Biodiversity forecasting and planning with AI
Bonjour-hi, better forecasts!
Four in-person workshops at IVADO bringing AI and quantitative ecology together to predict biodiversity change on policy-relevant timescales.
April–August 2027 · Montreal, QC
What we’re tackling
01
Complexity
Models that capture ecological processes and biodiversity together
02
Timescales
Forecasting to answer questions in years not decades in the future
03
Uptake
Open and transparent tools and indicators that decision-makers can use
Qu’est-ce qui se passe?
Le Summer of Biodiversité
A hands-on bootcamp focused scientific workshops, and decision-makers in the same room as the modellers all in Montreal (home of MILA, IVADO, GEO BON, the UN CBD Secretariat, and QCBS). One summer to turn better forecasts into better decisions for nature.
The Programme
01 · Bootcamp
April 19–23
Ecological complexity with statistics and ML
Species interactions, non-stationarity and sparse, biased data defeat simple models. When should we use statistics, when ML, and how do we combine them?
02 · Science-Policy
June 7–8
Using AI for better decisions for nature
AI is transforming biodiversity monitoring and data integration, but rarely reaches decision-makers. We want to go beyond proofs of concept, toward AI tools that directly shape conservation and policy decisions. Held just before Living Data 2027 (June 9–12), so you can join both in one trip.
03 · Cutting-edge SDMs
July 5–9
Advances in species distribution modelling
Distribution models still ignore species interactions and struggle with unstructured data and shifting climates. Next-generation SDMs need to link species, data streams and extreme events.
04 · Anticipating change
August 2–6
Early warnings for biodiversity
We often detect biodiversity change too late to act. Adaptive monitoring and short-term forecasts can turn early signals into rapid indicators for GBF targets.
One week not enough?
Apply for a month-long visit, ideally early June to early July (dates flexible).
Directors
Laura Pollock
Biology, McGill | QCBS
David Rolnik
Computer Science, McGill | MILA
Committee: Tanya Berger-Wolf (Ohio State), Melissa Chapman (ETH Zurich), Sara Beery (MIT), Justin Kitzes (Pittsburgh), Lily Xu (Columbia)