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Data Visualisation Looking for insights in datasets Visualising trapline route development A bumblebee worker repeatedly forages on the same flowers, gathering nectar to bring back to her nest. This video illustrates the way her route evolves over time: repeated parts of the route are reinforced and grow brighter, while abandoned portions fade away. After around 60 trips, she has settled on a repeatable and efficient flightpath (although it’s not the best possible route). Click here to read more and watch the route development of two more bees. The life story of a bumblebee We used harmonic radar technology to track every movement made by a worker bumblebee throughout her entire life. This video illustrates her whole life as a forager, from her first explorations of the world to her later dedication to collecting food for her nestmates. Click here to read more about how I created the video. Where do honeybee males look for sex? This image combines visuals from several analyses to summarise my research on the previously mysterious movements of male honeybees, known as drones. These drones have only one purpose in life - to try to mate - but because they do it high in the air, no-one quite knew where. We attached an electronic transponder to 76 drones (top right), which allowed us to track their movements using harmonic radar (top left), revealing their secrets for the first time. ​ Click here to learn more about what this picture tells us about honeybee mating behaviour. Comparing flight paths Flight 8 Flight 15 Comparison As a bumblebee worker gains experience foraging from an array of flowers, her flight path evolves. I developed a way to visualise how similar each flight is to the flight before. The first two images show two separate flights by the same bee at different stages of learning. The final image shows the probability that she utilised the same parts of the landscape on each flight. Some parts of the two flights were almost identical (the stretch from the nest to the first feeder at bottom right, and an unexpected, but very repeatable detour at the top of the image), while other legs of the journey (notably the movements between feeders in the centre of the image) changed drastically between flights. ​ Click here to learn more about how I created these images.

Bridges et al. (2023) Plos Biology 21(3): e3002019 Bumblebees acquire alternative puzzlebox solutions via social learning Researchers and philosophers fiercely debate what exactly culture is and whether non-human animals have it in some form, but one widely used definition is that culture involves shared, learned behavioural traditions which persist in a population over time. Alice Bridges’ PhD project was to investigate whether bees have culture under this definition. In this study, she painstakingly taught individual bees how to open a “puzzlebox” to get food, then returned them to their colonies. She showed that their untaught nest-mates picked up this behaviour by watching them. The box-opening skill spread through the colony and continued even after the original “demonstrator” bees had died. There’s no reason to suppose that new bees couldn’t learn from these second-generation learners so, hypothetically, box-opening could persist indefinitely. ​ Crucially, there were two possible ways to open the box, but learner bees were almost always faithful to the method used by their “demonstrator”, so each colony wound up with its own distinctive way of doing things. Even after some bees discovered the alternative method for themselves, they went back to doing it the way they were taught. In another experiment, four demonstrators were introduced to the colony, two trained with each method. In the end, one method became dominant throughout the colony while the other went extinct, apparently due to chance variation in how enthusiastic the demonstrators were, how quickly they died and so on. ​ This all suggests that purely arbitrary cultural traditions can become fixed in bee colonies and persist for long periods. The life cycle of bumblebees means that only queens survive across multiple seasons, so there is probably little chance of multi-generational cultural traditions being maintained in bumblebees as they are in humans, but it opens up the interesting possibility that there might be other insect species with something we’d recognize as culture. ​ I didn’t play a huge role in this one, mainly advising on how to analyse the data and report on the results, but it’s been great fun working with Alice and watching her ideas develop over the past few years.

