Study level

  • Honours

Faculty/School

Faculty of Science

School of Mathematical Sciences

Topic status

We're looking for students to study this topic.

Supervisors

Dr Matthew Adams
Position
Senior Lecturer in Mathematical Sciences
Division / Faculty
Faculty of Science
Associate Professor Kate Helmstedt
Position
Associate Professor in Operations Research
Division / Faculty
Faculty of Science

Overview

Antarctic ecosystems are complex, and data is limited since it is expensive to collect. Species interact in food webs which can be modelled as mathematical networks. The relationships between species are not always known, or we might know they interact but not how strongly. Noisy (or imperfect) data 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. In this project, we build upon a study from Dr Matthew Adams and Dr Kate Helmstedt to understand how this noisy data can be useful.

Reference: Adams et al. (2020) Informing management decisions for ecological networks, using dynamic models calibrated to noisy time‐series data. Ecology Letters 23: 607-619.

Research activities

  • Adapt existing code to an Antarctic case study
  • explore mathematical approaches for a Value-of-Information analysis
  • write a report detailing findings for a technical audience.

Outcomes

We aim to determine what kinds of noisy data might be useful for different kinds of ecosystem management.

Skills and experience

Programming skills (preferably in R or Matlab), mathematical or statistical undergraduate experience, good written communication skills, and an interest in using mathematics to address applied ecology problems to inform environmental management.

Scholarships

You may be eligible to apply for a research scholarship.

Explore our research scholarships

Keywords

Contact

Contact the supervisor for more information.