Interactions within ecosystems are really complex, and we do not have a lot of data on them. We build models to predict the interactions between species in space and time. We then tie these predictions on network structure to functional properties of ecological networks.

Can we use ecological information to make predictions about emerging viral pathogens? We apply biotic interaction inference techniques, network analysis, and secies distributions models, to provide mapping of the risk posed by various groups of pathogens.

We believe that open data are a treasure trove of knowledge that has not been entirely used yet, and require ecologists to get curious about tools and practices from the field of data science. We explore applications of machine learning and deep learning to biodiversity.