Nous distinguons trois visions de la complexité afin de clarifier les contours de la recherche dans ce domaine. Nous utilisons le démon de Laplace comme référence pour présenter ces visions. La vision 1 brise le rêve du démon de Laplace en identifiant des systèmes particuliers qui lui résistent en mathématiques, physique et informatique. La vision 2 propose une nouvelle version du rêve de Laplace fondée sur la disponibilité récente de grandes quantités de données et de nouvelles technologies de programmation, de stockage et de calcul. La vision 3 met le démon face au défi de simuler la subjectivité et ses effets collectifs. We identify three different visions of complexity with the aim to clarify the research field on complex systems. We use Laplace’s demon as a reference for formulating these visions. Vision 1 breaks Laplace’s dream by exhibiting fundamental difficulties in mathematics, physics and computer sciences hampering to derive systemic properties from the knowledge of components characteristics and their interactions rules. Vision 2 reactivates Laplace’s dream, with new demons based on increased computation power and big data availability, yet subject to the limitation identified by vision 1. Finally, vision 3 challenges Laplace’s demon to simulate subjectivity, counterfactuals, empathy and their cascading collective effects.

Community dynamics is influenced by multiple ecological processes such as environmental spatiotemporal variation, competition between individuals and demographic stochasticity. Quantifying the respective influence of these various processes and making predictions on community dynamics require the use of a dynamical framework encompassing these various components. We here demonstrate how to adapt the framework of stochastic community dynamics to the peculiarities of herbaceous communities, by using a short temporal resolution adapted to the time scale of competition between herbaceous plants, and by taking into account the seasonal drops in plant aerial biomass following winter, harvesting or consumption by herbivores. We develop a hybrid inference method for this novel modelling framework that both uses numerical simulations and likelihood computations. Applying this methodology to empirical data from the Jena biodiversity experiment, we find that environmental stochasticity has a larger effect on community dynamics than demographic stochasticity, and that both effects are generally smaller than observation errors at the plot scale. We further evidence that plant intrinsic growth rates and carrying capacities are moderately predictable from plant vegetative height, specific leaf area and leaf dry matter content. We do not find any trade-off between demographical components, since species with larger intrinsic growth rates tend to also have lower demographic and environmental variances. Finally, we find that our model is able to make relatively good predictions of multi-specific community dynamics based on the assumption of competitive symmetry.