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Woodgate, Perl & Collett (2021) Journal of Experimental Biology 224: jeb242167 The routes of one-eyed ants suggest a revised model of normal route following Before I started to work with bees, I was fortunate enough to work with Professor Tom Collett at the University of Sussex, one of the world’s top experts on insect cognition, and especially navigation. We studied wood ants, the largest ant species in the UK; you can often find them in sunny woodland areas where they build huge mounds out of twigs, pine needles and so on, which may contain hundreds of thousands of ants. You will often see what look like ant highways near to nests, with hundreds of ants running back and forth with food. They eat insects and other invertebrates, but their main source of food is aphids. The ants don’t kill the aphids but suck up a sweet liquid, called “honeydew” that the aphids secrete (in return for the ants protecting them from predators). As you get further from the nest, the ant highways split into branching networks of smaller paths, each with fewer ants, so that the colony can collect food across a large area surrounding the nest. It’s well known that ants lay pheromone trails, allowing others to follow their nestmates. Because these trails evaporate over time, only trails that are frequently refreshed remain strong and attractive to follow, so the best sources of food are easily found, whereas routes fade away. What’s less well known is that individual ants mainly make use of these trails only when young and inexperienced. An ant that finds a reliable source of food will quickly learn to find its own way and individual wood ant foragers can learn to navigate complex routes hundreds of meters long very accurately, passing through multiple different types of terrain. They have various sources of information available to them – we often talk about a “navigational toolkit” – but, if necessary, can learn to follow these routes using their visual memory alone. Along with Tom and many of our colleagues, I want to understand how an animal with such a tiny brain can accomplish such difficult feats. Partly, this is a scientist’s fascination with a cool and impressive natural phenomenon, but there are bigger reasons to want to know what’s going on underneath the surface to enable ants to do what they do. Finding your way to important locations (like food sources) and safely home again is a problem that’s common to lots of animal species, particularly humans. Understanding exactly how different types of animals approach this common problem can shine a light on the evolution of brains and the process of thinking. It’s quite common to imagine that humans and large-brained vertebrates must be doing a lot of very computationally intensive processing to support the complex, intelligent behaviour we observe, but when you see an individual ant tackling the same problems – and often doing just as well – it becomes clear that there may be far more computationally efficient ways to achieve the same ends. Which brings us on to another reason for wanting to understand insect brains: finding computationally efficient solutions to complex problems is a major goal of AI and computing research. Historically, biology has inspired many engineering advances, and as we learn more about how thinking actually works, there is every reason to believe that animal cognition can inspire advances in artificial cognition. The neural networks that underly all current AI research were themselves inspired by brain architecture, but understanding the specific computations and mechanisms behind intelligent behaviour has the potential to unlock even greater advances. Over the last decade or so, the most influential idea about how insects learn routes has been a concept known as “alignment image matching”. The idea is simple: you memorise the view towards your goal from various points along the route. Later, when you try to follow the route again, you turn until your current view matches the view you memorised and then walk forward in that direction. If you stray slightly from the memorised route, it is not possible to make your current view match your memory exactly, but if you turn until you find the best fit, or the least-bad match, that will often be when you are facing in close to the right direction, so repeating this process will eventually get you back on track. Imagine, for example, that you are trying to reach a location at the base of a mountain. Even if you lose your path, turning to face the mountain will give you the view that’s closest to the one you want, and heading in that direction will bring you closer and closer to your goal. Of course, if you go too badly awry this doesn’t work – if the mountain you see in the distance is not the same one you are looking for, then moving towards it isn’t going to help. Perhaps surprisingly, though, it seems that in many natural scenes there is a fairly large “catchment area”, within which repeatedly heading towards the view that best matches your memory will eventually bring you to your goal. Algorithms based on this principal have had a surprising degree of success in replicating the achievements of navigating ants and given scientists great insights into how ant brains go about learning and following a route. But surely this can’t be all there is driving the apparently complex and sophisticated navigation we see? Although quite a lot of ant navigation can be explained by alignment image matching, there are some observations that suggest there are other factors in play; for example, desert ants can follow a route when walking backwards, dragging heavy biscuit crumbs! Tom, Craig Perl and I deliberately set up a situation in a simple, controlled environment, in which alignment image matching would fail, in order to find out whether and how ants could still follow a route. Instead of walking hundreds of meters through a complex forest to collect honeydew from aphids, we asked ants to walk about 60 cm over a featureless, flat table to collect a mixture of sugar and water from a microscope slide. The table sits inside a large, featureless cylinder and we have shown in the past that if you create a “panorama” by arranging a swathe of black cloth around the walls, ants can learn to navigate to the food, using the panorama as though it was a natural skyline. We can rotate the entire cylinder between tests to make sure that the ants are really relying on the panorama, rather than using the earth’s magnetic field, or the position of the sun, or any other unknown cues. For this study, we made ants wear an “eyepatch” over their left eye, so they could not see anything in the left of their visual field. We made sure that the panorama was completely blank on the right-hand side and only had useful information on the left. This meant that the position of the black shapes on the panorama still specified exactly where to find the food, but whenever an ant was actually facing toward the food, all it could see was blank walls. An alignment image matching algorithm could never succeed in this situation: the “reference image” it tried to match to would just be a pure white wall and so facing in almost any direction (except with the black shapes in view of the good eye) would be an equally good match, so it could never decide which direction to travel in. Our ants needed a little help in the form of a short wooden channel that ensured they were heading in the right direction at the start, but they soon learned to find the food almost as well as two-eyed ants. When we messed with them by moving the black shape on the panorama left or right, the ants’ shifted their paths to the left or right as well, confirming that it was the position of the black shape that was telling them where to go. When we removed the black shape entirely, the results were interesting: the mean direction of all the ants was quite close to the food, suggesting that the little starting channel did give them some information about which direction to go in, but the ants’ routes were much more circuitous and variable than before, confirming that they needed to use the black shape to control their route effectively. This experiment shows that ant navigation is not so simple as turning the body until their current view matches a memory and then walking forwards. One hint that this might be the case can be found in the way ants walk. Far from walking in a straight line, always facing their goal, most ants walk with a distinctive zig-zag motion and actually spend more time looking in directions to either side of the goal than at the goal itself. This behaviour may have evolved to help ants follow pheromone trails or may even be a leftover from much further back in evolutionary history; but it may also play a role in visual navigation, allowing ants to constantly scan the scene in front of them. When we looked closely at the turns our ants made, we found that a lot of the leftward zig-zags ended with the ant facing toward the black shape on the panorama and a lot of right turns ended with the ant facing directly toward the food, even though there were no visible cues to tell the ant where to face. It may be that the ants had learned how large a right turn they needed to turn away from the bar toward the goal direction and were able to use this to keep them on course: seeing the black shape during a leftward turn would trigger a right turn of exactly the right size needed to ensure they were still pointed in the right direction. This probably isn’t a special tactic to deal with being forced to wear an eyepatch but might instead be how ants typically navigate. Of course, under normal situations there would probably be shapes on the skyline on both sides and they would also be able to learn what size of turn to make in the other direction. They could even learn to recognise lots of landmarks on the horizon and remember what size of turn was needed from each one; see my 2016 paper with Cornelia Buehlmann and Tom, below, for further evidence for this idea. I’m unlikely to continue this project further, but in the future experimenters could carefully change the positions of the shapes in the panorama to test whether the sizes of the zig-zags made by ants are tuned to correspond to the size of turns they need to make in order to keep facing their goal.

