Monday, January 27, 2025

‘Buzz me in:’ Bees wearing itty bitty QR codes reveal hive secrets

Penn State

QR code on bee — image:
Researchers attached QR codes to the backs of thousands of bees to track when and for how long they left their hives.
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Credit: Provided by Margarita López-Uribe, Robyn Underwood, Julio Urbina, Diego Penaloza-Aponte and team

UNIVERSITY PARK, Pa. — Several hundred bees in rural Pennsylvania and rural New York are sporting tiny QR codes on their backs. More than the latest in apiarian fashion, the little tags serve a scientific purpose: tracking when bees go in and out of their hives to better understand how long honey bees spend foraging for food outside of their hives. The work, a collaboration among entomologists and electrical engineers at Penn State, is the first step in solving a long-standing mystery of how far bees travel from their hives to collect pollen and nectar.

So far, the researchers have learned that while most trips outside of the hive last mere minutes, a small minority of bees can spend more than two hours away. The team said they expect to learn much more, thanks to the system they developed to track honey bees’ time out of the hive.

“This technology is opening up opportunities for biologists to study systems in ways that weren’t previously possible, especially in relation to organic beekeeping,” said Margarita López-Uribe, the Lorenzo L. Langstroth Early Career Professor, associate professor of entomology and author on the paper published in HardwareX, an open-access journal that details the exact equipment and methods researchers use in their work so that it might be replicated or built upon by others. “In field biology, we usually just look at things with our eyes, but the number of observations we can make as humans will never scale up to what a machine can do.”

Like workers at a high-security building, the bees “buzz” their way in and out of the hive, flashing the pass on their back. They have free access, but they are digitally tracked via an automated imaging system the team developed to monitor when bees leave the hive and when they return via a customized entrance with a camera sensor. The QR codes glued to bees’ backs are known as fiduciary tags, which carry the smallest amount of identification information and can be quickly detected and logged via the imaging system, even in low-resolution conditions. The system is a break with conventional entomology field work in which researchers visually observe bees for limited periods, enabling far more comprehensive and expanded observations, the researchers said.

Barriers to organic beekeeping

In general, organic beekeeping means that hives are kept free of chemical pesticides, herbicides and synthetic chemical treatments, and are situated away from polluted areas. While the U.S. Department of Agriculture’s National Organic Standards Board recommended specific standards for certifying honey and other bee products as “organic” in 2010, they were never passed. Honey bees are capable of flying long distances when they need to — estimated to be able to fly up to 10 kilometers from their hive — but the team hypothesized that such distance is uncommon and bees generally fly much shorter distances, perhaps less than one kilometer, according to López-Uribe. As such, the forage and surveillance zone requirements recommended for organic beekeeping in 2010 may be unnecessarily large.

That could change with a better, more precise understanding of bee foraging range, the researchers said. Honey bees communicate where the food sources are to other bees in the colony with a physical behavior called the “waggle dance.” López-Uribe said researchers spend significant time observing and attempting to decode the waggle dance to determine how far bees travel from their colonies — a process that could be aided by accurately tracking how long individual bees spend foraging.

“The waggle dance is the best source of information that we have about bee foraging, but that’s based on human observations, with maybe an hour of observations made once a day over two weeks. So, we approached the electrical engineering team to see if there might be technology that could better make these observations,” López-Uribe said. “The goal is to understand if that 10-kilometer estimation is biologically accurate. Can we determine exactly how far honey bees travel from their hives?”

Buddying up for the bees

The entomologists turned to Julio Urbina, professor of electrical engineering and co-corresponding author on the study, who tapped Diego Penaloza-Aponte, a doctoral student in electrical engineering and co-corresponding author on the study.

“There wasn’t anything available like this before,” Urbina said. “This paper is the first step moving forward in the right direction, with opportunities to do more — in large part because of the growing synergy across our teams.”

The researchers emphasized that this was not a collaboration of silos, where biologists and engineers pieced together separate contributions. Rather, the experts spent time as novices in each other’s disciplines to better understand the specific needs and limitations. The electrical engineers worked in the field, learning first-hand how to handle and monitor bees, while the entomologists visited the lab to learn what considerations go into designing and building automated technology.

“Systems built in the past to monitor bees were developed to run in or near controlled laboratory environments,” Penaloza-Aponte said. “Our goal was to develop something that could run in a rural environment, away from the lab, on solar power and to make everything open source. Anyone can use this system and modify it.”

According to Penaloza-Aponte, access was also of concern. All of the equipment used is commercially available and cost less than $1,500 in total per apiary, which includes six colonies.

