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Understorey communities can dominate forest plant diversity and strongly affect forest ecosystem structure and function. Understoreys often respond sensitively but inconsistently to drivers of ecological change, including nitrogen (N) deposition. Nitrogen deposition effects, reflected in the concept of critical loads, vary greatly not only among species and guilds, but also among forest types. Here, we characterize such context dependency as driven by differences in the amounts and forms of deposited N, cumulative deposition, the filtering of N by overstoreys, and available plant species pools. Nitrogen effects on understorey trajectories can also vary due to differences in surrounding landscape conditions; ambient browsing pressure; soils and geology; other environmental factors controlling plant growth; and, historical and current disturbance/management regimes. The number of these factors and their potentially complex interactions complicate our efforts to make simple predictions about how N deposition affects forest understoreys. We review the literature to examine evidence for context dependency in N deposition effects on forest understoreys. We also use data from 1814 European temperate forest plots to test the ability of multi-level models to characterize context-dependent understorey responses across sites that differ in levels of N deposition, community composition, local conditions and management history. This analysis demonstrated that historical management, and plot location on light and pH-fertility gradients, significantly affect how understorey communities respond to N deposition. We conclude that species' and communities' responses to N deposition, and thus the determination of critical loads, vary greatly depending on environmental contexts. This complicates our efforts to predict how N deposition will affect forest understoreys and thus how best to conserve and restore understorey biodiversity. To reduce uncertainty and incorporate context dependency in critical load setting, we should assemble data on underlying environmental conditions, conduct globally distributed field experiments, and analyse a wider range of habitat types.