Honey bee communication and collective decision-making

Project overview: Honey bees are social insects with tens of thousands of bees per colony that have evolved amazing strategies to collectively solve problems. For example, they have a unique communication signal called a waggle dance that allows them to communicate the direction/distance to a rewarding resource. Scientists can "eavesdrop" on these conversations between bees and map the locations that dancing bees advertise. Previous work has shown that honey bees are more likely to perform a waggle dance if the nectar source that they visited was more profitable (greater ratio of energy gained to energy spent per foraging trip), which allows a colony of honey bees to focus its foraging efforts on the best resources. However, we do not know whether honey bee foragers also assess the size of a flower patch (and thus how many other bees could also exploit it) when deciding whether and how persistently to recruit their sister foragers. We address this question using a combination of computer modeling, field experiments, and computer vision.


Subproject 1 (CS/Biology): We use computer modeling to understand how changing the rules that govern honey bee recruitment behavior affects colony foraging success in different landscapes. Currently, we are working with an agent-based simulation written by a former student that simulates a colony of honey bees and its interactions with flower patches in a surrounding landscape. This model was written in a multi-agent programmable environment called NetLogo. We plan to expand the capabilities of this model. To accomplish that, we plan to first re-write the model in Python.


Subproject 2 (Biology): To test whether and how flower patch size affects the communication behavior of real honey bee foragers, we will set up experiments at the Bernard Field Station this summer. We will video record honey bee colonies housed in glass-walled observation hives in order to map their waggle dance communications. These bees will have access to the surrounding landscape, including varying patch sizes of California buckwheat flowers growing at the field station. This subproject will involve surveying the surrounding flowers and recording honey bee visits to those flowers. It will also involve marking individual honey bees and training them to visit sugar-water feeders at different distances from their hives so that we can record and analyze their waggle dances.


Subproject 3 (CS/Biology): To process all of the videos of bees in these observation hives, we will be testing and adapting code that automatically detects and decodes honey bee waggle dances. This Python code was recently developed by researchers at the University of London and uses the library OpenCV for computer vision and machine learning. It currently extracts the information needed to map the flower patches advertised in waggle dances, but we plan to adapt it so that we can extract other important information about dancing behavior as well. We will use the data generated in coordination with another summer project in the Bee Lab (Mapping Flowers to Save the Bees- see associated software pipeline) to better assess how the distribution of flowers affects recruitment behavior.


Essay Prompts:

What interests you most about the project, what do you hope to get out of the research project and how does it fit with your long-term goals? 


How do your current skills and experience make you a good fit for one or more of the subprojects?


To complete your application for summer research in Biology, please contact me to discuss the project and submit this google form by Feb 28. If you have any questions or want to learn more, I encourage you to contact me before submitting your application.

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

The HMC Bee Lab is an interdisciplinary group that includes students from Biology, CS, Math, Engineering, and other fields studying a wide variety of questions about collective decision-making in both bees and ants. In this lab you will develop new skills and get a sense of many different kinds of research and approaches to answering questions. You could also potentially continue your work in a senior thesis project, present at regional or national conferences, and/or co-author a publication.

Logistics Information:
Project categories
Computer Science
Computer Vision
Natural Resources and Conservation
Student ranks applicable
Student qualifications

Because this project involves several subprojects that you may work on, we encourage students with a variety of interests and backgrounds to apply. All applicants should have a strong interest in understanding the natural world. First- and second-year students are welcome to apply as well as more advanced Biology and CS students interested in applying their knowledge/skills to practical problems. A knowledge of Python programming would be helpful, especially some experience with OpenCV, but this experience is not required.

Time commitment
Summer - Full Time
Paid Research
Number of openings
Techniques learned

Depending on which subproject(s) you work on, you may learn:

  • Python coding skills
  • computer modeling skills
  • computer vision techniques (OpenCV)
  • ecological study design and data analysis
  • field work techniques (vegetation surveys, behavioral observations, etc.)
  • animal care experience (beekeeping skills)

All students will learn to read and discuss scientific literature, and to communicate across disciplinary boundaries and with the public about their work.

Contact Information:
Mentor name
Morgan Carr-Markell
Mentor email
Mentor position
Postdoctoral Fellow
Name of project director or principal investigator
Morgan Carr-Markell, Matina Donaldson-Matasci
Email address of project director or principal investigator
2 sp. | 28 appl.
Hours per week
Summer - Full Time
Project categories
Computer Science (+3)
BiologyComputer ScienceComputer VisionNatural Resources and Conservation