ABSTRACT
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 hypothesized 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.
Keywords
Correlation analysis; Synergies; Trade-off; Biodiversity; Temporal
Variability; Ecosystem function relationships
INTRODUCTION
Land management and policy aim to improve human well-being by providing
multiple ecosystem services, i.e., ecosystem multifunctionality (Dade,
Mitchell et al. 2018). The Millennium ecosystem assessment (2005)
defined ecosystem services (ES) as the ’benefits people obtain from
ecosystems’, e.g., food, water, timber, and cultural values.
Ecosystem
services derive from ecosystem functions (EF) (Balvanera, Pfisterer et
al. 2006, Costanza, de Groot et al. 2017), which describe the
biogeochemical processes that are influenced by the organisms and their
traits to sustain an ecosystem (Millennium ecosystem assessment 2005,
Reiss, Bridle et al. 2009). These ecosystem functions can be used to
measure the flow and exchange of materials and energy in ecosystems
directly (Naeem 1998), or indirectly via ecosystem properties, such as
storage and retention of water or nutrients (Costanza, de Groot et al.
2017). In the last decades, the average global crop yields have been
rising due to more intensive management practices in agriculture (Foley,
Ramankutty et al. 2011). These
management practices had negative side effects on the environment, such
as declines in native pollinators, and increases in pests and diseases,
degrading land and water (Gordon, Peterson et al. 2008, Foley,
Ramankutty et al. 2011). On the other hand,
one important aim of nature
conservation is to protect areas in order to preserve important ES, such
as carbon sequestration and climate regulation, and to avoid widespread
biodiversity declines (Watson and Venter 2017). While ES
multifunctionality may be an implicit or explicit management aim,
current management strategies often focus on providing single ecosystem
services, e.g., maximising productivity or the value for nature
conservation. ES multifunctionality requires EF multifunctionality
(Manning, van der Plas et al. 2018). Since many ecosystem functions
improve with increasing plant species richness (Scherber, Eisenhauer et
al. 2010, Weisser, Roscher et al. 2017), diversifying ecosystems has
been proposed as an alternative management target, and studies have
found a generally positive relationship between plant species richness
and multifunctionality
(Cardinale, Srivastava et al.
2006, Gamfeldt, Hillebrand et al. 2008, Pasari, Levi et al. 2013,
Dooley, Isbell et al. 2015, Finney and Kaye 2017, Hautier, Isbell et al.
2018, Meyer, Ptacnik et al. 2018).
One challenge of promoting multifunctionality is that the simultaneous
enhancement of all EFs is likely impossible because there are trade-offs
between EFs (Rodríguez, Beard Jr et al. 2006, Manning, van der Plas et
al. 2018, Meyer, Ptacnik et al. 2018). Such trade-offs occur when the
provisioning of one EF improves at the expense of another EF. For
example, under conventional management of single crops, high
productivity often is associated with soil degradation (Kleinman,
Sharpley et al. 2011, Pereira, Bogunovic et al. 2023).
In contrast, synergies among EFs
occur when EFs are co-varying in the same direction (Rodríguez, Beard Jr
et al. 2006). For example, high below-ground biomass production is
related to a high below-ground carbon storage (Hanisch, Schweiger et al.
2020).
Two mechanisms can cause correlations between EFs. The first mechanism
consists of common drivers affecting multiple EFs (Bennett, Peterson et
al. 2009), referred to as the common-driver-mechanism in the following.
Environmental conditions can improve one EF while deteriorate another EF
(Bradford, Wood et al. 2014), thereby causing a trade-off between the
two EFs or a synergy if both EF would improve or deteriorate in the same
way in response to the environmental condition. For example, Maestre,
Quero et al. (2012) found that an increase in temperature decreased
multifunctionality, which could indicate that either individual EFs are
negatively affected by increasing temperature, or that higher
temperature can cause weaker synergies and/or stronger trade-offs among
EFs. The second mechanism consists of physiological or ecological
constraints among EFs (Bennett, Peterson et al. 2009), referred to as
ecological-constraints-mechanism in the following. As resources are
limited within an ecosystem, not all EFs can be improved simultaneously,
independent of external drivers. Carbon sequestration, for example, can
be enhanced by afforestation, but during tree growth, evapotranspiration
is increased, and water availability deteriorated (Engel, Jobbágy et al.
