This data set contains three time series of flower visitor, blossom cover and flower trait diversity indices from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown in the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, or 4 functional groups). Plots were maintained by bi-annual weeding and mowing.
Flower Trait measurements were conducted for 44 of the 60 plant species found in flower during the 2005, 2006 and 2008 flower visitor observation periods. All traits were measured on ten blossoms of different plant individuals from the monoculture plots at the experimental field sites during the vegetation period in 2011. No trait measurements were taken during flower visitor observations, but flower cover of these observations was used in combination with the 2011 trait measurements to calculate trait diversity indices. Diversity cannot sensibly be calculated for plots that did not contain any flower, these plots were therefore excluded from calculation. Two flower visitor variables, pollinator visitation rate and species richness, and all trait indices were calculated on the plot level. Functional diversity was calculated using the function dbFD of the R package 'FD' in the software R (R Development Core Team 2014; (Laliberté and Legendre 2010)). We used the index of functional diversity based on the quadratic entropy of Rao (Rao 1982) that incorporates both the relative abundances of species (in our study: blossom cover) and a measure of the pairwise functional differences between species (Fdis) as suggested by (Botta-Dukát 2005).Trait selection was based on their assumed importance for flower-pollinator interactions. Table 1 in Fornoff et al. 2016, shows these traits, their unit, range and data structure. All traits listed in Table 1 (n = 13) were included to calculate overall trait diversity (Fdis). Each trait was individually used (n = 1) to calculate single trait diversity (FDtrait).
In addition to trait diversity, we calculated community-weighted means (CWM) for each individual trait. CWM values express the abundance-weighted mean values of numerical variables or the percentage of relative abundance of each factor level of categorical variables.
