Skip to content

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

  • In-person Location (optional): 202 Deering Hall

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 hands-on coding activities, and paper discussions. 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.

The general structure of a class period will be something like this:

  • 1hr paper discussion
  • 1hr lecture/student presentations
  • 1hr hands-on coding activity

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.

BIO597 AI Policy Draft

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.