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).