The Australian Institute of Marine Science (AIMS) is tasked with monitoring submerged shoals off the coast of Western Australia.
This project will develop new statistical methodology in adaptive Bayesian design to help AIMS monitor more effectively.
You will work closely with AIMS and also other members of our ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).
There will be three steps to this project. The first is building a model to describe the variability in the collected submerged shoals data. This will facilitate an understanding of relationships between variables which may exist, and also provide an understanding of the spatial extent/variability of different habitats, functional groups and species, and how this changes over time.
Secondly, given the model developed in step 1, the following questions will be explored:
- What impact sizes could be detected (with reasonable power) if AIMS continue with the current monitoring practices?
- How could the current sampling protocol be changed to gain equivalent (or near equivalent) information but with less resources?
- If the spatial and temporal variability is not well understood, how can we monitor to learn about both of these?
- How can we leverage the developed model to monitor more efficiently and potentially gain more information from the same resources?
The final step of the project will be to develop recommendations about how AIMS can implement a cost effective monitoring framework into the future.
The outcomes of the project will include:
- development of new Bayesian computational algorithms
- submission of papers to top journals in statistics.
You will develop close connections with AIMS and provide them with recommendations for how they can monitor into the future.
Skills and experience
Ideally, you will have coding experience (for example in Matlab or R), and experience with Bayesian statistics.
You may be able to apply for a research scholarship in our annual scholarship round.
Contact the supervisor for more information.