Aims: Terrestrial ecosystems are changing in biodiversity, species composition and functional trait composition. To understand the underlying causes of these changes and predict the long-term resilience of the ecosystem to withstand future disturbances, we can evaluate changes in diversity and composition from fossil pollen records. Although diversity can be well estimated from pollen in temperate ecosystems, this is less clear for the hyperdiverse tropics. Moreover, it remains unknown whether functional composition of plant assemblages can be accurately predicted from pollen assemblage composition. Here, we evaluate how community-weighted mean (CWM) traits and diversity indices change along elevation. Location: Amazon–Andes elevation gradient in Peru. Methods: We used 82 modern pollen samples and 59 vegetation plots along the elevation gradient, and calculated CWM traits and diversity indices for each pollen sample and vegetation plot. We also quantified the degree to which taxa are over- or underrepresented by their pollen, by dividing the relative pollen abundance by the relative basal area abundance in the nearby vegetation survey plots (i.e. the R-rel values). Results: We found that CWM wood density increased, and CWM adult height and leaf area decreased with elevation. This change was well predicted by pollen assemblages, indicating that CWM trait–environment relationships based on pollen abundance data provide meaningful results. Diversity (richness, Shannon and Simpson) decreased with elevation for vegetation plots, but these trends could not be observed from pollen assemblages. Conclusions: Our results demonstrate that more research is needed to develop methods that lead to accurate diversity estimates from pollen data in these tropical ecosystems, but that CWM traits can be calculated from pollen data to assess spatial shifts in functional composition. This opens opportunities to calculate CWM traits from fossil pollen data sets in the tropics, with broad implications for improving our understanding and predictions of forest dynamics, functioning and resilience through time.