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Social resilience in a warming world: How is climate change reshaping bee societies?
Project Description
Cooperation is an important strategy for coping with harsh and unpredictable climates. However, we are only beginning to understand how climate change is reshaping animal societies. Understanding these consequences is particularly urgent for ecologically important groups like bees that span a broad spectrum of social complexity. This project will investigate the effects of acute and chronic climate stressors on bee social dynamics, and explore the behavioral mechanisms that support climate resilience across bee taxa. You will conduct field and lab experiments investigating bee social behaviour and thermal physiology in natural and managed populations. This project will also provide the opportunity to develop skills in AI approaches to behavioural research, by adapting and developing AI-based video tracking systems to automate behavioural analysis. This project will generate valuable insights into the behavioural consequences of a warming planet for social systems and the ecosystem services they sustain.
Research themes
Project Specific Training
The student will receive training in behavioural ecology, thermal physiology, and behavioural analysis using AI. They will gain experience designing and conducting field and lab experiments, analysing thermal and behavioural data, and applying and developing AI-based video tracking tools for automated behavioural analysis. Training will be delivered through one-to-one supervision and hands-on instruction with the research group. There will be additional opportunities for collaboration with project partners (e.g., agricultural partners like the Orchard Project). There will also be the opportunity to interact with QMUL's vibrant AI for ecology research group and the Digital Environment Research Group.
Potential Career Trajectory
This project will equip the student with a versatile skill set spanning behavioural ecology, physiology, data science, and AI-based image analysis. These skills provide a strong foundation for academic careers in ecology, evolution, and environmental science, as well as interdisciplinary research in climate resilience and biodiversity informatics. Beyond academia, the training is highly transferable to roles in environmental consultancy, conservation management, government and NGO research, and emerging sectors applying AI and data analytics to sustainability and natural resource management. The student will also gain experience in scientific communication and collaboration across disciplines.
Project supervisor/s
Madeleine Ostwald
Biology
Queen Mary, University College London
m.ostwald@qmul.ac.uk
Rachel Parkinson
Biology
Queen Mary, University College London
r.parkinson@qmul.ac.uk