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Meteorological measurements recorded in the Dischma valley from 2014-2016. In 2014 and 2015 we used 11 mobile weather stations from sensorscope to record meteorological parameter in the upper Dischma valley in the closer surroundings of the Gletschboden area. The meteorological stations are eqiupped with at least air temperature/humidity, wind velocity and wind direction sensors. Some stations are additionally equipped with precipitation, shortwave radiation and snow surface temperature sensors.
Three transects were installed at different aspects and were equipped with air temperature/humidity and wind sensors. Transect 1 (stations 2-4) provides meteorological Information on an east-north-east facing slope at elevations ranging between 2100 m and 2500 m. Transect 2 (stations 5-7) provides meteorological Information on a south-west slope and transect 3 (stations 8-10) on a north-west slope. Station 1 is fully equipped with meteorological sensors (temperature/humidity, wind, IR, up and downwand shortwave radiation and precipitation).
In 2016, mobile stations from sensorscope were replaced with six permanent meteorological stations. Meteorological stations 1-3 are equipped with an air temperature/humidity sensor, two wind speed sensors, a wind direction sensor and an incoming and outgoing shortwave radiation sensor. Stations 4 and 6 are equipped with an air temperature/humidity sensor and a wind speed/direction sensor. Station 5 is a equipped with an air temperature/humidity sensor, a wind speed/direction sensor, a snow surface temperature sensor, an incoming and outgoing shortwave radiation sensor and an incoming longwave radiation sensor.

en: meteo-station dischma meteorological-data meteorological-measurements dischmex

en: DISCHMEX - Meteorological measurements

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    Description: Location of the meteorological stations