Buzzing with new knowledge

The researchers used AprilTags, a QR code smaller than a person’s pinky nail that could be glued to the worker bee without impeding her movement or causing harm. Every two weeks throughout the active spring and summer season, the team tagged 600 young bees that had just emerged from their cells across six colonies. In total, they tagged over 32,000 bees across six apiaries.

“We targeted young bees so we could track their age more accurately, especially when they start to fly and when they stop,” said Robyn Underwood, Penn State Extension educator in apiculture and co-author on the paper. She explained that young bees are also softer and don’t sting yet, so they’re easier to handle. “Once the bee was old enough to fly, it would leave the colony and be seen under the camera. In real time, our sensor would read the QR code and capture the bee ID, date, time, direction of movement — leaving or entering the hive — and the temperature. Throughout the season, we could track individual bees. When did she leave? When did she come back? What was she up to?”

The researchers found that most trips typically lasted one to four minutes, which could be to check the weather prior to foraging or to defecate outside of the hive. Longer trips typically lasted less than 20 minutes, but 34% of the tagged bees spent more than two hours away from the hive. This could reflect an unusually long foraging trip, a bee who never returned to the hive or a missed detection if the bee entered the hive upside down, the researchers said. During some weeks, with fewer flowers available, more bees spent more time foraging, likely because they had to travel farther to find adequate food.

“We also found that bees are foraging for a lot longer over their lifetimes than initially thought,” said Underwood, explaining that honey bees are believed to live for about 28 days. “We’re seeing bees foraging for six weeks, and they don’t start foraging until they are already about two weeks old, so they live a lot longer than we thought.”

The cameras at each hive, running 24 hours a day and seven days a week, were each connected to a microcomputer, and the researchers uploaded the data to their laptops at weekly visits.

The researchers encountered an expected issue early in the monitoring — bees loitering in the hive entrance. The camera would detect their individual QR codes upwards of hundreds of times in a day.

“Turns out, some bees just like hanging out in the entrance, and the camera will read them every time they walk by,” Diego said. “That’s why the programming is so handy. It can cut that outlier data and help make sure we’re tracking what’s actually meaningful.”

Waggling into the future

The researchers are now collaborating with a team at Virginia Tech to assess how foraging duration times match decoded waggle dances. Next, the researchers said they hope to tag and track other bee species, as well as other types of honey bees, such as drone bees or queen bees to learn more about those aspects of the colony. They also plan to host workshops for scientists and beekeepers to learn how to build and use their own monitoring systems.

Other co-authors on this paper include Sarabeth Brandt, doctoral student in electrical engineering, and Selina Bruckner, postdoctoral scholar in entomology, Penn State; Erin Dent, an undergraduate student at Texas A&M University who participated on the project as a Project Drawdown scholar in summer of 2023; and Benedict DeMoras, with the Department of Entomology at Cornell University. This team is also collaborating with Margaret Couvillon and Lindsay Johnson, with the Department of Entomology at Virginia Tech; and Scott McArt, with the Department of Entomology at Cornell University.

The U.S. Department of Agriculture’s National Institute of Food and Agriculture funded this work.

The researchers used commercially available equipment to install a tracking camera that runs 24 hours a day, seven days a week at the entrance of every colony across six apiaries. On this colony, the camera is housed in a protective box, labeled W5, above a small slit for bees to enter or exit. The equipment cost less than $1,500 in total per apiary, which includes six colonies.

Researchers attached QR codes to the backs of young bees who were not yet able to fly and who did not yet have a sharp stinger. Over one season, they tagged over 32,000 bees.

Credit

Provided by Margarita López-Uribe, Robyn Underwood, Julio Urbina, Diego Penaloza-Aponte and team

Journal

HardwareX

DOI

10.1016/j.ohx.2024.e00609

Method of Research

Observational study

Subject of Research

Not applicable

Article Title

Automated entrance monitoring to investigate honey bee foraging trips using open-source wireless platform and fiducial tags

WORKING CLASS PSYCHOGEOGRAPHY

London cabbies’ planning strategies could help inform future of AI

University of York

Researchers have measured the thinking time of London taxi drivers - famous for their knowledge of more than 26,000 streets across the city - as part of a study into the future of AI route-mapping.

Unlike a satnav, which calculates every possible route until it gets to the destination, researchers at the University of York, in collaboration with University College London and the Champalimaud Foundation, found that London taxi drivers rationally plan each route by prioritising the most challenging areas first and filling in the rest of the route around these tricky points.

