Project overview
Movement is a fundamental feature of animal life, and we are gathering more and more data on animal movements using tracking devices attached to animals. A key assumption in our studies of animal movements is that the spatial movement paths (or ‘tracks’) of animals reflect different behavioural states. By detecting these behavioural states in tracking data, we can predict and map these behaviours across land- and seascapes, creating movescapes that tell us how animals use areas.
These movescapes will be particularly informative and valuable for our understanding and management of marine ecosystems, which are under immense pressure from growing human impacts.
Using large datasets on the movements of marine vertebrates such as marine mammals, the aim of our broader research agenda is to develop and apply machine learning methods to define and predict these movescapes. In the current project--in partnership with CLS-Argos and using an interdisciplinary approach across ecology, oceanography and computer science--we explore the creation and application of a semi-supervised workflow that uses big data and machine learning approaches to do this.
These movescapes will be particularly informative and valuable for our understanding and management of marine ecosystems, which are under immense pressure from growing human impacts.
Using large datasets on the movements of marine vertebrates such as marine mammals, the aim of our broader research agenda is to develop and apply machine learning methods to define and predict these movescapes. In the current project--in partnership with CLS-Argos and using an interdisciplinary approach across ecology, oceanography and computer science--we explore the creation and application of a semi-supervised workflow that uses big data and machine learning approaches to do this.