Visualising trapline route development A bumblebee worker repeatedly forages on the same flowers, gathering nectar to bring back to her nest. This video illustrates the way her route evolves over time: repeated parts of the route are reinforced and grow brighter, while abandoned portions fade away. After around 60 trips, she has settled on a repeatable and efficient flightpath (although it’s not the best possible route). Bees, and other animals that feed on renewable resources, like nectar, develop habitual foraging routes, known as traplines. People commonly describe a trapline route as one that revisits the same locations (flowers or flower patches, for bees) in the same sequence each time, and the idea is that this allows the animal to exploit predictable resources in the most efficient way. In reality, things are more complicated than that, and there is still a lot we don’t understand about trapline foraging. I was interested in how these trapline routes develop over time, so James Makinson and I used harmonic radar technology to track every foraging flight made by individual bees, as they learned where they could find feeders and developed a trapline route. To understand how each bee’s route changed over time, I developed a visualization inspired by the pheromone trails of ants. Individual ants lay scent trails as they go, allowing nestmates to follow their route and discover sources of food. The pheromones they deposit on the ground evaporate over time, unless they are reinforced by another ant laying down more scent. Because of this, the most popular paths, walked by many ants, develop strong odour trails and can be easily followed by new recruits, whereas the road less travelled gradually fade away. This helps the colony concentrate on the most profitable foraging areas and allows them to be flexible over time: if a food source dries up, disappointed ants will stop laying pheromones and what was once a highway will dwindle away, ensuring that new ants don’t waste their time following a path to nowhere. This network of pheromone trails is a useful metaphor for thinking about the way the route of a single bee develops with time. At the start of her foraging career she must explore widely, sampling flowers in different locations. As she learns what’s available, she can forage more efficiently by flying between the best or most reliable flowers, pruning her network of possible routes util she is left with an efficient flight path. In this video, we visualize the bee’s flight as a white-hot line she trails behind her as she flies. This metaphorical trail gradually cools and fades away, unless she flies back over the same area, in which case it is reinforced. As we watch over the course of about 60 foraging trips, we can see that her early, very convoluted routes gradually fade away, to be replaced by straight, efficient flights between feeders. Some segments of the route quickly stabilize: the initial leg, starting from the nest (at the bottom of the screen) and flying to the lower right hand feeder, and the stretch from the lower left to middle left feeders, are almost unchanged throughout. Other parts of the route continue to change and develop for longer, and despite the dogma that trapline routes stabilize onto a single, stereotyped sequence, the flightpath never becomes completely stable. There is a clear pattern in which the early, length exploratory loops beyond the boundaries of our feeder array get largely eliminated, as do returns to empty feeders, but even after several days of refining her route, the bee still does these digressions from time to time. This makes sense when you recall that flowers are ephemeral resources: previously reliable flowers die and new ones are growing all the time. Thinking back to the ant trails, you want unprofitable branches of the trail to dwindle so that you don’t waste resources looking for food where there isn’t any, but you don’t necessarily want those branches to die out altogether: if conditions change, you want to be able to respond by changing your foraging patterns quickly, so keeping your information on alternative food sources and routs up-to-date is a wise strategy. To make this video, I started with data from the harmonic radar which tell me the bees location, relative to the radar, once every three seconds (which is the time it takes the radar to scan through 360 degrees). First, I converted the bee position data into latitude and longitude coordinates, by triangulating her position using known landmarks in the landscape. The next step was to use Brownian Bride estimation to calculate a probability density function for the bee’s position between each set of radar coordinates. This allowed me to divide the landscape into ‘pixels’ – little squares, one meter on each side – and to calculate the probability that the bee flew through each pixel. I can visualize these probabilities as a heatmap, where each pixel is coloured in proportion to how likely it was that the bee flew through it. Each frame of the video updates the heatmap to incorporate what the bee did next. As time goes by, the ‘probability’ value of each pixel goes down with each time step, so the colours will gradually fade away over several seconds of video. But the bee is constantly moving, so new values are always being generated, representing the probability that she has flown over each pixel of ground since the last frame of video was generated. These are combined with the decaying values from previous frames to come up with a new frame of video, each of which has a ‘heat’ value which summarises where the bee has flown, with a higher weighting given to recent movements, and lower weightings given to older movements. The entire thing is sped up (by around 500x) allowing us to watch two days of route development in less than a minute. The route development of several other bees is shown below. You can see that, while certain trends remain constant (they switch from long, digressive exploratory flights to efficient, straight line movements between feeders; and they reduce the amount of back-tracking to empty feeders; but none of them ever fully stabilizes their route), each bee’s flight path evolves in a different way and they never end up with the exact same solution to the problem of how to fill up on nectar in the most efficient way.

