Description: Concern about the functional consequences of unprecedented loss in biodiversity has prompted biodiversity–ecosystem functioning (BEF) research to become one of the most active fields of ecological research in the past 25 years. Hundreds of experiments have manipulated biodiversity as an independent variable and found compelling support that the functioning of ecosystems increases with the diversity of their ecological communities. This research has also identified some of the mechanisms underlying BEF relationships, some context-dependencies of the strength of relationships, as well as implications for various ecosystem services that humankind depends upon. In this chapter, we argue that a multitrophic perspective of biotic interactions in random and non-random biodiversity change scenarios is key to advance future BEF research and to address some of its most important remaining challenges. We discuss that the study and the quantification of multitrophic interactions in space and time facilitates scaling up from small-scale biodiversity manipulations and ecosystem function assessments to management-relevant spatial scales across ecosystem boundaries. We specifically consider multitrophic conceptual frameworks to understand and predict the context-dependency of BEF relationships. Moreover, we highlight the importance of the eco-evolutionary underpinnings of multitrophic BEF relationships. We outline that FAIR data (meeting the standards of findability, accessibility, interoperability, and reusability) and reproducible processing will be key to advance this field of research by making it more integrative. Finally, we show how these BEF insights may be implemented for ecosystem management, society, and policy. Given that human well-being critically depends on the multiple services provided by diverse, multitrophic communities, integrating the approaches of evolutionary ecology, community ecology, and ecosystem ecology in future BEF research will be key to refine conservation targets and develop sustainable management strategies.

Description: This data collection contains species-specific aboveground plant biomass that was collected from the Trait Based Experiment in 2012. (Sown plant species, Weed plant biomass, the biomass of dead plant material, and the biomass of unidentified plant material) per plots collected in 2012 from a grassland trait diversity experiment (the Jena Trait Based Experiment). The data collection also contains the traits of the species measured in their monoculture. The experiment consists of 20 plant species that were assigned to one of three species pools: 1. Species that vary along a gradient of spatial leaf and root trait similarity, 2. Species that vary along a gradient of phenological trait similarity and 3. Species that vary along a gradient of both spatial and phenological similarity (see Ebeling et al. 2014). The experiment consists of 138 grassland plots 3 x 3 m in size that was established within the Jena Experiment, Germany, in 2011. Plots vary in plant species richness (1, 2, 4, or 8 species) and functional diversity (1, 2, 3, 4 functional diversity levels, where 1 indicates species are most similar and 4 being most dissimilar in functional traits). Plots were maintained by manual weeding in March, July and September. Biomass was harvested twice in 2012 (during peak standing biomass in late May and in late August) on all experimental plots. Plots were mown to the same height directly following biomass harvest. Plant biomass was harvested by clipping the vegetation at 3 cm above ground in two 0.2 x 0.5 m quadrats per plot. The harvested biomass was sorted into categories: individual species of the sown plant species, 'Weed' plant species (species not sown in a plot), detached 'Dead' plant material, and remaining plant material that could not be assigned to any category ('Rest'). All biomass was dried to constant weight (70°C, 〉= 48 h) and weighed. The data from individual quadrats were averaged. The traits measured are: Flowering initiation, Flowering cessation, specific leaf area (SLA), leaf dry matter content (LDMC), leaf area, maximum canopy height, specific root length (SRL), mean rooting depth (MRD), root mass density (RMD) and root length density (RLD). Flowering initiation and cessation were measured respectively as the week in which flowering was first observed and flowering senesce had completed throughout the plot. Leaf area, leaf fresh mass were measured on approximately five fully expanded leaves from different individuals. These leaves were dried at 65°C for over 48 hours and massed to calculate the specific leaf area (SLA, area per dry mass), and the leaf dry matter content (LDMC, dry mass per fresh mass). Maximum canopy height was measured during peak biomass in May by taking the average of five measurements along a transect. Root traits were measured by taking soil cores, 4 cm in diameter and 40 cm deep and sectioned by depth: 0-5, 5-10, 10-20, 20-30 and 30-40 cm. Roots were washed and roots 〈 2 mm in diameter were stored in 70 % ethanol. Root length was determined by scanning stained roots with neutral red and scanning roots using WinRhizo software. Root traits were only measured in species pool 1 and 2. Roots were then dried at 65°C for over 48 hours and massed to determine the specific root length (SRL, root length per mass), mean rooting depth (MRD, the average depth weighed by root mass per depth), root mass density (RMD, the average root mass per cubic cm volume) and root length density (RLD, root mass per root length).

