Technical advances have made it beneficial for ecologists to collaborate with computer scientists and software engineers. Fortunately, there are many technical students entering the workforce who have an interest in ecological applications. These students benefit from interacting with ecologists on projects during their university training in Computer Science (CS). This case study samples six years (2016-2023) of projects favored by students in a 400-level, project-based CS capstone course. Data include the project descriptions, project motivations, preferred qualifications, and deliverables from over 200 projects. These data are analyzed to discover themes and trends in the following: computer languages; tools (e.g., AWS, GIS, software/hardware); libraries (e.g., PyTorch, TensorFlow); and topics (e.g., data science, machine learning, robotics, IoT, Augmented Reality, web development). In addition, a sub-sample of 15 ecologically-focused projects are further analyzed, highlighting how they compare to the general sample. Recommendations from the two analyses include (1) tips for how to collaborate with CS students and faculty through project-based learning, including how to frame project descriptions and deliverables to appeal to CS undergrads and faculty; (2) examples of tools, languages, libraries, and topics that might be useful, but overlooked in ecologically-themed projects (e.g., a student conversant in machine learning might assist in new analysis techniques; a student team interested in graphics might provide creative and powerful ways to approach visualizing study results; a student team interested in sensors might suggest new ways to gather and display data); and (3) how to include project-based learning with CS undergrads as part of a broader-impact plan.
