|Title||Inferring dominance interactions from automated feeder data|
|Supervisor(s)||Krista van den Heuvel, dr. Barbara Tomotani, Prof. dr. Kees van Oers (NIOO-KNAW)|
|Examiner(s)||Krista van den Heuvel, dr. Barbara Tomotani, Prof. dr. Kees van Oers (NIOO-KNAW)|
|Contact email@example.com, firstname.lastname@example.org or email@example.com|
|Description||An individual�s position in a dominance hierarchy may influence their ability to gain access to food resources, territories and potential mates. Therefore, an individuals� dominance rank can have an important influence on their fitness. We are specifically interested to know how dominance rank relates to behavioural traits such as cognition and personality as well as circadian rhythm and foraging activity.
Many studies have relied on obtaining the dominance hierarchies of groups by recording social interactions, such as fight, submission displays or displacement. This is typically either observed directly or recorded on video, and either way requires a large amount of effort to obtain sufficient data. Therefore, there is much to be gained from using individual tracking techniques such as RFID (radio frequency identification) that can monitor the presence of certain individuals at a specific time and locations.
We have such data available for great tits in aviaries using feeders equipped with an RFID antenna, which were simultaneously video recorded. We would like to produce a code that uses this RFID data to extract interactions at the feeder to generate dominance hierarchies. This code can then be validated with the video data that is also available.
We are looking for a highly motivated and independent master student (thesis or internship) interested in programming, movement ecology and behavioural ecology to work on the code to extract dominance hierarchies from RFID feeder data and to analyse video data for validation of the code output.
The total duration can be adjusted to the students� needs, preferably starting before October 2021.
|Used skills||This project offers the opportunity to become affiliated with programming (R), video analysis, data analysis. Additionally, the student will develop his or her research skills. The student will be able to work on various research question(s) depending on their own interests (for example, but not exclusively, personality, cognition and/or circadian rhythms).|