Figure 3: The contribution of SR, plot ID, season and year to
total covariance between the 116 pairs of EFs, separated for EF pairs
showing positive relationships (synergies, 78 pairs) and negative
relationships (trade-offs, 38 EF pairs) according to their mean
correlation. The violin plots show for each driver the mean (solid
line), the standard error, and the distribution of contributions to
covariance of EF pairs. Positive contributions indicate that the driver
causes positive covariances between pairs of EFs, synergistically
driving the two individual EFs. Negative contributions indicate that the
driver causes negative covariances between pairs of EFs, driving the two
individual EFs antagonistically. Results are derived by partitioning
overall covariances into contributions of the different drivers; see
method section for explanation. In this graph, only effects are shown,
which on average explain >5% of covariance. In Supporting
information F), the same graph with all variables (including FGR, and
the presence/ absence of grasses, legumes and herbs) is shown.
DISCUSSION
We investigated the variation in the correlations between different EFs
and the drivers of these relationships. We found that correlations were
variable, and correlations between two particular EFs could range from
weak to strong or from negative to positive among the repeated
measurements. Overall, EF pairs generally showed an increasing variation
in their correlations with a higher number of times the EF-pair was
measured. The correlations among pairwise EFs were differently affected
by the identity of time points (years and seasons). That means that some
EF-pairs showed more stable correlations throughout time, whereas other
EF-pairs were more affected by differences in years and seasons and
therefore showed a higher temporal variation. Species richness and plot
identity (including the presence of legumes, grasses, herbs) explained
the largest fraction of covariance among EFs, while the effects of time
(year, season, and their interaction) explained little covariance. 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%). Time explained
13.4% of covariance for trade-offs but little for synergies (3.3%).
Correlations among EFs and the drivers of these correlations varied over
time. These results indicate the importance of repeated measurements of
ecosystem functions (EFs) over time to avoid spurious conclusions, and
suggests that land management practices that promote biodiversity and
reduce negative identity effects can enhance multifunctionality in
grasslands.
We found that even under the controlled conditions of our experiment,
correlations among EFs were variable. High temporal variation of
individual EFs had been documented before (Carpenter, Mooney et al.
2009, Cardinale, Duffy et al. 2012, Gaglio, Aschonitis et al. 2020, Qiu,
Carpenter et al. 2020, van der Plas, Schröder-Georgi et al. 2020).
However, until now, inconsistent
correlations between EFs or classes of EFs have only been found when
different studies were compared (Lee and Lautenbach 2016). Although all
functions were measured with a consistent methodology at a single field
site. Trade-offs were as variable as synergies (Supporting information
E, Fig. S5), and relationships for many pairs of functions could range
from synergy to trade-off when correlations were calculated for
different time points, which confirms the previous study of Lee and
Lautenbach (2016). Lee and Lautenbach (2016) found that the agreement on
the type of relationship for a particular pair of EFs, i.e. synergy,
trade-off, or no-effect relationship, decreased the more often the
relationships were measured. Similarly, we found that the variability of
EF relationships increased with the number of measurements
(Supplementary information E, Fig. S6), which indicates, that single
measurements can be misleading when EF relationships are identified.
Furthermore we showed, that not the identity of time points (years and
seasons), but the identity of EF pairs were associated with a high
variation in EF relationships (Supporting information, Fig S7). That
means, that it depends on the particular EF pair, whether their
correlation was highly variable because of differences between years or
seasons. One explanation could be that ecosystem processes vary caused
by a change or adjustment of biotic assemblages as a response to their
environmental conditions (Turner and Chapin 2005), leading to changes in
EF relationships or multifunctionality with changing environmental
conditions (Zirbel, Grman et al. 2019). In our study, the variation in
relationships between EFs originated from the temporal variation in EF
drivers (possible reasons could be inter-annual variation in rainfall,
temperature or other cyclic patterns such as boom and bust cycles of
herbivory), while in Lee and Lautenbach (2016), the variation in the
relationships among classes of EFs was introduced by different studies,
and therefore additional site-dependent contexts.
