Our work aims at developing quantitative methods to understand the various drivers of the dynamics of ecological communities in spatially-extended and temporally-varying environments. To achieve this overarching goal, we devise innovative monitoring protocols based on remote sensing and field sampling (Pottier & Jabot 2017), develop individual-based models grounded on ecological knowledge (Rougier et al. 2014, Read more about Spatio-temporal ecological dynamics: metapopulations and metacommunities[…]

We develop statistical algorithms to calibrate individual-based models with data, to assess their validity and to compare, select or average alternative models. These algorithms are mainly designed for Approximate Bayesian Computation (ABC). We also develop a R package “EasyABC” (Jabot et al. 2013) that encapsulates our various contributions (Lenormand et al. 2013, Jabot et al. Read more about Computational statistics: calibration, inference, ABC[…]

In the past half century, the key role of chance in environmental dynamics has been progressively recognized in most biological fields from molecular biology to ecology and evolution. Our work on stochastic processes encompass theoretical studies in probability theory (Bansaye, Pardo & Smadi 2013, Smadi & Vatutin 2016), simulation-based analyses of individual-based models (Jabot 2010, Read more about Stochastic processes: probability theory, simulations, applications[…]

We develop some algorithms to determine viability kernels in large dimensions. Various strategies have been used, based on SVM (support vector machines, Deffuant et al. 2007), kd-Trees (Alvarez et al. 2013) or recursive simplex stars (Deffuant 2017). Our work consists in designing these algorithms, determining mathematically their convergence properties, and developing computer software that implement Read more about Viability Algorithms[…]