Figure 2: Variation in the correlations between pairs of ecosystem
functions (EFs) (lower triangle) and average correlation between these
EFs (upper triangle). The different EFs (see list in table 1) were
grouped into classes. Mean correlations were calculated using Fisher’s Z
transformation of EF correlations per season and year that were averaged
over time. The standard deviation of the EF correlations per year and
season was calculated to estimate the variation of EF correlations. When
no average correlation is shown, the respective EF was not measured in
the same season and year. A missing standard deviation for an EF pair
shown to have a correlation coefficient represents cases where a
correlation coefficient could only be calculated for a single time
point.
Drivers of the covariance of EF
pairs
To test if the drivers year or season,
species richness, and plot identity affect relationships among EFs, we
quantified the covariance between all pairs of EFs and the contribution
of each driver to these covariances in percentage. These percentages
were signed because the drivers can contribute to the EF covariances by
affecting the underlying EFs synergistically (signed positive) or
antagonistically (signed negative). Importantly, the contribution of
individual drivers can have antagonistic (more negative covariance) or
synergistic (more positive covariance) effects irrespective of the
overall relationship between the respective EF being a synergy or a
trade-off.
All tested drivers (year, season, SR,
and plot ID) affected the covariances between EFs. The largest fraction
of covariance among EFs was explained by SR and plotID. However, effects
differed between EF pairs in synergies and trade-offs (defined by the
sign of the mean correlation, Fig. 2, upper triangle). For synergies,
most of the covariance was explained by SR (26.5%). In contrast, for
trade-offs, most of the covariance was explained by plot identity
(–29.5%, Fig. 3), with the negative value indicating that the
individual EFs were driven antagonistically, causing a trade-off. When
further investigating plotID, the presence of herbs and legumes already
explained half of the effect of plot ID (Supporting information F, Table
S4). For synergies, plot ID had intermediate positive effects (18%),
mainly due to the presence of grasses and herbs. Year and season caused
both positive and negative covariances, so that the average percentages
explained by year and season were low (2.8% and -0.6%). For
trade-offs, the average percentage of explained covariance by SR was low
(–4.2%), contributing positively and negatively to covariance. Season
contributed an additional –12.1% to covariance, while year explained
very little (-1.3%). Interactions between drivers explained very little
covariance (Supporting Information E, Table S3). Unexplained residual
covariance was, on average, |4.5%| of the covariance
(for synergies 5.5% and for trade-offs –3.6%), suggesting a low
amount of random covariation between EFs.