dischma

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dischmex

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meteo-station

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meteorological-data

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meteorological-measurements

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$oid: OBSOLETE

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This dataset contains all data on which the following publication below is based.
Paper Citation:
 - Risch AC, Ochoa-Hueso R, van der Putten WH, Bump JK, Busse MD, Frey B, Gwiazdowicz DJ, Page-Dumroese DS, Vandegehuchte ML, Zimmermann S, Schütz M. Size-dependent loss of aboveground animals differentially affects grassland ecosystem coupling and functions. 2018. Nature Communications 9: 3684.  [doi: 10.1038/s41467-018-06105-4](https://doi.org/10.1038/s41467-018-06105-4).
Please cite this paper together with the citation for the datafile.
Methods
Study sites
The experimental exclosure setups were installed within the SNP (IUCN category Ia preserve; Dudley 2008), in south-eastern Switzerland. The park covers 172 km2 of forests and subalpine and alpine grasslands along with scattered rock outcrops and scree slopes. The entire area has been protected from human impact (no hunting, fishing, camping or off-trail hiking) since 1914. Large, fairly homogenous patches of short- and tall-grass vegetation, which originate from different historical management and grazing regimes, cover the park’s subalpine grasslands entirely. Short-grass vegetation developed in areas where cattle used to rest (nutrient input) prior to the park’s foundation (14th century to 1914) (Schütz and others 2003, 2006) and is dominated by lawn grass species such as Festuca rubra L., Briza media L. and Agrostis capillaris L. (Schütz and others 2003, 2006). Today, this vegetation type is intensively grazed by diverse vertebrate and invertebrate communities that inhabit the park and consume up to 60% of the available biomass (Risch and others 2013). Tall-grass vegetation developed where cattle formerly grazed, but did not rest, and is dominated by rather nutrient-poor tussocks of Carex sempervirens Vill. and Nardus stricta L. (Schütz and others 2003, 2006). This vegetation type receives considerably less grazing, with only roughly 20% of the biomass consumed (Risch and others 2013). Consequently, the two vegetation types together represent a long-term trajectory of changes in grazing regimes. Underlying bedrock of all grasslands is dolomite, which renders these grasslands rather poor in nutrients regardless of former and current land-use regimes.
Experimental design
To progressively exclude aboveground vertebrate and invertebrate animals, we established 18 size-selective exclosure setups (nine in short-grass, nine in tall-grass vegetation) distributed over six subalpine grasslands across the SNP (Risch and others 2013, 2015). Elevation differences of exclosure locations did not exceed 350 m (between 1975 and 2300 m a.s.l.). The exclosures were established immediately after snowmelt in spring 2009 and were left in place for five consecutive growing seasons (until end of 2013). They were, however, temporarily dismantled every fall (late October after first snowfall) to protect them from avalanches. They were re-established in the same location every spring immediately after snowmelt. Each size-selective exclosure setup consisted of five plots (2 x 3 m) that progressively excluded aboveground vertebrates and invertebrates from large to small. The plots are labelled according to the guilds that had access to them “L/M/S/I”, “M/S/I”, “S/I”, “I”, “None”; L = large mammals, M = medium mammals, S = small mammals, I = invertebrates, None = no animals had access. As we only had permission to have the experimental setup in place for five consecutive growing seasons, the experiment had to be completely dismantled in the late fall of 2013 and all material removed from the SNP.
Our exclosure design was aimed at excluding mammalian herbivores, but naturally also excluded the few medium and small mammalian predators, as well as the entire aboveground invertebrate food web. A total of 26 large to small mammal species can be found in the SNP, but large apex predators are missing (wolf, bear, lynx) . Reptiles, amphibians and birds are scarce to absent in the subalpine grasslands under study. Only two reptile species occur in the park and they are confined to rocky areas that warm up enough for them to survive. One frog species spawns in an isolated pond far from our grasslands. Only three bird species occasionally feed on the subalpine grasslands. Using game cameras (Moultrie 6MP Game Spy I-60 Infrared Digital Game Camera, Moultrie Feeders, Alabaster, AL, USA), we did observe that the medium- and small-sized mammals (marmot/hares and mice) were not afraid to enter the fences and feed on their designated plots. We never spotted reptiles, amphibians or birds on camera. We distinguished between 59 higher aboveground-dwelling invertebrate taxa that our size-selective exclosures excluded (see also methods for aboveground-dwelling invertebrates below).
The “L/M/S/I” plot (not fenced) was located at least 5 m from the 2.1 m tall and 7 x 9 m large main electrical fence that enclosed the other four plots. The bottom wire of this fence was mounted at 0.5 m height and was not electrified to enable safe access for medium and small mammals, while fencing out the large ones. Within each main fence, we randomly established four 2 x 3 m plots separated by 1-m wide walkways from one another and from the main fence line: 1) the “M/S/I” plots were unfenced, allowing access to all but the large mammals; 2) the “S/I” plots (10 x 10 cm electrical mesh fence) excluded all medium-sized mammals. Note that the bottom 10 cm of this fence remained non-electrified to enable safe access for small mammals; 3) the “I” plots (2 x 2 cm metal mesh fence) excluded all mammals. We double-folded the mesh at the bottom 50 cm to reduce the mesh size to smaller than 1 x 1 cm openings; and 4) the “None” plots were surrounded by a 1 m tall mosquito net (1.5 x 2 mm) to exclude all animals. The top of the plot was covered with a mosquito-meshed wooden frame mounted to the corner posts (roof). We treated these plots a few times with biocompatible insecticide (Clean kill original, Eco Belle GmbH, Waldshut-Tiengen, Germany) to remove insects that might have entered during data collection or that hatched from the soil, but amounts were negligible and did not impact soil moisture conditions within these plots.
To assess whether the design of the “None” exclosure (mesh and roof) affected the response variables within the plots and, therefore, influenced the results, we established an additional six “micro-climate control” exclosures (one in each of the six grasslands) (Risch and others 2013, 2015). These exclosures were built as the “None” exclosures but were open at the bottom (20 cm) of the 3-m side of the fence facing away from the prevailing wind direction to allow invertebrates to enter. A 20-cm high and 3-m long strip of metal mesh was used to block access to small mammals. Thus, this construction allowed a comparable micro-climate to the “None” plots, but also a comparable feeding pressure by invertebrates to the “I” plots. We compared various properties within these exclosures against one another to assess if our construction altered the conditions in the “None” plots. We showed that differences in plant (e.g., vegetation height, aboveground biomass) and soil properties (e.g., soil temperature, moisture) found between the “I” and the “None” treatments were not due to the construction of the “None” exclosure, but a function of animal exclusions, although the amount of UV light reaching the plant canopy was significantly reduced (Risch and others 2013).
Aboveground invertebrate sampling
Aboveground invertebrates were sampled with two different methods to capture both ground- and plant-dwelling organisms: 1) we randomly placed two pitfall traps (67 mm in diameter, covered with a roof) filled with 20% propylene glycol in one 1 x 1 m subplot of the 2 x 3 m treatment plots in spring 2013 (May) and emptied them every two weeks until late September 2013 (Vandegehuchte and others 2017b, 2018). A pitfall trap consisted of a plastic cylinder (13 cm depth, 6.75 cm diameter). Within each cylinder we placed a 100 ml plastic vial with outer diameter 6.70 cm and on top of the cylinder we placed a plastic funnel to guide the invertebrates into the vials. Each trap was cover with a cone-shaped and transparent plastic roof to protect the trap from rain (Vandegehuchte and others 2017b, 2018). Note that in the “None” plots only one trap was placed as control to check for effectiveness of the exclosure. 2) We vacuumed all invertebrates from a 60 x 60 cm area on another 1 x 1 m subplot with a suction sampler (Vortis, Burkhard manufacturing CO, Ltd., Rickmansworth, Hertfordshire, UK) every month from June to September 2013 (Vandegehuchte and others 2017b, 2018). For this purpose, we quickly placed a square plastic frame (60 x 60 x 40 cm) with a closable mosquito mesh sleeve attached to the top edge into the plot from the outside. The suction sample was then inserted into through the sleeve and operated for 45 s to collect the invertebrates (Vandegehuchte and others 2017b, 2018).
We sorted the ≈100 000 individuals collected with both methods by hand and identified each individual morphologically to the lowest taxonomic level feasible (59 taxa, including orders, suborders, subfamilies, families; phylum for Mollusca). These taxa belonged to the following feeding types: 19 herbivores, 16 detritivores, 9 predators, 8 mixed feeders, 5 omnivores and 2 non-classified feeders (or not feeding as adults) (Vandegehuchte and others 2017b). We summed the numbers from the two pitfall traps and the suction sampling over the course of the 2013 season to represent the aboveground invertebrate abundance and community composition of a plot. Note: we did not specifically attempt to catch flying invertebrates with e.g., sticky traps, thus a few flying insects may have been missed with our vacuum sampling approach.
Sampling of plant properties
The vascular plant species composition was assessed at peak biomass every summer (July) by estimating the frequency of occurrence of each species with the pin count method in each plot (Frank and McNaughton 1990). A total of 172 taxa occurred within our 90 plots and we calculated plant species richness for each plot separately. We used the 2013 data in this study. Plant quality was assessed every year in July and September; here we use plant quality at the end of the experiment (September 2013). Two 10 x 100 cm wide strips of vegetation per plot were clipped, combined, dried at 65°C, and ground (Pulverisette 16, Fritsch, Idar-Oberstein, Germany) to pass through a 0.5 mm sieve. Twenty randomly selected samples across all treatments were analysed for N (Leco TruSpec Analyser, Leco, St. Joseph, Michigan, USA) (Vandegehuchte and others 2015). Nitrogen concentrations of the other samples were then estimated from models established for the experiment and the entire SNP relating Fourier transform-near infrared reflectance (FT-NIR) spectra to the measured values of N using a multi-purpose FT-NIR spectrometer (Bruker Optics, Fällanden, Switzerland) (Vandegehuchte and others 2015). Root biomass was sampled every fall by collecting five 2.2 cm diameter x 10 cm deep soil samples (Giddings Machine Company, Windsor, CO, USA) per plot (450 samples year-1). The samples were dried at 30 °C and roots were sorted from the sample by hand. We sorted each sample for 1 h which allowed to retrieve over 90% of all roots present in the samples (Risch and others 2013). The roots were then dried at 65 °C for 48 and weighed to the nearest mg. We averaged the values per plot and used the 2013 data only in this study.
Sampling of edaphic communities