Description: This data set contains plant species traits: Flowering initiation, Flowering cessation, specific leaf area (SLA), leaf dry matter content (LDMC), leaf area, maximum canopy height, specific root length (SRL), mean rooting depth (MRD), root mass density (RMD) and root length density (RLD). The traits were measured during the summer of 2012 on the plants grown in monoculture within a grassland trait diversity experiment (the Jena Trait Based Experiment). The experiment consists of 20 plant species that were assigned to one of three species pools: 1. Species that vary along a gradient of spatial leaf and root trait similarity, 2. Species that vary along a gradient of phenological trait similarity and 3. Species that vary along a gradient of both spatial and phenological similarity (see Ebeling et al. 2014). The plots were 3 x 3 m in size and established within the Jena Experiment, Germany, in 2011. Plots were maintained by manual weeding in March, July and September. Traits were measured during the summer of 2012. Flowering initiation and cessation were measured respectively as the week in which flowering was first observed and flowering senesce had completed throughout the plot. Leaf area, leaf fresh mass were measured on approximately five fully expanded leaves from different individuals. These leaves were dried at 65 C for over 48 hours and massed to calculate the specific leaf area (SLA, area per dry mass), and the leaf dry matter content (LDMC, dry mass per fresh mass). Maximum canopy height was measured during peak biomass in May by taking the average of five measurements along a transect. Root traits were measured by taking soil cores, 4 cm in diameter and 40 cm deep and sectioned by depth: 0-5, 5-10, 10-20, 20-30 and 30-40 cm. Roots were washed and roots 〈 2 mm in diameter were stored in 70 % ethanol. Root length was determined by scanning stained roots with neutral red and scanning roots using WinRhizo software. Root traits were only measured in species pool 1 and 2. Roots were then dried at 65 C for over 48 hours and massed to determine the specific root length (SRL, root length per mass), mean rooting depth (MRD, the average depth weighed by root mass per depth), root mass density (RMD, the average root mass per cubic cm volume) and root length density (RLD, root mass per root length).

Description: 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.

Description: The present study was conducted at the Jena Experiment field site from 2011 to 2015. The 48 experimental plant communities included twelve monocultures (of which one was removed from all analyses because it was planted with the wrong species), twelve 2-species mixtures, twelve 4-species mixtures and twelve 8-species mixtures. We used two community-evolution treatments (plant histories); plants with eight years of co-selection history in different plant communities in the Jena Experiment (communities of co-selected plants) and plants without such co-selection history (naïve communities). Community-level plant productivity was measured each year from 2012 to 2015 by collecting species-specific aboveground biomass twice per year in May and August. There are a total of seven harvests included in this dataset. We harvested plant material 3 cm aboveground from a 50 x 20 cm area in the centre of each half-quadrat, sorted it into species, dried it at 70°C and weighed the dry biomass. We also include a datafile with the stability metrics presented in the paper, such as resistance, recovery, and resilience to the flood, population stability and temporal stability.

Description: This data set contains information about the functional structure (overall biomass; abundance of consumers: in different habitat strata; of different food resource specialization, feeding strategies and aerial mobility) of aboveground consumers (herbivores, carnivores, omnivores and decomposers) per plots from a grassland plant diversity experiment (the Jena Experiment; Roscher et al. 2004). The experiment was established in 2002, and consists of 80 grassland plots. Plots vary in plant species richness (1, 2, 4, 8, 16, or 60 species). All plots are mown twice per year, and weeded three times per year to maintain the experimental diversity gradient. We collected ground-associated arthropods over 125 days from May until September 2010 using two pitfall traps of 4.5 cm diameter per plot. During the sampling periods, the field traps were filled with 3% formalin and after emptying the traps, animals were stored in 70% ethanol. Vegetation-associated arthropods were collected by suction sampling in early June and August (during the peak biomass of the plant communities) using a modified commercial vacuum cleaner. We randomly chose three subplots of 0.75 m x 0.75 m within each plot, covered them with a gauze cage of the same size, and sampled arthropods by vacuuming the inside of the cages until we spotted no arthropods anymore. Samples were identified to species level, except for Hymenoptera, which were identified to the level of family or subfamily. We pooled data of all sampling campaigns in 2010 and standardized the resulting abundances between zero and one, separately for pitfall and suction sampling to account for different sampling intensities between the two methods (Hertzog et al. 2016). We focused on species that we sampled more than once during the whole vegetation period.