Instructor: Tom Hobbs
Office: B227 NESB
Office Hours: Tuesday, Thursday 11:00 – 12:00
Phone: 491-5738 (o), 482-0903 (h)
Email: Tom.Hobbs@ColoState.EDU
2012 Spring Semester, Lecture: Tues. and Thurs. 9:30-10:50 in NESB A302, Lab: Wed. 1:00-3:00 in NESB B302
Rationale: Virtually all insight in ecology depends on bringing models together with data. This course is about gaining understanding of ecological systems using mathematics, statistics, and observations. The ultimate goal of the course is to master the fundamental principles needed for analysis of a broad range of problems in ecological research. Although this course is about methods, it will emphasize the principles that support those methods. This emphasis comes from my belief that what you know is not as important as what you are capable of learning. A principles-based approach assures you will be able to continue learning quantitative approaches to insight throughout your career.
Target audience: Graduate students and advanced undergraduates.
Objectives:
1. Develop understanding of the fundamental principles of using models to gain insight from data.
2. Master basic techniques for formulating static and dynamic models of ecological processes.
3. Learn modern methods for estimating model parameters, estimating associated uncertainties, and for evaluating alternative models based on data.
4. Provide grounding needed for effective collaboration with mathematicians and statisticians.
5. Give students the quantitative confidence needed to use mathematical and statistical models in their research and to support a lifetime of self-teaching.
Prerequisites: Ideally, students should have a semester of calculus, a basic ecology course, and an introduction to statistics (ST 301 or ST 307/EH 307). None of these are absolute requirements; I will review key background concepts as part of the lectures. However, that said, if you don’t have at least two of these background courses, you should be prepared to do some remedial work on your own.
Content and teaching approach: The course is faithful to its title—we will study principles and methods for building models and assimilating them with data. There will be a firm emphasis on learning by doing. Students will be taught to use R and JAGS as a "modeling workbench." The open-source computing language R has become the standard for scientific computation worldwide. Without doubt, it is the preeminent system for bringing models together with data. R is somewhat challenging to learn and a fundamental goal of this course is to make that challenge manageable even for students with minimal programming background. In addition, we will learn to use JAGS (Just Another Gibbs Sampler), stunningly powerful software for implementing contemporary Bayesian methodsYou might be more familiar with the software WinBUGS or OpenBUGS. JAGs has syntax that is almost identical with these programs, but in my experience, it is easier to use, less fussy, more powerful, and in many cases, faster. Moreover, it runs on all major platforms-Windows, Mac OS, Linux– in native mode, which makes it particularly well suited for this class. I have used all three of these Gibbs samplers. In my experience, JAGS is the best of the lot by a wide margin.. Weekly laboratory sessions will challenge students to build models and assimilate them with data. It is imperative that students keep up with laboratory work. The lab is the foundation of the course.
Texts: A manual introducing R programming is required for the laboratory. These are available for the cost of copying and binding ($15 ish?) at the bookstore. There is also a required text, McCarthy, M. A. 2007. Bayesian Methods for Ecology. Cambridge University Press, Cambridge, U. K., also available at the bookstore. The material in this book will compliment the material covered in class and lab. If you want to emerge from this course with a first rate understanding of modern methods for using models and data in ecology, I urge you to read the chapters assigned on the web site and work the problems in each chapter. Papers relevant to each week's lecture topics will be posted on the class web site.
Readings: A few readings, available in PDF format on the class web site, are offered to supplement the lectures, . We will not discuss the readings regularly in class. They are intended to offer a different slant on the material I present, thereby complimenting the lectures rather than duplicating them. However, some of the particularly technical material will be covered in lecture and directly reinforced in the readings. With the notable exception of the material that I provide as primers and handouts, it is entirely possible to do well in this course (in terms of your grade) without doing the reading. So, if all you want is a reasonable grade, then neglecting the reading probably won’t hurt you. Alternatively, if you want to master the material, the readings will contribute meaningfully to meeting your goal.
Working in groups: You will be assigned to a lab group including two other colleagues. I feel strongly that your success in ecology depends on your ability to work effectively with others. Moreover, a team approach to the work in the laboratories allows you to teach each other as well as to learn from the TA’s and me. It will lighten the work load by allowing you to share tasks. And, it is more fun.
