ÉPICBiodiversity
We are a question-driven group with interests in many processes, systems, and techniques.
The common thread to our research is: how can we measure and predict complex ecological systems more accurately, so we can turn this knowledge into more efficient decisions and actions? We conduct research that ranges from the fundamental to the translational, in biodiversity sciences, community ecology, and disease ecology.
Understanding the links between biodiversity and disease emergence
A lot of infectious disease originate in wildlife. We look at reservoirs and infectious diseases through the lens of community ecology, landscape ecology, and biogeography, to figure out where the risk of disease emergence is located, where possible reservoirs will move under climate change, and what the mechanisms that link biodiversity to disease emergence are.
Predicting and detecting species interactions
Species interactions underpin a lot of ecological processes, and yet they are notoriously difficult to sample. We develop techniques from applied machine learning to predict species interactions from functional traits and phylogenetic information, and build sampling algorithms to better understand where we should go look for new interactions.
Mapping (un)certainty
Although applied machine learning techniques are really effective at making predictions, they often come at the cost of a lower interpretability. We work at adapting tools from explainable machine learning to provide spatially explicit measures of uncertainty and explanations, and devise algorithms to turn them into concrete sampling recommendations.