Just as boats leave a wake as they travel over the surface of the water, underwater animals also leave a trail of water disturbance as they move through their environment. These trails, termed ‘hydrodynamic motion cues’, hold information about the size of the animal that was travelling through that location in the past, what type of animal it was, and how that animal was moving. In effect, these hydrodynamic motion cues represent a ‘liquid fingerprint’, holding information about the animal itself and and how it was travelling through that location in the past. Many species of fishes are able to detect the hydrodynamic pressure of these liquid fingerprint, however, we know little about how the hydrodynamic information detected by animals is used in their behavioural decision-making. In this project, we will test whether fishes that sense the hydrodynamic cues generated from the past movements of other animals use this information in different behavioural tasks. For example, some prey fishes may use these cues to find other group members, while other predatory species may use these cues to track their prey. Using a combination of behavioural experiments, modern sensing technology, and machine learning techniques, this project aims to understand how hydrodynamic motion cues are used by fishes in search and navigation tasks. This work will not only allow us to understand some of the extraordinary sensory and behavioral adaptations that animals have evolved in underwater environments, but will also be informative for engineering applications, including the design of autonomous sensing vehicles.