Regarding the identified EF relationships (mean correlations among all
the different EFs), we found both, synergies and trade-offs that can be
explained by biological processes and therefore confirm other studies
investigating the individual EFs (Jarrell and Beverly 1981, Allan,
Weisser et al. 2013). For example, EFs of the classes plant nutrients
and plant productivity showed often a trade-off, indicating a dilution
effect. i.e. when plant growth improved, plant nutrient concentrations
decreased in the plant tissue (Jarrell and Beverly 1981). However, the
carbon concentration of plants (PlantC) showed mainly synergies with EFs
of the class plant productivity. One reason could be that a high biomass
reflects a high nutrient-efficiency and thus comparatively low nutrient
concentrations and correspondingly high C concentrations (Allan, Weisser
et al. 2013). Furthermore, organic carbon in the soil (SoilDOC,
SoilCorg) was positively related to plant productivity (Fig. 2). This is
consistent with studies, showing that a high biomass production leads to
an accumulation of dead plant material in the soil (Post and Kwon 2000)
or root exodation of plants (Raich and Tufekciogul 2000). As the Jena
experiment was established on depleted arable soil, a higher carbon
concentration in the soil occured faster with higher biomass production,
but in the end the carbon concentration might be the same on all plots
due to accelerated litter decomposition (Weisser, Roscher et al. 2017).
EFs of the class Invasion resistance showed synergies with EFs of the
class plant productivity. This is consistent with former studies,
showing that a high biomass of the native species suppressed invasive
species (Yannelli, MacLaren et al. 2020, Rojas-Botero, Kollmann et al.
2022), often due to a more complete use of available resources (Hector,
Dobson et al. 2001, Roscher, Beßler et al. 2009). Summarizing these
examples, the relationships identified here for the classes of EFs are
consistent with the underlying biological processes.
We found that synergies and trade-offs have different drivers. Species
richness is a known driver of many EFs (Gamfeldt and Roger 2017,
Weisser, Roscher et al. 2017, Craven, Eisenhauer et al. 2018) and
ecosystem services (van der Plas 2019). When two EFs improve with higher
SR, a positive covariance is introduced, strengthening their
relationship. The fact that we showed that SR affected the majority of
investigated EFs positively explains that the large majority of SR
effects on the covariance between EFs were positive and that EFs in
synergies were stronger effected by SR than EFs in trade-offs (Fig. 2).
However, the relationship between two EFs weakens when SR has
contrasting effects on the two EFs, as indicated by a few pairs of EFs
for which we showed SR to cause negative covariance. In our study, plot
identity represents all other differences among plant communities within
a diversity level, such as FGR, the presence of different functional
groups (Supporting information F, Fig S8), species identity, and the
presence of other groups of organisms (e.g. microbes, insects)
associated with particular plant communities. These identity effects
were mostly positive for EFs in synergies and mostly negative for EFs in
trade-offs. This can be explained by selection effects, which have been
documented repeatedly by comparing the performance, such as biomass
production, of plant communities (Marquard, Weigelt et al. 2009). A high
performance of a plant community may be associated with a high abundance
of certain species (Loreau and Hector 2001) and therefore with many
simultaneously occurring EFs (many synergies). A low performance is
associated with a negative selection effect (Loreau and Hector 2001) and
could be related to the occurrence of just a few EFs, as they are
restricted by trade-offs. Outside an experimental setup, the equivalent
of SR and plotID would be the different communities associated with
landscape patches. As a consequence of different biotic communities,
different levels of individual EFs would occur in these patches and
different correlations among EFs could be identified. While the effect
of different aspects of SR and plotID (FGR, presence/absence of
functional groups or individual species) on individual EFs is frequently
investigated, further research is needed to identify how different SR/
plotID impacts the relationships between EF.
