Tracking Ant Movement in Artificial Trees

What do ant colonies and railroad systems have in common? Both serve to transport goods and individuals from place to place, and need to balance the often competing goals of doing so efficiently and at low cost, while also remaining robust to potential disruptions to the network. We are currently working to understand how simple organisms can work together in groups to create and maintain such transportation networks, using a multidisciplinary combination of field and laboratory experiments with turtle ants, along with mathematical and computational models.

We have conducted a series of experiments designed to discover how ants move within a tree-like branching structure, and how individual movement choices lead to collective decisions about where to nest within that structure. We now have a good understanding of how individual ants make choices at branching junctions and are now working to describe how these choices are modified when many ants are exploring together. To do this, we are using our own software pipeline, which uses computer vision to automatically track ant movement from videos taken in the lab. This has allowed us to collect much more data on ant turning choices in the challenging context when multiple ants are navigating the same structure. We would now like to apply this pipeline to collect data on our most recent experiment, which was designed to explore how competition between ant colonies affects exploration and nest choice.

Name of research group, project, or lab
HMC Bee Lab
Why join this research group or lab?

You will be part of a team of students working on a set of related interdisciplinary projects, using biology, mathematics, computation, and engineering to solve problems of biological interest. The variety of techniques and approaches will give you an opportunity to explore your interests and develop new skills. This project has connections to the study of complex systems and artificial intelligence, and has potential applications to the development of computational optimization algorithms. There may be opportunities to continue the work in a senior thesis, present at a regional or national conference, and/or co-author future publications. The work is funded by the National Science Foundation.

Representative publication
Logistics Information:
Project categories
Biology
Computer Science
Computational Biology
Computer Vision
Data Science
Student ranks applicable
Sophomore
Junior
Senior
Student qualifications

A course in statistics (e.g. BIOL 154) and programming experience in R and/or Python are required. 

Here is a list of other skills/interests that could be useful, or which you might develop along the way. They're not requirements, but if you already have experience or interest in any of them, be sure to mention it in your application.

  • Data science and visualization with R
  • Linux/UNIX command line and shell scripting, high performance computing
  • Computer vision and image processing, e.g. using OpenCV
  • Ecology and animal behavior

While this project is likely best suited for a sophomore or higher, first-years with an exceptional background are also welcome to apply.

Time commitment
Fall - Part Time
Compensation
Academic Credit
Number of openings
1
Techniques learned

In this project, you will

  • work with a software pipeline written in Python to track ants
  • run, monitor and troubleshoot/optimize Linux shell scripts for collecting data
  • extract, analyze and visualize data resulting from the pipeline

You will also learn to read and discuss scientific literature, and to communicate across disciplinary boundaries and with the public about your work.

Project start
Fall 2024
Contact Information:
Mentor
mdonaldsonmatasci@hmc.edu
Associate Professor of Biology
Name of project director or principal investigator
Matina Donaldson-Matasci
Email address of project director or principal investigator
mdonaldsonmatasci@g.hmc.edu
1 sp. | 3 appl.
Hours per week
Fall - Part Time
Project categories
Computer Vision (+4)
BiologyComputer ScienceComputational BiologyComputer VisionData Science