Trait-based classifications can efficiently capture species' responses to environmental gradients and their impacts on ecosystem functioning. Thus, the clustering of phytoplankton species into functional groups can improve the understanding of their relationships with the environment and help to predict their response to environmental changes. Accordingly, this study aimed to create habitat templates of Reynolds phytoplankton functional groups (RFGs) in tropical drinking water reservoirs to describe, explain, and predict their occurrence and formation of blooms. We analyzed the structure of RFGs in 10 tropical reservoirs, in humid and semiarid regions of Brazil, and defined their relationships with 10 environmental variables. We designated the habitat template based on niche differentiation, thresholds for the occurrences and bloom formation, cluster analyses, and generalized additive models. We identified 136 species, assembled in 20 RFGs. Six groups of habitat templates were recognized based on environmental conditions and dominant RFGs, usually represented by bloom-forming species of cyanobacteria, dinoflagellates, green algae, and diatoms. The functional groups D, X1, and P presented the most restrictive occurrences, while RFGs M and S-N displayed the widest, occurring in almost all sets of conditions. Moreover, salinity was the best predictor of RFGs' biomass (higher R-2), followed by depth, soluble reactive phosphorus, irradiance, water transparency, and dissolved inorganic nitrogen. Our approach improves the understanding of how RFGs interact with environmental gradients in tropical reservoirs, helping water managers to adopt sustainable practices to control algal blooms, based on predictions of the future state of dominance.