Floral preferences of bumblebees across a range of European climates https://www.findaphd.com/phds/project/floral-preferences-of-bumblebees-across-a-range-of-european-climates/?p137456 Deadline: 7th January 2022 To apply: https://www.iapetus2.ac.uk/how-to-apply/ Background. Bumblebees are agriculturally important pollinators, but are currently declining in abundance in the UK and around the world, in part due to climate change (Soroye et al. 2020). Understanding these declines requires research on the biology and ecology of these species. Bumblebees are thought to be generalists, pollinating a variety of flower species. However, our preliminary observations conducted in Durham in summers 2020 and 2021 indicate that different bumblebee species prefer different plants (see also Sikora et al. 2020). Bumblebees have been a preferred insect model for neuroethology and sensory neuroscience, and a wealth of earlier work has focussed on the importance of visual cues and nectar/pollen reward for foraging honeybees and bumblebees (Latty and Trueblood 2020). In contrast, the importance of floral smells is less well known, although some works report the essential role of flower volatiles in bumblebees’ floral choice (Galen and Kevan 1983; Suchet et al. 2011; Haber et al. 2019). This project will investigate olfactory preferences of commonly occurring bumblebees (e.g. Bombus terrestris, Bombus pascuorum and Bombus lapidarius) to naturally-occurring floral volatiles, and how these preferences are affected by climatic conditions and background plant communities in Norway (Kløfta), UK (Durham and Stirling), Germany (Würzburg), Italy (Milan) and Portugal (Braganca). We expect the plants that the bumblebees forage on to differ between these location, due to different climatic condition. We hypothesise that, despite the differences in plant species, the key components of floral bouquets will be very similar across test locations. Aims. 1) To identify plants that bumblebees forage on in the five countries, to establish plant preferences for bumblebee species; 2) Collect floral volatiles from the plants identified in Aim 1, as well as florals that bumblebees do not forage on, as controls; analyse these volatiles by GC/MS ; 3) Establish behavioural preferences of bumblebees in response to full floral bouquets and components of bouquets, fractions and synthetic components of that are specific for focal plant species. Methodology: Bee and plant collections will be conducted in the areas around Durham, Stirling, Kløfta, Würzburg, Milan and Braganca in March-September during the local bumblebee foraging periods. The student will be advised and assisted during field collection by OR and local members of the supervisory team. Student will be trained to identify plants and bumblebees via morphological cues and DNA barcoding. Floral volatiles will be collected at the same time as bumblebees by using standard volatiles traps, and will be analysed by the student via gas chromatography-mass spectrometry in TS laboratory. Behavioural olfactory assays on bees will be conducted in the field or either in the glasshouse at the Biocentre, University of Würzburg or in a glasshouse at Durham Botanical garden. The bees will be given a choice between 2 stimuli, or stimulus and a control, and their preference for a smell will be inferred from the tendency of a bee to land at the stimulus. Training and skills: The student will receive training: 1) by supervisors with complementary skills and expertise; 2) by collaborators and postdocs in the seven participating institutions; 3) by attending summer courses, conferences and Durham-run training events; 4) by participating in regular public outreach activities; 5) by helping OR to supervise UG students; 6) by presenting their work at lab meetings and conferences. The student will acquire knowledge and skills in: 1) insect chemical ecology and neuroethology; 2) gas chromatography/mass spectrometry and collection of volatiles; 3) bumblebee rearing; 4) identification of bumblebees and plants; 5) molecular biology methods; 6) cutting-edge techniques for behavioural analysis; 7) presentation and scientific writing; 8) research supervision; 9) Impact and public outreach. Requirements: We are looking for an independent and enthusiastic student able to develop the project and drive it forward. Interest in sensory ecology, neuroethology, animal behaviour, chemical ecology and previous research experience are a plus. You should be available to conduct field and lab work in the UK and in continental Europe. The peak time for field work is in March – September. Further information:Informal enquiries ARE STRONGLY ENCOURAGED and should be directed to Dr Lena Riabinina, olena.riabinina@durha.ac.uk, +44-191-334-1282