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Gallo et al. (2023) Journal of Comparative Physiology A Sub-cell scale features govern the placement of new cells by honeybees during comb construction Vince Gallo is a computer expert and keen beekeeper. When he discovered that scientists don’t truly understand how bees construct their perfect honeycomb, he joined Prof. Lars Chittka’s lab as a PhD student to find out. This is the first of a series of experiments with which Vince added more to our knowledge of honeybee architecture than has been done for a generation. Honeybees building comb seem to work haphazardly, with different individuals coming and going, adding a piece of wax here, removing a piece there, often altering or undoing the work of other bees. There is no evidence to suggest that they are organised into teams and there is certainly no foreman or architect directing their efforts and yet from this seeming chaos emerges one of the most regular, geometrically perfect constructions to be found in nature. Vince’s key insight is that this process is managed by stigmergy, a form of self-organisational process in which the state of the workpiece itself guides each subsequent step. In other words, when a bee discovers certain features in the wax of the comb, they stimulate behaviours – likely following a small number of fairly simple rules – that change the state of the comb. The new shape of comb triggers a new set of actions (either in the same bee or a new one that stumbles across the partially built comb) which modify it still further, until a complete honeycomb has been built (and the process doesn’t even stop then, since comb must be constantly maintained and is often modified to suit the changing need of the colony, all probably directed through stigmergic processes). In this paper, Vince examined how honeycomb is first started and then extended, cell by cell, but with each hexagonal cell neatly adjoined to its neighbours to tile the entire comb. He inserted small sheets of wax with various features into a hive and waited for the bees to construct honeycomb on them. Vince developed special software allowing him to precisely compare photographs of the stimuli with the resulting comb, making it clear how the final state of the comb was influenced by its starting conditions. When presented with small indentations pressed into flat sheets of wax, it appears that bees start to deposit wax around the rim, beginning the process of building the walls that surround each hexagonal cell. They then extend the original depression, enlarging it to the required cell size. The wall of the eventual cell coincides with the original depression to a greater extent than expected by chance. When the initial wax had two indentations, close to one another, the bees built a pair of cells separated by a common wall which ran neatly between the pits, demonstrating that the choice of where to begin building a cell affects not only the position and spacing of cells, but also their orientation. A v-shaped piece of wax attached to the starting stimulus slightly resembles the corner of a finished cell and Vince showed that that was enough to cause bees to build two new cells either side of the v, with their common wall extending from its point, to form the triple junction that defines the corners of all cells in a hexagonal grid. However, when the starting stimulus had both v-shapes and pits that were misaligned, the final construction had walls that were more closely aligned to the pits than to the v-shape, which makes it clear that it is the pits that trigger the building of each new cell which control wall placement, and not the corner of a previously built cell. In fact, it seems likely that when a bee encounters the outer wall of a cell rising from a flat base, that creates the same conditions as a depression and triggers the building of a new, adjoining cell. Tiling the comb with hexagonal cells is the most efficient use of space, creating strong walls while using the minimum amount of wax. This work demonstrates how bees, working haphazardly, without any central organising force and almost certainly with no individual having a detailed plan or overview of the entire construction, can build such an impressive construction. A difference in height caused by a small depression, or a low wall can trigger the construction of a new cell wall, while the placement of individual cells, or even their starting conditions, causes new cells to be built in the right place to adjoin them. In truth, honeycomb is far from perfect – you can often see visible seams where separate piece of comb have merged, scars from previous damage, gradual changes in cell orientation, and odd spaces where cells don’t quite fit together – but the existence of simple rules that allow the current state of the comb to direct future construction explain how bees are flexible enough cope with these irregularities. As well as completing his own PhD, Vince has been instrumental over the past few years in helping to develop the radars I use to track bee movements. Not only did he write a lot of the software I rely on, but he taught me a huge amount about computers, coding and, especially, technical project management. In return, I hope I was able to help him adapt his skills for academic research and teach him a little about experimental design, data analysis, scientific writing and so on. Other than that general advice over the course of the project, my contribution to this one was mainly in figuring out how to analyse the data and make it answer our questions.