In 2009, 2010, and 2011 we collected three composited soil samples (5 cm diameter x 10 cm depth; AMS Samplers, American Falls, ID, USA) and assessed bacterial community structure using T-RFLP profiling (Liu and others 1997; Blackwood and others 2003; Hodel and others 2014). We detected a total of 89 operational taxonomic units (OTUs). These values are in accordance with other studies reporting OTU richness (Wirthner and others 2011; Zumsteg and others 2012; Meola and others 2014) using T-RFLP profiling, a method that detects the most abundant, and thus likely, the most relevant, taxa. We averaged the data over the three years of collections for our calculations. Microbial biomass carbon (MBC) was determined with the substrate-induced method (Anderson and Domsch 1978) every fall (September) between 2009 and 2013 by collecting three mineral soil samples (5 cm diameter × 10 cm mineral soil core, AMS Samplers, American Falls, ID, USA). The three samples were combined (90 samples for each sampling year), immediately put on ice, taken to the laboratory, passed through a 2-mm sieve and stored at 4°C. Again, we only used the 2013 data in this study.
Soil samples (5 cm diameter x 10 cm depth) to extract soil arthropods were collected in June, July, and August 2011 with a soil corer lined with a plastic sleeve to ensure an undisturbed sample (total of 270 samples). The plastic line core was immediately sealed on both ends using cling film and put into a cooler. All plots were sampled within three days and the extraction of arthropods started the evening of the sampling day using a high-gradient Tullgren funnel apparatus (Crossley and Blair 1991; Vandegehuchte and others 2015). Samples were kept in the extractor for four days and the soil arthropods were collected in 95% ethanol. All individuals were counted and each individual was identified morphologically to the lowest level feasible [76 taxa, including orders, suborders, subfamilies, families (Protura, Thysanoptera, Aphidina, Psylina, Coleoptera, Brachycera, Nematocera, Auchenorryncha, Heteroptera, Formicidae); sub-phylum for Myriapoda, for Acari and Collembola also including morpho-species). Note that we also included larval stages (nine of the 76 taxa) (Vandegehuchte and others 2015). All data were summed over the season. A detailed species list for mites and collembolans is published (Vandegehuchte and others 2017a) [https://doi.org/10.1371/journal.pone.0118679.s001]. Earthworms are rare in the SNP and therefore were not included. We collected eight random 2.2 cm diameter x 10 cm deep soil cores from each plot in September 2013 to determine the soil nematode community composition. The samples were mixed and the nematodes were extracted from 100 ml of fresh soil using Oostenbrink elutriators (Oostenbrink 1960). All nematodes in a 1 ml of the 10 ml extract were counted, a minimum of 150 individuals sample-1 were identified to genus or family level using (Bongers 1988), the numbers of all nematodes were extrapolated to the entire sample and expressed for a 100 g dry sample. In total we identified 63 genus or family levels (Vandegehuchte and others 2015). The list of all the nematodes found is published (Vandegehuchte and others 2015) [http://www.oikosjournal.org/appendix/oik-03341] or DOI: [doi: 10.1111/oik.03341].
We are aware that sampling soil microbes from 2009 to 2011 and soil arthropods in 2011 was not ideal, but we are positive that this does not bias the results. Most of the parameters measured in our experiment either already showed a treatment response after the first growing season (e.g., plant biomass) or did not respond over the entire time experiment (e.g., microbial biomass C). The microbial community composition (2009 – 2011) was highly influenced by inter-annual differences in temperature and precipitation, but did not differ between treatments or vegetation types (Hodel and others 2014). We therefore felt comfortable using the 2009 through 2011 data for describing the soil microbial community in our experimental treatments. Similarly, we are positive that our soil arthropod data are representative. We did assess soil arthropods in August 2012 and found no differences to the August 2011 data. However, we did not feel comfortable combining the 2011 June, July, August data with only August data for 2012 for our analyses.
Sampling of soil properties
We collected three soil samples (5 cm diameter x 10 cm depth) in each plot in September 2013 after removing the vegetation. First, we collected the top layer of mineral soil rich in organic matter, the surface organic layer or rhizosphere, typically 1 to 3 cm in depth with a soil corer (AMS Samples, American Falls, Idaho, USA). Second, we collected a 10 cm mineral soil core beneath this surface layer. The cores for each layer were composited, dried at 65 °C for 48 h and fine-ground to pass a 0.5 mm screen. We then analysed all samples for total C using a Leco TruSpec Analyser (Leco, St. Joseph, Michigan, USA). Mineral soil pH was measured potentiometrically in 1:2 soil:CaCl2 solution with an equilibration time of 30 min.
Soil net N mineralisation was assessed during the 2013 growing season (Risch and others 2015). For this purpose, we randomly collected a 5 cm diameter x10 cm deep soil sample with a soil corer (AMS Samples, American Falls, Idaho, USA) after clipping the vegetation in June 2013. After weighing and sieving (4 mm mesh) the soil, we extracted a 20 g subsample in 1 mol l-1 KCl for 1.5 h on an end-over-end shaker and thereafter filtered it through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnenmühle FineArt GmbH, Dassel, Germany). From these filtrates NO3- concentrations were measured colorimetrically (Norman and Stucki 1981) and NH4+with flow injection analysis (FIAS 300, Perkin Elmer, Waltham Massachusetts, USA) (Risch and others 2015). We dried the rest of the sample 105 °C to constant mass to determine fine,fraction bulk density. A second soil sample was collected within each plot in June 2013 with a corer lined with a 5 x 13 cm aluminium cylinder. The corer was driven 11.5 cm deep into the soil so that the top 1.5 cm of the cylinder remained empty. Into this space we placed a polyester bag (250 µm) filled an ion-exchanger resin to capture the incoming N. The bag was filled with a 1:1 mixture of acidic and alkaline exchanger resin (ion-exchanger I KA/ion exchanger IIIAA, Merck AG, Darmstadt, Germany). We then removed 1.5 cm soil at the bottom of the cylinder and placed a second resin exchanger bag into this space to capture the N leached from the soil column. To assure that the exchange resin was saturated with H+ and Cl- prior to filling the bags, the mixture was stirred with 1.2 ml l-1 HCl for 1 h and then rinsed with demineralized water until the electrical conductivity of the water reached 5 µm cm-1. The cylinder with the resin bags in place was reinserted into the soil with the top flush to the soil surface and incubated for three months. We recollected the cylinders in September 2013. Each resin bag and 20 g of sieved soil (4 mm mesh) from each cylinder were then separately extracted with KCl and NO3- and NH4+ concentrations were measured. Nitrate and NH4+ concentrations of all samples were then converted to a content basis by multiplying their values with fine fraction bulk density. Net N mineralisation was thereafter calculated as the difference between the N content of the samples collected at the end of the three-month incubation (including the N extracted from the bottom resin bag) and the N content at the beginning of the incubation (Risch and others 2015).
Soil CO2 emissions were measured every two weeks between 0900 and 1700 hrs from early May through late September 2013 with a PP-Systems SRC-1 soil respiration chamber (15 cm high, 10 cm diameter; closed circuit) attached to a PP-Systems EGM-4 infrared gas analyser (PP-Systems, Amesbury, MA, USA) on two locations per plot (Risch and others 2013). The chamber was placed on randomly placed, permanently installed PVC collars (10 cm diameter) driven 5 cm into the soil at the beginning of the study (Risch and others 2013). Freshly germinated plants growing within the collars were removed prior to each measurement to avoid measuring plant respiration or photosynthesis. The two measurements collected per plot and sampling date were averaged.
Soil moisture (with time domain reflectometry; Field-Scout TDR-100, Spectrum Technologies, Plainfield, Illionois, USA) and temperature (with a waterproof digital pocket thermometer; Barnstead International, Dubuque, Iowa, USA) were measured at five random locations per plot every two weeks during the growing seasons during the experiment for the 0 to 10 cm depth (Risch and others 2013, 2015). As soil moisture and soil temperature were highly negatively correlated (Risch and others 2013), we only used soil moisture for this study. We used plot-level averages of all values available to capture soil moisture variability during the five years of the experiment. The results remained unchanged when we only used soil moisture from the 2013 growing season.
Numeral calculations and statistical analyses
Ecosystem coupling. We conducted principal component analyses (PCAs; unscaled) at the complete dataset level using the abundances of each taxonomical entity to describe each of the five different communities used in this study: aboveground-dwelling invertebrates, vascular plants, soil microorganisms, soil arthropods and soil nematodes. We retained the first two components (PCA axis 1 and PCA axis 2) of each analysis as we found them to adequately represent the temporal and spatial variability of our 90 treatment plots in previous studies55,67. Together they explained a total of 71.70% of the variation for aboveground invertebrates, 44.36% for plants, 44.85% for soil microorganisms, 61.85% for soil arthropods and 77.19% for soil nematodes. In addition, we used soil pH and soil organic C content as a proxy for soil chemical properties, soil bulk density as a proxy for soil physical properties and soil moisture (negatively correlated with soil temperature) as a proxy for soil micro-climatic conditions for an overall total of fourteen constituents.
We calculated ecosystem coupling9 for each exclosure treatment within each vegetation type (i.e., 2  5 treatment combinations in total) as an integrated measure of pairwise ecological interactions between ecosystem constituents representing ecological communities and the soil abiotic environment. These ecological interactions are defined by non-parametric Spearman rank correlation analyses between two constituents, excluding interactions involving two abiotic constituents (e.g., soil pH vs. soil moisture) and interactions between the first (PC1) and second (PC2) component of each community type, as these are orthogonal by definition. Interactions between abiotic constituents were excluded from the analyses because the focus of our study was on communities and how they interact with one another and their surrounding environment; therefore, including abiotic-abiotic interactions was not of interest here. Given that the effectiveness of our experimental design resulted in that no community composition data of aboveground-dwelling invertebrates was available for the “None” plots (all animals excluded), only thirteen instead of fourteen constituents were included in the ecosystem coupling calculations for this treatment. The complete absence of aboveground invertebrates represents the most extreme case of disturbance between aboveground animal communities and the rest of the ecosystem constituents. This may have resulted in a slight overestimation of ecosystem coupling for these plots.
	Average ecosystem coupling was calculated as follows:
Ecosystem coupling=
where Xi is the absolute Coupling was calculated value of the Spearman’s rho coefficient of the ith correlation for each treatment within each vegetation type (i.e., based on nine replicates each), considering and n is the number of pairwise comparisons considered (n = a total of 80;  interactions (56 in the case of the “None” treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the “None” treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the “None” treatment).	Coupling was calculated for each treatment within each vegetation type (i.e., based on nine replicates each), considering a total of 80 interactions (56 in the case of the “None” treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the “None” treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the “None” treatment).
To establish whether constituents were significantly and positively coupled within treatments (i.e., the average of their correlation coefficients were greater than in a null model where correlation only happens by chance), we calculated one-tailed p-values based on permutation tests with 999 permutations.

