Understanding how climate change could impact population dynamics is of primary importance for species conservation. In species-rich ecosystems with many rare species, the small population sizes hinder a good fit of species-specific models. We propose a mixture of regression models with variable selection allowing the simultaneous clustering of species into groups according to vital rate information (recruitment, growth, and mortality) and the identification of group-specific explicative environmental variables. We illustrate the effectiveness of the method on data from a tropical rain forest in the Central African Republic and demonstrate the accuracy of the model in successfully reproducing stand dynamics and classifying tree species into well-differentiated groups with clear ecological interpretations.