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PROJECT MANAGEMENT Advanced SPANISH FLUENCY Intermediate

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Buehlmann*, Woodgate* & Collett* (2016) Current Biology 26: 1-6 On the Encoding of Panoramic Visual Scenes in Navigating Wood Ants It’s well known (among the sort of people who think it’s a good idea to administer intelligence tests to ants) that navigating animals extract useful information from the shape of the skyline to guide their routes. In fact, if you replace the real skyline with a crude facsimile (scaled down but far closer to the ant), they are unfazed and carry on navigating to the same place they originally learned. We took advantage of this by transferring wood ant colonies to a lab and letting them learn to find food, guided only by a panorama made up of pieces of cloth pegged to the walls of a big white cylinder. We then tried to deduce exactly what the ants had learned by changing the shapes and looking at where the ants search for food: if we choose our shapes cleverly enough, the kinds of mistakes they make should let us know what rules they are trying to use. One big question is whether ants segment a scene into component shapes, the way we do. When you look at the horizon, you don’t simply see it as a single undulating line; instead, without any conscious effort on your part, you see a mountain over here, a tall tree over tree, a stand of woodland in the distance (is that one object or many?). Do ants do something similar? Do they draw their mental dividing lines in the same places we do? And however they divide the scene into components, how do they store information about those shapes so that it can be used to guide them to their food? We trained ants to find food at a point between several large skyline shapes, so that rather than heading toward a particular shape, they had to learn something about the relative positions of the shapes on either side of the goal. Then we removed or altered the shapes to see where they would go. Replacing the learned shapes with different ones in the same positions had no effect on the ants’ ability to find the food, so they were evidently able to apply their stored memories of the relative positions of shapes to a new panorama even though the shapes didn’t match at all. When we trained them to head in a direction specified by the positions of a rectangle and a triangle and then tested them with just one of those shapes present, you might expect that they would just retrieve the memory associated with the one shape that remained; so if the food was usually found 30 degrees to the right of the rectangle and 60 degrees to the left of the triangle, and they were tested with just the rectangle, they might head 30 degrees to its right. In fact, this is not what happened, and the ants always seemed to take a compromise heading that didn’t match the feeder’s position with respect to any single component of the training panorama. The different tests we performed, and their analyses, get a little complicated, here, but the overall pattern of the results suggests that the ants have learned information about each shape (probably including the angles of the edges and some measure of its size or “visual mass”) along with its position relative to the feeder. When the ant encounters an ambiguous view, it can retrieve all of these memories each of which suggests a direction of travel. To anthropomorphise, the thought process might be: “if I’m looking at the rectangle I should turn 30 degrees to the right, but if I’m looking at the triangle, I should turn 60 degrees to the left”. These separate possible headings are then recombined according to some algorithm we don’t yet fully understand, to come up with a best guess compromise heading. Not every memory is given equal weighting: it seems that how well the shape matches plays an important role (so if the test shape is a rectangle, the rectangle memory will play more important role in generating the compromise and the ant will head to the right, just not so far right as it normally heads, relative to the rectangle; but if it’s a triangle, the triangle plays a more important role and the ant heads further left, just not so far left as normal). The distance of each shape from the food also influences the eventual heading, with more weight given to shapes that are normally close to the food than those that are more distant. It’s likely that other factors also play a role and that the compromise heading is constantly updated to reflect the ant’s level of certainty about how its current view matches the stored memories. Tom Collett suggested a useful analogy to visualize this procedure: imagine the ant wearing a loop of wire like a crown, with a few brightly coloured beads in certain locations around the ring. These beads match the memorized positions of the shapes on the skyline. To navigate toward the food, the ant turns until the beads all align with the shapes on the skyline and walks in that direction. When the skyline is changed, there is no way to perfectly align the beads, so the ant finds a position which in some way provides the least-bad fit. Perhaps imagine that the ant turns repeatedly this way and that, matching one bead to the shape on the horizon, realizing that the other beads are unaligned and turning again to match one of them, instead. It lingers the longest or returns most frequently to the positions in which the match is best, and quickly turns away when the match is bad, and in this way a sort of average direction of travel gradually emerges from the disorder. Ants do walk with a sinuous zig-zag path, so it’s not impossible that something like this repeated turning and matching really does take place, but we suggest it only as an analogy to show how different, incompatible memories can be reconciled in the brain to come up with a best guess direction of travel. This process, in which the brain simultaneously comes up with several independently calculated estimates of where to head, and only combines them to produce a single direction of travel at a much later stage of processing, seems to be a general principle of insect navigation. For example, in addition to their visual memories, ants use a process called “path integration” to keep track of their position and can use it to plan a direct route home, even if their outbound route was very tangled. Many species of ant can also follow scent trails laid by their nestmates. These different ways to plan a route are kept separate in the brain and play a larger or smaller role in the eventual choice of travel direction depending on the context and the ant’s estimates of their reliability: when the path integrator gives a clear indication of the homeward direction, ants will generally follow it even if the skyline indicates they are on the wrong track, but when they are close to home it is less reliable and visual cues play a bigger role; meanwhile, an inexperienced ant will often follow pheromone trails even if they conflict with the other sources of information, but experienced ants rely on their own private knowledge (like visual memories or path integration) in preference to following trails. *These authors contributed equally to the study.