We considered six ecosystem functions and process rates commonly used to assess ecosystem functioning (Meyer and others 2015; Manning and others 2018). Plant N content represents a measure of forage quality, while plant richness has been shown to stabilise biomass production, thus allowing the system to respond to changes in herbivory. Soil net N mineralisation, soil respiration, root biomass, and microbial biomass represent fluxes or stocks of energy. For all functions and processes higher values represent higher functioning (Manning and others 2018). All these variables were measured in the last year of the experiment (2013). We then quantified ecosystem multifunctionality using the multiple threshold approach (Byrnes and others 2014; Manning and others 2018), which considers the number of functions that are above a certain threshold, over a series of threshold values (typically 10-99%) that are defined based on the maximum value of each function. We weighted all our functions equally for these calculations (Manning and others 2018). The number of functions in a plot with values higher than a given threshold value for the respective function is summed up. The sum represents ecosystem multifunctionality for that plot. Given that choosing any particular threshold as a measure of ecosystem multifunctionality is arbitrary, we calculated the average of thresholds from 10-90% (in 10% intervals) as a more integrated representation of ecosystem multifunctionality.
We used Pearson correlations to explore the relationships between ecosystem coupling (all interactions, biotic-biotic interactions, abiotic-biotic interactions involving above- and belowground constituents, and all interactions, biotic-biotic interactions, abiotic-biotic interactions involving belowground constituents only) and ecosystem multifunctionality by calculating the slopes of all relationships between ecosystem coupling and multifunctionality for all thresholds between 10 and 99%. We also related ecosystem coupling with the average of multifunctionality at thresholds between 30-80% as explained before and considered this correlation as a robust indication of the type of association between these two variables. In addition, we explored the relationships between ecosystem coupling (all interactions, biotic-biotic interactions, abiotic-biotic interactions involving above- and belowground constituents, and all interactions, biotic-biotic interactions, abiotic-biotic interactions involving belowground constituents only) and individual ecosystem functions. The effects of exclosures and vegetation type on individual functions and multifunctionality were evaluated using linear mixed effects models ('lme' function of the nlme package), with exclosure and vegetation type as fixed effects and fence as a random factor. All statistical analyses and numerical calculations were done in R version 3.4.0 (R Core Team 2016).
References
- Anderson J, Domsch K. 1978. A physiological method for the quantitative measurement of microbial biomass in soil. Soil Biol Biochem 10:215–21.
- Blackwood CB, Marsh T, Kim S-H, Paul EA. 2003. Terminal Restriction Fragment Length Polymorphism Data Analysis for Quantitative Comparison of Microbial Communities. Appl Environ Microbiol 69:926–32. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC143601/
- Bongers T. 1988. De nematoden von Nederland. Schoorl, The Netherlands: Pirola
- Byrnes JEK, Gamfeldt L, Isbell F, Lefcheck JS, Griffin JN, Hector A, Cardinale BJ, Hooper DU, Dee LE, Duffy JE. 2014. Investigating the relationship between biodiversity and ecosystem multifunctionality: Challenges and solutions. Methods Ecol Evol 5:111–24.
- Crossley DAJ, Blair JM. 1991. A high-efficiency low-technology Tulgren-type extractor for soil microarthopods. Agric Ecosyst Environ 34:187–92.
- Dudley N. 2008. Guidelines for applying protected area managment categories. Gland: IUCN
- Frank DA, McNaughton SJ. 1990. Aboveground biomass estimation with the canopy intercept method: A plant growth form caveat. Oikos 57:57–60.
- Haynes AG, Schütz M, Buchmann N, Page-Dumroese DS, Busse MD, Risch AC. 2014. Linkages between grazing history and herbivore exclusion on decomposition rates in mineral soils of subalpine grasslands. Plant Soil 374.
- Hodel M, Schütz M, Vandegehuchte ML, Frey B, Albrecht M, Busse MD, Risch AC. 2014. Does the aboveground herbivore assemblage influence soil bacterial community composition and richness in subalpine grasslands? Microb Ecol 68:584–95.
- Liu WT, Marsh TL, Cheng H, Forney LJ. 1997. Characterization of microbial diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S rRNA. Appl Environ Microbiol 63:4516–22. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC168770/
- Manning P, van der Plas F, Soliveres S, Allan E, Maestre FT, Mace G, Whittingham MJ, Fischer M. 2018. Redefining ecosystem multifunctionality. Nat Ecol Evol 2:427–36. https://doi.org/10.1038/s41559-017-0461-7
- Meola M, Lazzaro A, Zeyer J. 2014. Diversity, resistance and resilience of the bacterial communities at two alpine glacier forefields after a reciprocal soil transplantation. Environ Microbiol 16:1918–34. https://onlinelibrary.wiley.com/doi/abs/10.1111/1462-2920.12435
- Meyer ST, Koch C, Weisser WW. 2015. Towards a standardized Rapid Ecosystem Function Assessment (REFA). Trends Ecol Evol 30:390–7. http://www.sciencedirect.com/science/article/pii/S0169534715000968
- Norman R., Stucki JW. 1981. The determination of nitrate and nitrite in soil extracts by ultraviolet spectrophotometry. Soil Sci Soc Am J 45:347–53.
- Ochoa-Hueso R. 2016. Non-linear disruption of ecological interactions in response to nitrogen deposition. Ecology 87:2802–2814.
- Oostenbrink M. 1960. Estimating nematode populations by some selected methods. In: Sasser NJ, Jenkins WR, editors. Nematology. Chapel Hill, NC, USA: University of North Carolina Press. pp 85–101.
- R Core Team. 2016. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing
- Risch AC, Haynes AG, Busse MD, Filli F, Schütz M. 2013. The response of soil CO2 fluxes to progressively excluding vertebrate and invertebrate herbivores depends on ecosystem type. Ecosystems 16:1192–202.
- Risch AC, Schütz M, Vandegehuchte ML, Van Der Putten WH, Duyts H, Raschein U, Gwiazdowicz DJ, Busse MD, Page-Dumroese DS, Zimmermann S. 2015. Aboveground vertebrate and invertebrate herbivore impact on net N mineralization in subalpine grasslands. Ecology 96:3312–22.
- Schütz M, Risch AC, Achermann G, Thiel-Egenter C, Page-Dumroese DS, Jurgensen MF, Edwards PJ. 2006. Phosphorus translocation by red deer on a subalpine grassland in the Central European Alps. Ecosystems 9:624–633.
- Schütz M, Risch AC, Leuzinger E, Krüsi BO, Achermann G. 2003. Impact of herbivory by red deer (Cervus elaphus L.) on patterns and processes in subalpine grasslands in the Swiss National Park. For Ecol Manage 181:177–88.
- Vandegehuchte ML, van der Putten WH, Duyts H, Schütz M, Risch AC. 2017a. Aboveground mammal and invertebrate exclusions cause consistent changes in soil food webs of two subalpine grassland types, but mechanisms are system-speciﬁc. Oikos 126:212–23.
- Vandegehuchte ML, Raschein U, Schütz M, Gwiazdowicz DJ, Risch AC. 2015. Indirect short- and long-term effects of aboveground invertebrate and vertebrate herbivores on soil microarthropod communities. PLoS One 10:e0118679.
- Vandegehuchte ML, Schütz M, de Schaetzen F, Risch AC. 2017b. Mammal-induced trophic cascades in invertebrate food webs are modulated by grazing intensity in subalpine grassland. J Anim Ecol 86:1434–46.
- Vandegehuchte ML, Trivellone V, Schütz M, Firn J, de Schaetzen F, Risch AC. 2018. Mammalian herbivores affect leafhoppers associated with specific plant functional types at different timescales. Funct Ecol 32:545–55.
- Wirthner S, Frey B, Busse MD, Schütz M, Risch AC. 2011. Effects of wild boar (Sus scrofa L.) rooting on the bacterial community structure in mixed-hardwood forest soils in Switzerland. Eur J Soil Biol 47:296–302. http://dx.doi.org/10.1016/j.ejsobi.2011.07.003
- Zumsteg A, Luster J, Göransson H, Smittenberg RH, Brunner I, Bernasconi SM, Zeyer J, Frey B. 2012. Bacterial, Archaeal and Fungal Succession in the Forefield of a Receding Glacier. Microb Ecol 63:552–64. https://doi.org/10.1007/s00248-011-9991-8

