Format and Content


The format of the intensive summer school has been revised to take into account COVID restrictions on travel and large group activities. Activities will be accessible remotely but local hubs may be organized if the situation allows it. The course will consist of five one-day sessions alternating with a day off, spread over two weeks. 

The course is structured to minimize lectures and emphasize practice. 

Short lectures and exercises will be given in the morning. The emphasis will be on practice under the supervision of instructors. The afternoon will be organized around one or two higher-level problems to solve. A guest lecture will be offered at the end of the day to provide perspective on the topic of the day. Students will have the opportunity to keep working on the problem during the day off, and the solution will  be provided at the following session.

Instructors will be available during exercises and the day breaks.


The toolbox for the analysis of ecological data is expanding at unprecedented rates and it is almost impossible to track all of the novel methods available. Their high degree of sophistication is also very restricting, limiting their applicability and our appreciation of some innovative studies. It is out of reach to learn each of them and  advanced training in data modelling is by far the best asset to learn about new methods and develop the one's best suited for an ecological problem. The objective of this course is to train students methods and skills to fit models to ecological data. 

The course will introduce notions of probabilities, maximum likelihood and Bayesian modelling. Emphasis will be put on algorithms and computational methods in order to develop abilities to solve a wide range of problems. By the end of the course, students will be able to define their modelling problem, arrange their data properly, develop the equations to describe the phenomenon of interest and evaluate parameters using information based and Bayesian approaches.

Tentative Schedule  

August 16th, 2021
Probabilities and distributions: know your data, define your problem.

August 18th, 2021
Maximum likelihood methods: write down equations for your model.

August 20th, 2021
Optimization algorithms: reveal the capacities of computers to solve complex problems numerically.

August 23rd, 2021
Bayesian statistics: how to properly represent uncertainty in ecological models.

August 25th, 2021
Sampling algorithms: evaluating posterior distributions by your own.