Community ecology has had a strong focus on single snapshots of species compositional variation in time. However, environmental change often occurs slowly at relatively broad spatio-temporal scales, which requires historically explicit assessments of long-term metacommunity dynamics, such as the order of species arrival during community assembly (i.e., priority effects), a theme that merits further empirical quantification. In this study, we applied the Bayesian inference scheme of Hierarchical Modeling of Species Communities together with information on functional traits and evolutionary dependencies to efficiently explore the question of how ecological communities are organized in space and time. To do this, we used a comprehensive time-series dataset from boreal lake plants and adopted the perspective that more sound conclusions on metacommunity dynamics can be gained from studies that consider a historically integrative approach over long timeframes. Our findings revealed that historical contingency via priority effects can profoundly shape community assembly under the influence of environmental change across decades (here, from the 1940s to the 2010s). Similarly, our results supported the existence of both positive and negative species-to-species associations in lake plants, suggesting that functional divergence can switch the inhibition–facilitation balance at the metacommunity level. Perhaps more importantly, this proof-of-concept study supports the notion that community ecology should include a historical perspective and suggests that ignoring priority effects may risk our ability to identify the true magnitude of change in present-day biotic communities.