I lead and contribute to a range of research projects, and also supervise postgraduate research students.

More ideas for potential student projects are available here: Student Projects, and information on current and past students is here People.

My main area of research is computational simulation modelling of plants in complex biological, agricultural and ecological systems, including areas such as:

  • evolution of herbicide resistance in weeds in agricultural systems and the effect of different genetics and management strategies on the rate of this evolution
  • competition between species, individual plants, and parts of plants and the effect of spatial patterns on this competition
  • weed seed bank population dynamics and the effects of different seed biology and management strategies
  • seed dormancy, germination and persistence, and the influence of environmental factors and management options
  • the way that plant structure emerges over time in relationship with physiology and environment
  • the optimisation of land use sequencing and analysis of tactical and strategic decisions in agricultural systems, taking into account the effects of factors such as weeds, disease, plant nutrients, yields, economics and climate variability
  • water use, root architecture, drought and climate change, in relation to applications such as the prediction and management of the establishment and survival of annual and perennial crop and pasture plants in drought-susceptible environments, or the long-term health of natural ecosystems in the face of climate change
  • the spread of invasive biological organisms such as weeds, insects and plant diseases
  • predicting the fate of plant species under climate change, accounting for landscape characteristics, population dynamics and dispersal
  • constructing useful decision support systems for managing agricultural and natural ecosystems

This model simulates the dynamic interactions between a community of perennial plants.

The modelling approaches in which I have interest and experience include:

  • individual-based models and simulation (IBMs)
  • L-systems and functional-structural plant models (FSPMs) for modelling dynamic plant architecture
  • continuous and discrete dynamical systems, including chaotic systems and S-systems
  • constructing statistical 'summary models' to represent more complex bio-physical simulation models
  • numerical/computational simulation of the movement and spread of biological substances or organisms through IBMs or integro-difference equations
  • applied statistical modelling of biological data, including linear and non-linear modelling, multivariate analysis, Bayesian analysis, and mixed-effect models