• Energy Research

Many scientific disciplines currently are experiencing a “reproducibility crisis” because numerous scientific findings cannot be repeated consistently. A novel but controversial hypothesis postulates that stringent levels of environmental and biotic standardization in experimental studies reduces reproducibility by amplifying impacts of lab-specific environmental factors not accounted for in study designs. A corollary to this hypothesis is that the deliberate introduction of controlled systematic variability (CSV) in experimental designs can increase reproducibility. We tested this hypothesis using a multi-laboratory microcosm study in which the same ecological experiment was repeated in 14 laboratories across Europe. Each laboratory introduced environmental and genotypic CSV within and among replicated microcosms established in either growth chambers (with stringent control of environmental conditions) or glasshouses (with more variable environmental conditions). The introduction of genotypic CSV led to lower among-laboratory variability in growth chambers, indicating increased reproducibility, but had no significant effect in glasshouses where reproducibility also was lower. Environmental CSV had little effect on reproducibility. Although there are multiple causes for the “reproducibility crisis”, deliberately including genetic variation may be a simple solution for increasing the reproducibility of ecological studies performed in controlled environments.

The dataset contains calibrated isotopic data (∆13C, ∆15N) and relative biomasses of spiders and ground beetles, sampled in 2019 in Lower Saxony, Germany, within the RTG 2300 project "Enrichment of European beech forests with conifers". Arthropods were sampled with pitfall traps and identified to species level by Kriegel et al (2021) and Matevski and Schuldt (2023). Natural abundance isotopic ratios (δ13C, δ15N) were determined by the Centre for Stable Isotope Research Analysis at the University of Göttingen and thereafter calibrated with leaf litter isotopic ratios. Calibrated isotopic values (∆) were used to calculate multidimenional isotopic metrics, which were compared between different stand types and regions. The stand types were Douglas fir, beech-Douglas fir, beech, beech-spruce and spruce.

Earth is home to over 350,000 vascular plant species that differ in their traits in innumerable ways. A key challenge is to predict how natural or anthropogenically driven changes in the identity, abundance and diversity of co-occurring plant species drive important ecosystem-level properties such as biomass production or carbon storage. Here, we analyse the extent to which 42 different ecosystem properties can be predicted by 41 plant traits in 78 experimentally manipulated grassland plots over 10 years. Despite the unprecedented number of traits analysed, the average percentage of variation in ecosystem properties jointly explained was only moderate (32.6%) within individual years, and even much lower (12.7%) across years. Most other studies linking ecosystem properties to plant traits analysed no more than six traits and, when including only six traits in our analysis, the average percentage of variation explained in across-year levels of ecosystem properties dropped to 4.8%. Furthermore, we found on average only 12.2% overlap in significant predictors among ecosystem properties, indicating that a small set of key traits able to explain multiple ecosystem properties does not exist. Our results therefore suggest that there are specific limits to the extent to which traits per se can predict the long-term functional consequences of biodiversity change, so that data on additional drivers, such as interacting abiotic factors, may be required to improve predictions of ecosystem property levels.

AbstractBiodiversity experiments revealed that plant diversity loss can decrease ecosystem functions across trophic levels. To address why such biodiversity-function relationships strengthen over time, we established experimental mesocosms replicating a gradient in plant species richness across treatments of shared versus non-shared history of (1) the plant community and (2) the soil fauna community. After 4 months, we assessed the multitrophic functioning of soil fauna via biomass stocks and energy fluxes across the food webs. We find that soil community history significantly enhanced belowground multitrophic function via changes in biomass stocks and community-average body masses across the food webs. However, variation in plant diversity and plant community history had unclear effects. Our findings underscore the importance of long-term community assembly processes for soil fauna-driven ecosystem function, with species richness and short-term plant adaptations playing a minimal role. Disturbances that disrupt soil community stability may hinder fauna-driven ecosystem functions, while recovery may require several years.