2005).
Management
strategies cannot easily overcome ecological constraints. Consequently,
correlations among EFs need to be understood to mitigate trade-offs and
enhance synergies (Shen, Li et al. 2020). One decision strategy for
ecosystem management could be to consider the occurring species traits
to avoid potential trade-offs, as species traits link EFs with each
other (Hanisch, Schweiger et al. 2020). An attempt to consider species
traits is to maximize the number of species present, as each species
possesses a large number of traits, or to consider functional groups,
classifying groups of plant species according to plant traits, which
seem more likely to influence EFs (Tilman 2001, Roscher, Schumacher et
al. 2004). Consequently, correlations among EFs and the underlying
drivers need to be understood to mitigate trade-offs and enhance
synergies (Shen, Li et al. 2020), which is essential to manage
ecosystems for multifunctionality.
For example, for the EF-classes
’Nutrition biomass’ and ’Life cycle maintenance, habitat and gene pool
protection’, as many as 50-75% of the case studies reported a
trade-off, whereas 25-50% reported a synergy or no relationship between
these classes. For the EF classes ’Intellectual and representative
interactions’ and ’Physical and experiential interactions’, 50-75% of
the studies reported synergies, while 25-50% reported the opposite or
no relationship. The underlying causes of these conflicting results are
still subject to debate (Dade, Mitchell et al. 2018).
There are several possibilities why the relationship between two
particular EFs could differ among studies. First, the relationship
between EFs can change based on the scale or land system considered,
e.g. urban area vs. agricultural area (Adhikari and Hartemink 2016, Lee
and Lautenbach 2016). Second, most studies investigated EF relationships
based on single measurements. However, ecological drivers, such as
diversity or nutrient availability, can change over time and cause
variation in relationships between EFs (Crouzat, Mouchet et al. 2015,
Torralba, Fagerholm et al. 2018, Zheng, Wang et al. 2019). Third,
differences in the ecosystem investigated, or in abiotic conditions
among sites, can cause variation regarding EF relationships among
studies. Land-use type (Li, Chen et al. 2018), management intensity
(Rodríguez, Beard Jr et al. 2006), and environmental factors like
climate and soil pH have been shown to strongly affect individual EFs
(Wang, Liu et al. 2021), and the correlations between EFs (Spake,
Lasseur et al. 2017). If these drivers affect EFs differently, a change
in the driver will change the relationship between these EFs. One
example would be EFs dependent on water availability, such as shoot
length and root length, being positively related within a year of high
precipitation (Pérez-Ramos, Roumet et al. 2012), and showing a weaker
relationship at low precipitation, when plants invest more in roots than
shoots (Mokany, Raison et al. 2006). In addition, previous studies have
found that drivers of individual EFs are of different importance at
different places and time points (Isbell, Calcagno et al. 2011, Crouzat,
Mouchet et al. 2015, Torralba, Fagerholm et al. 2018, Zheng, Wang et al.
2019, Martin, Durand et al. 2020, Shen, Li et al. 2020, Willemen 2020).
This implies that also the variability in EF relationships may differ
among places and time points as these drivers can influence EF
relationships directly by changing the ecological dependency of the two
EFs or indirectly by affecting EFs individually and therefore causing a
change in their covariance. Finally, also differences in the statistical
methods used to evaluate relationships between EF classes can bias
results (Lee and Lautenbach 2016).
For example, no-effect
relationships were more likely to be found when correlation coefficients
were used, whereas descriptive methods such as GIS-analyses, which
quantify and describe EF relationships based on cooccurence of EF at the
same location, showed a higher probability to identify trade-offs (Lee
and Lautenbach 2016). In summary, there are several reasons why
relationships between different EFs may vary. Whereas a few studies
recorded the variation of individual EFs (van der Plas, Schröder-Georgi
et al. 2020) and their drivers over time (Gaglio, Aschonitis et al.
2020, van der Plas, Schröder-Georgi et al. 2020), such studies are
lacking for EF relationships.
To understand whether EF relationships are inherently variable or
whether meta-analyses detected variability because of differences among
studies, studies investigating EF relationships repeatedly under
comparable conditions are needed. Furthermore, the drivers of EF
relationships need to be investigated to understand, what might cause
variability in EF relationships. Drivers and variability of EF
relationships might depend on the individual EFs or their proxies
investigated. For example, it was shown, that plant diversity has
particularly strong effects on lower trophic levels and effects dampen
with increasing trophic levels (Scherber, Eisenhauer et al. 2010).