Article Recommendations Short pieces on papers I've admired, written for Faculty Opinions Wormholes in virtual space: From cognitive maps to cognitive graphs. Warren WH, Rothman DB, Schnapp BH, Ericson JD. Cognition 2017 09; 166:152-163 ​ Some of the most interesting, and most fiercely contested, questions in cognition research are those relating to how spatial memories are stored and the mechanisms by which they are processed to allow navigation to desired locations. It has long been proposed that humans, and other animals, have a ‘cognitive map’, often taken to mean that the locations of places are encoded as coordinates within a common, Euclidean reference frame. Warren and colleagues investigated the nature of humans’ spatial memories using a genuinely innovative study in which participants were asked to navigate around a virtual reality maze containing ‘wormholes’ which instantaneously ‘teleported’ them from one place to another while simultaneously rotating them. The subjects learned to navigate easily and efficiently within the maze without even realising that it was geometrically impossible, but in doing so violated various postulates of Euclidean geometry. The authors propose that the behaviour of participants in the wormhole maze was best explained by the hypothesis that they organised their knowledge of the maze as a ‘labelled graph’ in which the distances and angles between pairs of locations were stored, but locations were not encoded in a common coordinate system. While the results are open to multiple interpretations, they present powerful evidence against the idea of a Euclidean map in the brain, and the idea of navigation through ‘impossible’ virtual words is a powerful one with great potential to enrich our understanding of spatial cognition. ​ View at Faculty Opinions ​ Taking an insect-inspired approach to bird navigation. Pritchard DJ, Healy SD. Learn Behav 2018 03; 46(1):7-22 The need to navigate accurately to particular locations in space, whether to find food, shelter, mates or other resources, is common to animals across a wide range of taxa, body types and brain sizes. Indeed, a powerful argument for studying navigation behaviour is that examining how widely varying species cope with a common challenge could shed light on the evolution of cognitive strategies, yet the approaches of researchers to studying navigation in insects and vertebrates have diverged widely. The popular hypotheses to explain spatial cognition, as well as the experimental approaches used to test those hypotheses, differ markedly between the two fields, but it is far from clear whether these differences reflect real differences in the cognitive strategies used by the two groups or might, instead, stem from the ways they have been studied. In this fascinating and thoughtful paper, Pritchard and Healy use hummingbirds as a case study to ask whether taking inspiration from the ideas and approaches current in studies of insect navigation (predominantly ants and bees), might explain some perplexing results seen in birds and provide inspiration for productive future study. They identify two themes, in particular, that are common in insect studies but rarely considered in the bird literature: first, that various strategies by which the animal compares its current view to memorised ones - as opposed to constructing cognitive models of space that are independent of the animal’s current orientation - can explain a surprisingly broad array of results; and second, that the spatial information available to an animal is inextricably linked to its sensory systems and its movements within its environment. Not only do they convincingly argue that cognitive mechanisms like those proposed in ants and bees might explain the way hummingbirds look for food but they raise the question of whether an insect-inspired approach might spark advances in our understanding of vertebrate navigation as a whole. View at Faculty Opinions ​ Schematic representations of local environmental space guide goal-directed navigation. Marchette SA, Ryan J, Epstein RA. Cognition 2017 01; 158:68-80 Many animals, including humans, need to remember the locations of places of interest in a way that allows navigation at different spatial scales. For example, to retrieve your spectacles you need to recall not only in what room you left them, but where in the room they can be found. An intuitively appealing idea might be that the brain stores spatial memories in a ‘zoomable’ map, so that the spatial coordinates of each known location are stored in a single reference frame and, by varying the resolution at which those coordinates are retrieved, we can use the same information to navigate towards something on both global and local scales. The authors tested this hypothesis in a virtual reality environment by asking human participants to re-find the locations of objects displayed in four virtual ‘museums’. Imperfect human navigators demonstrated a striking pattern of errors in which they frequently went to the ‘right’ place within a room, but in the wrong building entirely, demonstrating that their memories of each object’s location within its museum could not be nested within the larger scale memories of the location of each building. Further experiments showed that these ‘schema-preserving’ errors persisted even when the buildings differed in their local geometries and even in their shapes, casting doubt on the proposition that the local positions of objects were stored as Euclidean coordinates even within a local reference frame and suggesting instead a schematic encoding of locations relative to walls, doors, etc. Intriguingly, when the buildings themselves were removed so that the objects were arranged within a single park-like environment, these errors disappeared, with participants almost always locating the correct grouping of objects, even if they sometimes found the wrong object within a group. This strongly suggests that spatial boundaries play an