Much like us, when a bird is sick, it reduces activity, stops eating and loses weight. This is a core part of the immune response, helping to conserve energy and fight the infection. It also reduces the change of transmitting infection to other birds. However, if birds are exposed to repeated infections, it may no longer be beneficial to have repeated bouts of fasting and inactivity.
Bird feeders could be a source of infection, and birds that visit feeders could be exposed to repeated infections at feeders. In turn, these birds are expected to be better able to cope with sickness and continue to be active while sick, which could increase the chances of transmitting infection to other birds and becoming ‘superspreaders’.
Activity loggers (accelerometers), placed on the back of the bird, allow the remote study of activity and behaviour, by measuring acceleration. To be able to distinguish different behaviours from the acceleration data, we can film birds while carrying loggers so we can link specific behaviours to unique acceleration profiles. The student will, firstly, annotate videos of birds to pair behaviours – such as flying, resting, eating, or preening – with the corresponding acceleration data. A behavioural classification model (using supervised machine learning methods) built on this data will then be applied to acceleration data from birds who have been exposed to a simulated infection.
In this project, the student will gain core skills in data handling and analysis in R and learn about movement ecology, immune function and avian ecology.
Contact Hannah Watson for more details: hannah.watson@biol.lu.se
https://portal.research.lu.se/en/persons/hannah-watson/
If you want to know more…
Yu et al. 2024. Flight activity ad effort of breeding pied flycatchers in the wild, revealed with accelerometers and machine learning. Journal of Experimental Biology, 227:jeb247606. https://doi.org/10.1242/jeb.247606
Yu et al. 2023. Accelerometer sampling requirements for animal behaviour classification and estimation for energy expenditure. Animal Biotelemetry, 11:28. https://doi.org/10.1186/s40317-023-00339-w