en: subalpine grazing swiss-national-park invertebrates exclusion grassland vertebrates ecosystem-multifunctionality ecosystem-functions ecosystem-coupling

envidat

approval_status: approved

fr: EnviDat est le portail de données du WSL; il offre un accès uniformisé et réglementé aux données de surveillance et de recherche en matière d’environnement. Ce portail est capable d’héberger et de publier des ensembles de données précieuses et uniques, qui sont partagées aux quatre coins de la planète. Ce faisant, EnviDat favorise la recherche et contribue à l’avancement des sciences environnementales.

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

it: EnviDat è il portale dati del WSL che fornisce un accesso unificato e gestito ai dati di monitoraggio ambientale e di ricerca. Il portale ha la capacità di ospitare e pubblicare serie di dati preziosi e unici che vengono condivisi in tutto il mondo. Così facendo, EnviDat promuove la ricerca e contribuisce al progresso della scienza ambientale.

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

en: Ecosystem coupling and multifunctionality - exclosure experiment

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

format: XLS

ecosystem-coupling

display_name: ecosystem-coupling

ecosystem-functions

display_name: ecosystem-functions

ecosystem-multifunctionality

display_name: ecosystem-multifunctionality

exclusion

display_name: exclusion

grassland

display_name: grassland

grazing

display_name: grazing

invertebrates

display_name: invertebrates

subalpine

display_name: subalpine

swiss-national-park

display_name: swiss-national-park

vertebrates

display_name: vertebrates


herb-layer-biomass-in-swiss-forests

en: The purpose of this project was to develop a model to estimate herb layer biomass and carbon stock based on the categorical cover estimate on each NFI sample plot. To this end, biomass and cover of the six main plant groups in the herb layer were collected from 405 1x1 m subplots on 135 study sites (15 sites in 9 strata) which were selected based on a stratified sampling approach. To ensure consistency with NFI methodology, study sites corresponded to the design of regular NFI sample plots and plant cover was estimated by trained field-crew members. Based on the dry weight of the plant biomass and the cover estimate on each subplot, a linear regression model was developed and applied to estimate herb layer biomass on each NFI sample plot.

en: ferns herbs shrubs biomass national-forest-inventory grasses sedges

en: Herb layer biomass in Swiss forests

title_for_slug: herb-layer-biomass-in-swiss-forests

de:
Resource Title: Final Report
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    Description: Final Report of the project submitted to the Swiss Federal Office for the Environment.

format: PDF

media_type: application/pdf

de:
Resource Title: Repeated samples
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    Description: Data collected during re-sampling on sub-sample of sites

de:
Resource Title:
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    URL: https://www.envidat.ch/dataset/d92e9a5c-fc2f-4023-8f3a-b73cdf06ed9f/resource/3f97f6e7-838a-4c74-ab6e-3eecec1cf35a/download/cndata.xlsx
    Description: Measured C and N contens [%] of collected herb layer biomass samples.

de:
Resource Title: Collected herb layer data
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    Description: Table with primary data collected in the field

biomass

display_name: biomass

ferns

display_name: ferns

grasses

display_name: grasses

herbs

display_name: herbs

national-forest-inventory

display_name: national-forest-inventory

sedges

display_name: sedges

shrubs

display_name: shrubs


en:
Decomposition of plant litter is a key process for the transfer of carbon and nutrients in ecosystems. Carbon contained in the decaying biomass is released to the atmosphere as respired CO2, and may contribute to global warming. Litterbag studies have been used to improve our knowledge of the drivers of litter decomposition, but they lack comparability because litter quality is plant species-specific. The use of commercial tea bags as a standard substrate was suggested in order to harmonize studies, where green tea and rooibos represent more labile and more recalcitrant C compounds as surrogates of local litter.
The tea bag approach was implemented on eight sites of the Swiss long-term Forest Ecosystem Research (LWF) network (https://www.wsl.ch/LWF). This allowed us to take advantage from the existing infrastructure and data from a previous litterbag study with local litter. In Beatenberg and Schaenis, additional elevation transects were established (1200-1800 m and 540-1150 m, respectively) to examine particularly the effect of temperature on decomposition. In Pfynwald (https://www.wsl.ch/de/ueber-die-wsl/versuchsanlagen-und-labors/flaechen-im-wald/pfynwald.html) and Salgesch, infrastructure of running projects was used to examine the effect of drought and understory removal, respectively. In Novaggio, tea bags were incubated in summer and winter to study the effect of seasonality particularly precipitation. Tea bags are collected after 3, 12, 24, and 36 months; for the two time-shifted experiments additionally after 6 and 9 months.
The study has two primary objectives. Firstly, it contributes to TeaComposition initiative (http://teacomposition.org/) which aims at investigating long-term litter decomposition and its key drivers at present as well as under different future climate scenarios using a common protocol and standard litter (tea) across nine terrestrial biomes. Secondly, the data are used to further develop decomposition models such as Yasso (http://en.ilmatieteenlaitos.fi/yasso) which is used by several countries, including Switzerland to estimate the annual carbon fluxes in dead wood, litter, and soil for reporting in National Greenhouse Gas Inventories under the United Nations Framework Convention on Climate Change and the Kyoto Protocol.

en: verification decomposition green-tea tea-bag litter roiboos modeling carbon forest

en: LWF-Tea bag sites

format: CSV

carbon

display_name: carbon

decomposition

display_name: decomposition

forest

display_name: forest

green-tea

display_name: green-tea

litter

display_name: litter

modeling

display_name: modeling

roiboos

display_name: roiboos

tea-bag

display_name: tea-bag

verification

display_name: verification


en:
The dataset comprises - 90 000 records from inventories in 54 strict forest reserves in [Switzerland](https://www.wsl.ch/de/wald/biodiversitaet-naturschutz-urwald/naturwaldreservate.html) and [Lower Saxony / Germany](http://naturwaelder.de/) along a considerable environmental gradient. It was used to develop parsimonious, species-specific mortality models for 18 European tree species based on tree size and growth as well as additional covariates on stand structure and climate.
 Inventory data
Measurements had been conducted repeatedly on up to 14 permanent plots per reserve for up to 60 years with re-measurement intervals of 4 - 27 years. The permanent plots vary in size between 0.03 and 3.47 ha. The inventories provide diameter measurements at breast height (DBH) and information on the species and status (alive or dead) of trees with DBH ≥ 4 cm for Switzerland and ≥ 7 cm for Germany.
 Data selection
We excluded three permanent plots where at least 80 % of the trees died during an interval of 10 years, and mortality could be clearly assigned to a disturbance agent. Mortality in the remaining stands was rather low, with a mean annual mortality rate of 1.5 % and strong variation between plots from 0 to 6.5 % (assessed for trees of all species with DBH ≥ 7 cm).
We only used data from permanent plots with at least 20 trees per species to obtain reliable plot-level mortality rates even for species with low mortality rates (about 5 % during 10 years), and selected tree species occurring on at least 10 plots to cover sufficient ecological gradients. This led to a dataset of 197 permanent plots and 18 tree or shrub species: _Abies alba_ Mill., _Acer campestre_ L., _Acer pseudoplatanus_ L., _Alnus incana_ Moench., _Betula pendula_ Roth, _Carpinus betulus_ L., _Cornus mas_ L., _Corylus avellana_ L., _Fagus sylvatica_ L., _Fraxinus excelsior_ L., _Picea abies_ (L.) Karst, _Pinus mugo_ Turra, _Pinus sylvestris_ L., _Quercus pubescens_ Willd., _Quercus_ spp. (_Q. petraea_ Liebl. and _Q. robur_ L.; not properly differentiated in the Swiss inventories), _Sorbus aria_ Crantz, _Tilia cordata_ Mill. and _Ulmus glabra_ Huds..
 Predictors of tree mortality
We considered tree size and growth as key indicators for mortality risk. Radial stem growth between the first and second inventory and DBH at the second inventory were used to predict tree status (alive or dead) at the third inventory. To this end, the annual relative basal area increment (relBAI) was calculated as the compound annual growth rate of the trees basal area. Additional covariates on stand structure and climate comprise mean annual precipitation sum (P), mean annual air temperature (mT), the mean and the interquartile range of DBH (mDBH, iqrDBH), basal area (BA) and the number of trees (N) per hectare.
 Further information
For further information, refer to Hülsmann _et al_. (in press) How to kill a tree – Empirical mortality models for eighteen species and their performance in a dynamic forest model. _Ecological Applications_.

