Background: Natural disturbance is a fundamental component of the functioning of tropical rainforests let to natural dynamics, with tree mortality the driving force of forest renewal. With ongoing global (i.e. land-use and climate) changes, tropical forests are currently facing deep and rapid modifications in disturbance regimes that may hamper their recovering capacity so that developing robust predictive model able to predict ecosystem resilience and recovery becomes of primary importance for decision-making: (i) Do regenerating forests recover faster than mature forests given the same level of disturbance? (ii) Is the local topography an important predictor of the post-disturbance forest trajectories? (iii) Is the community functional composition, assessed with community weighted-mean functional traits, a good predictor of carbon stock recovery? (iv) How important is the climate stress (seasonal drought and/or soil water saturation) in shaping the recovery trajectory? Methods: Paracou is a large scale forest disturbance experiment set up in 1984 with nine 6.25 ha plots spanning on a large disturbance gradient where 15 to 60% of the initial forest ecosystem biomass were removed. More than 70,000 trees belonging to ca. 700 tree species have then been censused every 2 years up today. Using this unique dataset, we aim at deciphering the endogenous (forest structure and composition) and exogenous (local environment and climate stress) drivers of ecosystem recovery in time. To do so, we disentangle carbon recovery into demographic processes (recruitment, growth, mortality fluxes) and cohorts (recruited trees, survivors). Results: Variations in the pre-disturbance forest structure or in local environment do not shape significantly the ecosystem recovery rates. Variations in the pre-disturbance forest composition and in the post-disturbance climate significantly change the forest recovery trajectory. Pioneer-rich forests have slower recovery rates than assemblages of lat