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  • 1
    Online Resource
    Online Resource
    San Diego :Elsevier Science & Technology,
    Keywords: Biodiversity. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (386 pages)
    Edition: 1st ed.
    ISBN: 9780081029138
    Series Statement: Issn Series
    DDC: 333.95
    Language: English
    Note: Front Cover -- Mechanisms underlying the relationship between biodiversity and ecosystem function -- Copyright -- Contents -- Contributors -- Preface: Mechanistic links between biodiversity and ecosystem functioning -- Acknowledgements -- References -- Further reading -- Chapter One: A multitrophic perspective on biodiversity-ecosystem functioning research -- 1. What are the key achievements of BEF research? -- 1.1. A short history of BEF research -- 1.2. A new BEF era provides novel insights -- 1.3. Identification of BEF mechanisms -- 1.4. BEF in multitrophic communities -- 1.5. BEF implications for ecosystem services -- 2. What are the key challenges of future BEF research? -- 2.1. Non-random biodiversity change across trophic levels -- 2.2. Predicting the strength of BEF relationships across environmental contexts -- 2.3. Spatial scaling of BEF relationships -- 2.4. Eco-evolutionary implications of multitrophic BEF -- 2.5. FAIR data and beyond -- 2.6. Operationalizing BEF insights for ecosystem management, society, and decision making -- 3. Concluding remarks -- Acknowledgements -- References -- Chapter Two: Above- and belowground overyielding are related at the community and species level in a grassland biodiversit ... -- 1. Introduction -- 2. Methods -- 2.1. Site description -- 2.2. Biomass sampling -- 2.3. Estimating species root biomass using molecular methods -- 2.4. Data analysis -- 3. Results -- 3.1. Hypothesis 1: At the community level, above- and belowground overyielding are correlated -- 3.2. Hypothesis 2: At the pool level, species from the `spatial pool overyield more aboveground, whereas the species from ... -- 3.3. Hypothesis 3: At the species level, some species exhibit trade-offs between above- and belowground overyielding -- 4. Discussion -- 4.1. Are above- and belowground overyielding correlated at the community level?. , 4.2. Above- and belowground overyielding relationships differ between species pools -- 5. Conclusions -- Authorship statement -- Acknowledgements -- References -- Chapter Three: Lost in trait space: species-poor communities are inflexible in properties that drive ecosystem functioning -- 1. Introduction -- 2. Methods -- 2.1. Study sites and experimental designs -- 2.1.1. Cedar Creek -- 2.1.2. Jena -- 2.2. Data -- 2.3. Calculation of functional and phylogenetic indices and statistical analyses -- 3. Results -- 3.1. Temporal shift of the functional and phylogenetic diversity of plant communities -- 3.2. Temporal shifts in community trait space -- 4. Discussion -- 4.1. Temporal shift of the functional and phylogenetic diversity of plant communities -- 4.2. Temporal shift of the trait space of plant communities -- 5. Conclusions -- Acknowledgements -- References -- Chapter Four: Terrestrial laser scanning reveals temporal changes in biodiversity mechanisms driving grassland productivity -- 1. Introduction -- 2. Material and methods -- 2.1. Study site and trait based-experiment -- 2.2. Terrestrial laser scanning: Data acquisition and processing -- 2.3. Diversity drivers: Functional diversity, functional identity, and species richness -- 2.4. Data analyses -- 2.4.1. Estimation of plant biomass from terrestrial laser scanning metrics -- 2.4.2. Temporal changes in diversity effects on plant productivity -- 3. Results -- 3.1. Intra- and inter-annual variation in mean height as a proxy for abovegroundbiomass -- 3.2. Intra-annual diversity and identity effects on plant development -- 4. Discussion -- 4.1. Intra-annual changes in functional diversity effects on plant development -- 4.2. Intra-annual changes in identity effects on plant development -- 4.3. Species richness effects and inter-annual differences. , 4.4. A new method for biodiversity-ecosystem functioning research in grasslands -- 5. Conclusions -- Acknowledgements -- References -- Chapter Five: Plant functional trait identity and diversity effects on soil meso- and macrofauna in an experimental grassland -- 