My research aims to contribute to improved recognition of Indigenous landscape management by developing methods that can quantify and distinguish human fire effects from climate-driven processes.​

• PhD project title: “Sharp boundaries, complex origins: The role of anthropogenic fire in forest-grassland ecotone dynamics”​

• What's the problem? We don't really understand how people’s use of fire to manage landscapes has affected where forests end and grasslands begin. These boundaries between forest and grassland are really important for ecosystems, but it's hard to tell whether changes are caused by climate, soils, “natural” fires, or human fire management—they're all mixed together in the real world.​

• Why is this hard to study? These landscape changes happen slowly (over decades or centuries), and you can't exactly run experiments where you burn half a landscape one way and half another way to see what happens. Also, we don't have good methods to study how human decision-making about when and where to burn actually translates into ecological effects.​

• What am I doing about it? I’m building computer models that can simulate these processes over long time periods. But first, I need to figure out three key things: 1) What actually drives people's decisions about when and where to use fire, 2) How complex do these decision-making processes need to be in a computer model to get realistic results, and 3) How I go about connecting human fire decisions to what actually happens to the vegetation.

• How will this help? Once I have a working model, I can run "experiments" on the computer—testing different scenarios like "what if people didn’t burn at all?" or "what if climate was different?" This will help us understand how Indigenous fire management (which has been going on for thousands of years) actually shapes landscapes, and could inform better land management decisions today.​​

• The big picture: I’m essentially trying to untangle the role of humans in shaping some of our most important ecosystems, using computer modelling since it's near impossible to study directly in the real world.