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Multivariate Applications in Ecology (BSC 747) Class Links

Syllabus | Schedule and Files | Printer Friendly Version


Instructor:Jake Schaefer
Phone: 601-266-4928
Course Homepage:
Office hours: Monday, Wednesday 8:00-10:00 am. If these office hours don't fit your schedule please see me for alternate times when we can schedule a meeting. Feel free to contact me after class or through email to set up a meeting time.
Office: 1004 Johnson Science Tower

Lectures: 3:30-4:45 Monday, Wednesday in JST 210

Textbook: Borcard,D., F. Gillet and P. Legendre. 2011. Numerical Ecology with R. ISBN: 978-4419-7975-9

Software: R. A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. I also suggest you use rstudio:

Course Objectives

This course will provide an introduction to statistical techniques used to analyze complex multivariate ecological datasets. Each week the class will focus on one type of analysis in one lecture and one recitation meeting. The lecture portion of the course will give an introduction to the various techniques as they are applied to common ecological questions. In addition to textbook reading material, students will be given peer reviewed literature utilizing each technique. Class discussions will include assessment of the efficacy and appropriateness of the analyses used in regards to the research question being asked. We will discuss whether or not the correct analyses was used and if the presentation of the analyses was both clear and suitable. The recitation portion of the class will focus on implementation of these techniques in analyzing provided datasets or students own data if available. We will use the R statistical language for all analyses. R is an open source (and free) statistical tool capable of performing most commonly used multivariate analyses in ecology. Early recitation activities will include an introduction to R to familiarize students with the language. When a new analysis is introduced, the workings of the analysis in R will be demonstrated. Students will be responsible for analyzing data and turning in summaries of each analysis. By the end of the course, the students will be capable of independently analyzing their own multivariate dataset as well as reading and understanding these analyses in the literature.

Grading Policy

There will be no exams, your grade will be based entierly on written assignments.

For each weekly assignment, you will need to turn in:

  1. R code for performing the analysis
  2. A 1 page synthesis of ouput with appropriate interpretation

Please turn these assignments in via email to me with BSC 747 in the subject line.

Each assignment will be graded on a 0-3 scale (0=not turned in, 1=turned in but code problematic, 2=turned in, code OK but synthesis lacking, 3= code and synthesis OK).

Grades will be assigned on the following scale:

–A = 32-42
–B = 28-31
–C = 24-27
–D = 20-23
–F = <=19

Attendance Policy

I strongly encourage your attendance at lecture. Although your attendance record is not calculated into your overall course grade, experience has shown me that students having several absences do not perform well on exams. If you miss a lecture, find someone in class to tell you what you missed or see me during office hours. It would be wise to borrow a classmate's notes and/or read the textbook because we cover important material every day. If you have to miss class I would be happy to go over what you missed during my office hours.

Dates of Interest

For information on the last day to drop this class, see the academic calendars published by the Office of the Registrar.

Class Disruptions

Please respect the other students in the class by not causing disruptions. This includes cell phones ringing, having conversations with others in the class or other disruptive behavior.

Missed Exams

In general, all exams must be taken in class on time. If there is some reason you can not take an exam at that time, please see me ahead of time. If you miss an examination for medical reasons, please give me a written statement to that effect signed by the attending physician. Documentation will be accepted only up to one week following the missed examination. Exams missed without medical documentation or prior consent of the instructor result in a grade of 0. There are no exceptions to this policy.

Disability Support Services

If any student has special needs they can contact the Office for Disability Accommodations for assistance.

Academic Dishonesty

Academic misconduct includes cheating (using unauthorized materials, information, or study guides), plagiarism (submitting others work as if it were your own), falsification of records, unauthorized possession of examinations, intimidation, and any other action that may improperly affect the evaluation of your performance. It also includes assisting others in any such acts or attempts to engage in such acts. Penalties may range from grade penalties (including lowering a student's semester grade, failing a student for the course, or requiring a substitute exam or paper) to official disciplinary action. For more information, see the sections on Student Policies and Procedures in the USM Student Handbook.

Note: This syllabus is subject to change at the discretion of the instructor. All changes will be announced in class and on the course web page