1. Introduction -- 2. Material and methods -- 2.1. Experimental design -- 2.1.1. Soil fauna sampling -- 2.1.2. Plant cover measurement -- 2.2. Plant community indices -- 2.3. Statistical analyses -- 3. Results -- 3.1. Plant species richness effects -- 3.2. Trait-based models -- 3.3. Comparison of plant species richness-based and trait-based models -- 4. Discussion -- 4.1. Plant species richness has a weak effect on soil communities -- 4.2. Plant traits as more powerful predictors of soil fauna communities -- 4.3. The importance of plant trait identity effects across soil fauna groups -- 4.4. Soil fauna responses to spatial resource acquisition traits -- 4.5. Soil fauna responses to temporal resource acquisition traits -- 5. Conclusions -- Acknowledgements -- References -- Further reading -- Chapter Six: How plant diversity impacts the coupled water, nutrient and carbon cycles -- 1. Introduction -- 2. Plant diversity effects on the soil microbial community and soil processes and functions -- 2.1. Microbial community composition and diversity -- 2.2. Soil water balance -- 2.3. Nutrient cycling -- 2.4. Plant carbon allocation to soil, microbial net assimilation and microbial carbon storage -- 3. Consequences of the element and water cycles and their coupling for the BEF relationships -- Acknowledgements -- References -- Chapter Seven: A new experimental approach to test why biodiversity effects strengthen as ecosystems age -- 1. Introduction -- 2. Methods -- 2.1. Study site -- 2.2. The DeltaBEF experiment -- 2.3. Measurements -- 2.4. Data analysis -- 2.5. The soil barrier experiment. , 3. Results -- 3.1. Establishment of the treatments -- 3.1.1. Treatment effects on plant communities -- 3.1.2. Treatment effects on soil properties -- 3.1.3. Treatment effects on the plant diversity-productivity relationship -- 4. Discussion -- 4.1. Establishment of the treatments -- 4.2. Treatment effects on the plant diversity-productivity relationship -- 5. Conclusions -- Acknowledgements -- References -- Chapter Eight: Linking local species coexistence to ecosystem functioning: a conceptual framework from ecological first pr ... -- 1. Introduction -- 2. Jointly emerging local coexistence and ecosystem functioning from ecological first principles -- 2.1. Abiotic conditions -- 2.1.1. Resources -- 2.1.2. Other abiotic factors -- 2.2. Biotic conditions -- 3. Population level effects of abiotic and biotic conditions on fecundity, growth, and survival (Fig. 2) -- 3.1. Abiotic conditions -- 3.1.1. Resources -- 3.1.2. Other abiotic factors -- 3.2. Biotic conditions -- 4. How ecological first principles influence trade-offs between fecundity, growth, and survival and in turn influence loc ... -- 4.1. Productivity -- 4.2. Root decomposition -- 5. Conclusion -- Author contributions -- Acknowledgements -- References -- Further reading -- Chapter Nine: Mapping change in biodiversity and ecosystem function research: food webs foster integration of experiments ... -- 1. Topic networks as a way to visualize global conversation about biodiversity and ecosystem functioning -- 1.1. Core research domains persist through time: `BEF experiments and `Science policy -- 1.2. Integrative research domains connect the scientific landscape: Aquatic food webs and agricultural landscapes -- 2. Divisions among research domains: influences on food webs -- 2.1. Baseline comparisons across research domains: Random, null, and gradient based hypotheses. , 2.2. Scaling multi-trophic diversity -- 2.3. Currency across domains: Biomass, energy, valuation -- 3. Summary and outlook: towards integrative food-web ecology -- Acknowledgements -- References -- Chapter Ten: Transferring biodiversity-ecosystem function research to the management of `real-world ecosystems -- 1. Introduction -- 2. Small-grain and highly-controlled experiments (Cluster A) -- 2.1. What can be transferred -- 2.2. Barriers to transfer and directions for future research -- 3. Small-grain studies with low experimental control (Cluster B) -- 3.1. What can be transferred -- 3.2. Barriers to transfer and directions for future research -- 4. Large-grain studies without experimental control (Cluster C) -- 4.1. What can be transferred -- 4.2. Barriers to transfer and directions for future research -- 5. Conclusion -- Acknowledgements -- References -- Back Cover.
