Syllabus¶

Course Information¶

Course: BIO 597 Spatial Analysis of Biodiversity
Term: Fall 2026
Instructor: Isaac Overcast (isaac.overcast@maine.edu, he/him)
Format: Seminar plus applied computational lab
Location: Online synchronous
Day/Time: Fridays 10:00-12:50AM (Eastern)
Prerequisites: Prior coursework in ecology, evolution, biogeography, GIS, statistics, or permission of instructor. Some programming experience in R or Python is recommended.
Credits: 3
Office hour: Scheduled upon request

Course Description¶

An advanced survey of tools for modeling and analysis of spatial patterns of biodiversity, including species distributions, biodiversity metrics, endemism, phylogenetic diversity, and landscape connectivity. The course emphasizes environmental drivers, including remote sensing and microclimate data, within predictive frameworks such as species distribution models and machine learning models.

Organization¶

Each meeting will be a mix of lecture, in-class "active" learning, and group activities. In addition a lot of work is assigned outside of class, including assignments, reading, and watching video tutorials. An example session would include a lecture to introduce a general concept with examples from biological research, followed by a group active-learning exercise in which students implement the method applied to real datasets.

Learning Outcomes¶

By the end of the course, students should be able to:

Work with geographic coordinates and spatial biodiversity datasets.
Use vector and raster geospatial data in Python.
Acquire and clean biodiversity and environmental data.
Compute and interpret biodiversity, endemism, and phylogenetic diversity metrics.
Perform spatial joins, overlays, buffering, distance calculations, and spatial sampling.
Analyze spatial patterns of species distributions and biodiversity.
Quantify environmental niches and species–environment relationships.
Account for spatial autocorrelation and spatial sampling bias.
Perform spatial interpolation and prediction.
Analyze community composition across space.
Build reproducible spatial workflows and communicate spatial results.

Assessment/Grades¶

Grades are based on participation (attendance and discussions), assignments, projects proposals, and project presentations, and the final project.

30% - Weekly applied exercises
15% - Short spatial-analysis exercises
15% - Project proposal and progress check (midterm)
10% - Participation/discussion
30% - Final project

Course Policies¶

Assignments: Code-based assignments are used to assess comprehension of new concepts learned in class. These are graded on effort, accuracy, and style.

Projects: A course project will be developed by each student. We will begin planning these very early in the class, well before you will have learned the skills to develop them. As we progress through class we will revisit and revise project proposals, while also discussing how the concepts we learn may be usefully applied to aspects of the projects. A formal project proposal is due halfways through class. Near the end of class students will give presentations on the status of their projects. The final projects will be published as open source code on github and evaluated on the basis of documentation, style, and a working example.

Attendance policy: This course relies upon student participation and, thus, attendance is expected. Absences will incur a grade penalty unless excused. Students who are unable to attend class for health or other personal reasons should reach out to the instructors. We understand that there are many legitimate reasons for absences, so do please reach out.

Statement on policy for students with disabilities: If you have a disability for which you may be requesting an accommodation, please contact Student Accessibility Services, 121 East Annex, 581.2319, as early as possible in the term. Students who have already been approved for accommodations by SAS and have a current accommodation letter should meet with me (the instructor of the course) privately as soon as possible.

Statement on use of generative AI in coursework:

EXERCISE FOR DAY 1: Generate our own Acceptable Use of AI Policy for the class.

This is a previous version I had used, but don't peek at it:
Students are not discouraged from using AI tools, such as ChatGPT or similar technologies, to support their learning and assignments. However, the use of AI must be acknowledged clearly in your submissions. For example, you can include a brief note stating, "This analysis was generated with the assistance of [AI tool name]."

While AI can be a powerful resource, it is not infallible. These tools can occasionally produce incorrect or misleading information, and relying on them exclusively may hinder your ability to critically evaluate data, methods, and results. Therefore, you are expected to: - Verify Information: Cross-check AI-generated outputs against authoritative sources or class materials. - Develop Independent Skills: Engage with the concepts and tools independently to ensure you can identify when AI outputs are inaccurate or suboptimal. - Take Responsibility: Ultimately, you are accountable for the content and quality of your work, regardless of whether it was AI-assisted.

By using AI judiciously and critically, you will enhance your ability to evaluate the reliability of data and tools, which is a vital skill in data science. This statement on the use of generative AI was generated with the assistance of ChatGPT.

Statement of academic integrity: Academic dishonesty is a serious offense and will not be tolerated in the class. Students are expected to reference sources appropriately in any work. Students are allowed to discuss homework assignments but should respond to questions and tasks on their own, not using a group answer. Violation of the University of Maine rules of academic integrity (e.g., plagiarizing materials), will result in automatic failure of the course. Rules and consequences are outlined in the University of Maine System Academic Integrity Policy.

Equipment¶

All software and materials for the course are open access (available online for free) including assigned readings and videos. Students will learn to code in a production-quality virtual environment provided by the University of Maine ARCSIM.

Required Materials¶

Readings will be assigned from journal articles, open textbooks, software documentation, and technical reports. All computational work will use open-source tools.