The effect of trait‐based diversity on productivity results mainly from intraspecific trait variability in the macrophyte community

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Abstract Trait‐based methods are key to understanding the biodiversity–productivity relationship (BPR) of macrophyte communities. Community‐weighted mean traits (i.e., community trait structure) have been proven to have more influence on macrophyte community productivity than species richness. However, the underlying mechanism by which community trait structure variation affects macrophyte community productivity along an environmental gradient is still not well understood. A mesocosm experiment was used to investigate how community trait structure shapes macrophyte community productivity along the water depth gradient. Three submerged macrophyte species (Myriophyllum spicatum, Vallisneria natans and Potamogeton malaianus) were assembled into all possible combinations (one, two and three species per community) at three different water depths (1, 2.5 and 4 m). We fitted the relationships between community‐weighted mean traits and community productivity, and between species richness and community productivity as a comparison. We compared functional traits under different water depths and species composition at the species and community levels and disentangled the community trait structure into species turnover and intraspecific trait variability effects. The results showed that community trait structure had various influences on macrophyte community productivity, which was based on traits per se. However, species richness had a non‐significant impact on macrophyte community productivity. Water depth had a significant impact on most traits of the three species at the species level, whereas both water depth and species composition had significant impacts on community trait structure which was mainly affected by intraspecific trait variability along the water depth gradient. Our findings highlight the importance of considering intraspecific trait variability and species turnover under water depth gradients to understand the relationship between trait‐based biodiversity and productivity.