Consequently, it can be expected, that EFs depending on different
components of the ecosystem (e.g. plant productivity and soil microbes)
show different EF relationships or a higher variability of EF
relationships. Furthermore, we
expect to see similar EF
relationships between EFs depending on the same components of the
ecosystem, e.g. between EFs representing plant productivity and EFs
representing invasion resistance.
Here we used data of 31 EFs
repeatedly measured during 5 to 19 years in a large-scale temperate
grassland biodiversity experiment, i.e., the Jena Experiment (Roscher,
Schumacher et al. 2004, Weisser, Roscher et al. 2017). The 31 EFs
covered different components of the ecosystem related to plant
productivity, plant nutrients, soil microbes, consumers, invasion
resistance, soil properties, and soil nitrogen and carbon
concentrations, which are called classes of EFs hereafter. Our study
aimed to systematically investigate the variability in the pairwise
relationships between EFs and the underlying drivers of variability.
Specifically, we addressed the following questions:
(1) How variable are EF relationships over time? Do pairs of EFs differ
in their relationship between replicated measurements?
(2) What drives the relationship among EFs? How much do years, seasons,
species richness and the identity of the plots (representing the
identity of the studied plant communities) contribute to these
relationships by affecting pairs of EFs in similar or opposing ways?
(3) Are synergies and trade-offs driven differently by years, seasons,
plant species richness and the identity of the studied plots?
METHODS
Study
site
In 2002, the Jena Experiment, a biodiversity experiment with 82 plots
was established at a former arable field near to the city of Jena
(Germany) (Roscher, Schumacher et al. 2004, Weisser, Roscher et al.
2017). The plots were sown in May 2002 with a species richness (SR) of
1, 2, 4, 8, 16 and 60 grassland plant species, with 16, 16, 16, 16, 14
and 4 replicates, respectively (each replicate was a unique species
composition, i.e. community, except for the highest richness level where
all replicates had the same species composition). Plot identity (”plot
ID”) represents the different plots containing different plant
communities with a variety of compositional features (Jochum, Fischer et
al. 2020). Plant species for communities with 1–16 species were
randomly chosen from a pool of 60 plant species typical forArrhenatherum grasslands with restrictions to create different
levels of functional-group richness within each level of species
richness. We distinguished three functional groups, namely grasses,
herbs (small herbs and tall herbs combined), and legumes, based on
ecologically relevant attributes (Roscher, Schumacher et al. 2004).
Species richness and functional group richness (FGR), number of
functional groups per community) were varied as independently as
possible (Roscher, Schumacher et al. 2004). All plots were mown twice a
year, did not receive any fertiliser, and were weeded two to three times
a year (Roscher, Schumacher et al. 2004). The chosen mowing regime
corresponds to the region’s typical management of extensively used hay
meadows (Weisser, Roscher et al. 2017). Two monocultures were given up
due to the weak establishment of the target species in the first years,
resulting in 80 plots used for this analysis.
Dataset
We based this analysis on 31 EFs measured during 5 to 19 years in the
Jena Experiment (full description in Supporting Information A, Table
S1). These EFs are indicative of eight classes of EFs: plant
producticity, plant nutrients, soil microbes, consumers, invasion
resistance, soil carbon, soil nitrogen, and soil properties (Table 1).
The EFs within one class of EFs are often related.
The data were categorised into spring (March, April, May), summer (June,
July, August), autumn (September, October, November) and winter
(December, January, February) according to the meteorological seasons of
the Northern Hemisphere. In the case of multiple measurements of the
same EF per season and year, the raw data were averaged per plot, year,
and season. The EFs were always measured on all plots but in different
numbers of years and seasons. The number of years ranged from 5 to 19,
and most EFs were measured once or twice a year. A dataset comprising
all plots is referred to as a measurement in the following. The number
of measurements ranged from a minimum of 5 (SoilDensity) to a maximum of
36 (PlantHeight). The inverse of some EFs was used to represent a
valuable function according to humans’ perspective enabeling to identify
synergies and trade-offs (Table 1).