Course Syllabus


FISH 511 A: Theory and Application of Stable Isotopes in Ecology

Stable isotopes are a powerful and now common tool used in ecological investigation. This course will explore the theory underlying the use of different stable isotopes to explore and quantify mechanisms driving patterns and processes in a variety of ecosystems. We will discuss applications of stable isotope approaches to i) estimate sources of organic matter and energy to consumers and ecosystems, ii) estimate sources of natural and anthropogenic nutrients to ecosystems, iii) identify migratory patterns of mobile animals, iv) reconstruct trophic linkages in food webs, v) identify hydrologic sources to aquatic ecosystems, and a variety of other applications that are common in ecology.

Meeting Times

        Thursday 2:30p-4:20p in FSH 203 (We will end at 4:00p on days when there is a seminar in SAFS).


Gordon Holtgrieve, School of Aquatic and Fishery Sciences,, 206-616-7041, FSH 316B

Schedule, Structure, & Readings


Papers from the primary literature relevant to the weekly topics are provided in advance of each class meeting via the course Canvas site. Everyone should read these before coming to class and be prepared to engage in the discussions. One or two students (depending on enrollment) will lead the discussion.  Signups will happen the first week of class.  The last couple of meetings will focus on the application (and pitfalls) of isotope mixing models.


Isotope Mass Balance Exercises (by hand or in Excel)

In Week 3 there will be a problem set in which you will be given two problems and asked to derive isotope mass balances from scratch.  The problem set can be found here.

Bayesian Isotope Mixing Models (in R)

Mixing models (typically Bayesian) are a common but rapidly changing means to determine the relative proportions of different organic matter sources (e.g., prey) to a pool (e.g., consumer) using isotope data.  There are currently at least a half-dozen model structures used in the literature, each with significant assumptions and limitations.  We will learn to implement the more common models in R and explore the assumptions and limitations using real and artificial data sets.  This will result in a short report on your findings.  Some basic ability to read, write, and implement R scripts will be required.  Most of the code will be provided. 

University Policy on Academic Integrity

Plagiarism, cheating, and other misconduct are serious violations of the student conduct code. We expect that you will know and follow the UW's policies on cheating and plagiarism. Any suspected cases of academic misconduct will be handled according to UW regulations. More information, including definitions and examples, can be found in the Faculty Resource for Grading and the Student Conduct Code (WAC 478-120).

Disability Accommodations

To request academic accommodations due to a disability, contact Disability Resources for Students, 448 Schmitz, 206-543-8924 (V/TTY). If you have a letter from that office indicating that you have a disability that requires academic accommodations, present the letter to the instructor so that we can discuss the accommodations needed for the class.

Course Summary:

Date Details