The UCLA Library DataSquad is a team of undergraduate students who support data-related projects at UCLA. As part of the UCLA Library Data Science Center, the DataSquad works with students who need help with their data projects and highlights the work of researchers using data at UCLA.
Amanda Robin is a Ph.D. candidate in the Department of Ecology and Evolutionary Biology, researching the behavior and locomotion of wild squirrels. In 2019, Robin and her team equipped wild California ground squirrels (Otospermophilus beecheyi) with motion-sensitive collars. These collars allowed researchers to record accelerometer data at 100 values per second as the squirrels moved through their environment.
Robin and the rest of the Squirrel Gazer team observed the squirrels’ behaviors and characterized their activities with labels such as “sitting,” “standing,” “laying down” and more. Using the motion data and the created labels, Robin wants to utilize machine learning to better understand behavior and patterns. The Data Science Center is helping her use R and R packages (statistical programming languages) to analyze the data and develop neural networks to predict a squirrel’s activity and behavior.
The DataSquad interviewed Amanda to learn more about her inspiration and her project.
What got you interested in ecology and evolutionary biology?
Squirrels! When I was a community college student, I thought I wanted to be a clinical psychologist; however, when I got to UC Berkeley, I learned more about animal psychology. I started in the Jacobs Lab of Cognitive Biology(opens in a new tab) and found that I liked animal psychology more than human psychology. Currently, I am in the Nonacs Lab at UCLA. One of my advisors is Dr. Jennifer E. Smith, an evolutionary behavioral ecology researcher from Mills College. It’s been fun trying to figure out what animals are doing in nature!
Why did you choose to specifically study the behavior patterns of California ground squirrels?
California ground squirrels are ecosystem engineers! They help provide protection and even housing for other animals. We are looking at how different squirrels behave underground because it never really has been done before. Patterns such as which squirrels are in charge of building more than others (female or male, for example) can be discovered.
To research these squirrels, we needed to know how to get underground. We couldn’t cast the burrows, similar to how researchers study ant burrowing, because the tunnels and pathways were too extensive. People have tried cameras, but it's difficult to determine measurements with them. That’s when we decided to use accelerometer, gyroscope collars to model the 3D paths! Eventually, we want to use the collars and our machine learning algorithms to study Californian squirrels and their behavior underground.
What has been the most challenging part of the experiment so far?
We started trying to build the collars, but that was a mess. Eventually, we bought the collars that we use now. Also, following the squirrels for three hours and tracking their every move in summer is very difficult. When you are trying to connect data collected on wild animals in the field to computer science, there are so many other steps that you have to connect to make sure you are providing the machine learning algorithms with meaningful data.
Thank you to Amanda Robin for talking with the DataSquad about your work!