Tag Archives: #honeybee

Radar Tracking Uncovers Mystery Of Where Honeybee Drones Have Sex (Biology)

Scientists from Queen Mary University of London and Rothamsted Research have used radar technology to track male honeybees, called drones, and reveal the secrets of their mating behaviours.

The study suggests that male bees swarm together in specific aerial locations to find and attempt to mate with queens. The researchers found that drones also move between different congregation areas during a single flight.

Drones have one main purpose in life, to mate with queens in mid-air. Beekeepers and some scientists have long believed that drones gather in huge numbers of up to 10,000 in locations known as ‘drone congregation areas’. Previous research has used pheromone lures to attract drones, raising concerns that these lures could have inadvertently caused these congregations. This new study is the first ever attempt to track the flight paths of individual drones and observe them in the absence of lures.

Similar mating sites, in which large numbers of males gather, have been observed in other animals but this is the first time males have been observed to move between multiple locations, hinting at the discovery of a new type of animal mating system.

The research is published today in the journal iScience and coincides with the UN designated World Bee Day (20 May), which aims to raise awareness of the importance of pollinators, the threats they face and their contribution to sustainable development.

To track the flight paths of drones, researchers attached a small antenna-like electronic device, known as a transponder, to the back of individual honeybees. When the transponder receives a radar signal from the transmitter, it absorbs its energy and converts it into a higher frequency signal, which is then detected by the radar antenna. As the transponders signal is twice the frequency of the initial signal, it is easily identifiable and cannot be confused with reflections of the original signal from objects in the surrounding environment, such as trees of buildings.

Using this system the researchers are able to track the bee’s position relative to the radar every 3 seconds with an accuracy of around 2m. The team then used the positions of known landmarks within the outdoor experimental field site to determine the true GPS position of each bee.

The scientists found that drones alternated between periods of straight and convoluted, looping flight patterns within a single flight. On further investigation they showed that phases of looping flight were associated with four distinct aerial locations where drones congregated and these specific areas were consistent over a two year period.

The researchers propose that drone congregation areas could function like ‘leks’, mating systems in which large numbers of males gather solely in an attempt to mate. Lek systems are most well known in vertebrates, like deer and grouse, and males are typically faithful to a single lek location.

Dr Joe Woodgate, a Postdoctoral Researcher at Queen Mary and lead author of the study, said: “By using harmonic radar technology to track the bees, we found that individual flight paths show a clear change of behaviour from straight flight to looping flight. Periods of looping flight were clustered in particular locations and repeatable over two years, confirming that stable drone congregation areas, similar to ‘leks’ in other species, do exist.”

“We show that drones frequently visited more than one congregation area on a single flight. This is the first evidence for males of any species routinely moving between lek-like congregations and may represent a new form of lek-like mating system in honeybees.”

Interestingly, the study highlights similarities between the behaviour of drones within these congregation areas to swarms of midges or mosquitos. The researchers observed that when drones are making looping flight in one of these areas, the further they go from the centre, the harder they accelerate back towards it. This creates an apparent force, drawing bees toward the centre and leading to a stable, coherent swarm despite individual drones only spending a short time at each location.

The researchers still don’t understand how the drones find these congregation areas in the first place. Drones are born in Summer and their average lifespan is only around 20 days, so new generations can’t find these areas by following older drones. “Our findings suggest drones locate congregation areas as early as their second ever flight, without apparent extensive search. This implies that they must be able to get the information required to guide them to a congregation from observing the landscape close to their hive. In the future, we will look at how they accomplish this feat,” said Professor Lars Chittka, Professor of Sensory and Behavioural Ecology at Queen Mary and supervisor of the project.

The work was supported by grants from the European Research Council and Engineering and Physical Sciences Research Council (EPSRC).

Dr Joe Woodgate, the lead researcher for the study is also part of the EPSRC-funded ‘Brains on Board’ programme that aims to create robots with the navigational abilities of bees. He added: “We believe that bee-inspired robotics will play a role in improving robotics and artificial intelligence in the future. Understanding how bees select and find distant goals based on their explorations of their surroundings will be important for this.”


Research publication: ‘Harmonic radar tracking reveals that honeybee drones navigate between multiple aerial leks’ Joseph L. Woodgate, James C. Makinson, Natacha Rossi, Ka S. Lim, Andrew M. Reynolds, Christopher J. Rawlings, Lars Chittka, iScience, DOI: https://doi.org/10.1016/j.isci.2021.102499.


Provided by Queensmary University of London

Unexpected Similarity Between Honey Bee And Human Social Life (Biology)

Bees and humans are about as different organisms as one can imagine. Yet despite their many differences, surprising similarities in the ways that they interact socially have begun to be recognized in the last few years. Now, a team of researchers at the University of Illinois Urbana-Champaign, building on their earlier studies, have experimentally measured the social networks of honey bees and how they develop over time. They discovered that there are detailed similarities with the social networks of humans and that these similarities are completely explained by new theoretical modeling, which adapts the tools of statistical physics for biology. The theory, confirmed in experiments, implies that there are individual differences between honey bees, just as there are between humans.

