computational ecology

Spatial ecology of species interactions

Why is there variation in the way species interact over space or time? We analyse spatially and temporally replicated datasets of surveys of species interactions to measure the variation in the structure of ecological networks, and describe its dynamics.

Data science for biodiversity research

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.

Connectivity and ecosystem integrity assesment

We use techniques from graph theory and circuit theory to measure the connectivity of different landscapes in Qu├ębec. We are particularly interested in the blue/green connectivity.

Functional consequences of network structure

We are interested in turning the structure of networks into a predictive variable for community ecology; we are particularly interested in the role of trophic interactions in ecosystem functioning.

Numerical tools for network analysis

Addressing these new questions often requires to develop new tools. We develop statistical and mathematical approaches, implement them, and release them as free and open source software to make network analysis user-friendly.

Prediction of species interactions

Can we predict inter-specific interactions? We are interested in developing predictive models that would use different sources of information (functional traits, local abundances, previous knowledge, …) to predict the probability that two species will interact.