Current computational models to understand human planning systems are challenging to apply to the ‘real world’ or at large scale, and so researchers measured the thinking time of London taxi drivers while they planned travel journeys to various destinations in the capital city.

Previous studies have shown the uniqueness of the London taxi driver’s brain; they have a larger posterior hippocampus region than the average person, with their brain changing in volume as a result of their cab driving experience.

Dr Pablo Fernandez Velasco, British Academy Postdoctoral Fellow at the University of York, said: “London is incredibly complex, so planning a journey in a car ‘off the top of your head’ and at speed is a remarkable achievement.

“If taxi drivers were planning routes sequentially, as most people do, street-by-street, we would expect their response times to change significantly depending on how far they are along the route.

“Instead, they look at the entire network of streets, prioritising the most important junctions on the route first, using theoretical metrics to determine what is important. This is a highly efficient way of planning, and it is the first time that we are able to study it in action.”

Researchers showed that taxi drivers use their cognitive resources in a much more efficient way than current technology, and argue that learning about expert human planners can help with AI development in a number of ways.

Dan McNamee from the Champalimaud Foundation said: “The development of future AI navigation technologies could benefit from the flexible planning strategies of humans, particularly when there are a lot of environmental features and dynamics that have to be taken into account.

“Another way to enhance these technologies would be to integrate the information about human experts into AI algorithms designed to collaborate with humans. This is a very important point, because if we want to optimize how an AI algorithm interacts with a human, the algorithm has to ‘know’ how the human thinks.”

Professor Hugo Spiers from University College London added: “This study certainly confirms what other studies have found - the London taxi driver’s brain is incredibly efficient and its larger volume is put to good use in making sense of a highly complex city like London.”

The research is supported by the British Academy, the EPSRC UK, and Ordnance Survey and published in the journal PNAS.

Journal

Proceedings of the National Academy of Sciences

London taxi drivers have specialized human mental strategies for expertly navigating their city’s streets, scientists find

Champalimaud Centre for the Unknown

Video 1 — video:
A taxi driver calls out the names of streets and the turns on his path while the traject appears segment by segment on the map.
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Credit: Iva Brunec

An international team of scientists, including a principal investigator in neuroscience from the Champalimaud Foundation, in Lisbon, has analysed the way expert London taxi drivers plan their route in advance every time they pick up passengers. It is the first time a study about human planning has been performed in a real-world, large-scale environment. Their results are published today (23rd of January) in the Proceedings of the National Academy of Sciences.

London’s licensed taxi drivers are not unknown to research: they were found, some 25 years, to have a distinctive brain feature due to their navigation expertise. Indeed, the volume of their hippocampus, a structure in the brain that plays an important role in learning and memory, is different from that of people who do not drive taxis.

The new study focused on their planning behaviour when doing their job. “Expert navigators can plan routes efficiently and quickly in enormous and intricate environments, such as cities”, the authors write in their paper. “[But] most studies to date have employed small-scale and/or abstract environments with naive participants. Here, we surmount these challenges by asking London taxi drivers – famous for their expert knowledge of the London street network, composed of over 26,000 streets – to plan routes through London.”

“Cities epitomize the intricacy of planning in real-world environments”, says Pablo Fernandez Velasco, from the University of York, the joint first author of the study. “If we considered planning a path across a large city that, between start and destination, involved 30 streets, using a minimal tree-search algorithm would involve an evaluation of over one billion potential street sequences.” If anything, this makes it clear that humans do not use such an algorithm to choose their route.

Time to think, time to name streets

The researchers analised the drivers’ response times – “as a proxy of thinking times” – to see how these drivers rationally organised their route-planning process.

“Our goal was to understand how expert humans make plans in very complex situations”, says Daniel McNamee, Group Leader of the Natural Intelligence Lab at the Champalimaud Foundation and one of the senior authors of the study. “We also wanted to compare expert humans versus AI algorithms. Our hypothesis was that humans were somehow doing something different. AI algorithms couldn't explain the behavior expert humans exhibit in this sort of situation.”

London taxi drivers are a very unique population of humans, in particular because when they train for their licence they are not allowed to use tools such as Google maps to help them; they have to rely solely on their memory storage and retrieval skills to efficiently navigate tens of thousands of city streets.

“They are a unique group when you want to answer questions such as how do expert humans organize their memory, what internal representations do they use or how do they retrieve pieces of information from their memory”, McNamee explains. “And that gives us a unique window into the highest level of human decision-making, knowledge representation and processing.”