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  • 2
    Publication Date: 2020-10-07
    Description: Concern about the functional consequences of unprecedented loss in biodiversity has prompted biodiversity–ecosystem functioning (BEF) research to become one of the most active fields of ecological research in the past 25 years. Hundreds of experiments have manipulated biodiversity as an independent variable and found compelling support that the functioning of ecosystems increases with the diversity of their ecological communities. This research has also identified some of the mechanisms underlying BEF relationships, some context-dependencies of the strength of relationships, as well as implications for various ecosystem services that humankind depends upon. In this chapter, we argue that a multitrophic perspective of biotic interactions in random and non-random biodiversity change scenarios is key to advance future BEF research and to address some of its most important remaining challenges. We discuss that the study and the quantification of multitrophic interactions in space and time facilitates scaling up from small-scale biodiversity manipulations and ecosystem function assessments to management-relevant spatial scales across ecosystem boundaries. We specifically consider multitrophic conceptual frameworks to understand and predict the context-dependency of BEF relationships. Moreover, we highlight the importance of the eco-evolutionary underpinnings of multitrophic BEF relationships. We outline that FAIR data (meeting the standards of findability, accessibility, interoperability, and reusability) and reproducible processing will be key to advance this field of research by making it more integrative. Finally, we show how these BEF insights may be implemented for ecosystem management, society, and policy. Given that human well-being critically depends on the multiple services provided by diverse, multitrophic communities, integrating the approaches of evolutionary ecology, community ecology, and ecosystem ecology in future BEF research will be key to refine conservation targets and develop sustainable management strategies.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , peerRev
    Format: application/pdf
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  • 3
    Publication Date: 2020-11-26
    Description: 1. Plant diversity is an important driver of belowground ecosystem functions, such as root growth, soil organic matter (SOM) storage, and microbial metabolism, mainly by influencing the interactions between plant roots and soil. Dissolved organic matter (DOM), as the most mobile form of SOM, plays a crucial role for a multitude of soil processes that are central for ecosystem functioning. Thus, DOM is likely to be an important mediator of plant diversity effects on soil processes. However, the relationships between plant diversity and DOM have not been studied so far. 2. We investigated the mechanisms underlying plant diversity effects on concentrations of DOM using continuous soil water sampling across 6 years and 62 plant communities in a long‐term grassland biodiversity experiment in Jena, Germany. Furthermore, we investigated plant diversity effects on the molecular properties of DOM in a subset of the samples. 3. Although DOM concentrations were highly variable over the course of the year with highest concentrations in summer and autumn, we found that DOM concentrations consistently increased with plant diversity across seasons. The positive plant diversity effect on DOM concentrations was mainly mediated by increased microbial activity and newly sequestered carbon in topsoil. However, the effect of soil microbial activity on DOM concentrations differed between seasons, indicating DOM consumption in winter and spring, and DOM production in summer and autumn. Furthermore, we found increased contents of small and easily decomposable DOM molecules reaching deeper soil layers with high plant diversity. 4. Synthesis. Our findings suggest that plant diversity enhances the continuous downward transport of DOM in multiple ways. On the one hand, higher plant diversity results in higher DOM concentrations, on the other hand, this DOM is less degraded. The present study indicates, for the first time, that higher plant diversity enhances the downward transport of dissolved molecules that likely stimulate soil development in deeper layers and therefore increase soil fertility.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 4
    Publication Date: 2018-01-10
    Description: The research of a generation of ecologists was catalysed by the recognition that the number and identity of species in communities influences the functioning of ecosystems. The relationship between biodiversity and ecosystem functioning (BEF) is most often examined by controlling species richness and randomising community composition. In natural systems, biodiversity changes are often part of a bigger community assembly dynamic. Therefore, focusing on community assembly and the functioning of ecosystems (CAFE), by integrating both species richness and composition through species gains, losses and changes in abundance, will better reveal how community changes affect ecosystem function. We synthesise the BEF and CAFE perspectives using an ecological application of the Price equation, which partitions the contributions of richness and composition to function. Using empirical examples, we show how the CAFE approach reveals important contributions of composition to function. These examples show how changes in species richness and composition driven by environmental perturbations can work in concert or antagonistically to influence ecosystem function. Considering how communities change in an integrative fashion, rather than focusing on one axis of community structure at a time, will improve our ability to anticipate and predict changes in ecosystem function.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 5
