Ecosystem Ecology

Courtney Campany
Colgate University


This course covers topics related to terrestrial ecosystem ecology. By utilizing lectures, journal discussions, grand challenges and a data science tool kit students are immersed in the modern field of ecology that utilizes local, regional or global scale patterns to unpack broad ecological processes.

Course Size:

Course Format:
Students enroll in one course that includes both lecture and lab. The lecture and the lab are both taught by the professor.

Institution Type:
Private four-year institution, primarily undergraduate

Course Context:

This is an upper level ecology course designed for students majoring in biology or environmental science degrees. This course has a required lab.

Course Content:

This course focuses on the foundational principles of terrestrial ecosystem ecology. Topics of lectures and journal discussion include Earth's energy budge and climate systems, nutrient cycling, productivity and biomass distributions, disturbance, succession and global change. The lab portion of the course uses mystery data sets for students to explore patterns in many of the lecture topics. Finally, team-based student-driven group projects are assigned to tackle complex socio-ecological issues. These mentored group projects promote active self-learning, diversity driven group work, networking and ecological activism.

Course Goals:

Course Goals:
1) To help you understanding the basic principles and concepts of ecosystem ecology, the impacts of human-induced global changes and how these changes are affecting ecosystem processes.
2) To provide you with modern data science skills that are increasingly required in post-collegiate careers
3) To critically evaluate and understand the primary scientific literature.
4) To engage environmental activism through grand challenge initiative projects

Course Features:

The lecture portion of the class includes a weekly lecture on a foundational ecology topic followed by a jigsaw journal discussion group of 2-3 recent articles on the same topic. Jigsaw discussion involve small groups reading 1 journal article and teaching it to other student groups. This lecture slot concludes with a holistic discussion of the ecological topic and it's direction in current and future ecological research.

The lab portion of this course is split into two formats; a grand challenge initiative (GCI) team-based group project and bi-weekly data science in ecology assignments. Teams of students are assigned a large-scale environmental problem that has been proposed for the class an outside source. Students are tasked with creating a solutions based ecological action plan, as if this were a real-world job assignment. Bi-weekly meetings with each group are used to discuss progress, set deadlines and give presentations. Mystery data science problems are drawn from published open source data sets and the NEON data portal. Students are taught data science tools in the R coding language and are tasked with interpreting each data set (without knowing it's source). A large focus is on visualizing and manipulating the data sets and not statistics. After assignment submission the source of each data set is revealed and is discussed relative to their own interpretation.

Course Philosophy:

The lecture portion of the course is meant to break from static class room teaching techniques by getting students actively engaged in current topics, controversies and directions of large scale ecology research. As an upper level course, this approach engages students in critical thinking to evaluate how the fundamental principles in ecology are being tested, challenged or utilized in modern research.

The lab portion of the course is mean to engender the difficulties in solving real-world ecological problems and also to build a data-science driven skill set for undergraduate students. The grand challenge questions expose students to the difficulties in addressing ecological issues that may cross scientific, political or cultural boundaries. The data science portion of the lab allows students to work with open-source large data sets that encompass many ecological topics covered in lecture. Data science assignments are built around both data exploration and applied problems to prepare students for both academic or management-type environmental careers.


Lecture grades are assigned based on testing of lecture topics combined with test questions drawn during each journal group discussion.

Lab grades are assigned based on the ecological action plan submitted for grand challenge initiative combined with a self-assessment from each individual regarding their role within the group project. Data science problems are assessed by the ability of the student to produce fully reproducible html reports of each mystery data set, that show proficiency in (1) data visualization and (2) interpretation of the data without the need for statistics.


[file Ecosystem Ecology syllabus (Acrobat (PDF) 503kB May2 19) 'Syllabus']

References and Notes:

Principles of Terrestrial Ecology / F. Stuart Chapin III, Pamela A. Matson, Peter M. Vitousek
Classic ecosystem ecology textbook
A Learning Guide to R /Remko Duursma, Jeff Powell & Glenn Stone
Great open source manual for the R coding language
Students are exposed to somewhere between 30-40 scientific papers throughout the course, either through direct reading or being taught from other student groups.