Remotely sensing motion: the use of multiple biologging technologies to detect fine-scale, at-sea behaviour of breeding seabirds in a variable Southern Ocean environment

• Main Author: Schoombie, Stefan
• Other Authors: Ryan, Peter G, Wilson, Rory P
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Faculty of Science 2021
• Subjects: Biological Sciences; Gough; Southern Ocean
• Online Access: http://hdl.handle.net/11427/36061; https://open.uct.ac.za/bitstream/11427/36061/1/thesis_sci_2021_schoombie%20stefan.pdf

The at-sea behaviour of seabirds, such as albatrosses and petrels (order Procellariiformes), is difficult to study because they spend most of their time on the ocean and have extremely large ranges. In the early 2000s, behavioural studies of seabirds were dominated by diving patterns of diving birds or spatial studies from satellite telemetry. Recent advances in biologging technologies have opened up new avenues for studying the at-sea behaviour of farranging seabirds in their natural environment. Bio-logging devices are now small enough to be attached to flying seabirds where multiple sensors record data at infrasecond sampling rates. These data can be used to infer, inter alia, body posture, activity (e.g. flapping, takeoff, landing, etc.), magnetic heading and spatial distribution at a resolution that was not previously possible. Bio-logging devices are battery powered and a tradeoff exists between the length of deployments and sampling frequencies, however not a lot of study has been done on what the effect of coarse sampling rates are on data quality. Together with the masses of data that are generated by bio-logging devices, analytical tools have also become available to extract useful metrics from the data. This thesis utilized some of the latest bio-logging technology to study the at-sea behaviour of several procellariiforms, breeding on Marion, Gough and Nightingale Islands, from finescale data. After describing the loggers used and the methods of deployment in Chapter 2, I assess the effect that sampling rates have on metrics derived from GPS loggers in Chapter 3. This was done by sub-sampling GPS tracks recorded at 1-s sampling intervals, showing the effect that different sampling intervals have on metrics, including the total distance travelled and behavioural states derived from path length and turning angles. I show that for larger sampling intervals, the total distance travelled will be underestimated at varying degrees depending on flight sinuosity. By varying sampling rates when estimating ...