Computational ecology

We use a range of tools to explore a diverse suite of ecological questions.  Nearly all of our research is underpinned by some form of empirical investigation (field and/or lab work) but we also use statistical and simulation modelling to try to synthesise and make sense of these data.  To this end, we have used machine-learning methods to reconstruct disturbance patterns, statistical modelling to understand the role of tree-ferns in NZ’s temperate forests, spatial simulation to unravel vegetation-fire feedbacks, and individual (agent)-based models to explore forest dynamics and the movement of seabirds.  We are (generally) agnostic about languages and platforms, but most of us tend to use R, Python and/or NetLogo.