en: empirical-mortality-models tree-growth inventory-data forest-reserves tree-mortality

en: Calibration data for empirical mortality models of 18 European tree species

empirical-mortality-models

display_name: empirical-mortality-models

forest-reserves

display_name: forest-reserves

inventory-data

display_name: inventory-data

tree-growth

display_name: tree-growth

tree-mortality

display_name: tree-mortality


en:
This dataset contains eddy-covariance measurements in the ablation period of 2014-2016. Measurements were taken from two turbulence towers over a long-lasting snow patch, which are 5 m apart from each other (2014 and 2015). The turbulence towers were equipped with two YOUNG ultrasonic anemometers mounted 0.7 m (in 2014) and 3.3 m (in 2015) above snow-free ground, two ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) mounted at 2.6 m (in 2014) and 2.2 m (in 2015) above snow-free ground and one anemometer (DA-600, Kaijo Denki) mounted at 0.3 m above snow surface.
The measurement setup changed in 2016 and includes a measurement above the snow-free ground in upwind direction (Swiss coordinates: 790191/176689). The measurement tower is equipped with one ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 3.3 m above the snow-free ground. Additionally, one measurement tower is installed above the long-Lasting snow patch and equipped with the same setup as 2015.
Turbulence data were sampled at a frequency of 20 Hz. The processing of the data to quality controlled fluxes has been done with the Biomicrometeorology flux software (Thomas et al., 2009). The program applies plausibility tests and a despiking test after Vickers and Mahrt (1997) on the measured data. The routine further applies a time-lag correction and considers the deployment (e.g.  the sonic azimuth). A frequency response correction (Moore, 1986) is done and a three-dimensional rotation is performed. Finally, quality assurance/quality control (QA/QC) flags after Foken et al., (2004) are issued and fast Fourier transform power and co-spectra are calculated.  The change in snow height is considered in the post-processing for every measurement day. The turbulence data were averaged to 30 minute intervals.

en: eddy-covariance-measurements dischmex turbulent-sensible-heat-fluxes snow-patch patchy-snow-covers

en: DISCHMEX - Impact of extreme land-surface heterogeneity on micrometeorology over spring snow-cover

de:
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    Description: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.2 m.

format: ZIP

media_type: application/zip

de:
Resource Title: S5 2015
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    Description: Eddy-covariance measurement with an ultrasonic anemometer (DA-600, Kaijo-Denki) in 0.3 m on a daily basis.

de:
Resource Title: S1 2014
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    URL: https://www.envidat.ch/dataset/7e8c6dac-80cf-49d7-bd85-2a61d3336e6f/resource/486e31a9-78e2-4736-9cad-a40c0958662c/download/s1-2014.zip
    Description: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.6 m.

de:
Resource Title: S5 2016
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/7e8c6dac-80cf-49d7-bd85-2a61d3336e6f/resource/43081c3a-5d6c-4f7c-9259-b221e82f9b61/download/s5-2016.zip
    Description: Eddy-covariance measurement with an ultrasonic anemometer (DA-600, Kaijo-Denki) in 0.3 m on a daily basis.

de:
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    Format: application/zip
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    Description: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.3 m.

de:
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/7e8c6dac-80cf-49d7-bd85-2a61d3336e6f/resource/60ca7742-4ab9-49f6-9318-3dac0c4ae4bd/download/s4-2014.zip
    Description: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 0.7 m.

de:
Resource Title: S4 2015
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/7e8c6dac-80cf-49d7-bd85-2a61d3336e6f/resource/3e7cd0af-60cb-4026-8e8f-7c0736c45f5b/download/s4-2015.zip
    Description: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.2 m.

de:
Resource Title: S3 2014
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/7e8c6dac-80cf-49d7-bd85-2a61d3336e6f/resource/96640b5b-18e4-4de4-b19b-d8be3fabe9a6/download/s3-2014.zip
    Description: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 0.7 m.

de:
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/7e8c6dac-80cf-49d7-bd85-2a61d3336e6f/resource/81cb8412-b9bf-4ac8-a65b-7fdd0c597087/download/s2-2015.zip
    Description: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.3 m.

de:
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/7e8c6dac-80cf-49d7-bd85-2a61d3336e6f/resource/fb937402-fc94-4121-9d55-809664005628/download/s5-2014.zip
    Description: Eddy-covariance measurement with an ultrasonic anemometer (DA-600, Kaijo-Denki) in 0.3 m on a daily basis.

de:
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    URL: https://www.envidat.ch/dataset/7e8c6dac-80cf-49d7-bd85-2a61d3336e6f/resource/25ce04fb-452c-439e-b1fe-8ef2ccb1ea4e/download/s2-2014.zip
    Description: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.6 m.

de:
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    URL: https://www.envidat.ch/dataset/7e8c6dac-80cf-49d7-bd85-2a61d3336e6f/resource/e78a7861-5a9d-458f-b66d-d180292f6840/download/s2-2016.zip
    Description: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific Inc.) in 3.2 m on a daily basis.

de:
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    Description: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.5 m.

de:
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de:
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format: PNG

media_type: image/png

de:
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dischmex

display_name: dischmex

eddy-covariance-measurements

display_name: eddy-covariance-measurements

patchy-snow-covers

display_name: patchy-snow-covers

snow-patch

display_name: snow-patch

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes


en: Switzerland has a reliable and cost efficient energy system. Due to phase  out of nuclear energy it is necessary to find new options to maintain this powerful energy system. The Swiss energy strategy 2050 aims to reduce CO2-emissions, increase efficiency and promote renewable energies. The Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) examined relevant woody and non-woody biomass quantities (cubic meters, fresh-, dry weight) and their energy potentials (in Petajoules: primary energy and biomethane) with a similar methodological approach. The work was done within the frame of the Swiss Competence Centers for Energy Research (SCCER) especially in line with the SCCER Biomass for Swiss energy future (Biosweet). With a uniform and consistent approach for the current potentials ten biomass categories were estimated and aggregated for the whole of Switzerland. In this context solutions for the technical, social and political challenges are promoted. First, considering the different biomass resources characteristics and available data, appropriate methods at the finest scale possible were elaborated to estimate the annual quantities which could theoretically be collected (theoretical potential). Then, explicit and rational restrictions for sustainable bio-energy production were defined according to the current state of the art and subtracted from the theoretical potential to obtain the sustainable potential. The main restrictions are competing material utilizations, environmental factors and supply costs. Finally, the additional sustainable potential was estimated considering the current bioenergy production. Our main purpose was to provide potentials for developing conversion technologies as well as a detailed and comprehensive basis of the Swiss biomass potentials for energy use for economic and political decision makers. The complete report is available under https://www.dora.lib4ri.ch/wsl/islandora/object/wsl%3A13277/datastream/PDF/view

en: bioenergy potential-assessment availability biomass-resources sustainability

envidat

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: Potentials of domestic biomass resources for the energy transition in Switzerland

envi

de: Umwelt

de:
Resource Title: Domestic Biomass Potential Theoretic Sustainable Absolute per Municipality
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    Description:

de:
Resource Title: Biomass Potentials Switzerland Cantons
    Size: 138287 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/21599a72-b714-4d11-a748-644d0016fae9/resource/65b8fe6d-81ee-4b05-ab8c-c46ece1d3d25/download/biomasspotentials_cantonal-level.xlsx
    Description:

de:
Resource Title: Agricultural Potential 2018 Municipalities 2022 with Monthly Repartition
    Size: 5600768 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/21599a72-b714-4d11-a748-644d0016fae9/resource/dbb6ec20-05c8-4957-99ec-8aef86cb3f73/download/agricultural-potential_2018_municipalities2022_with_monthly-repartition.xls
    Description:

de:
Resource Title: Biomass Supply Costs
    Size: 194315 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/21599a72-b714-4d11-a748-644d0016fae9/resource/d20fa98a-3fcc-4ada-ab31-c302d0c26f9f/download/biomasssupplycost.docx
    Description:

de:
Resource Title: Sustainable Absolute 2014 per Month
    Size: 1686824 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/21599a72-b714-4d11-a748-644d0016fae9/resource/edea557c-892d-439a-acab-c235fc099a11/download/sustainable_absolute_2014_per_month.xlsx
    Description:

de:
Resource Title: Biomass Potentials Switzerland
    Size: 10164033 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/21599a72-b714-4d11-a748-644d0016fae9/resource/67ecd9fa-b920-40b0-a5ed-6209360fa645/download/biomasspotentials_detailed_national-level.xlsx
    Description:

de:
Resource Title: Biomass Potential Switzerland Municipalities
    Size: 1145439 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/21599a72-b714-4d11-a748-644d0016fae9/resource/30bcdb8e-f0eb-4eb3-8d14-1456a416255d/download/domestic_biomass_potential_theoretic_sustainable_absolute-per-municipality.xlsx
    Description: Domestic biomass potential based on 2014 data at the municipality level for the theoretical and sustainable potential for 10 types of biomass.