A critical difficulty was getting access to taxi drivers. Hugo Spiers, Professor of Cognitive Neuroscience at University College London (UCL) – the other senior co-author of the study – has spent 20 years studying their brains, but this is the examination of their planning. “These taxi drivers are famous for their larger posterior hippocampus” says Spiers. “Now, thanks to collaboration across UCL and the Champalimaud, we have revealed how they put that enhanced brain to use.”

The team gave a group of 43 taxi drivers a start location in London and a “goal” location. The drivers were then asked to verbally call out the sequence of turns and streets they would take to go from the route’s origin to destination. “This is a very familiar task for these expert taxi drivers because, during their specialized training for the job, they're tested on many of these start-goal combinations before they get their taxi driver license”, McNamee points out.

For each route (there were 315 such possible “runs”, as they say in taxi driver-speak), the researchers measured the response time between the task presentation and the first call-out of a street name by each driver. This they called the “offline thinking time”: it corresponds to the phase during which the taxi driver silently considers the route. Next, they measured the times between each call-out of street or turns by the drivers, which they called the “online phase”.

Organising decisions about a route

One of the things the team wanted to study was what decisions the drivers made about their route during their offline thinking time. “One possibility”, says McNamee, “was that they organise their decisions in the sequence of streets and junctions that they are actually going to physically encounter during the ride – that the first thing they think about is the first street they're going to take, then the second street, the third street, and so on and so forth.” This means they will be slower in naming subsequent streets as they go along the route.

The results contradicted this hypothesis. What the results suggest is that “during the offline thinking time, the taxi drivers prioritize high-complexity, non-local parts of the London space”, McNamee continues. “It seems that they immediately jump to figuring out a higher-order global structure [of the route], based on critical junction states, using the physical structure of London.” The technical expression for this is “non-local precaching of critical junctions”. And then, “once they've done that, they start verbalizing and filling in the rest of the details”.

The drivers also took longer to think about longer streets in London. “This suggests they have spatial awareness of the actual physical structure of London, as opposed to a more abstract kind of representation where distances are not taken into account”, McNamee points out.

“I was surprised by this result”, he says. “I expected the taxi drivers would have a less spatially embodied sense of the London state space. I did not expect that the length of the street in the real world would slow them down, but that they would have a more abstract notion [of their city]. But maybe the fact that they have this more embodied spatial awareness is useful for more flexible planning, where they can take into account if one street is usually more busy than another at a given time of day, and they're also encoding all sorts of additional spatial variables and integrating them into their thinking. However, this is not something we specifically addressed in our study.”

“This result is consistent with reports from previous research” says Pablo Fernandez Velasco. “Taxi drivers have reported that when they have to plan a route, they sometimes imagine going through it, both from an aerial and from a first-person perspective. If they imagine the physical space to some degree, it makes sense that the length of the street segment impacts the length of their street planning”.

A different order of priorities in “London space”

In sum, expert London taxi drivers prioritize important streets and junctions in order to determine a more global structure of the run. “What we see with the taxi drivers is that they consider space out of the order in which they would actually experience them in the real world”.

“If they first take time offline to think about it differently, then they'll be much faster to call out streets during the online phase”, adds McNamee. This is clearly a much more efficient way of thinking than a simple sequential strategy.

What does McNamee think is the most interesting result of this work? “I think it is the fact that we were able to identify and predict that, during the offline thinking time, when the taxi drivers are in deep silent thought, they prioritize highly complex, and spatially remote junctions and streets in London”, he answers. “That had never been shown in human experts – decision makers or planners – in what I would refer to as an ecologically valid task for them, that is, a task they accomplish every day.”

“In fact, what we were studying here is a kind of expert human intuition, an innate sense of what to think about before you even start thinking about it, in a sense”, he ponders. “That's a key area of interest in other work we do in my lab: intuitive human reasoning.”

One thing seems certain, according to McNamee: human experts have a qualitatively different algorithmic strategy to AI systems. Nonetheless, AI algorithms could benefit, in the future, from results about how human experts think. “They could benefit in, I'd say, two ways”, he explains. “One has to do with more flexible planning, particularly when there are a lot of environmental features and dynamics that have to be taken into account.”

“The other would be to integrate the information about human experts into AI algorithms designed to collaborate with humans. This is a very important point, because if we want to optimize how an AI algorithm interacts with a human, the algorithm has to ‘know’ how the human thinks.”

“And that's something to consider”, McNamee concludes.

All the routes planned by the taxi drivers across London