Time (year, season, and their interaction) explained some, albeit
little, covariance among EFs and affected synergies and trade-offs
differently. Time can become a driver of EF relationships when
environmental conditions vary over time, e.g., temperature and extreme
events, affecting the biological activity of organisms. Trade-offs were
more affected by temporal effects than synergies. Season was among the
main drivers of trade-offs, reflecting the pronounced change in abiotic
conditions among seasons in the temperate zone. As an example, soil
microbial activity strongly responds to climatic conditions, affecting
carbon and nutrient cycling (Frey, Lee et al. 2013). Further,
competition strongly affects trade-offs, which can be changed by
altering the environmental conditions, such as the availability of water
or light that fluctuate with time. Consequently, temporal variability in
these drivers can induce variability in EF correlations underlying the
importance of repeated measurements to identify the true relationships
between EF.
When investigating the drivers of EF correlations, we found that some
covariance among EFs could not be explained by any of the drivers tested
in our study. We interpret this unexplained covariance as EF pairs
affected by the ecological-constraints-mechanism. Plants have access to
a limited pool of resources they can invest in, e.g. in growth or
defence against natural enemies, resultingin a growth-defense trade-off
(Karasov, Chae et al. 2017). Because providing unlimited resources
within a local patch is impossible, ecological trade-offs resulting from
resource limitation are inevitable. Further, the simultaneous provision
of EFs can be limited by competition. For example, in our study,
improved plant productivity was associated with higher invasion
resistance (considered good), likely due to intensified competition for
space and light between the resident plant community and potential
invading plant individuals in our plots. Furthermore, the higher the
root biomass was, the lower were the soil nutrient concentrations,
implying competition among plant species for available nutrients.
Resource limitations and competition may limit biological activities,
leading to trade-offs between EFs. These trade-offs can be weakened when
competition in diverse communities is reduced by complementarity between
species (Weisser, Roscher et al. 2017). Understanding how ecological
constraints affect relationships between EFs is an important topic for
further investigation.
Our results have implications for land management aiming at promoting
multifunctionality. Relationships among EFs affect multifunctionality
since they can either promote (synergies) or limit (trade-offs)
multifunctionality. Analysing the drivers of relationships between EFs,
we showed that SR can promote synergies among EFs, resulting in
increased multifunctionality. Consequently, promoting diversity is a
mean to foster multifunctionality, confirming previous empirical
biodiversity multifunctionality relationships (Isbell, Calcagno et al.
2011, Lefcheck, Byrnes et al. 2015, Meyer, Ptacnik et al. 2018).
Further, we showed that plot identity effects, including functional
group richness and the presence/absence of individual functional groups,
were important drivers for trade-offs between EFs. While we tested for
identity effects of plots, there are likely individual plant species
that cause these trade-offs by maximising some EFs at the expense of
other EFs. When future research can identify such plant species with
strong effects on trade-offs, land management can target low densities
of these disadvantageous species to reduce trade-offs between EF and
promote multifunctionality. Nevertheless, competition for resources and
the resulting ecological trade-offs between EFs are challenging to
resolve.
CONCLUSIONS
Our study showed that even under the controlled conditions of a single
experimental field site, correlations among EFs were variable over time.
Consequently, repeated measurements of EF are needed to avoid spurious
and non-generalisable conclusions about relationships among EFs.
Moreover, our results show the potential for land management to promote
multifunctionality in both the establishment and the management phase by
incorporating two principles. First, maintaining or increasing
biodiversity of grasslands, which we showed increases synergies among
EFs, promoting multifunctionality. Second, reducing negative identity
effects by reducing the proportion of disadvantageous species or
communities as indicated by the strong effect of plot identity
strengthening trade-offs.
Future studies should continue to investigate the drivers of EF
relationships to identify common drivers causing trade-offs and separate
common drivers from potential ecological constraints. Importantly, these
studies should also address how environmental conditions can change
these relationships and identify influential species enabling
recommendations on how to adapt management for maximising
multifunctionality.