Forest structure plays an important role in determining habitat suitability for plants and animals, but these relationships are poorly characterized for different biological communities in tropical forests. We used ground-based lidar to quantify structural metrics and determine their contribution in predicting species diversity and compositional changes between plots for nine biological groups in an Amazonian forest. For each group, we calculated Fisher’s alpha index and summarized community composition using Principal Coordinates Analysis. As biological organisms may also react directly to hydro-edaphic conditions, we carried out variance partitioning analysis using linear regressions to disentangle the relative contribution of structural metrics and hydro-edaphic variables. Forest structure was related to species diversity and composition of some groups, specifically for plants, anurans, and birds. Mean canopy height, leaf area height volume, and skewness explained more than one-third of species diversity of palms and trees, with higher values relating to higher species diversity. Hydro-edaphic variables were the most important predictors of the main compositional axis for plant groups, but some structural metrics explained more than 30% of the secondary compositional axis for ferns + lycophytes, trees, birds, and anurans. Vegetation height and variability, vegetation quantity, and vertical structure, but not canopy openness, were the main structural characteristics modulating species diversity and composition. Our findings reinforce the potential to estimate species diversity and compositional changes across structural gradients using lidar-derived metrics in a hyper-diverse forest. Understanding these relationships advances our ability to make community predictions useful for conservation and provides new avenues to investigate the mechanisms impacting diversity.