Species distribution models (SDMs) commonly rely on climatic predictors, although species distributions are also shaped by historical and spatially structured processes. Macroecological regionalizations are increasingly used in biogeography and may potentially improve SDM performance by representing ecological and historical connectivity among regions. However, their actual predictive value remains poorly tested. In this study, we evaluated the contribution of macroecological regionalizations to SDMs of 18 freshwater mollusk species in Kazakhstan (Central Asia). We constructed ensemble models using climatic variables, land-cover predictors, terrestrial ecoregions, freshwater ecoregions, drainage basins, and an artificial equal-area spatial grid used as a biologically neutral control. Model performance was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), the True Skill Statistic (TSS), and Cohen's k (Kappa) metrics. All models showed generally high predictive performance. However, the inclusion of ecoregions and drainage basins resulted in little or no improvement relative to climate-only models. Notably, the contribution of the artificial spatial grid frequently equaled or exceeded that of biologically meaningful zonation, suggesting that territorial predictors may primarily capture residual spatial structure rather than biologically informative processes per se. In contrast, land-cover predictors consistently improved model performance across most algorithms and metrics. Our results indicate that landscape structure may be substantially more important for freshwater mollusk SDMs than broad macroecological regionalizations and highlight the necessity of testing biologically neutral spatial controls when evaluating territorial predictors in SDMs.