This data set contains measures of energy-use efficiency, energy flow, and energy storage in units of dry biomass that quantify the multitrophic ecosystem functioning realized in grassland ecosystems of differing plant diversity. Given are both the measures integrated over whole ecosystems (total network measures) as well as the energy dynamics associated with individual ecosystem compartments including the entire biological community and detrital compartments across the above- and belowground parts of the ecosystem.Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment, see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Study plots are grouped in four blocks in parallel to the river in order to account for any effect of a gradient in abiotic soil properties. Each block contains an equal number of plots of each plant species richness and plant functional group richness level. Plots were maintained in general by bi-annual weeding and mowing. Since 2010, plot size was reduced to 5.5 x 6 m and plots were weeded three times per year.Trophic-network models were constructed for 80 of the experimental plots, and represent the ecosystem energy budget in the currency of dry-mass (g m-2 for standing stocks and g m-2 d-1 for flows). All trophic networks have the same topology, but they differ in the estimated size of the standing stock biomass of individual compartments (g m-2) and flows among the compartments (g m-2 d-1). Each trophic network contains twelve ecosystem compartments representing distinct trophic groups of the above- and belowground parts of the ecosystem (i.e., plants, soil microbial community, and above- and belowground herbivores, carnivores, omnivores, decomposers, all represented by invertebrate macro- and mesofauna) and detrital pools (i.e., surface litter and soil organic matter). Vertebrates were not considered in our study due to limitations of data availability and because the impact of resident vertebrates in our experimental system is expected to be minimal. Larger grazing vertebrates were excluded by a fence around the field site, though there was some occasional grazing by voles.Compartments are connected by 41 flows. Flows (fluxes) constitute 30 internal flows within the system, namely feeding (herbivory, predation, decomposition), excretion, mortality, and mechanical transformation of surface litter due to bioturbation plus eleven 11 external flows, i.e. one input (flows entering the system, namely carbon uptake by plants) and ten output flows (flows leaving the system, namely respiration losses). The ecosystem inflow (a flow entering the system) and outflows (flows leaving the system) represent carbon uptake and respiration losses, respectively. In the case of consumer groups, the food consumed (compartment-wide input flow) is further split into excretion (not assimilated organic material that is returned to detrital pools in the form of fecesfaeces) and assimilated organic material, which is further split into respiration (energy lost out of the system to the environment) and biomass production, which is further consumed by higher trophic levels due to predation or returned to detrital pools in the form of mortality (natural mortality or prey residues). In case of detrital pools (i.e. surface litter and soil organic matter), the input flows are in the form of excretion and mortality from the biota compartments, and output flows are in the form of feeding by decomposers and soil microorganisms (i.e. decomposition). Surface litter and soil organic matter are connected by flows in the form of burrowing (mechanical transportation) of organic material from the surface to the soil by soil fauna. Organism immigration and emigration are not considered in our study due to limited data availability.Flows were quantified using resource processing rates (i.e. the feeding rates at which material is taken from a source) multiplied with the standing biomass of the respective source compartment. To approximate resource processing rates, different approaches were used: (i) experimental measurements (namely the aboveground decomposition, fauna burial activity (bioturbation), microbial respiration, and aboveground herbivory and predation rates); (ii) allometric equations scaled by individual body mass, environmental temperature and phylogenetic group (for the above- and belowground fauna respiration rates and plant respiration); (iii) assimilation rates scaled by diet type (for quantification of belowground fauna excretion and natural mortality); (iv) literature-based rates scaled by biomass of trophic groups (for microbial mortality); and (v) mass-balance assumptions (carbon uptake, plant and aboveground fauna mortality, belowground decomposition, belowground herbivory, and belowground predation). Mass-balance assumption means that the flows are calculated assuming that resource inputs into the compartment (i.e. feeding) balance the rate at which material is lost (i.e. the sum of through excretion, respiration, predation, and natural death). We used constrained nonlinear multivariable optimization to perturb the initial flow rates estimated from the various sources. We assigned confidence ratings for each flow rate, reflecting the quality of empirical data it is based on. We then used the 'fmincon' function from Matlab's optimization toolbox, which utilizes the standard Moore-Penrose pseudoinverse approach to achieve a balanced steady state ecological network model that best reflects the collected field data. Measured data used to parameterize the trophic network models were collected mostly in the year 2010.Network-wide measures that quantify proxies for different aspects of multitrophic ecosystem functioning were calculated for each experimental plot using the 'enaR' package in R. In particular, total energy flow was measured as the sum of all flows through each ecosystem compartment. Flow uniformity was calculated as the ratio of the mean of summed flows through each individual ecosystem compartment divided by the standard deviation of these means. Total-network standing biomass was determined as the sum of standing biomass across all ecosystem compartments. Community maintenance costs were calculated as the ratio of community-wide respiration related to community-wide biomass. Supplement to: Buzhdygan, Oksana Y; Meyer, Sebastian Tobias; Weisser, Wolfgang W; Eisenhauer, Nico; Ebeling, Anne; Borrett, Stuart R; Buchmann, Nina; Cortois, Roeland; De Deyn, Gerlinde B; de Kroon, Hans; Gleixner, Gerd; Hertzog, Lionel R; Hines, Jes; Lange, Markus; Mommer, Liesje; Ravenek, Janneke; Scherber, Christoph; Scherer-Lorenzen, Michael; Scheu, Stefan; Schmid, Bernhard; Steinauer, Katja; Strecker, Tanja; Tietjen, Britta; Vogel, Anja; Weigelt, Alexandra; Petermann, Jana S (2020): Biodiversity increases multitrophic energy use efficiency, flow and storage in grasslands. Nature Ecology & Evolution Most of the data used to parameterize these trophic networks were collected in 2010.A diagram depicting the conceptual trophic-network model developed to describe multitrophic ecosystem functioning can be found in the paper (REF to the NEE paper). This paper also shows the relationship between the individual flows and compartment sizes as well as the network-wide measures with plant species richness. Further sensitivity analyses for the influence of including the highest diversity level are also provided in the paper.