Long-term monitoring data is central for the analysis of biodiversity change and its drivers. Time series allow a more accurate evaluation of diversity indices, trait identification and community turnover. However, evaluating data collected across different monitoring programs remains complicated because of data discrepancies and inconsistencies. Here we propose a method for aggregating datasets using diffusion maps. The method is illustrated by aggregating long-term phytoplankton abundance data from the Wadden Sea and Southern North Sea gathered by two institutions located in Germany and The Netherlands. The aggregated data allowed us to infer species traits, to reconstruct the main trait axis which drives community functionality, ultimately quantifying functional diversity of the individual samples, having used only the co-occurrence of species in samples. Although functional diversity varies greatly among sampling stations, we detect a slight positive trend in German stations, which contrasts with the clear decreasing trend observed in most of the Dutch Wadden Sea stations. At the Terschelling transect, in Southern North Sea, the stations also showed contrasting estimations of functional diversity between off-shore and in-shore stations. Our research provides further evidence that traits and functional diversity can be robustly reconstructed from monitoring data alone, showing that data aggregation can increase the accuracy of this reconstruction, being able to aggregate heterogeneous datasets.