10 research outputs found
Plant soil feedbacks (PSF) as affected by soil microarthropods
No full textKuťáková, E.; Cesarz, S.; Münzberová, Z. & Eisenhauer, N. 2018. Soil microarthropods
alter the outcome of plant-soil feedback experiments. Scientific Reports in press. DOI: 10.1038/s41598-018-30340-wSee the reference (Materials and Methods) for a detailed description of the PSF experiment and the individual measured variables.Plant data measured in the feedback phase refer to the mean values of plant individuals in each microcosm.Nematode counts refer to the numbers of individuals per gram soil dry weight (the actual numbers of determined nematodes being extrapolated to the total nematode counts in the respective sample).</div
Thakur et al 2015 Cascading effects of belowground predators on plant communities are density dependent
No full textThakur et al 2015 Cascading effects of belowground predators on plant communities are density dependen
Supplement 1. Complete data set of all measured variables of 2011 and 2012.
No full textFile List
data.txt (MD5: 425b0832b1d6aa48ae879c9b3044ba68)
Description
The data.txt file is a tab-separated file. It contains the raw data on soil microbial biomass and functions.
Column definitions
Plotcode
Year (2011/2012)
Plant species richness (1, 4, 16)
Temperature (ambient, + 1.5°C, + 3°C)
Plant functional groups: Legumes (0,1)
Plant functional groups: Forbs (0,1)
Plant functional groups: Woddy species (0,1)
Plant functional groups: C3-grasses (0,1)
Plant functional groups: C4-grasses (0,1)
Water content %
Microbial biomass Cmic
Microbial Growth after addition of C
Microbial Growth after addition of CN
Microbial Growth after addition of CP
Microbial Growth after addition of CNP
Enzyme activity: ß-1,4-glucosidase
Enzyme activity: Cellobiohydrolase
Enzyme activity: ß-1,4-N-acetylglucosaminidase
Enzyme activity: Acid phosphatase
Enzyme activity: Phenol oxidase
Enzyme activity: Peroxidase
Enzyme activity: Urease
Mass specific enzyme activity: ß-1,4-glucosidase
Mass specific enzyme activity: Cellobiohydrolase
Mass specific enzyme activity: ß-1,4-N-acetylglucosaminidase
Mass specific enzyme activity: Acid phosphatase
Mass specific enzyme activity: Phenol oxidase
Mass specific enzyme activity: Peroxidase
Mass specific enzyme activity: Urease
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Appendix A. Linear Model table of F and P values on the effects of plant diversity (PD: 1, 4 and 16 plant species) on mass enzyme activity of 1,4 -β-glucosidase, cellobiohydrolase, 1,4-β-N-acetyl-glucosaminidase, phosphatase, peroxidase, phenol oxidase, and urease of 2011 and 2012.
No full textLinear Model table of F and P values on the effects of plant diversity (PD: 1, 4 and 16 plant species) on mass enzyme activity of 1,4 -β-glucosidase, cellobiohydrolase, 1,4-β-N-acetyl-glucosaminidase, phosphatase, peroxidase, phenol oxidase, and urease of 2011 and 2012
Supplementary information from Insect communities under skyglow: diffuse night-time illuminance induces spatio-temporal shifts in movement and predation
No full textTables S1-S11 and Figures S-S4 from: Insect communities under skyglow: diffuse nighttime illuminance induces spatiotemporal shifts in movement and predatio
Data from 14 labortories testing the impact of introduced variability on the reproducibility of a microcosm ecological experiment
No full textAlthough microcosm experiments are a frequent tool used to address fundamental ecological questions, there has been no quantitative assessment of the reproducibility of any microcosm experiment. This dataset contains the response variables measured in a multi-laboratory microcosm study in which the same microcosm experiment was repeated in 14 laboratories across Europe. All laboratories simultaneously run a simple microcosm experiment using grass (Brachypodium distachyon L.) monocultures and grass and legume (Medicago truncatula Gaertn.) mixtures. All twelve variables were then used to calculate the effect of the presence of nitrogen-fixing legume on the grass-legume mixtures (i.e. the net legume effect).
The project tested a controversial hypotheses postulating 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 the experimental design. This implies that the deliberate introduction of controlled systematic variability (CSV) in experimental designs can increase reproducibility. To test this hypothesis, each laboratory followed the same experimental protocol and introduced environmental and genotypic controlled systematic variability (CSV) within and among replicated microcosms established in either growth chambers (with stringent control of environmental conditions) or glasshouses (with more variable environmental conditions). Data were used to test the extent to which the effect size of the net legume effect varied with the CSV treatment and to estimate the number of laboratories that produced results that can be considered reproducible
Data and R code from "Grazing and ecosystem service delivery in global drylands"
No full textThere are two zip files with the data and R scripts used in the article "Grazing and ecosystem service delivery in global drylands".
The file "Main_Data_code.zip" contains the data and R code used in the main analyses of the paper. These data also include the location and major environmental characteristics of the plots surveyed.
The file "Livestock_data_code.zip" contains the data and R code used in the characterization and validation of grazing pressure levels (see Methods). Readme and metadata files including a description of the files, variables and units are provided.
All the methodological details can be found in the article.
Additional authors from the BIODESERT consortium not included in the author list (we reached the maximum number of authors allowed by figshare) include: Víctor Rolo, Juan G. Rubalcaba, Jan C. Ruppert, Ayman Salah, Max A. Schuchardt, Sedona Spann, Ilan Stavi, Colton R. A.Stephens, Anthony M. Swemmer, Alberto L. Teixido, Andrew D. Thomas, Heather L. Throop, Katja Tielbörger, Samantha Travers, James Val, Orsolya Valkó, Liesbeth van den Brink, Sergio Velasco Ayuso, Frederike Velbert, Wanyoike Wamiti, Deli Wang, Lixin Wang, Glenda M. Wardle, Laura Yahdjian, Eli Zaady, Yuanming Zhang and Xiaobing Zhou </p
