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An image obtained from the system showing bar code bees in the observation hive. Contours indicate whether a barcode could be successfully decoded (green), could not be decoded (red) or was not recognized (no contour). The beehive entrance is in the lower right corner, and the inset shows two bees that were automatically recognized by the trophy taxis. Photo credit: Tim Gernat, University of Illinois
Bees and humans are about as different organisms as you can imagine. Despite their many differences, surprising similarities have been recognized in the way they interact socially in recent years. A team of researchers from the University of Illinois, Urbana-Champaign, has now experimentally measured honeybees’ social networks and their development over time, based on previous studies. They discovered that there are detailed similarities with human social networks and that these similarities are fully explained by new theoretical models that adapt the tools of statistical physics for biology. The theory, confirmed in experiments, implies that there are individual differences between honeybees, just as there are between humans.
The study, which for the first time measures the extent of individual differences in the networking of honeybees, was carried out by Sang Hyun Choi, Postdoctoral Fellow Vikyath D. Rao, Adam R. Hamilton and Tim Gernat, Swanlund Chair of Physics Nigel Goldenfeld (BCXT- Head / GNDP) and Swanlund Chair in Entomology and IGB Director Gene E. Robinson (GNDP). The collaboration included experimental measurements of honey bee social behavior performed by Hamilton, Gernat and Robinson, with data analysis by Rao and theoretical models and interpretations by Choi and Goldenfeld. Their findings were published in a recent article in the journal Procedure of the National Academy of Science.
“Originally, we wanted to use honeybees as a practical social insect to find ways to measure and think about complex societies,” Goldenfeld said. “A few years ago, Gene, Tim, Vikyath and I worked together on a large project where bees were ‘barcodes’ so we could automatically monitor where they were going in the beehive, which direction they were pointing and each interaction partner. That way, we could build a social network known as a temporal network in time. ”
This study, conducted several years ago, involved high-resolution imaging of honeybees with barcode customization, with algorithms capturing interaction events by mapping the bees’ position and orientation in the images. In these studies, when measuring social interactions between honeybees, researchers focused on trophallaxis – the process of transferring liquid food from mouth to mouth. Trophallaxis is used not only for nutrition but also for communication and is thus a model system for studying social interactions.
“We chose the trophallaxis because it’s the type of honeybees social interaction that we can closely track,” said Choi. “Since honey bees are physically connected to each other through nose contact during trophallaxis, we can determine whether or not they are actually interacting. In addition, each honey bee is tagged so that we can identify each person involved in each interaction event. ”
“In our previous work, we asked how long bees spend between events where they met other bees, and we showed that they interact in uneven ways,” said Goldenfeld. “Sang Hyun and I took the same dataset, but now asked a different question: What about the duration of interaction events, not the time between interactions?”
When looking at the individual interactions, the time spent varied from short interactions to long interactions. Based on these observations, Choi developed a theory in which bees exhibited an individual trait of attractiveness that could be compared to human interaction. For example, people might prefer to interact with friends or family members rather than strangers.
“We have developed a theory for this that is based on a very simple idea: When a bee interacts with another bee, you can think of it as a kind of” virtual source “between them,” said Goldenfeld. “The strength of the source is a measure of how much they are drawn to one another. If the source is weak, the bees quickly break the source and walk away, perhaps trying to find another bee to interact with. When the feather is strong, they can interact longer. We call this theoretical description a minimal modelbecause it can quantify the phenomenon of interest without requiring undue and unnecessary microscopic realism. Non-physicists are often surprised to learn that detailed understanding and predictions are possible with a minimum of descriptive input. ”
Goldenfeld explained that the mathematical framework for her theory came from a branch of physics called statistical mechanics, which was originally developed to describe gas atoms in a container and has since been extended to all states of matter, including living systems. Choi and Goldenfeld’s theory made correct predictions about the experimental honeybee data set previously collected.
Out of curiosity, the theory was then applied to human data sets, revealing patterns similar to the honeybee data set. Choi and Goldenfeld then applied an economic measure of wealth and income differences in humans – the so-called Gini coefficient – to show that bees exhibited different differences in attractiveness in their social interactions, if not as different as humans. These results show a surprising universality in patterns of social interaction in both honeybees and humans.
“It’s obvious that human individuals are different, but it’s not that obvious to honey bees,” Choi said. “So we examined the inequality in honeybees activity levels in a way that is independent of our theory to see if honeybee workers are actually different. Previous work in our group has used the Gini coefficient to quantify inequality in foraging by honeybees. Therefore, we thought that this method could also be used to study the inequality in trophallaxis activity. ”
“Finding such striking similarities between bees and humans arouses interest in discovering universal principles of biology and the mechanisms underlying them,” said Robinson.
The researchers’ results suggest that complex societies can have surprisingly simple and universal regularities that may shed light on how resilient and resilient communities emerge from widely differing social roles and interactions. The researchers predict that their minimal theory could be applied to other eusocial insects as the theory does not include features specific to honeybees.
In future studies, the same techniques from statistical mechanics can be applied to understand the cohesion of communities through well-characterized pair interactions, according to Choi and Goldenfeld.
“This was my first project after joining Nigel’s group and it was taking a long time to figure out how to properly address the problem,” said Choi. “It was fun and challenging to work on such an interdisciplinary project. As a physics student studying biological systems, I never expected to use concepts from business. ”
“It was very exciting to see how simple physical ideas can explain such a complex and widespread social phenomenon and also provide some organizational insight,” said Goldenfeld. “I was very proud of Sang Hyun for having the persistence and insight to find out. Like any transdisciplinary science, this was a really difficult problem, but incredibly fascinating when it all came together. This is the kind of advancement that results from the joint settlement of different scientists within the same laboratory – in this case the Carl R. Woese Institute for Genome Biology. ”
Reference: “Individual variations lead to universal and cross-species patterns of social behavior” by Sang Hyun Choi, Vikyath D. Rao, Tim Gernat, Adam R. Hamilton, Gene E. Robinson and Nigel Goldenfeld, November 30, 2020, Procedure of the National Academy of Sciences.
DOI: 10.1073 / pnas.2002013117