An image obtained from the system showing barcoded bees inside the observation hive. Outlines reflect whether a barcode could be decoded successfully (green), could not be decoded (red), or was not detected (no outline). The hive entrance is in the lower-right corner, and the inset reveals two bees that were automatically detected performing trophallaxis. ©Tim Gernat, University of Illinois

The study, which measures the extent of individual differences in honey bee networking for the first time, was carried out by first author physics PhD student Sang Hyun Choi, postdocs 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 faculty at the Carl R. Woese Institute for Genomic Biology at Illinois, of which Robinson is the director. The collaboration comprised experimental measurements of honey bee social behavior performed by Hamilton, Gernat and Robinson, with data analysis by Rao and theoretical modeling and interpretation 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,” said Goldenfeld. “A few years ago, Gene, Tim, Vikyath and I collaborated on a large project that put “bar codes” on bees so that we could automatically monitor everywhere they went in the hive, every direction in which they pointed, and every interaction partner. In this way, we could build a social network in time, something known as a temporal network.”

That study, done a few years ago, involved high-resolution imaging of barcode-fitted honey bees, with algorithms detecting interaction events by mapping the position and orientation of the bees in the images. In those studies, researchers focused on trophallaxis — the act of mouth-to-mouth liquid food transfer — when measuring the social interactions between honey bees. Trophallaxis is used not only for feeding but for communication, making it a model system for studying social interactions.

“We chose to look at trophallaxis because it is the type of honey bee social interaction that we can accurately track,” said Choi. “Since honey bees are physically connected to each other by proboscis contact during trophallaxis, we can tell whether they are actually engaging in an interaction or not. In addition, each honey bee is tagged so we can identify each individual engaged in each interaction event.”

“In our earlier work, we asked how long bees spend between events where they meet other bees, and we showed that they interact in a non-uniform way,” said Goldenfeld. “Sang Hyun and I took the same data set, but now asked a different question: What about the duration of interaction events, not the time between interactions?”

In looking at the individual interactions, the time spent varied from short interactions to long interactions. Based on these observations, Choi developed a theory where bees exhibited an individual trait of attractiveness that could be likened to human interaction. For example, humans might prefer to interact with friends or family members 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 sort of “virtual spring” between them,” said Goldenfeld. “The strength of the spring is a measure of how attracted they are to each other so if the spring is weak, then the bees will quickly break the spring and go away, perhaps to find another bee with whom to interact. If the spring is strong, they may stay interacting 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 detailed understanding and predictions can be made with a minimum 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 since extended to encompass all states of matter, including living systems. Choi and Goldenfeld’s theory made correct predictions about the experimental honey bee dataset that was previously collected.

Out of curiosity, the theory was then applied to human datasets, revealing similar patterns as with the honey bee dataset. Choi and Goldenfeld then applied an economic measure for wealth and income disparities in humans — termed the Gini coefficient — to show that bees displayed disparities in attractiveness in their social interactions, although not as different as humans. These results indicate a surprising universality of the patterns of social interactions in both honey bees and humans.

“It is obvious that human individuals are different, but it is not so obvious for honey bees,” said Choi. “Therefore, we examined the inequality in the activity level of the honey bees in a way that is independent of our theory to verify that honey bee workers are indeed different. Previous work done in our group has used the Gini coefficient to quantify the inequality in honey bee foraging activity so we thought that this method would also work to examine the inequality in trophallaxis activity.”

“Finding such striking similarities between bees and humans spark interest in discovering universal principles of biology, and the mechanisms that underlie them,” said Robinson.

The researchers’ findings suggest that complex societies may have surprisingly simple and universal regularities, which can potentially shed light on the way that 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 honey bee-specific features.

In future studies, the same techniques from statistical mechanics can be applied to understand the cohesiveness of communities through well-characterized pair interactions, said Choi and Goldenfeld.

“This was my first project after I joined Nigel’s group, and it took a long time for me to figure out the right way to approach the problem,” said Choi. “It was fun and challenging to work on such an interdisciplinary project. As a physics student studying biological systems, I had never expected myself to use concepts from economics.”

“It was very exciting to see how simple physical ideas could explain such a seemingly complex and widespread social phenomenon, and also give some organismal insights,” said Goldenfeld. “I was very proud of Sang Hyun for having the persistence and insights to figure this out. Like all transdisciplinary science, this was a really tough problem to solve, but incredibly fascinating when it all came together. This is the sort of advance that arises from the co-location of different scientists within the same laboratory — in this case the Carl R. Woese Institute for Genomic Biology.”

Provided by University of Illinois