Last update to syllabus: 11/04/2025

Overview

Course number: WILD 7870

Instructor: Liam A. Berigan
Assistant Professor
College of Forestry, Wildlife, and Environment
Office: 2349 CFWE Building, 602 Duncan Dr.
Phone: (334) 844-1044
Email:
Office hours: Friday, 10–11:30am
Statistical consulting: Mon–Fri, 10am–4pm (when door is open)

Course Description: It has been said that the term “spatial ecology” is redundant because ecology is an inherently spatial discipline. Nonetheless, in this course we will explicitly consider how ecological processes play out over space, and how space feeds back to influence ecological processes. We will combine lectures, discussions, and hands-on lab-based learning to explore the theory, tools, and challenges underlying the spatial aspects of ecology. This course will cover a breadth of topics rather than focusing on any one in-depth, although students will be provided resources to dig deeper into issues of particular interest.

Credit Hours: 4

Course Prerequisites: Students should have a strong background in ecology, equivalent to an undergraduate degree in biology or a related discipline. A prior inferential statistics course (such as WILD 5750/6750) is also highly recommended. Experience in R programming will be helpful during the course, but is not a prerequisite.

Outcomes and Objectives

Student Learning Outcomes

  • You will be able to use common geospatial tools (e.g., R, Google Earth Engine) to conduct spatially explicit analyses and understand the unique data formats used by these tools
  • You will be able to efficiently convert data from spreadsheets to spatially explicit formats (e.g., shapefiles and rasters) and back again
  • You will be able to describe how spatially explicit regression tools differ from traditional generalized linear models
  • When presented with information on a species, you will be able to identify the multi-scaled factors driving its distribution in space and time
  • You will distinguish among spatial models of a landscape and be able to describe concepts of habitat amount, fragmentation, and connectivity and why they can be controversial
  • When presented with an ecological research question, you will be able to identify the pros and cons of taking a spatially explicit approach and when it is/isn’t necessary
  • You will utilize spatial principles in real-world applications including reserve design, wildlife management, and forest stewardship

Objectives:

  • Learn the theoretical principles underlying spatial statistics
  • Practice using statistical tools for analyzing spatially explicit data
  • Read and analyze seminal works in spatial ecology
  • Learn about the multi-scaled factors that structure plant and animal distributions across the world
  • Discuss conceptual models of habitat and population structure
  • Learn how spatial ecological principles can be used in real-world conservation and management applications

Assignments, Grading, and Class Materials

Assignments

In general, each week will consist of one lecture (Tuesday), one lab (Wednesday), and one discussion (Thursday).

  • Labs (50% of grade): In the lab, we will explore concepts from the week’s lecture and discussion through data, simulations, and analyses. A lab report will be due one week after it is assigned, and lab assignments will be worth 50% of your grade. You should complete your lab assignments in R Markdown and submit them via Canvas. Your code must be fully reproducible (i.e., produce the desired result when running the code sequentially) in order to receive full points.
  • Final projects (25% of grade): For their final exam, students will be required to complete and present a project that uses tools and theory from class to test a hypothesis of their choosing (using data of their choosing) in a spatially explicit framework. This project will be worth 25% of your grade.
  • Discussions (25% of grade): The last part of your grade will be based on attendance at lectures and participation in discussions.

Project details: There are a few rules for your final project, but otherwise, I consider it to be relatively wide open in terms of the direction you want to take it.

  • You should identify a hypothesis of interest to you and test it using relevant data in a spatially explicit framework.
  • You will develop a 1–2 page proposal by mid-semester and present it to me for feedback to ensure you are on the right track.
  • You will develop an ~5,000 word document formatted just like a scientific manuscript with an abstract, introduction, methods, results, discussion, and literature cited sections.
  • You will give a 12 minute PowerPoint (or similar) presentation on your project to the class.

This project is about you demonstrating that you have learned something in this class and can apply it to real-world questions. My hope is that you can use this project as a springboard for something you were interested in working on for your thesis/dissertation. However, if you do not have data or are not testing questions related to spatial ecology in your own work, see me, and I will be happy to help you develop a project.

Example project ideas:

  • Test whether the scale at which some life history characteristic affects a group of species is mediated by life history characteristics
  • Identify priority conservation areas for a target species
  • Examine factors influencing movement or dispersal patterns
  • Build a landcover classification model and examine how the pattern of some class of interest varies in space and/or time
  • Examine how variation in scale affects the answer to your question
  • Explicitly compare range maps for a species of interest with fine-grained species distribution model and evaluate the implications for conservation planning
  • Build a metapopulation model
  • Test effects of matrix composition on movement or population dynamics
  • Quantify factors influencing colonization and extinction of habitat patches for a target species
  • Evaluate how the spatial configuration of a landcover type has changed in space and time
  • Test fragmentation effects on a response of interest using different fragmentation models

Grading and Evaluation Procedures

The grading scale for this class is as follows: A (100–90), B (89–80), C (79–70), D (69–60), and F (< 60). Labs will comprise 50% of your grade, the final project will be 25%, and the remaining 25% will be based on your participation in lectures and discussions. Students may withdraw without grade penalty until the 15th class day, and until mid-semester (although a W will appear on the student’s transcript if the student withdraws between the 16th and 36th class day). Students who withdraw from the course between the 6th class day and the 15th class day will pay a course drop fee of $100.

Classroom Policies

Attendance, late assignments, missed work, etc.

