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IMAGE: An image obtained by the system showing bar-coded bees inside the observation hive. The outlines reflect whether a barcode could be successfully decoded (green), could not be decrypted (red), or if it was … More
Credits: Tim Gernat, University of Illinois
Bees and humans are as diverse organisms as you can imagine. However, despite their many differences, striking similarities in the way they interact socially have begun to be recognized in recent years. Now, a team of researchers from the University of Illinois Urbana-Champaign, based on their previous studies, experimentally measured honeybees’ social networks and how they develop over time. They found that there are detailed similarities to the social networks of humans, and that these similarities are fully explained by the new theoretical modeling, which adapts the tools of statistical physics for biology. The theory, confirmed in experiments, implies that there are individual differences among honey bees, just as there are among humans.
The study, which measures for the first time the magnitude of individual differences in honey bee networking, was led by first author of physics graduate student Sang Hyun Choi, PhD students Vikyath D. Rao, Adam R. Hamilton and Tim Gernat, Swanlund Chair of Physics Nigel Goldenfeld and Swanlund Chair of Entomology Gene E. Robinson (GNDP). Goldenfeld and Robinson are also lecturers at the Carl R. Woese Institute for Genomic Biology in Illinois, of which Robinson is the director. The collaboration included experimental measurements of the social behavior of honey bees performed by Hamilton, Gernat, and Robinson, with data analysis from Rao and theoretical models and interpretations by Choi and Goldenfeld. Their findings were published in a recent article in the journal Proceedings of the National Academy of Science.
“Originally, we wanted to use honey bees as a convenient social insect to help us find ways to measure and think about complex societies,” Goldenfeld said. “A few years ago, Gene, Tim, Vikyath and I collaborated on a big project that put” barcodes “on bees so that we could automatically track wherever they went in the hive, every direction they pointed and every interaction partner. In this way, we could build a social network over time, something known as a time network. “
The study, conducted a few years ago, involved high-resolution imaging of barcoded honey bees, with algorithms that detect interaction events by mapping the position and orientation of bees in the images. In these studies, the researchers focused on trophylaxis – the act of mouth-to-mouth transfer of liquid food – when measuring social interactions between honey bees. Trophallaxis is used not only for nutrition but for communication, making it a model system for the study of social interactions.
“We chose to consider trophylaxis because it’s the kind of social interaction in honey bees that we can accurately track,” said Choi. “Since honey bees are physically connected to each other by proboscis contact during trophylaxis, we can tell whether they are actually engaged in an interaction or not. In addition, each honey bee is marked so that each individual involved in each event can be identified. of interaction. “
“In our previous work, we asked how long bees spend between events where they meet other bees, and we showed that they interact unevenly,” Goldenfeld said. “Sang Hyun and I took the same data set, but now we asked a different question: what about the duration of the interaction events, not the time between interactions?”
When observing individual interactions, the elapsed time ranged 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, humans may prefer to interact with friends or family rather than strangers.
“We developed a theory for this based on a very simple idea: if a bee is interacting with another bee, you can think of that as a kind of ‘virtual spring’ between them,” Goldenfeld said. “The strength of the spring is a measure of how attracted they are to each other, so if the spring is weak, the bees will quickly break the spring and leave, perhaps to find another bee to interact with. spring is strong, they can interact longer. We call this theoretical description a minimal model, because it can quantitatively capture the phenomenon of interest without requiring excessive and unnecessary microscopic realism. Non-physicists are often surprised to learn that it is possible to make a detailed understanding and predictions with a minimal amount of descriptive input. “
Goldenfeld explained that the mathematical framework for their theory originated from a branch of physics called statistical mechanics, originally developed to describe gas atoms in a container, and has since extended to encompass all states of matter, including systems. living. Choi and Goldenfeld’s theory made correct predictions on the experimental honey bee dataset that was previously collected.
Out of curiosity, the theory was then applied to human datasets, revealing patterns similar to those of the honey bee dataset. Choi and Goldenfeld then applied an economic measure for wealth and income inequality in humans – called the Gini coefficient – to show that bees exhibited inequalities of attractiveness in their social interactions, although not as diverse as humans. These results indicate a surprising universality of the patterns of social interactions in both honey bees and humans.
“It’s obvious that human individuals are different, but it’s not that obvious to honey bees,” Choi said. “Therefore, we examined the inequality in the activity level of honey bees in a way independent of our theory to verify that bee workers are indeed different. Previous work done in our group used the Gini coefficient to quantify the inequality in honey bee foraging activity, so we thought this method would also work for examining inequality in trophylaxis activity. “
“The discovery of such striking similarities between bees and humans ignites interest in the discovery of the universal principles of biology and the mechanisms behind them,” said Robinson.
The researchers’ findings suggest that complex societies can have surprisingly simple and universal regularities, which can potentially shed light on how resilient and robust communities emerge from very different social roles and interactions. The researchers predict that their minimal theory could be applied to other eusocial insects since the theory does not involve specific characteristics of honey bees.
In future studies, the same techniques of statistical mechanics can be applied to understand community cohesion through well-characterized pair interactions, Choi and Goldenfeld said.
“This was my first project after I joined Nigel’s group, and it took me a long time to find the right way to tackle it,” 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 economics.”
“It was very exciting to see how simple physical ideas could explain such a complex and widespread social phenomenon, and also provide some insights into the organism,” Goldenfeld said. “I was very proud of Sang Hyun for having the persistence and the insights to understand this. Like all transdisciplinary science, this was a really difficult problem to solve, but incredibly fascinating when it all came together. from the co-location of several scientists within the same laboratory, in this case the Carl R. Woese Institute of Genomic Biology “.
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