Machine-learning and image recognition to monitor spatio-temporal changes in the behaviour and dynamics of species interactions

Changes in land use and climate affect the distribution, abundance and behaviour of species globally. Management and mitigation of these processes requires a good understanding of how they affect wildlife, which requires effective monitoring over large areas and long time periods. Mammals are often scarce, shy, elusive or nocturnal, so are difficult to monitor. The increasing affordability of camera traps - devices that can be deployed over long periods to take photographs of passing wildlife, 24 hours a day - has contributed to their popularity as a survey method.

Camera trapping has revolutionised prospects for monitoring mammals but generates huge numbers of images to be analysed. Some projects have successfully turned to "citizen scientists" to help with image processing but demand is likely to outstrip supply in the near future.

Over the past decade, analytical techniques have been developed to enable the estimation of key community, population and behavioural parameters from camera trap imagery, yielding data on not only distribution and abundance, but also parameters related to activity, movement and inter-specific interactions, applicable across a wide range of both individually recognisable and unmarked species. However, these techniques can increase the burden of image processing, begging the questions:
- Can automated image analysis make these promising techniques more accessible and widely used?
- Can the widespread, long term use of these techniques catalyse a step change in our understanding of the status of mammal populations and the processes driving change?

In this project, working with an interdisciplinary team of ecologists and computer scientists, the student will mobilise machine learning techniques to push forward our ability to automatically process large quantities of camera trap imagery, with a particular focus on using the data generated to understand the dynamics of interactions between species and their environment. The ecological insights generated will be aimed at informing the management of threatened species.