Incorporating modelling and data to understand forest dynamics in a changing world

Climate change and changes in atmospheric composition are driving big changes in the lives of trees across the world's forests. In many places, these changes are expected to make trees grow more quickly. But they are also expected to drive increased levels of tree mortality - something which we are already starting to see in some regions. The net of these changes governs the carbon uptake by forest biomass, currently a sink for about 20% of humanity's CO2 emissions, as well as the future trajectory of these forests in terms of species composition and ecosystem services. Yet the size of this carbon uptake and how it is changing around the world is actually quite poorly constrained. In order to make a step change in our understanding of this uptake we have to push forward knowledge of how the underlying dynamics of tree growth and mortality are changing. Luckily, the tools to do this exist. Thousands of scientists make regular observations of these forest dynamics, providing direct evidence of changes in thousands of plots across the world. The latest computational models of forest dynamics have the potential to link directly to these observations and upscale them from plots to the global forest, allowing us to answer big questions about how the world's forests are changing.

In this PhD project you will combine ground observations of forest dynamics with computational modelling, collaborating with a large network of field scientists around the globe as well as being embedded in a large international collaboration developing a leading dynamic vegetation model (LPJ-GUESS, https://web.nateko.lu.se/lpj-guess/index.html).

In addition to pushing the boundaries of knowledge on forest dynamics. you will develop skills in big data analysis and development of forest models, providing strong transferable skills in analytical code development. There will be potential for you to link up with existing field campaigns to gain a breadth of experience that stretches from computational modelling and big data analysis, to on-the-ground measurements.