Questions How does plant community diversity influence variation in plant biomass? There are two competing hypotheses: the ‘biomass‐ratio hypothesis’, where biomass is influenced by the abundance and traits of the most dominant species, and the ‘diversity hypothesis’, where the diversity of organisms influences biomass through mechanisms such as niche complementarity. However, no studies have tested which one of these two hypotheses better explains the variation in plant biomass in the forest understorey. Location Temperate deciduous forests in Northern France. Methods For the forest understorey, we assessed species diversity and biomass as well as soil and light conditions in 133 forest plots of 100m² each. Using mixed‐effect models and after controlling for 2 Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site. potential confounding factors, we tested the ‘biomass‐ratio hypothesis’ by relating the relative abundance of the most dominant species across our study sites and the community‐weighted mean values (CWM) of plant traits (leaf area and plant height) to biomass. The ‘diversity hypothesis’ was tested by relating biomass to various measures of taxonomic, functional and phylogenetic diversity. Results Biomass of the forest understorey was mainly related to the relative abundance and the trait values of the most dominant species, supporting the ‘biomass‐ratio hypothesis’. In contrast with the ‘diversity hypothesis’, functional diversity indices had a negative impact on biomass. We found no contribution of taxonomic or phylogenetic diversity indices. Conclusion The abundance and traits of the most dominant species matter more than taxonomic, functional or phylogenetic diversity of the forest understorey in explaining its biomass. Thus, there is a need for experiments that aim to fully understand keystone species’ responses to ongoing changing biotic and abiotic conditions and to predict their effects on ecosystem functioning and processes.