An individual project: The single exception to group work will be an individual project that you will complete at the end of the course. I will offer several datasets spanning the breadth of ecology. You will choose a dataset appropriate to your interests and will develop an analysis to gain insight from the chosen data. You may also work on your own data, but you must get approval from me to do so. I want to be sure that the problem you propose is tractable using the data you have in hand. You will prepare a report of the project as if it were a submission of an article to Ecology, complete with cover-letter.
A weekly question: On Friday of each week, please send me an email with a question about the material we covered during the week. I will compile these and will cover common themes in lecture. The TA's and I will work with you in lab to be sure you get your questions answered.
Grading: If you complete the assigned work with attention and care, you will get an A in this course. I am far more interested in your mastery of the material than I am in making academic comparisons among you. The material in this course can appear intimidating at first, but the last thing I want is for you to be anxious about it. Everyone who has taken this course has emerged with a sturdy understanding of the key concepts and methods. It may seem daunting at first. Relax. We will get through it.
Seventy percent of your grade will be based on 8-10 lab write-ups, each worth 50 points / week required to complete. So, if I allocate 2 weeks to a lab topic it is worth 100 points, etc. The remaining 30% of your grade will be based on the individual project, described above. Lab work will require programming in R and JAGS as well as some work with paper and a sharp pencil. For each assignment, each lab group will turn in a single electronic copy of a write-up that includes text and figures communicating your results. You should also turn in a hard copy of documented code. Grading will be based on the following:
1. Quality of approach to problem: Did you use a logical, thoughtful process for solving the problem?
2. Quality of presentation: Did you present your findings in a literate document? Did you clearly communicate how you solved the problem, showing mathematical steps or a computer algorithm? Was your document attractive and well organized?
3. Quality of technique: Did you demonstrate mastery of the appropriate methods? Your lab reports should describe model results and discuss them as appropriate. Include figures and/or tables, embedded in the text, not appended at the end. All figures must have captions. All tables must have headings. Provide an appendix documenting your code. I urge you to use the LaTeX endfloat package to give you practice in the type of format that you will use for writing articles for submission to journals.
Preparing reports: All lab write-ups and your the report of you individual project must be prepared in LyX or LaTeX. Submit your work in a .pdf file and a .lyx or .tex file attached to an email with NR 575 lab number _ in the subject line for lab reports and NR 575 individual project for your final project.
Things you need: A large amount of computer programming will be necessary to successfully complete the course, so students will need easy access to workstations running R, JAGS, LyX, and LaTeX, all of which are free, open-source software. We will learn how to load these in the lab. It will be very helpful if you have your own laptop. You should always bring it to lab and you will occasionally need it in class. I will let you know when to bring it to class. It will also be useful to bring a regular, old fashioned notebook or tablet to class. There are topics that I feel are best presented at the board and for which you should take notes. I do this not to avoid preparing handouts, which I will likely do anyway, but instead to make the lecture more intimate and interactive, to make your learning a bit more active, and to slow me down.
Accommodation of individual learning needs: If you have learning needs that may affect your performance (sight, hearing, language, or any other reason), please let me know at the beginning of the course. We will work out ways to accommodate your needs. I am deaf as a post in my right ear, so you may need to accommodate me as well.
Interaction outside of class: My office hours will 11:00-12:00 on Tuesdays and Thursdays and by appointment. Appointments can be informal–if you stick your head in my door, I am will often be able to help you. The TA's and I will be available via email to answer questions on your R programming. When you have R questions, be sure to include the script causing you problems in your email including everything we need to run your code. Your learning will be meaningfully enhanced if you struggle with a problem before you ask a question, but we don't want you to struggle excessively. If you truly aren't getting anywhere on your own, don't spin your wheels. Contact us with a well-framed question.
My contact information is above; the TA's emails are:
Nathan Galloway nathan.galloway@colostate.edu
Joe Northrup, Joe.Northrup@colostate.edu
TA’s office hours will be announced in lab.
Class notes: Notes for each lecture will be available as PDF files on the class web site. The updated, current version of the notes will be available no later that 8:30 the day of lecture. I revise every lecture I give, so you would be wise to print the notes the day of the lecture.
My travel: Although I avoid travel during the spring semester, there will be occasions when I must be away. On the rare occasions when I must miss class, I will provide some alternative learning activity. Teaching assistants will conduct laboratories when I am traveling. I will also try to find an alternative time slot to meet the class.