Natural disturbances are the dominant form of forest regeneration and dynamics in unmanaged tropical forests. Monitoring the size distribution of treefall gaps is important to better understand and predict the carbon budget in response to land use and other global changes. In this study, we model the size frequency distribution of natural canopy gaps with a discrete power law distribution. We use a Bayesian framework to introduce and test, using Monte Carlo Markov Chain and Kuo-Mallick algorithms, the effect of local physical environment on gap size distribution. We apply our methodological framework to an original Light Detecting and Ranging dataset in which natural forest gaps were delineated over 30000¿ha of unmanaged forest. We highlight strong links between gap size distribution and environment, primarily hydrological conditions and topography, with large gaps being more frequent in floodplains and on hillslopes. In the future, we plan scale up testing of our methodology using satellite data. Additionally, although gap size distribution variation is clearly under environmental control, gap process variation over time should be tested against climate variability.