Institution: University of Exeter
Lead Supervisor: Dr Joah Madden
Collecting simultaneous, high frequency, location data from multiple individuals is difficult, traditionally requiring a high degree of human effort and physical effort in the field risking perturbing the study species and hence biasing data. Automated measurement systems reduce such costs. An individual’s location can be determined by trilateration based on radio-reception of ‘ping’ signals conveying the ID of the individual. An innovative system (ATLAS), developed and deployed in Israel, achieves this and I will deploy the system this summer to track movement and fates of released pheasants as part of my existing ERC funded research project on the ecology and cognition of pheasants.
This Placement would constitute an independent facet of the main project yet benefit from infrastructure and resources already in place. The work would take place on our field site and I would supervise the student directly, and s/he would benefit from interactions with my team of post-docs and PhD students and Israeli collaborators. I provide established marked bird populations, computers and lab space. The RTSG would buy tracking equipment (Software defined radio modules e.g. Fun-cube dongles & Raspberry Pi computers) to construct local radio nodes. Other equipment is supplied by the project.
The Placement involves fieldwork, with the student being responsible for tagging birds, retrieving and processing data, and truthing data with field observations of actual movement patterns. The student will develop algorithms to smooth movement tracks, accounting for missing data and noise inherent in the system, and an automatic ‘death detection’ algorithm by which motionless birds are highlighted and can be retrieved. Each bird is monitored at 1-2Hz 24hrs/day. We will release ~200 tagged birds. This will generate ~700 million location points over 6 weeks. The student will gain skills in: practical bird handling and husbandry; direct behavioural observation; radio positioning techniques; analysis of movement data; international collaboration with Sivan Toledo in Computer Sciences, Tel Aviv University. If the project progresses as planned, there will be an opportunity to contribute to writing the work up for publication.
Indicative timescale for project (subject to change)
During weeks 1-4 the student will work with others on my research team to familiarise themselves with the ATLAS system, including both the software and hardware. They will learn about pheasant biology and ecology, basic field observation skills and will construct, program and assist in deployment of the individual ‘pinger’ tags. Once the birds have been released at the end of July, the student will spend weeks 5-10, in collaboration with my PhD student, extracting and processing movement data using MySQL, GME, R and ARC. They will use both real and toy data to construct smoothing and death detection algorithms. They will assess the efficacy of these algorithms through observations on the ground and searches for dead birds/preplaced ‘lost’ pingers. The student will be trained in bird catching and handling skills to ensure competency required by the Home Office Licence governing the project.