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Other Writing Writing about science for technical and non-technical audiences We tracked male honeybees for two years to find out where they look for sex. An article I wrote for The Conversation, explaining the science behind my research paper into honeybee drone behaviour . This one caught people's imagination: it has been read more than 76,000 times and was the most read article by any Queen Mary University of London researcher in 2021. ​ While some bees are workers, others are born to bee free. Another piece for The Conversation, explaining a study with James Makinson and others in which we told the life stories of individual bees by tracking every flight they ever made. This one had more than 23,000 views and was widely republished. ​ Central Place Foraging. Encyclopedia of Animal Cognition and Behavior. Vonk, J., Shackelford, T. (eds). I was asked to write a piece for a new encyclopedia. This short article explains the science of central place foraging, a common tactic in which an animal has a central base, usually a home or nest, from which it searches for food or other resources. Click here to read the full text or view a pdf copy. ​ Faculty Opinions (formerly Faculty of 1000), is a social network in which working scientists can recommend the most important or relevant new research results to one another. As an associate member, I have written a number of short pieces, explaining the relevance of new papers that have excited me. Click here to read them or for links to the original versions on the Faculty Opinions site.

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Published Papers Research papers are the main output from most academic research. We use them to let our colleagues know about the work we’ve done and what we think it means. In the following section, you can find out more about my published research. Below, I will give a brief summary of each of my published papers. Click on any title to visit the journal’s website or on the image to view or download a pdf copy of the paper. In most cases, clicking on the image for any of my papers will also take you to a more detailed summary, written for a more general audience. Bridges et al. (2023) Plos Biology 21(3): e3002019 Bumblebees acquire alternative puzzlebox solutions via social learning Cultures arise when local behavioural traditions spread throughout a group and are maintained through long periods. Highly elaborate cultural traditions can develop when these behaviours are added to by successive generations. There is considerable controversy over the extent to which animal groups possess culture. In this important study, Alice Bridges shows that bumblebee societies possess the necessary traits for cultural development: learned techniques for solving a “puzzlebox” to get food spread throughout a colony, even when they were too difficult for any individual bee to figure out for themselves and completely arbitrary local variants persisted over time, even when equally good alternatives were introduced. Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. Gallo et al. (2023) Journal of Comparative Physiology A Sub-cell scale features govern the placement of new cells by honeybees during comb construction Honeycomb is a remarkable example of precision engineering in nature, yet it grows organically, can be adapted to fill all sorts of irregular spaces and is built by bees with no central intelligence guiding their work. We still don’t know exactly how it’s done, but Vince Gallo has made great strides forward in our understanding. In this paper, we show that the placement of new cells, as the comb expands, is controlled by the positions of existing walls. This is a specific example of stigmergy, in which the next steps in a construction process are guided by the state of the work piece, itself. ​ Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. Pan et al. (2023) Microscopy and Microanalysis 29(2) 786–794 Fine structure of the compound eyes of male and female Heortia vitessoides Moore (Lepidoptera: Crambidae) I study the behaviour of insects because they are interesting, and particularly because I’m fascinated by the efficient ways in which small-brained insects manage to tackle complex tasks that we currently need supercomputers and the energy budget of a small country to even approximate. But other researchers have a more immediate, practical reason: insects have major effects on the human economy, from pollinators like my bees, who help us out, to insect pests which cause huge losses for farmers worldwide. The more we understand about the physiology and behaviour of pests, the more likely we are to come up with clever and economical ways to beat them. In this paper, my collaborators in China made a detailed study of the eyes of an important crop pest species, revealing details that will help come up with more effective pest control strategies. ​ Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. Guiraud et al. (2022) Plos One 17(6): e0263198 Discrimination of edge orientation by bumblebees Complex cognitive tasks, such as visual processing, build on the outputs from simpler, upstream, neural circuits; for example, modules that detect edges in the visual field and their orientation. We hope eventually to model the entire visual processing chain – which will provide great insight into how brains accomplish difficult tasks as well as making huge steps forward in computer vision – but to do that, it is necessary first to understand the building blocks. This paper looks at how good bumblebees actually are at detecting angled edges, by setting them a foraging task in which good “flowers” can be identified by the precise angle of a coloured bar. We show that bumblebees could discriminate really well between bars that differed by just 7°, and that this doesn’t seem to be affected by orientation. Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. Woodgate et al. (2021) iScience 102499 Harmonic radar tracking reveals that honeybee drones navigate between multiple aerial leks My major focus for around eight years was running a research program using harmonic radar technology to reveal how bees use the landscape and what cognitive mechanisms underlie their often very complex and adaptable behaviour. By using a very small tracker on the bee and a very big radar unit in the field, I could track a bee’s flight path under unrestricted conditions. This paper is the fruits of the single biggest project I undertook, along with my indefatigable colleague James Makinson. We tracked hundreds of male honeybees (or drones) over two years and confirmed what some scientists and beekeepers had long suspected: drones gather together in huge numbers in specific locations, known as drone congregation areas, which astonishingly can persist in the same place for years, perhaps decades, at a time. We were able to identify the flight dynamics that allow these swarms to form; identify the complex spatial structure of congregation areas and the network of interconnected aerial highways that join them; and place this apparently unique behaviour in the context of the sexual selection pressures that shaped it, showing the differences as well as the parallels to mating systems across the animal kingdom. Click here to visit the journal’s website, or here to read (much) more or view a pdf copy of the paper. Brebner et al. (2021) Animal Behaviour 179: 147e160 Bumble bees strategically use ground level linear features in navigation One of my major research interests is how animals like bees look at the world and extract information that can be used to help them move around it. Something that particularly fascinates me is how the ability to fly opens up new possibilities for learning about the world. For this study, we tracked the flight of bumblebees in a completely flat landscape: with no useful landmarks on the horizon, bees needed to look down and find ways to make use of what they could see below them. Our field site had a regular gridwork of roads and field borders and we found that they made extensive use of them: following them like a road network when they were unfamiliar with the landscape, using them to guide their approach to the nest, and using appropriate context to reduce the area they needed to search when lost. What’s really exciting is not just that bees can learn to use highways, but that they can do so in creative and flexible ways, making different use of them under different circumstances as and when needed. This was my first last-author paper (this typically indicates a supervisory or mentor role in academic publishing). ​ Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. 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 used radar to track bees, I used cameras to track ants on a much smaller scale. Even smaller and tinier-brained than a bee, individual ants can learn to follow long, complex routes over rough terrain to find food. That such a small brain can drive this apparently complex behaviour suggests that they must use very efficient computational methods, so computer scientists are very interested in learning how, in the hope of making similarly impressive robot navigators. One clever model, known as alignment image matching, works by assuming that an ant turns until what it sees gives the best match to a previously stored memory and then walks forward, and despite its simplicity has proven capable of imitating many of the properties we associate with ant navigation. We suspected that there is more to real ant behaviour than this, so we engineered a situation in which the model would fail, because there is nothing to see when the ant is facing in the right direction. With practise, ants were still able to find their way to food, by remembering how far they needed to turn away from a direction in which there was a landmark to see. We argue that this wasn’t a new technique, learned under unusual conditions, but that similar controlled turns are used to follow routes under normal conditions, too. This information, along with other recent discoveries, can now be used to make even better navigation algorithms. Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. Williams et al. (2021) Computers and Electronics in Agriculture 184: 106065 Early prediction of bumblebee flight task using machine learning Back in 2016, James Makinson and I used harmonic radar to track every move made by individual forager bumblebees throughout their entire lives. We argued that each trip outside the nest could be classed as either an exploration flight, in which they seem to mix searching for flowers with learning about their surroundings, or an exploitation flight in which they focussed on efficiently collecting pollen or nectar from previously discovered flower patches. In this clever work, Sam Williams and colleagues at Bangor University came up with an efficient way to automate the categorisation of each flight. The really clever bit is that the algorithm could categorise flights with high accuracy from just the first few datapoints in a radar track. In other words, they can predict what the purpose of a trip is before the bee has even got to the flowers. This opens up lots of possibilities for automatically monitoring bee activity on a far greater scale than we can currently do, and the Bangor team hope to deploy it on a fancy new drone-based bee tracker. I didn’t play a huge role in this one, mostly providing training data and a bit of advice. Click here to visit the journal’s website, or here to view a pdf copy of the paper. Makinson*, Woodgate* et al. (2019) Scientific Reports 9:4651 Harmonic radar tracking reveals random dispersal pattern of bumblebee (Bombus terrestris ) queens after hibernation In this study, we used harmonic radar tracking to investigate what queen bumblebees get up to during a mysterious phase of their lives. In the spring, queens emerge from the earth, where they have been hibernating all winter, but they do not search for nesting sites and start building new bumblebee colonies for several weeks. Our tracking data, confirmed by old fashioned visual observations, revealed that this period is actually a formerly unknown new stage in bumblebee life-history, characterised by the need to rest and rebuild fat reserves before they can start to lay eggs. During this period, bumblebee queens spend almost all of time they are not feeding resting on the ground, often napping with their heads tucked under a fallen leaf. They fly only rarely and for short distances, so it is essential for them that there is plenty of leaf litter on the ground for them to hide in and that there are unbroken bridges of habitat between wild areas, since they cannot cross large inhospitable areas in one go. Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. ​ *These authors contributed equally to the study. Woodgate et al. (2017) Scientific Reports 7: 17323 Continuous radar tracking illustrates the development of multi-destination routes of bumblebees ​ Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. Woodgate et al. (2016) PLoS ONE 11(8): e0160333 Life-long radar tracking of bumblebees ​ Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. Buehlmann*, Woodgate* & Collett* (2016) Current Biology 26: 1-6 On the Encoding of Panoramic Visual Scenes in Navigating Wood Ants Ants that have discovered a good food source can learn to follow long and complex routes to and from the food using just visual memories of the shape of the skyline. We don’t yet know exactly what information about the skyline they remember, or how they store it efficiently in memory. This study is one of a series in which we trained ants to find food in a controlled, lab environment, guided by a simple skyline made of geometric shapes; we then carefully alter the skyline shapes in various ways to see how their routes are affected. When we replace the familiar shapes with different ones in the same places, ants can still navigate well, so they clearly store information about the relative positions of landmarks independently of information about the shapes themselves. When one shape is removed from a multi-shape skyline, there is ambiguity: which remembered shape corresponds to the remaining ones? The ants’ routes are perturbed in complex ways which suggest that, rather than picking a single “best-fit”, they work out which direction would be indicated if the remaining shape was each of the remembered shapes and pick a compromise direction that combines them all in a weighted average. Greater weighting seems to be given to shapes that better match the original skyline shapes and to those lying closer to the goal. This approach is likely to allow quite robust navigation even under changing conditions, while memorizing only a fairly small amount of information. Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. *These authors contributed equally to the study. Woodgate, Buehlmann & Collett (2016) Journal of Experimental Biology 289: 1689-1696 When navigating wood ants use the centre of mass of a shape to extract directional information from a panoramic skyline ​ Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. Woodgate et al. (2014) Evolution 68: 230-240 Environmental and genetic control of brain and song structure in the zebra finch ​ Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. Woodgate et al. (2012) Animal Behaviour 83: 773-781 Male song structure predicts reproductive success in a wild zebra finch population ​ Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. Woodgate et al. (2011) Behavioral Ecology 22: 566-573 Developmental stressors that impair song learning in males do not appear to affect female preferences for song complexity in the zebra finch ​ Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. Paul et al. (2011) Evolution and human Behavior 32 (2011) 21–28 Mood and the speed of decisions about anticipated resources and hazards This paper was based on my undergraduate honours project at the University of Bristol. We’re all familiar with the saying that an optimist sees the glass as half full while a pessimist sees it as half empty but, remarkably, this may well be literally true: your mood and expectations about the world can actually influence your perceptions. We created a task in which human participants had to judge whether a cross on a screen was closer to one end of a line than the other, and in which they were rewarded or punished for correct or incorrect choices with pictures of cute or scary animals! When the cross was in the middle, less anxious participants were more likely than their anxious classmates to perceive it as being closer to the “reward” end of the line. Similar results are found in various animal species and support a growing body of evidence that emotional states allow quick and flexible decision making under uncertain conditions and do so in part by affecting the way animals perceive the world. ​ Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper. Woodgate et al (2010) Animal Behaviour 79: 1381-1390 Developmental stress and female mate choice behaviour in the zebra finch ​ Click here to visit the journal’s website, or here to read more or view a pdf copy of the paper.

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Williams et al. (2021) Computers and Electronics in Agriculture 184: 106065 Early prediction of bumblebee flight task using machine learning Back in 2016, James Makinson and I used harmonic radar to track every move made by individual forager bumblebees throughout their entire lives. We argued that each trip outside the nest could be classed as either an exploration flight, in which they seem to mix searching for flowers with learning about their surroundings, or an exploitation flight in which they focussed on efficiently collecting pollen or nectar from previously discovered flower patches. In this clever work, Sam Williams and colleagues at Bangor University came up with an efficient way to automate the categorisation of each flight. The really clever bit is that the algorithm could categorise flights with high accuracy from just the first few datapoints in a radar track. In other words, they can predict what the purpose of a trip is before the bee has even got to the flowers. This opens up lots of possibilities for automatically monitoring bee activity on a far greater scale than we can currently do, and the Bangor team hope to deploy it on a fancy new drone-based bee tracker. I didn’t play a huge role in this one, mostly providing training data and a bit of advice.

Click here to read the original article ​

Contact me for information about purchasing or commissions Traveller, you have reached a place where only monsters dwell. Turn back now, before it's too late.