important role in determining the spatial scales at which location memories are grouped. View at Faculty Opinions ​ Male bumblebees perform learning flights on leaving a flower but not when leaving their nest. Robert T, Frasnelli E, Collett TS, Hempel de Ibarra N. J Exp Biol 2017 03 01; 220(Pt 5):930-937 When leaving their nest for the first time, worker bees perform characteristic looping flights, turning back to look at the nest. These learning flights help them to learn the location of the nest, which is essential to a central place forager like a worker bee. Male bumblebees of the species Bombus terrestris, on the other hand, live a very different lifestyle from the female workers: leaving their natal nest for the first time they never look back, metaphorically speaking; instead they spend the rest of their lives fending for themselves and searching for a mate. In this paper, Robert et al. used high speed cameras to investigate the structure of male flights when leaving the nest and demonstrate that the bees literally don’t look back and show no sign of female-like learning flight. After feeding from a rewarding artificial nectar source, however, male bees did perform learning flights, facing back toward the feeder in a similar manner to females. These results suggest that the behaviours bumblebees perform to facilitate learning are closely related to their ecological needs. View at Faculty Opinions Continuous lateral oscillations as a core mechanism for taxis in Drosophila larvae. Wystrach A, Lagogiannis K, Webb B. eLife 2016 10 18; 5 Occam’s razor suggests that, given several competing hypotheses, the simplest should a priori be considered most likely. When it comes to understanding the neural mechanisms that underlie complex behaviours, though, it can often be hard to comprehend what the minimal requirements to reproduce a set of behaviours might be. Even an animal as simple as a Drosophila larva exhibits what appears to be a repertoire of different behavioural states that allow the larva to move up and down chemical, light or temperature gradients. Attempts to understand this behaviour have posited a system requiring some level of ‘decision making’ and switching between behavioural programs. In this study, Wystrach et al. examined high resolution video recordings of moving larvae and identified a regular lateral body oscillation. Both turns and head ‘casting’ behaviour could be explained as modulations in the size of these oscillations. Modelling this system revealed that a remarkable amount of the larva’s behavioural repertoire could be reproduced by a simple oscillating system, receiving direct sensory inputs without complicated processing. Thus, what appears to be a complex set of behaviours involving switches between distinct states might turn out to emerge naturally from a far simpler mechanism. View at Faculty Opinions Prenatal acoustic communication programs offspring for high posthatching temperatures in a songbird. Mariette MM, Buchanan KL. Science 2016 08 19; 353(6301):812-814 Many songbirds call while incubating their eggs and can even communicate with their embryos in this way. In this fascinating study, Mariette and Buchanan discovered that zebra finches produce a particular incubation call only in the later stages of embryonic development and only when the ambient temperature is unusually high. Using a series of thorough experimental manipulations, they show that nestlings that were exposed to these calls before hatching showed different patterns of growth and begging than controls. Birds that had heard the incubation calls and were reared in hot nests grew more slowly than those reared in cooler nests, while control birds showed the opposite trend. The effects continued into adulthood, with birds that grew slowly in hot nests or fast in cool nests achieving greater reproductive success than their less well adapted counterparts. Finally, males that had been exposed to incubation calls and reared in hot nests were more likely themselves to choose hot nests in which to raise their own offspring. This all adds up to a very exciting story in which parent birds are able to communicate to their unhatched offspring regarding the environmental conditions under which they can expect to grow up and the offspring respond by altering their behaviour and developmental trajectory in order to thrive under those conditions. View at Faculty Opinions Caffeinated forage tricks honeybees into increasing foraging and recruitment behaviors. Couvillon MJ, Al Toufailia H, Butterfield TM, Schrell F, Ratnieks FLW, Schürch R. Curr Biol 2015 Nov 02; 25(21):2815-2818 Over the course of a hard working day, many of us will look forward keenly to our next cup of coffee; this fascinating paper suggests that honeybees may feel the same way! Caffeine is a naturally occurring substance in many species of plants and is often found, at low concentrations, in nectar. To test whether caffeinated nectar affects the behaviour of insects that feed on it, Couvillon et al. monitored the foraging activity of honeybees trained to feed on artificial feeders in the field. When the sucrose rewards in the feeder were laced with a realistic concentration of caffeine, not only did individual bees visit the feeder more often, but they were more likely to communicate the feeder’s position to their nest mates using the waggle dance, dancing more enthusiastically and for longer. The end result was that recruitment of new bees to the caffeinated feeder was four times greater than to a control feeder. The authors suggest that the caffeine manipulated the bees’ perception of the nectar quality so that they react as though to nectar with a higher sugar concentration. Plants with caffeine in their nectar may be able to attract more bees, increasing the efficiency of pollination, without having to invest more in producing sweeter nectar. Not only does this study provide an intriguing insight into the ways in which plants can manipulate their insect