MAKING PREDICTIONS FROM SATELLITE IMAGERY
Earth observation satellites have captured a huge amount of imagery of the Earth in recent decades. I am interested in combining this imagery with data science approaches like machine learning, to understand and predict the future of biodiversity in the face of global change. I am particularly interested in forest ecosystems, due to their high biodiversity value (approximately 70% of terrestrial species live in forests) and their sequestration of large amounts of carbon.
I am especially interested in understanding the resilience, resistance and elasticity of forests; our ability to predict forest loss with early warning signals; and the presence of tipping points in forest systems.
I use species interaction networks to understand how biodiversity is responding to global change and predict what will happen to it in the future.
Species interaction networks are an incredibly powerful tool for understanding community-scale biodiversity responses as they allow a simultaneous consideration of the species in the community and the structure of the community as a whole. Moreover, networks are highly amenable to analyses using data science methods.
Particularly,I am interested in predicting the structure and functioning of networks across space and time; understanding the impacts of stressors on network structure and functioning; understanding the processes that give rise to network structure; and developing new software tools to analyse ecological networks (see software).