I will record all lectures, labs, and discussions using Panopto and post them online. If you miss a class, I expect you to watch the material. If you miss a lab, the due date for your lab report does not change. If you miss a discussion, you can make up your participation points by submitting a two paragraph document to me within one week of the missed class. The first paragraph should contain a summary of the reading. The second paragraph should contain your contribution to our in-class discussion. That is, you will need to demonstrate that you have watched the recording of our discussion and weigh-in with your thoughts.

Excused absences: Students are granted excused absences from class for the following reasons: illness of the student or serious illness of a member of the student’s immediate family, death of a member of the student’s immediate family, trips for student organizations sponsored by an academic unit, trips for University classes, trips for participation in intercollegiate athletic events, subpoena for a court appearance and religious holidays. Students who wish to have an excused absence from this class for any other reason must contact the instructor in advance of the absence to request permission. The instructor will weigh the merits of the request and render a decision. When feasible, the student must notify the instructor prior to the occurrence of any excused absences, but in no case shall such notification occur more than one week after the absence. The instructor may request documentation for excused absences depending on the circumstances.

Late assignments: Twice per semester a student may turn in her/his weekly lab assignment up to one week later than the due date. Any assignment turned in more than one week late will receive a zero. If two assignments have already been turned in late, all other late assignments will receive a zero unless the student has extenuating circumstances that they have discussed with the instructor ahead of the due date.

Canvas: Weekly assignments and other course notifications will be disseminated via Canvas. Students are responsible for checking Canvas to ensure they are up-to-date on course information. Note that each student has control over her/his notifications via Canvas and can edit settings to alert them when an announcement is posted, an assignment is due, a grade is released, etc.

Academic Honesty: All portions of the Auburn University Student Academic Honesty code (Title XII) found in the Student Policy eHandbook will apply to this class. All academic honesty violations or alleged violations of the SGA Code of Laws will be reported to the Office of the Provost, which will then refer the case to the Academic Honesty Committee.

Classroom Behavior: The Auburn University Classroom Behavior Policy is strictly followed in the course; please refer to the Student Policy eHandbook for details of this policy.

Emergency Contingency: If normal class and/or lab activities are disrupted due to illness, emergency, or crisis situation, the syllabus and other course plans and assignments may be modified to allow completion of the course. If this occurs, an addendum to your syllabus and/or course assignments will replace the original materials.

Tentative schedule

Spring break (March 9–13)

Generative Artificial Intelligence Tools

AI Policy: Permitted in this Course with Attribution

In this course, students are encouraged to use generative AI tools like ChatGPT to support their work. Note that this comes with several conditions:

  1. All work submitted must reflect your original thought. AI can be used to help implement your ideas (e.g., suggesting functions, troubleshooting error messages when coding), but cannot be used as a replacement for your critical thinking during assignments (e.g., plugging assignment questions into generative AI tools and copy-pasting the results).

  2. You are ultimately responsible for the factual accuracy of everything that you submit. Generative AI tools are known to hallucinate (i.e., present incorrect material as factual), with consequences ranging from broken code to completely fabricated citations. When coding, check the recommendations of generative AI against documentation (accessed using the help() function), and use tests and checks to ensure that the resulting code works as intended.

  3. If you elect to use AI-generated material in assignments, you should include an AI statement during submission. This statement can be entered into the comments field on Canvas during the submission process. A sample AI statement might look as follows: “[Generative AI Tool Name] was used in the following way(s) in this assignment [e.g., brainstorming, grammatical correction, citation, which portion of the assignment].” If coding, provide additional information about any tests or checks you’ve conducted to ensure that AI-generated code works as intended.

Use caution and avoid sharing any sensitive or private information when using these tools. Examples of such information include private scientific data, personally identifiable information, protected health information, financial data, intellectual property, and any other data that might be legally protected.

Student Resources

Accommodations

Students who need accommodations should submit their approved accommodations through the AIM Student Portal on AU Access and follow-up with the instructor about an appointment. It is important for the student to complete these steps as soon as possible; accommodations are not retroactive. Students who have not established accommodations through the Office of Accessibility, but need accommodations, should contact the Office of Accessibility at or (334) 844-2096. The Office of Accessibility is located in Haley Center 1228.

Mental Health

If you are experiencing stress that feels unmanageable (personal or academic) during the semester, Auburn University’s Student Counseling & Psychological Services (SCPS) offers a variety of services to support you. The mission of SCPS is to provide comprehensive preventative and clinical mental health services to enhance the psychological well-being of individual students, as well as the broader campus culture. As an instructor, I am available to speak with you regarding stresses related to your work in this course, and I can assist in connecting you with the SCPS network of care. You can schedule an appointment yourself with the SCPS by calling (334) 844-5123 or by stopping by their offices on the bottom floor of Haley Center or the second floor of the Auburn University Medical Clinic. If you or someone you know needs to speak with a professional counselor immediately, the SCPS offers counseling during both summer term as well as the traditional academic year. Students may come directly to the SCPS and be seen by the counselor on call, or you may call (334) 844-5123 to speak with someone. Additional information can be found on the SCPS website.

Basic Needs

Any student who faces challenges securing their food or housing and believes this may affect their performance in the course or others is urged to contact Auburn Cares at (334) 844-1305, or explore the resources on their website. Furthermore, please notify me if you are comfortable in doing so as this will allow me to connect you with any other known resources.

Justification for Graduate Credit

This course is eligible for graduate credit because students will be learning advanced topics that are built on a strong undergraduate education in ecology, biology, statistics, and coding. This course will foster independent learning through lab assignments that will require students to explore published literature and think critically about how findings from their lab work interact with ecological theory. In addition, students will be tasked with applying the knowledge they obtain to their own research projects and presenting their findings.