Predicting the Ecological Impacts of Future Fire Activity on a Global Scale

Wildfire is the most important type of natural disturbance impacting the terrestrial biosphere. The nature of the fire regime affects vegetation on multiple space and time scales: small fires can trigger gap dynamics at a local scale, while the expression of fire-related trait syndromes is strongly controlled by the frequency and intensity of large fires. The prevalence of wildfire is also implicated in the maintenance of specific vegetation types, notably savannas and grasslands. The incidence of wildfire is influenced by multiple factors, including long-term climate conditions, short-term weather, vegetation type and productivity, and human factors affecting land-use. These factors may have different effects on different aspects of the fire regime: human ignitions affect the number of fires and their seasonal distribution, for example, but have little impact on fire spread and the total area burned. This complexity means it is difficult to predict the impact of future changes in climate, climate-induced changes in vegetation, and human activities on fire regimes solely using empirical evidence, and the situation is further complicated because there are multiple feedbacks between these different controls. Coupled fire-vegetation models are the only way of predicting future changes in large-scale fire regimes and exploring how these will affect regional vegetation. However, although several such models have recently been developed, there are large differences in their past and future predictions1 and uncertainties in model structure and parameterisation that make it hard to know whether modern fire regimes are correctly simulated for the right reasons.

The overarching goal of this project is to improve modelling capacity to address the ecological impacts of future change in wildfire. You will use advanced statistical techniques, including generalized linear and mixed-effect modelling, with remote-sensing observations (e.g.2) to explore what controls different aspects of wildfire regimes (numbers of fire starts, fire type, fire seasonality, frequency and intensity, burned area, and emissions) and whether these controls vary regionally. You
ill use these analyses to inform the design of "perturbed-parameter" experiments with a simple global fire model (INFERNO3). In these experiments, key model processes are specified using a plausible range of possible parameter values to investigate which uncertainties have the largest impact on wildfire regimes. These analyses will help quantify uncertainties in projections but will also lead to improvements to the fire model. In the final step of the project you will couple INFERNO to a simple model of vegetation productivity (P4) to investigate how future change in climate and other fire controls might affect fire regimes, the expression of fire-related recovery traits (resprouting, serotiny, re-seeding) and the balance between grass and woody cover (and thus the extent of savannas, forests and grasslands).