based on five components:

1. In class quizzes (25% of course grade). Quizes will cover assigned readings as well as lecture material.
2. Completion of homework assignments. (50% of couse grade).
3. A final project that presents an analysis of a data set of the student's choosing. (10% of course grade).
4. A corresponding class presentation on this analysis (10% of course grade).
5. Student attendance and participation in class. (5% of course grade).
   

Syllabus (tentative): We will adjust the class schedule during the semester based based on our rate of progress.

(B1~Bolker Chapter 1; L1~Lavine Chapter 1)

lab1.pdf Intro1.R Intro2.R  Chlorellagrowth HW 1 Solutions 

lab2.pdf seedpred.dat ewcitmeas.dat HW 2 Solutions  

lab3.pdf

HW Lab 5 (Due 28 Feb)

HW Lab 6 (Due 2 Mar)

MLE code

HW13 Pine Cone Data