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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 TEC 106 and GCRL FSB 102

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 in a .R file
  2. A 1 page synthesis of ouput with appropriate interpretation in a word file
  3. Please do not resend the dataset
  4. Email assignments by midnight each Friday
  5. Put your name and the week of the semester in the subject (e.g. Smith week 2)

Each assignment will be graded on a 0-5 scale (0=not turned in, 5=code performs analysis correctly, and interpredation is accurate and well written). I will take off points for any problems with code, not doing the analysis correctly, problems with interpretation, and gramatical or other problems with the synthesis.

Grades will be assigned on the following scale:

–A = 49.5+
–B = 44-48.4
–C = 38.5-43.9
–D = 33-38.4
–F = <=32.9

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.

The final exam will be administered at the scheduled time, see the final exam schedule 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.

Disability Support Services

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

Academic Integrity

All students at the University of Southern Mississippi are expected to demonstrate the highest levels of academic integrity in all that they do. Forms of academic dishonesty include (but are not limited to):

Engaging in any of these behaviors or supporting others who do so will result in academic penalties and/or other sanctions. If a faculty member determines that a student has violated our Academic Integrity Policy, sanctions ranging from resubmission of work to course failure may occur, including the possibility of receiving a grade of "XF" for the course, which will be on the student's transcript with the notation "Failure due to academic misconduct." For more details, please see the University's Academic Integrity Policy. Note that repeated acts of academic misconduct will lead to expulsion from the University.

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