    Publication Date: 2020-04-22
    Description: Current analyses and predictions of spatially‐explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long‐term average thermal conditions at coarse spatial resolutions only. Hence, many climate‐forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing, or cold‐air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free‐air temperatures, microclimatic ground and near‐surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near‐surface temperature data from all over the world. Currently this database contains time series from 7538 temperature sensors from 51 countries across all key biomes. The database will pave the way towards an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Miscellaneous , notRev
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  • 6
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    PANGAEA
    In:  Supplement to: Wagg, Cameron; Ebeling, Anne; Roscher, Christiane; Ravenek, Janneke; Bachmann, Dörte; Eisenhauer, Nico; Mommer, Liesje; Buchmann, Nina; Hillebrand, Helmut; Schmid, Bernhard; Weisser, Wolfgang W (2017): Functional trait dissimilarity drives both species complementarity and competitive disparity. Functional Ecology, 31(12), 2320-2329, https://doi.org/10.1111/1365-2435.12945
    Publication Date: 2023-05-20
    Description: This data collection contains species-specific aboveground plant biomass that was collected from the Trait Based Experiment in 2012. (Sown plant species, Weed plant biomass, the biomass of dead plant material, and the biomass of unidentified plant material) per plots collected in 2012 from a grassland trait diversity experiment (the Jena Trait Based Experiment). The data collection also contains the traits of the species measured in their monoculture. The experiment consists of 20 plant species that were assigned to one of three species pools: 1. Species that vary along a gradient of spatial leaf and root trait similarity, 2. Species that vary along a gradient of phenological trait similarity and 3. Species that vary along a gradient of both spatial and phenological similarity (see Ebeling et al. 2014). The experiment consists of 138 grassland plots 3 x 3 m in size that was established within the Jena Experiment, Germany, in 2011. Plots vary in plant species richness (1, 2, 4, or 8 species) and functional diversity (1, 2, 3, 4 functional diversity levels, where 1 indicates species are most similar and 4 being most dissimilar in functional traits). Plots were maintained by manual weeding in March, July and September. Biomass was harvested twice in 2012 (during peak standing biomass in late May and in late August) on all experimental plots. Plots were mown to the same height directly following biomass harvest. Plant biomass was harvested by clipping the vegetation at 3 cm above ground in two 0.2 x 0.5 m quadrats per plot. The harvested biomass was sorted into categories: individual species of the sown plant species, 'Weed' plant species (species not sown in a plot), detached 'Dead' plant material, and remaining plant material that could not be assigned to any category ('Rest'). All biomass was dried to constant weight (70°C, 〉= 48 h) and weighed. The data from individual quadrats were averaged. The traits measured are: Flowering initiation, Flowering cessation, specific leaf area (SLA), leaf dry matter content (LDMC), leaf area, maximum canopy height, specific root length (SRL), mean rooting depth (MRD), root mass density (RMD) and root length density (RLD). Flowering initiation and cessation were measured respectively as the week in which flowering was first observed and flowering senesce had completed throughout the plot. Leaf area, leaf fresh mass were measured on approximately five fully expanded leaves from different individuals. These leaves were dried at 65°C for over 48 hours and massed to calculate the specific leaf area (SLA, area per dry mass), and the leaf dry matter content (LDMC, dry mass per fresh mass). Maximum canopy height was measured during peak biomass in May by taking the average of five measurements along a transect. Root traits were measured by taking soil cores, 4 cm in diameter and 40 cm deep and sectioned by depth: 0-5, 5-10, 10-20, 20-30 and 30-40 cm. Roots were washed and roots 〈 2 mm in diameter were stored in 70 % ethanol. Root length was determined by scanning stained roots with neutral red and scanning roots using WinRhizo software. Root traits were only measured in species pool 1 and 2. Roots were then dried at 65°C for over 48 hours and massed to determine the specific root length (SRL, root length per mass), mean rooting depth (MRD, the average depth weighed by root mass per depth), root mass density (RMD, the average root mass per cubic cm volume) and root length density (RLD, root mass per root length).
