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Wasof, S., Lenoir, J., Hattab, T., Jamoneau, A., Gallet-Moron, E., Ampoorter, E., et al. (2018). Dominance of individual plant species is more important than diversity in explaining plant biomass in the forest understorey. J. Veg. Sci., 29(3), 521–531.
Résumé: QuestionsHow 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. LocationTemperate deciduous forests in northern France. MethodsFor the forest understorey, we assessed species diversity and biomass as well as soil and light conditions in 133 forest plots of 100m(2) each. Using mixed-effect models and after controlling for 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 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. ResultsBiomass 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 to the diversity' hypothesis, functional diversity indices had a negative impact on biomass. We found no contribution of taxonomic or phylogenetic diversity indices. ConclusionThe 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 on-going changing biotic and abiotic conditions and to predict their effects on ecosystem functioning and processes.