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.

We are working on understanding the type of problems that we can solve without having to do new sampling, and how to handle errors and uncertainty.

As a bonus, we use the tools we develop to improve the monitoring and reporting abilities of various stakeholders. One of our key question related to this topic at the moment is to examine the “suitability” of existing data to address a given question. We work on measures to identify areas in which information is low, which should be sampled with a higher priority.

Selected articles

Testing Predictability of Disease Outbreaks With a Simple Model of Pathogen Biogeography
Dallas, T. A. Carlson, C. J. Poisot, T.
Artificial Intelligence for Ecological and Evolutionary Synthesis
Desjardins-Proulx, P. Poisot, T. Gravel, D.
Revisiting the Links-Species Scaling Relationship in Food Webs
MacDonald, A. Banville, F. Poisot, T.
Ecological Data Should Not Be So Hard to Find and Reuse
Poisot, T. Bruneau, A. Gonzalez, A. Gravel, D. Peres-Neto, P.
Synthetic Datasets and Community Tools for the Rapid Testing of Ecological Hypotheses
Poisot, T. Gravel, D. Leroux, S. Wood, S. A. Fortin, M.J. Baiser, B. Cirtwill, A. R. Araújo, M. B. Stouffer, D. B.