Antarctic ecosystems are complex, and data is limited since it is expensive to collect. Species including penguins, seabirds, invertebrates, mosses, and marine species interact in food webs which can be modelled as mathematical networks. These networks can be large, span across terrestrial and marine systems, and are changing in response to environmental changes.
These ecological networks can be modelled using differential equation predator prey models like Lotka-Volterra to describe these interactions. However, the relationships between species are not always known, or we might know they interact but not how strongly.
Data is expensive to collect in Antarctica, so it may be patchy or noisy. We are then faced with the challenge of parameterising these food webs with this patchy data. This imperfect data still can be used to model these species interactions to give more certainty about how the ecosystem works as a whole – although the worse the data is, the less information it contributes.
This data might not be perfect, but it can still be very useful for planning management of the ecosystems. We can use Value-of-Information theory to determine what kinds of data will be most useful for building these network models, what level of precision will be most useful using a Bayesian analysis, and guide where it is most useful to invest in more refined and reliable data.
This project aims to develop a framework for determining the Value-of-Information for food web analysis, case studies involving Antarctic ecosystems, and policy advice for future ecosystem monitoring. You will become a member of the Australian Research Council Special Research Initiative for Securing Antarctica's Environmental Future, which will provide opportunities for interdisciplinary collaboration and a real pathway to informing management of Antarctic ecosystems.
Skills and experience
You will have:
- an undergraduate degree in mathematics, statistics, computer science, or a similarly quantitative field
- good skills in coding, e.g. coding models such as ordinary differential equations and/or statistical inference
- an interest in using mathematics and decision science techniques to address applied and/or theoretical ecology problems to inform environmental management
- an interest in working and communicating with a multidisciplinary team of researchers.
We are particularly interested in applicants from groups that have traditionally been excluded from academia, mathematics, and STEM based on gender, sexuality, race, culture, disability status. You will join an active and diverse research group (the Applied Mathematical Ecology Group), and we have a strong focus on supporting your research development.
You may be eligible to apply for a research scholarship.
- decision science
- Bayesian statistics
- penguin diet
- penguin predators
Contact the supervisor for more information.