Quantitative movement analysis: tracking sea turtles, seals and marine birds in the global ocean
1. Animal Movement and Behaviour
2. Quantitative Modelling
3. Remote sensing and animal tracking
4. Marine biology and ecology
The last decade has seen the development of reliable satellite tracking equipment which has allowed routine long-term (months to years) tracking of a range of marine vertebrates including turtles, seals and sea birds. My projects focus on examining a number of cutting-edge questions related to patterns of movement and habitat use including both “blue skies” questions on animal orientation as well as more applied questions on marine conservation planning.
I am especially interested into extracting information about movement at small spatio-temporal scales using high resolution Fastloc-GPS tracking data-sets. Fastloc-GPS accuracy is several orders of magnitude better that conventional Argos tracking or light-based geolocation. Speed of travel, animal bearing and other commonly used metrics can be calculated only few hours apart depending of the quality of the locations. This allows the analyse of a range of data-sets emerging from long-term animal tracking, and the examination of key biological parameters such as swimming speed, orientation efficiency or energy expenditure along migratory paths. For example, for species such as sea turtles, detailed analysis of habitat use on breeding or foraging sites will be used to develop informed conservation strategies in terms of protected area designation.
Another of my interests is using Lagrangian drifter trajectories and ocean particle tracking models to consider various drift scenarios describing the dispersal of hatchling sea turtles. This work is embedded in a broader context regarding the ontogeny of migration of sea turtles, the implications of climate change and the global distribution of sea turtles.
Based on those different skills, I am also interested in building a meta-analysis of movement patterns across diverse species (turtles, seals, birds) using comparable Argos and Fastloc-GPS data-sets. The purpose of such meta-analysis is to identify common patterns across taxa.
PhD Student, School of Life and Environmental Sciences, Deakin University, Warrnambool Campus, Australia