de:
Resource Title: Theoretical Absolute 2014 per Month
    Size: 1752360 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/21599a72-b714-4d11-a748-644d0016fae9/resource/5618198b-874c-4157-ab2d-44d0060d7adb/download/theoretical_absolute_2014_per_month.xlsx
    Description:

availability

display_name: availability

bioenergy

display_name: bioenergy

biomass-resources

display_name: biomass-resources

potential-assessment

display_name: potential-assessment

sustainability

display_name: sustainability


en: To study the performance of mature Scots pine (_Pinus sylvestris_ L.) under chronic drought conditions in comparison to their immediate physiological response to drought release, a controlled long-term and large-scale irrigation experiment has been set up in 2003. The experiment is located in a xeric mature Scots pine forest in the Pfynwald (46° 18' N, 7° 36' E, 615 m a.s.l.) in one of the driest inner-Alpine valleys of the European Alps, the Valais (mean annual temperature: 9.2°C, annual precipitation sum: 657 mm, both 1961-1990). Tree age is on average 100 years, the top height is 10.8 m and the stand density is 730 stems ha-1 with a basal area of 27.3 m2 ha-1. The forest is described as _Erico Pinetum sylvestris_ and the soil is a shallow pararendzina characterized by low water retention. The experimental site (1.2 ha; 800 trees) is split up into eight plots of 1'000 m2 each. During April-October, irrigation is applied on four randomly selected plots with sprinklers of 1 m height at night using water from an adjacent water channel. The amount of irrigation corresponds to a supplementary rainfall of 700 mm year-1. Trees in the other four plots grow under naturally dry conditions. Soil moisture has been monitored since the beginning of the project at 3 soil depths (10, 20 and 60 cm). The crown condition of each tree is being assessed each year since 2003. Tree measurement data such as diameter at breast height, tree height, and social status were assessed in 2002, 2009 and 2014. The duration of the irrigation experiment is planned for 20 years.

en: swiss-forest-lab diameter-at-breast-height crown-condition tree tree-species irrigation scots-pine forest dbh lwf drought

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: EnviDat: the Environmental Data Portal

en: Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland

de:
Resource Title: Long-term Irrigation	Experiment Pfynwald homepage
    Size: 1 bytes
    Format: text/html
    URL: https://www.wsl.ch/en/about-wsl/instrumented-field-sites-and-laboratories/experimental-sites-in-forests/pfynwald.html
    Description:

format: HTML

media_type: text/html

de:
Resource Title: Site photo 2
    Size: 9330316 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/0e18a914-f2e4-47df-a86c-a44a1b0055e4/resource/fd9b4ee3-e87e-4c0b-b7a8-832fb3f019e7/download/20150709pfyn117.jpg
    Description:

format: jpeg

de:
Resource Title: Site photo 1
    Size: 9330316 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/0e18a914-f2e4-47df-a86c-a44a1b0055e4/resource/e0eed15d-de04-44f0-8601-cb531f9fa916/download/20150709pfyn117.jpg
    Description:

de:
Resource Title: Shapefile	of measurement location
    Size: 0
    Format: No Info
    URL: https://map.geo.admin.ch/?topic=swisstopo&X=127951&Y=613350&zoom=11&lang=de&bgLay er=ch.swisstopo.pixelkarte-farbe&crosshair=marker
    Description:

de:
Resource Title: Site photo 3
    Size: 8186031 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/0e18a914-f2e4-47df-a86c-a44a1b0055e4/resource/e3c46133-0b7c-4d9c-a3d0-ba0e91e663b5/download/20150709pfyn218.jpg
    Description:

de:
Resource Title: Swiss Forest Health Inventory
    Size: 1 bytes
    Format: text/html
    URL: https://www.wsl.ch/en/forest/forest-development-and-monitoring/sanasilva-forest-health-inventory.html
    Description:

de:
Resource Title: Dataset
    Size: 1898851 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/0e18a914-f2e4-47df-a86c-a44a1b0055e4/resource/81280262-3628-458c-8dbf-33ed80eb222a/download/envidatpfyncrowncond2016.xlsx
    Description:

crown-condition

display_name: crown-condition

dbh

display_name: dbh

diameter-at-breast-height

display_name: diameter-at-breast-height

drought

display_name: drought

forest

display_name: forest

irrigation

display_name: irrigation

lwf

display_name: lwf

scots-pine

display_name: scots-pine

swiss-forest-lab

display_name: swiss-forest-lab

tree

display_name: tree

tree-species

display_name: tree-species


en:
Rufiberg is a pre-alpine meadow site in Switzerland where shallow landslides have been observed after past intense rain storms. In order to assess the triggering mechanisms of these landslides, a comprehensive investigation was conducted within the project TRAMM from Nov 2009 to Oct 2012. It included meteorological observations, soil moisture measurements, bedrock groundwater measurements. The Rufiberg is located at the NW side of the Gnipen to the north of the village Arth-Goldau in the Canton of Schwyz. In the summer months, the site is used for pasturing. Usually, from December to March a snow cover is present at the Rufiberg. The site is at an altitude between 1080 – 1180 m asl, is ENE oriented, and has an average slope of 30 -35°. The Subalpine Molasse in the region is inclined with 30 - 35° to SE. In the area of the field site, beds of conglomerate with several m of thickness alter with beds of sandstone and marlstone. A ca. 2 – 5 m thick eluvium/colluvium layer composed of silty and sandy clay covers the bedrock. This site has been chosen because on one hand, during heavy rainfall events, e.g. autumn 2005, numerous landslides occur in the region of the Gnipen and the Rufiberg. On the other hand, the Rufiberg is very appropriate for experiments due its location away from infrastructures and due to its accessibility. The goal of the investigation was to understand the hydrology and hydrogeology of the slope with regard to shallow landslides.
More information: Brönnimann, C., Stähli, M., Schneider, P., Seward, L. and Springman, S.M. 2013. Bedrock exfiltration as a triggering mechanism for shallow landslides. Water Resources Research, 49 (9): 5155–5167. DOI: 10.1002/wrcr.20386.