pollinators, but for those of us who study waggle dances it also suggests a valuable practical method to increase dances. View at Faculty Opinions Honeybees Learn Landscape Features during Exploratory Orientation Flights. Degen J, Kirbach A, Reiter L, Lehmann K ... Singh PK, Manz G, Greggers U, Menzel R. Curr Biol 2016 10 24; 26(20):2800-2804 Insect such as bees show remarkable abilities to navigate their environment, often over large distances, and technological advances are now allowing researchers to address important questions about what information they learn about the landscape and when they learn it (e.g. {1,2}). Honeybee workers taking up foraging for the first time usually perform a series of orientation flights, first circling the nest to learn its location, then flying further out into the landscape. Long-range orientation flights typically cover only a narrow sector of the surrounding environment, with consecutive flights often exploring in different directions, and it is unclear whether they serve the purpose of learning the area for navigational purposes, looking for forage locations or both. In this useful study, the authors used harmonic radar technology first to record exactly where individual bees flew during their orientation flights, and then to look at what they did when displaced either to locations they had just explored or to entirely novel locations. Bees that were displaced to areas they had explored during their orientation flights found their way home faster, taking straighter routes, than those that found themselves in novel locations, demonstrating that they had been learning how to get home during these flights. The authors argue that the bees' performance could not be explained by odour cues and that they take straight paths home from too far away to recognise landmarks associated with the nest, strongly suggesting that the bees memorise visual features of the landscape during orientation flights. References 1. Ontogeny of orientation flight in the honeybee revealed by harmonic radar. Capaldi EA, Smith AD, Osborne JL, Fahrbach SE, Farris SM, Reynolds DR, Edwards AS, Martin A, Robinson GE, Poppy GM, Riley JR. Nature. 2000 Feb 3; 403(6769):537-40 DOI: 10.1038/35000564 2. Life-Long Radar Tracking of Bumblebees. Woodgate JL, Makinson JC, Lim KS, Reynolds AM, Chittka L. PLoS ONE. 2016; 11(8):e0160333 DOI: 10.1371/journal.pone.0160333 View at Faculty Opinions Impacts of neonicotinoid use on long-term population changes in wild bees in England. Woodcock BA, Isaac NJ, Bullock JM, Roy DB, Garthwaite DG, Crowe A, Pywell RF. Nat Commun 2016 08 16; 7:12459 The controversy regarding whether neonicotinoid pesticides are responsible for declines in pollinator populations is one of the most important and pressing questions facing both the ecology and agriculture communities today. There has been an EU moratorium on their use in place since 2013 to allow more time for research and a consensus seems to be gradually appearing that neonicotinoids can cause potentially serious, sub-lethal effects in bees and other insect pollinators. However, the short-term nature of most scientific studies had led to a serious lack of information about the impact these relatively subtle deleterious effects may have on insect population structures in the long-term, hampering our ability to make constructive decisions regarding pesticide use. This important and timely paper used long-term observational data of wild bee distributions throughout England to look at whether neonicotinoid usage played a role in population persistence and local extinctions over the last 18 years. The results demonstrate a negative relationship between neonicotinoid usage and the survival of populations on a local scale, across 62 wild bee species. This effect was three times more pronounced among species that are known to forage on oilseed rape (OSR), one of the major crops on which neonicotinoids are used and one of the major sources of forage for bees in the UK. The authors’ analysis suggests that that, while OSR provides important resources for bees of many species, the advantages of wide OSR coverage are outweighed by the negative impact of using neonicotinoid pesticides. While it remains unclear how these effects at a local scale will impact loss of biodiversity at larger spatial scales, it is clear that the sub-lethal effects attributed to neonicotinoid use have the potential to contribute to long-term population declines and this evidence has an important role to play in the ongoing debate over pesticide use. View at Faculty Opinions Context odor presentation during sleep enhances memory in honeybees. Zwaka H, Bartels R, Gora J, Franck V, Culo A, Götsch M, Menzel R. Curr Biol 2015 Nov 02; 25(21):2869-2874 Why do animals sleep? Studies in vertebrates, particularly mammals, have shown that the consolidation of memory is perhaps the most important function of sleep. Sleep occurs in a wide variety of taxa, but less is known about the extent to which the functions and mechanism of sleep in invertebrates might resemble those in vertebrates. The authors used a proboscis extension reflex (PER) conditioning task to quantify honeybees' memory of a heat stimulus. When bees were exposed during deep sleep to a contextual odour that had been present during training, their memory performance was improved the following day. A clever and logical series of further experiments demonstrated that this performance boost only occurred when the bees experienced the particular odour associated with the learning trials, and only when they were exposed to it in deep sleep, rather than at random points during the night. Past experiments in mammals have suggested that the consolidation of memory during sleep functions by reactivation of the same neural pathways that were active during the initial learning experience. The fact that replaying the learning context (in the form of an odour) boosts memory performance in bees hints that a similar process may