Course Syllabus

Course description

Analysis of ecological data, focusing specifically on community-level data. Topics include distance measures, group comparison methods (Mantel test, permutational MANOVA), ordinations (PCA, DCA, NMS), methods of identifying groups (cluster analysis, classification trees), as well as Indicator Species Analysis, diversity measures, and related topics. 4 credits.  Pre- or co-requisite: QSCI 482. Offered: W.

Instructor

Professor: Dr. Jon Bakker
Office: Room 036, Merrill Hall, Center for Urban Horticulture
Phone: 206-221-3864
Email: sefs502@uw.edu
Office Hours: Please make an appointment via email

Course Goals

The goals of this course are to:

1. To introduce students to the range of techniques available for analyzing community-level ecological data.
2. To provide students with the ability to evaluate and choose appropriate statistical techniques, and the opportunity to apply them to their own data.
3. To expose students to the variety of ecological questions to which these statistical techniques have been applied.

Course Format

Classes are held on Wednesdays and Fridays from 9:00 to 10:50 am in BLD 261. This course is extensively computer-based. Detailed notes will be provided to students as a resource for use during this course and in the future.  In-class demonstrations will use example datasets so that we can detect and address procedural errors that might otherwise confound analyses.

Readings provide background material to techniques and examples of their application.

Assignments and student projects provide opportunities to apply techniques.  Periodically, there will be opportunities to work on analyses with other students, and thereby gain exposure to how techniques are applied to a wider range of ecological data than any one student focuses on for their project.

A visual display of the course content is available here.

Grading / Assessment

Assignments (equally weighted) - 40%

Project (see here for details) - 60%

Assignments and projects are due at the beginning of the class period on the due date. Late assignments will be penalized 10% per day. Final grades will be assigned based on the UW standard grading system.

Course Text and Other Resources

Required: Manly, B.F.J., and J.A. Navarro Alberto. 2017. Multivariate statistical methods: a primer. 4th edition. CRC Press, Boca Raton, FL. ISBN 978-1-4987-2896-6.

Recommended: Borcard, D., F. Gillet, and P. Legendre. 2018. Numerical ecology with R. 2nd edition. Springer, New York, NY. ISBN: 978-3319714035. Call number QH541.15.S72 B67 2018

Recommended: Gardener, M. 2014. Community ecology: analytical methods using R and Excel. Pelagic, Exeter, UK. ISBN: 978-1907807619. Call number QH541 .G374 2014

Note: Springer has a series of books about R, most of which are available as e-books through the UW library.

We will also use various articles from the primary literature. Articles are available through the course schedule below.

A large number of texts about R have been published. A partial list of these, and texts about related topics, is available here.

Software

Data should be stored and manipulated in Microsoft Excel or, if data quantities are large, Microsoft Access. These programs are part of Microsoft Office, and are available on most UW computers.

Most of our analyses will be conducted in the R statistical language. R is an open source system, and can be downloaded (free!) from http://cran.r-project.org/. The base system is updated frequently; the current version as of 181228 is v.3.5.2. Packages that conduct specific types of analyses in R have been written by folks around the world and can be downloaded from the same site. Packages that are directly applicable to this course are labdsv and vegan, among others. However, you can also write your own code to conduct, summarize, and graph your results - and we'll do so during the course! This software is available on the SEFS virtual machines.

RStudio provides a helpful environment in which to run R.  It is available in open source from http://www.rstudio.com/. The current version as of 181228 is 1.1.463. This software is available on the SEFS virtual machines.

Some analytical techniques may also be demonstrated in PC-ORD and/or PRIMER. These are for-profit programs and are not as freely available as R.  PC-ORD PC-ORD is produced by MjM Software (https://www.pcord.com/); the current version is v.7. PRIMER is produced by PRIMER-E, a UK company (https://www.primer-e.com/); the current version is v.7. There is also an extremely useful PERMANOVA+ add-on for PRIMER.  Both of these programs are available on the SEFS virtual machines.

Computational Facilities

Our classroom is BLD 261, a SEFS computer lab. The machines in this lab provide access to virtual machines via VMWare software; instructions are here. R is installed on the virtual machines, which can also be reached from your laptop or home machine with an internet connection. Students may also bring their own laptop into BLD 261 if they prefer. Required internet resources (course website, R packages, etc) may be accessed via the wireless network available in BLD 261.

Students may also prefer to store data on their own thumb drive (USB drive).  In addition, R can be installed, and run, from a thumb drive. Doing so will require a drive with at least 500 MB of storage space.

BLD 261 is not available for use outside of class, but several other on-campus labs are available:

• FSH 207.
• MER 020.
• Virtual Machines (VMWare).  Students in SEFS courses can access our virtual machines with R, RStudio, PC-ORD, and PRIMER installed.  Instructions are here.

In addition, since R is free, assignments and projects can be conducted on your home computer or laptop.

UW and CoEnv Course Policies

Academic Integrity: Plagiarism, cheating, and other types of misconduct are serious violations of your contract as a student.  I expect that you will know and follow the University's policies about academic integrity. Any suspected cases of academic misconduct will be handled according to University regulations. More information, including definitions and examples, are found here.

Disability Accommodations: To request academic accommodations for a disabiltiy, please contact Disability Resources for Students. More information is available here.

Online Privacy: The UW online privacy statement is available here.

Website Usage: The UW website terms and conditions of usage statement is available here.