Relationships between ecosystem functions are temporally variable and driven by plant species richness and plant community composition

Ecosystem management aims at providing many ecosystem services simultaneously. Such ecosystem multifunctionality can be limited by trade-offs and increased by synergies among the underlying ecosystem functions (EF), which need to be understood to develop targeted management. Previous studies found differences in the correlation between EFs. We hypothesised that correlations between EFs are variable even under the controlled conditions of a field experiment and that seasonal and annual variation, plant species richness, and plot identity (identity effects of plant communities such as the presence and absence of functional groups and species) are drivers of these correlations. We used data on 31 EFs related to plants, consumers, and physical soil properties that were measured over 5 to 19 years, up to three times per year, in a temperate grassland experiment with 80 different plots, constituting six sown plant species richness levels (1, 2, 4, 8, 16, 60 species). We found that correlations between pairs of EFs were variable, and correlations between two particular EFs could range from weak to strong correlations or from negative to positive correlations among the repeated measurements. To determine the drivers of pairwise EF correlations, the covariance between EFs was partitioned into contributions from plant species richness, plot identity, and time (including years and seasons). We found that most of the covariance for synergies was explained by species richness (26.5%), whereas for trade-offs, most covariance was explained by plot identity (29.5%). Additionally, some EF pairs were more affected by differences among years and seasons and therefore showed a higher temporal variation. Therefore, correlations between two EFs from single measurements are insufficient to draw conclusions on trade-offs and synergies. Consequently, pairs of EFs need to be measured repeatedly under different conditions to describe their relationships with more certainty and be able to derive recommendations for the management of grasslands.