Potential student projects

How to combine food webs with size-based models?

Marine ecosystems and fishing

Multi-species communities are often described by food webs, which focus on species as the key variable determining what an individual eats. But in marine ecosystems, fish can grow by several orders of magnitude during their lives, and their diet changes as they do so. This means that body size can be more important than species identity in describing predator-prey interactions. Size-specturm models focus on body size as a key variable and keep track of how biomass flows from prey to predator through mortality and growth. These models take the form of a system of nonlinear integro-partial differential equations. This project will use size-spectrum models to investigate the dynamics of multi-species communities. These models will also be used to investigate the effect of different fishing strategies on key outcomes such as fisheries yield and impact on ecosystem function and community structure.

Impeded random walks

In a classical random walk, agents move a short distance in a randomly chosen direction at each time step. If the agents all move independently, the average agent density is governed by the heat (or diffusion) equation. But in many real situations, agents interact with one another and the environment, e.g. via physical contacts or other signals. Their movement can also be impeded by obstacles. This project will investigate random walks where agent movement is impeded in this way, in particular looking at how this affects the diffusion PDE for agent density.
Experimental image of part of a cell population

Collective cell behaviour

Collective cell behaviour is the driving force behind many physiological processes, including embryonic development, tissue repair and tumour growth. Experiments on collective cell behaviour typically collect data at the level of the population rather than the individual cell. We'd like to be able to translate data from observing populations of cells into knowledge about how individual cells work and how they interact with their neighbours. This project will approach this problem using approximate Bayesian computation (ABC). At its simplest, this involves sampling model parameters from a prior distribution and simulating cell behaviour. If the model output is "close" to the experimental data, the parameter values are accepted as part of the posterior distribution, otherwise they are rejected. This will be used to estimate quantities such as cell proliferation and movement rates and the strength of interactions with neighbouring cells.

Spatial moment dynamics for invasive populations

Spatial moments are a way of describing the spatial distribution of a population that accounts for correlations between individual locations, e.g. whether individuals tend to occur together in clusters or tend to be spaced apart at regular intervals. Classical models based on average population density cannot distinguish between these and other cases, but they can crucially affect population dynamics. This project will use spatial moment dynamics to model an invasive population and investigate the two-way interplay between spatial structure and macroscopic outcomes such as population size and invasion speed. These models use a combination of individual-based stochastic models and an integro-partial differential equation for the second spatial moment coupled with an appropriate moment closure approximation.


Michael Plank, Room 614 Erskine Building, School of Mathematics and Statistics, University of Canterbury, Christchurch 8140, New Zealand
Tel: +64 3 3692462
Email: michael.plank@canterbury.ac.nz