2024 BACN Early-Career Prize Lecture Many of our decisions affect other people. Our choices can decelerate climate change, stop the spread of infectious diseases, and directly help or harm others. Prosocial behaviours – decisions that help others – could contribute to reducing the impact of these challenges, yet their computational and neural mechanisms remain poorly understood. I will present recent work that examines prosocial motivation, how willing we are to incur costs to help others, prosocial learning, how we learn from the outcomes of our choices when they affect other people, and prosocial preferences, our self-reports of helping others. Throughout the talk, I will outline the possible computational and neural bases of these behaviours, and how they may differ from young adulthood to old age.

This is my C-14 Impaler gauss rifle! There are many like it, but this one is mine!" - A terran marine If you have never heard of a terran marine before, then you have probably missed out on playing the very engaging and entertaining strategy computer game, StarCraft. However, don’t despair, because what we have in store might be even more exciting! In this interactive session, we will take you through, step-by-step, on how to train a team of terran marines to defeat a team of marines controlled by the built-in game AI in StarCraft II. How will we achieve this? Using multi-agent reinforcement learning (MARL). MARL is a useful framework for building distributed intelligent systems. In MARL, multiple agents are trained to act as individual decision-makers of some larger system, while learning to work as a team. We will show you how to use Mava (https://github.com/instadeepai/Mava), a newly released research framework for MARL to build a multi-agent learning system for StarCraft II. We will provide the necessary guidance, tools and background to understand the key concepts behind MARL, how to use Mava building blocks to build systems and how to train a system from scratch. We will conclude the session by briefly sharing various exciting real-world application areas for MARL at InstaDeep, such as large-scale autonomous train navigation and circuit board routing. These are problems that become exponentially more difficult to solve as they scale. Finally, we will argue that many of humanity’s most important practical problems are reminiscent of the ones just described. These include, for example, the need for sustainable management of distributed resources under the pressures of climate change, or efficient inventory control and supply routing in critical distribution networks, or robotic teams for rescue missions and exploration. We believe MARL has enormous potential to be applied in these areas and we hope to inspire you to get excited and interested in MARL and perhaps one day contribute to the field!

Research, whether direct or indirect, aims to advance knowledge and change the world for the better. But whether you are spike-sorting with high-performance computers, getting through 100 single-use plastic pipette tips in a day or receiving regular shipments of metal-rich equipment, your research is having a long-term and detrimental impact on the environment. This session will explore how life sciences research contributes to the climate crisis and negatively impacts local and global environments. Practical advice will be given on ways to reduce the footprint of your own research.

Climate change is one of our generation's greatest challenges, with increasingly severe consequences on global ecosystems and populations. Machine Learning has the potential to address many important challenges in climate change, from both mitigation (reducing its extent) and adaptation (preparing for unavoidable consequences) aspects. To present the extent of these opportunities, I will describe some of the projects that I am involved in, spanning from generative model to computer vision and natural language processing. There are many opportunities for fundamental innovation in this field, advancing the state-of-the-art in Machine Learning while ensuring that this fundamental progress translates into positive real-world impact.

Multi-agent reinforcement learning (MARL) has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource (CPR) management. Crucial CPRs include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere, of which proper management is related to some of society’s greatest challenges such as food security, inequality and climate change. This talk will consist of three parts. In the first, we will briefly look at climate change and how it poses a significant threat to life on our planet. In the second, we will consider the potential of multi-agent systems for climate change mitigation and adaptation. And finally, in the third, we will discuss recent research from InstaDeep into better understanding the behaviour of networked MARL systems used for CPR management. More specifically, we will see how the tools from empirical game-theoretic analysis may be harnessed to analyse the differences in networked MARL systems. The results give new insights into the consequences associated with certain design choices and provide an additional dimension of comparison between systems beyond efficiency, robustness, scalability and mean control performance.