occur in insects. This raises the interesting possibility that not only the function of sleep, but at least some of the mechanisms of memory consolidation are evolutionarily conserved from invertebrates to mammals. View at Faculty Opinions Seed coating with a neonicotinoid insecticide negatively affects wild bees. Rundlöf M, Andersson GK, Bommarco R, Fries I ... Klatt BK, Pedersen TR, Yourstone J, Smith HG. Nature 2015 May 07; 521(7550):77-80 This paper provides convincing evidence that neonicotinoid pesticides have a harmful effect on bees, in an extensive field study. The authors looked at a number of indicators of bee health across eight pairs of oil-seed rape fields in southern Sweden, in which one field of each pair was treated with a neonicotinoid-based commercial seed treatment. They present evidence for reduced density of wild bees, impaired colony growth and reproduction in bumblebee colonies, and reduced nesting success by solitary bees in treated fields. This study makes weighty contributions to our understanding in two key areas: first, some of the existing evidence for deleterious effects of neonicotinoids comes from laboratory studies in which the dosage or route of ingestion of neonicotinoids may not be realistic, and the bees have no opportunity to manage their exposure through their behaviour (for example, by foraging elsewhere). This study, by contrast, used a commercially available seed coating on an oil-seed rape crop, at the manufacturer's recommended concentration and on fields that were managed normally, ensuring that we get a true picture of the effects of neonicotinoids as used in agriculture. Second, many prior studies and risk assessments have used honeybees, Apis mellifera, as a model, so that less is known about the effects of neonicotinoids on the wider pollinator community. Here, the authors expanded the range of species examined, looking at the effects of pesticide treatment on bumblebees and solitary bees, in addition to honeybees. They provide evidence that wild bee numbers were reduced in treated fields and that the growth of bumblebee colonies was impaired. However, no treatment effect was seen on the health of honeybee colonies, suggesting that the effects of neonicotinoid use on wild pollinator populations may be greater than previously thought and implying that honeybee studies cannot be reliably extrapolated to other species. View at Faculty Opinions

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Anchor 1 EXPERIENCE 2017 - 2022 Researcher, Brains on Board, Queen Mary, University of London Working in Professor Lars Chittka's world-renowned lab, I led a programme of research for Brains on Board , a major interdisciplinary research project. ​ We aim to design autonomous flying robots with navigational and learning abilities inspired by those of honeybees. 2014 - 2017 Postdoctoral research fellow, Queen Mary, University of London I investigated how bees acquire and use information about the world for large-scale navigation, tracking their movements using harmonic radar technology and developing new methods for analysing their behaviour. ​ I also managed a major engineering project, developing a new generation of radars. 2012 - 2014 Post-doctoral research associate, University of Sussex I used high-speed camera systems to investigate how ants learn complex routes. I used Matlab to develop new tools to process and analyse large datasets of animal movements. ​ I also wrote and presented lectures on neuroscience and animal behaviour for undergraduate classes. 2011 - 2012 Postdoctoral research assistant, Deakin University, Australia Although the role of wildfire in ecological systems has become an important field of study in recent decades, almost nothing is understood about the immediate effects of fire on wild animal behaviour. I set up a novel research project investigating the impact of bush fires on bird movements. ​ I also lectured to undergraduate classes. 2011 Postdoctoral research assistant, Deakin University, Australia I was commissioned by the Australian Government to carry out an investigation into the role of pest bird species in spreading seed beyond the containment zones of GM crop trials. This project led to a presentation and the submission of an official report to the Office of the Gene Technology Regulator. With just six months of funding, I learned how to design experiments, collect and analyse data and deliver results on a short timescale. 2005 Field assistant, University of Bristol ​ I worked as a field assistant on a research project in South Australia, investigating the breeding success of Blue-cheeked rosella parrots. This involved monitoring egg-laying, tracking the growth of chicks, behavioural observations and assisting with ringing and taking blood samples from adult parrots. EDUCATION 2006 - 2011 PhD in sensory ecology, Cardiff University I was awarded a PhD in sensory ecology and physiology for my studies into the role of stress on the brain development and mate preferences of birds. ​ I won a fully funded Targeted Priority Studentship by the Biotechnology and Biological Sciences Research Council, and my doctoral research produced four papers in peer-reviewed scientific journals and oral presentations at two international conferences. 2002 - 2005 BSc (1st class) in Zoology, University of Bristol I undertook an undergraduate project in human psychology, investigating how mood and emotions influence judgement, which was subsequently published in a respected scientific journal . ​ My undergraduate studies in animal behaviour, psychology and physiology inspired a lifelong interest in cognition and how our brains process information. 1994 - 2001 Secondary education, The Skinners School, I was awarded four A-levels in 2001: Biology (A), Mathematics (B), Chemistry (B), General studies (B). ​ I achieved nine GCSEs in 1999: three A*s, five As, and one B. Anchor 2

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PRESS My research in the media