Understanding trophic interactions is critical for successful resource management. However, studying diet patterns (e.g., spatial and seasonal changes) can require extensive effort. Using individual analyses to interpret patterns may be further complicated by assumptions and limitations of the analytical approach. We investigated and compared predicted adult lake trout (Salvelinus namaycush) diet composition and patterns using stomach content analysis (SCA), fatty acid analysis (FAS), and stable isotope analysis (SIA) individually and simultaneously. The three analyses were conducted for fall-captured fish in Lake Ontario and provided different diet composition estimates; SCA suggested alewife (Alosa pseudoharengus) was dominant by frequency and mass, while FAA and SIA suggested rainbow smelt (Osmerus mordax) contributed the most based on similarity among fatty acid signatures and two-stable isotope (carbon and nitrogen) mixing models, respectively. We hypothesize the disagreement among diet estimates is a result of a seasonal shift in diet variably expressed due to differing extent of time reflected by the diet metric: hours to days for SCA, weeks to months for FAA and several months for SIA. Despite variability in diet composition estimates among methods, similar patterns in lake trout diet were observed among the three diet analyses; the contribution of alewife in lake trout diet was greater for larger individuals and for males compared to females, particularly in the east and northeast regions of the lake where alewife density was relatively low. Thus, the complementary results from the three analyses suggest that length, location, sex, and season all influence lake trout diet. Individually, analyses often failed to identify these patterns in lake trout diet with significance, and some of the patterns have not been observed in previous studies of lake trout diet in Lake Ontario. The thorough description of lake trout diet obtained from a single sampling season demonstrates how simultaneous use of multiple diet analyses may allow investigation of spatial and seasonal diet composition and with reduced sampling effort.