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Catherine Villeneuve

Doctorat - McGill
Superviseur⋅e principal⋅e
David Rolnick
Sujets de recherche
Apprentissage profond
Systèmes dynamiques
GitHub

Publications

BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models
Catherine Villeneuve
Benjamin Akera
Mélisande Teng
David Rolnick
Species distribution models (SDMs), which aim to predict species occurrence based on environmental variables, are widely used to monitor and… (voir plus) respond to biodiversity change. Recent deep learning advances for SDMs have been shown to perform well on complex and heterogeneous datasets, but their effectiveness remains limited by spatial biases in the data. In this paper, we revisit deep SDMs from a Bayesian perspective and introduce BATIS, a novel and practical framework wherein prior predictions are updated iteratively using limited observational data. Models must appropriately capture both aleatoric and epistemic uncertainty to effectively combine fine-grained local insights with broader ecological patterns. We benchmark an extensive set of uncertainty quantification approaches on a novel dataset including citizen science observations from the eBird platform. Our empirical study shows how Bayesian deep learning approaches can greatly improve the reliability of SDMs in data-scarce locations, which can contribute to ecological understanding and conservation efforts.
2026-03-13
AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Predicting space use patterns of a territorial top predator: from individual movement decisions to Arctic fox space use
Frédéric Dulude-de Broin
Dominique Berteaux
Joël Bêty
Catherine Villeneuve
Alexis Grenier-Potvin
Andréanne Beardsell
Jeanne Clermont
Audrey Durand
Pierre Legagneux
2025-09-30
bioRxiv (prépublication)
doi.org