    Keywords: JenExp; The Jena Experiment
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 7
    Publication Date: 2023-05-20
    Description: This data set contains plant species traits: Flowering initiation, Flowering cessation, specific leaf area (SLA), leaf dry matter content (LDMC), leaf area, maximum canopy height, specific root length (SRL), mean rooting depth (MRD), root mass density (RMD) and root length density (RLD). The traits were measured during the summer of 2012 on the plants grown in monoculture within a grassland trait diversity experiment (the Jena Trait Based Experiment). The experiment consists of 20 plant species that were assigned to one of three species pools: 1. Species that vary along a gradient of spatial leaf and root trait similarity, 2. Species that vary along a gradient of phenological trait similarity and 3. Species that vary along a gradient of both spatial and phenological similarity (see Ebeling et al. 2014). The plots were 3 x 3 m in size and established within the Jena Experiment, Germany, in 2011. Plots were maintained by manual weeding in March, July and September. Traits were measured during the summer of 2012. Flowering initiation and cessation were measured respectively as the week in which flowering was first observed and flowering senesce had completed throughout the plot. Leaf area, leaf fresh mass were measured on approximately five fully expanded leaves from different individuals. These leaves were dried at 65 C for over 48 hours and massed to calculate the specific leaf area (SLA, area per dry mass), and the leaf dry matter content (LDMC, dry mass per fresh mass). Maximum canopy height was measured during peak biomass in May by taking the average of five measurements along a transect. Root traits were measured by taking soil cores, 4 cm in diameter and 40 cm deep and sectioned by depth: 0-5, 5-10, 10-20, 20-30 and 30-40 cm. Roots were washed and roots 〈 2 mm in diameter were stored in 70 % ethanol. Root length was determined by scanning stained roots with neutral red and scanning roots using WinRhizo software. Root traits were only measured in species pool 1 and 2. Roots were then dried at 65 C for over 48 hours and massed to determine the specific root length (SRL, root length per mass), mean rooting depth (MRD, the average depth weighed by root mass per depth), root mass density (RMD, the average root mass per cubic cm volume) and root length density (RLD, root mass per root length).
