Disentangling the influences of multiple environmental factors on ecosystem integrity is not straight-forward because environmental factors may interact and biotic responses may be nonlinear. We aimed to understand better the relationships between freshwater invertebrate assemblages and multiple, interacting environmental factors. We analyzed stream monitoring data for 689 sampling sites in the state of Ohio (USA) with Boosted Regression Trees (BRTs). We used 16 environmental predictors covering geography, water chemistry, physical-habitat quality, and toxic pressure. We represented freshwater invertebrate assemblages by the Invertebrate Community Index (ICI) and its 10 component metrics. The ICI was mainly related to physical-habitat quality, nutrient concentrations (P and N), and pH. Responses of the ICI component metrics to physical-habitat quality and water-chemistry variables were similar and were associated with amplified importance of these predictors for the ICI, whereas heterogeneous responses of the component metrics to geographic variables appeared to cancel each other out at the level of the ICI. Models including predictor interactions explained 22 to 54% of the deviation in the biotic endpoints, whereas the no-interactions models explained 14 to 47%. The gain in predictive power was largest between the no- and the pairwise interaction models and decreased rapidly for each additional interaction level. We conclude that a focus on pairwise interactions is a good compromise between higher predictive power and interpretability of the results.