International climate change mitigation policies envisage carbon storage in tropical forests. Long-term studies have reported an increase of above-ground biomass (AGB) accumulation in tropical forest trees in recent decades, presumably due to an increase in atmospheric CO2, but this signal is heterogeneous across space and time. This heterogeneity limits our capacity to forecast future changes in AGB in response to climate change predictions. The spatio-temporal dynamics of AGB change are determined by the cumulative effects of interactions in local tree neighbourhoods influencing the growth and mortality of individual trees. Understanding these neighbourhood interactions is challenging because it requires spatially-explicit data-sets of tree demography over several decades, high-resolution data on environmental covariates, and complex statistical modelling techniques. SpatForest aims to address this challenge by developing statistical models of tree demography and AGB dynamics in tropical forest that explicitly account for variation in local biotic and abiotic environments. By including terms for environmental factors that are likely to respond to future climate change, the models developed during SpatForest will enhance our ability to forecast changes in AGB of tropical forests.