    Keywords: Block; Canopy height, maximum; Density; EXP; Experiment; Experiment week; Jena Experiment 2012; JenExp; JenExp_2012; Leaf area; Leaf area, specific, per mass dry weight; Leaf dry matter content, mass dry weight per mass wet weight; Length of roots, average; Plot; Root length, specific; Species; Species Pool; The Jena Experiment; Thuringia, Germany
    Type: Dataset
    Format: text/tab-separated-values, 335 data points
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  • 8
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    PANGAEA
    In:  Supplement to: Strecker, Tanja; Gonzalez, Odette; Scheu, Stefan; Eisenhauer, Nico (2016): Functional composition of plant communities determines the spatial and temporal stability of soil microbial properties in a long-term plant diversity experiment. Oikos, (accepted), https://doi.org/10.1111/oik.03181
    Publication Date: 2023-05-13
    Description: The study was carried out on the main plots of a large grassland biodiversity experiment (the Jena Experiment). 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). Plots were maintained by bi-annual weeding and mowing. We tracked soil microbial basal respiration (BR; µlO2/g dry soil/h) and biomass carbon (Cmic; µgC/g dry soil) over a time period of 12 years (2003-2014) and examined the role of plant diversity and plant functional group composition for the spatial and temporal stability (calculated as mean/SD) of soil microbial properties (basal respiration and biomass) in bulk-soil. Our results highlight the importance of plant functional group composition for the spatial and temporal stability of soil microbial properties, and hence for microbially-driven ecosystem processes, such as decomposition and element cycling, in temperate semi-natural grassland.
    Keywords: JenExp; The Jena Experiment
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 9
    Publication Date: 2023-05-13
    Description: The present study was conducted at the Jena Experiment field site from 2011 to 2015. The 48 experimental plant communities included twelve monocultures (of which one was removed from all analyses because it was planted with the wrong species), twelve 2-species mixtures, twelve 4-species mixtures and twelve 8-species mixtures. We used two community-evolution treatments (plant histories); plants with eight years of co-selection history in different plant communities in the Jena Experiment (communities of co-selected plants) and plants without such co-selection history (naïve communities). Community-level plant productivity was measured each year from 2012 to 2015 by collecting species-specific aboveground biomass twice per year in May and August. There are a total of seven harvests included in this dataset. We harvested plant material 3 cm aboveground from a 50 x 20 cm area in the centre of each half-quadrat, sorted it into species, dried it at 70°C and weighed the dry biomass. We also include a datafile with the stability metrics presented in the paper, such as resistance, recovery, and resilience to the flood, population stability and temporal stability.
    Keywords: asynchrony; co-occurrence history; disturbance; Flood; grassland biodiversity; JenExp; recovery; resistance; selection; The Jena Experiment
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 10
    Publication Date: 2023-05-13
    Description: The study was carried out on the main plots (Main Experiment) of a large grassland biodiversity experiment, the Jena Experiment. 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). Plots were maintained by bi-annual weeding and mowing. This data set consists of standard deviation (SD), mean and stability (stab) of soil microbial basal respiration (µl O2/h/g dry soil) and microbial biomass carbon (µg C/g dry soil). Data were derived by taking soil samples and measuring basal and substrate-induced microbial respiration with an oxygen-consumption apparatus. Samples for calculating the temporal stability were taken every year in May/June from 2003 to 2014, except in 2005. Oxygen consumption of soil microorganisms in fresh soil equivalent to 3.5 g dry weight was measured at 22°C over a period of 24 h. Basal respiration (µlO2/g dry soil/h) was calculated as mean of the oxygen consumption rates of hours 14 to 24 after the start of measurements. Substrate- induced respiration was determined by adding D-glucose to saturate catabolic enzymes of microorganisms according to preliminary studies (4 mg g-1 dry soil solved in 400 µl deionized water). Maximum initial respiratory response (µl O2/g dry soil/h) was calculated as mean of the lowest three oxygen consumption values within the first 10 h after glucose addition. Microbial biomass carbon (µg C/g dry soil) was calculated as 38 × Maximum initial respiratory response according to prelimiray studies.
    Keywords: Calculated; DEPTH, sediment/rock; EXP; Experiment; Experimental plot; Jena_Experiment; Jena Experiment; JenExp; Microbial biomass as carbon per soil dry mass; Microbial biomass as carbon per soil dry mass, standard deviation; Microbial respiration per soil dry mass; Microbial respiration per soil dry mass, standard deviation; Replicates; Temporal Stability; The Jena Experiment; Thuringia, Germany
    Type: Dataset
    Format: text/tab-separated-values, 1752 data points
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