Climate change is exerting a profound influence on the timing of seasonal development of vegetation, i.e. phenology, worldwide - and particularly in mountain grasslands. A key feature controlling the resilience of alpine plant communities to climate change is phenological plasticity: the ability to express different phenologies within a given plant assemblage can help the community to better cope with climate shifts and extremes. Recent work has shown a tight relationship between phenology and certain plant functional traits, especially those associated to competition and growth rate. Phenocameras proved to be an effective mean to monitor community-level phenology by retrieving average phenological signals across a portion of the field of view of digital cameras. Here we used pixel-level information to quantify spatially-explicit phenology from multi-year imagery acquired over 5 alpine grasslands in the western Alps. Concurrently, based on site-specific species inventories we retrieved information about functional traits from the global plant trait database TRY. The objectives of this work are: 1) to assess the link between phenological diversity and plant functional trait diversity in alpine grasslands; 2) to test the consistency in space and time of the functional diversity-phenological diversity relationship. A total of 19 year-sites of phenocam images were processed to obtain maps of phenological metrics. We focused on either spring (start of season, moment of greenness peak, spring recovery rate), autumn (end of season, start of senescence, autumn senescence rate) or full season (length of season, greenness integral) metrics. To describe the spatial distribution of phenological metrics we used indexes such as the Moran Index and the Entropy index. The FD R package was used to compute multidimensional functional diversity. We will illustrate the traits that best correlate with phenological diversity across the 5 different grasslands included in this study and discuss inter-year and inter-site variability in the relationships in the light of climate variability.