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. 2014) and those of external contributors. These computational statistics techniques are enabling innovative works in ecological modelling (Bridier et al. 2014, Lagarrigues et al. 2014, Rougier et al. 2015, Jabot & Lohier 2016).
Key contributed papers:
-Bridier, A., Briandet, R., Bouchez, T., & Jabot, F. (2014). A model-based approach to detect interspecific interactions during biofilm development. Biofouling, 30(7), 761-771.
-Jabot, F., & Lohier, T. (2016). Non‐random correlation of species dynamics in tropical tree communities. Oikos, 125(12), 1733-1742.
-Jabot, F., Faure, T., & Dumoulin, N. (2013). EasyABC: performing efficient approximate Bayesian computation sampling schemes using R. Methods in Ecology and Evolution, 4(7), 684-687.
-Jabot, F., Lagarrigues, G., Courbaud, B., & Dumoulin, N. (2014). A comparison of emulation methods for Approximate Bayesian Computation. arXiv preprint arXiv:1412.7560.
-Lagarrigues, G., Jabot, F., Lafond, V., & Courbaud, B. (2015). Approximate Bayesian computation to recalibrate individual-based models with population data: Illustration with a forest simulation model. Ecological modelling, 306, 278-286.
-Lenormand, M., Jabot, F., & Deffuant, G. (2013). Adaptive approximate Bayesian computation for complex models. Computational Statistics, 28(6), 2777-2796.
–Rougier, T., Lassalle, G., Drouineau, H., Dumoulin, N., Faure, T., Deffuant, G., … & Lambert, P. (2015). The combined use of correlative and mechanistic species distribution models benefits low conservation status species. PLoS One, 10(10), e0139194.