en: water-table landslides hydrology soil-moisture-water-content meadow

envidat

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: TRAMM project - experimental hydrological and hydrogeological dataset of a landslide prone hillslope. Rufiberg, Switzerland

envi

en: Environment

de:
Resource Title: Data Lateral subsurface water flow.xlsm
    Size: 2033894 bytes
    Format: application/vnd.ms-excel.sheet.macroEnabled.12
    URL: https://www.envidat.ch/dataset/5333838f-5da6-4574-8d28-ce1794c19631/resource/251578d0-3df2-44ed-9090-613a678bdbf8/download/data-lateral-subsurface-water-flow.xlsm
    Description:

de:
Resource Title: Data Groundwater level in Bedrock.xlsx
    Size: 939397 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/5333838f-5da6-4574-8d28-ce1794c19631/resource/0e09242b-0222-4b90-b247-4fa2c34bb8ca/download/data-groundwater-level-in-bedrock.xlsx
    Description: We drilled six boreholes in two clusters. The boreholes reached a depth between 2 and 9 m below the ground surface. The lower half of boreholes BH1 and BH6 were cored with a 9 cm-diameter rotational diamond drilling head. The other boreholes were drilled destructively with an 11 cm-diameter Mitsubishi hammer system. Boreholes BH1, BH2 and BH5 were equipped with open standpipe piezometers, whereas BH3, BH4 and BH6 were equipped with a special piezometer system composed of an enclosed measuring cell at the bottom of a 1 inch-PVC tube. The free groundwater level in the PVC tube is measured in the standpipe piezometer, but it doesn’t necessarily represent the water table in the soil layer because the pressure sensor is located in the sealed lower part of the piezometer in the enclosed measuring cell and thus is not in contact with the air. This system allows the hydraulic pressure of lithologies with low permeability to be measured directly. Two piezometers were installed in BH6 at different depths. The PVC tubes were slotted at the bottom for 0.5, 1 or 2 meters and the boreholes were sealed carefully with clay pellets above this filter section to avoid by-pass and infiltration from the surface. DCX-22 pressure sensors from Keller AG Winterthur, with integrated data loggers, were installed in each piezometer to measure the relative hydraulic pressure at a 30 minute-intervals from November 2010 to November 2011. The data were corrected for in-situ air-pressure fluctuations.

de:
Resource Title: Data Soil water content TDR.xlsm
    Size: 7866407 bytes
    Format: application/vnd.ms-excel.sheet.macroEnabled.12
    URL: https://www.envidat.ch/dataset/5333838f-5da6-4574-8d28-ce1794c19631/resource/fd31a45e-e5d0-476e-9a45-611f4d572d93/download/data-soil-water-content-tdr.xlsm
    Description: Four test pits were excavated in summer 2010 along the slope to install vertical profiles of Time Domain Reflectometry sensors [Topp et al., 1980] to measure soil water content at depths of 0.25 m, 0.7 m, 1.1 m and 1.5 m. The 15 cm long TDR rods were inserted horizontally into the vertical walls of the pits, and the four pits were carefully refilled with the excavated soil. The water content was recorded from summer 2010 to fall 2011 at 10-minute time intervals using a Campbell TDR 100 system.

de:
Resource Title: Data Groundwater level in Soil.xlsx
    Size: 5593516 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/5333838f-5da6-4574-8d28-ce1794c19631/resource/720b044d-3ee5-4104-ba45-4b5b8ee7bf3e/download/data-groundwater-level-in-soil.xlsx
    Description:

hydrology

display_name: hydrology

landslides

display_name: landslides

meadow

display_name: meadow

soil-moisture-water-content

display_name: soil-moisture-water-content

water-table

display_name: water-table


full-content-of-wsl-fauna-database

en: Complete extract of Fauna Database of WSL, containing all projects and all taxa. Meant as exchange and citation platform for sharing the data with the national data centre 'Centre Suisse de la Cartographie de la Fauna (CSCF)', and Info Fauna.

en: arthropods database invertebarates collections wsl

en: Full content of WSL Fauna Database

title_for_slug: full-content-of-wsl-fauna-database

envi

de: Umwelt

de:
Resource Title: FIDO_global_extract 2023-03-08_16-27-28 WSL-AllData-AllTaxa.sql.zip
    Size: 3662 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/8c409314-8cc5-45de-81a0-a5076c8219ab/resource/9c2db111-e28e-4b6e-84eb-905029533eaf/download/fido_global_extract-2023-03-08_16-27-28-wsl-alldata-alltaxa.sql.zip
    Description: Query executed to retrieve the complete dataset from WSL Fauna Database
FIDO_global_extract 2023-03-08_16-27-28 WSL-AllData-AllTaxa.sql compressed in zip-archive

de:
Resource Title: FIDO_global_extract 2023-03-08_16-27-28 WSL-AllData-AllTaxa.csv
    Size: 346030081 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8c409314-8cc5-45de-81a0-a5076c8219ab/resource/f0900bd4-d997-4328-b813-e69794203edb/download/fido_global_extract-2023-03-08_16-27-28-wsl-alldata-alltaxa.csv
    Description: Complete dataset of all projects and data collected in WSL Fauna Database

media_type: text/csv

de:
Resource Title: FIDO_global_extract 2023-04-17_17-00-48-WSL-AllData-AllTaxa
    Size: 474860156 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8c409314-8cc5-45de-81a0-a5076c8219ab/resource/e696663c-7879-4754-acc4-f7d72a73dc6e/download/fido_global_extract-2023-04-17_17-00-48-wsl-alldata-alltaxa.csv
    Description: Updated export with semicolon-delimited columns instead of comma delimited

arthropods

display_name: arthropods

collections

display_name: collections

database

display_name: database

invertebarates

display_name: invertebarates

wsl

display_name: wsl


en:
The dataset consists of the standardized precipitation (SPI) and the standardized precipitation evapotranspiration index (SPEI) index with a 30 arcsec (~1km) horizontal resolution.
Data for precipitation rates (pr) and potential evapotranspiration (pet) are taken from CHELSA V2.1 (https://doi.org/10.16904/envidat.228.v2.1). Both data sets are monthly time series from 1980 to 2018 at 30 arcsec. and have the unit of kg m-2 month-1. SPEI is a more reliable measure of drought than SPI as it additionally considers the effect of potential evapotranspiration. Potential evapotranspiration (pet) is the amount of water per area per time that can evaporate at the soil surface or be transpired by plants in absence of water supply limitations. SPEI effectively estimates for a given location how the climatic water balance, i.e., the difference between precipitation rate (pr) and potential evapotranspiration (pet), relates to the long-term mean. Here, pet was calculated with the Penman-Montheith approach, assuming surface conductance of a reference crop of 12 cm height following the definition of the Food and Agriculture Organization of the United Nations (FAO), originating from CHELSA-BIOCLIM+ database (https://www.doi.org/10.16904/envidat.332). To obtain global time series of SPEI, in each pixel the frequency distribution of water balance estimates (pr-pet) is approximated with a log-logistic probability distribution function. We calculate monthly SPEI using a 12-month memory and approximate the parameters of the log-logistic probability distribution based on the time series from 1980-2018. Such a 12-month time window is suitable to capture long-term drought events that are long enough to impose substantial impacts on agriculture, hydrology, and ecosystems while ones at shorter time scales would be more appropriate to detect short-term drought. For each month, SPEI is calculated considering the respective water-balance value and the values of the eleven preceding months. Here we use SPEI-12 in December to represent the overall water-balance condition of the respective year. The resulting SPEI data has a horizontal resolution of 130 arcsec (~1km). The calculation of SPI and SPEI is done in R with the package SPEI (https://www.doi.org/10.32614/CRAN.package.SPEI).

en: drought extreme-events multi-year-drought precipitation megadrought chelsa potential-evapotranspiration

en: High resolution global standardized drought indices

de:
Resource Title: Multiyear meteorological droughts (MYMD) statistics - Description
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    URL: https://www.envidat.ch/dataset/8670a317-ca6b-4bce-9191-428e2827184e/resource/a32ef5b4-fab9-4d15-b6da-c8244029027d/download/mymd_stats_description.csv
    Description: Description of column names, and units of the fields contained in the dataset "Multiyear meteorological droughts (MYMD) statistics".

de:
Resource Title: Multiyear meteorological droughts (MYMD10) statistics - Description
    Size: 1384 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/8670a317-ca6b-4bce-9191-428e2827184e/resource/c1c0dbc9-ec96-462e-bf62-bc90b6aad3ab/download/mymd10_stats_description.csv
    Description: Description of column names, and units of the fields contained in the dataset "Multiyear meteorological droughts (MYMD10) statistics".

de:
Resource Title: Kernel Normalized Difference Vegetation Index Anomaly (QKNDVI)
    Size: 6710886 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fglobal%2Fannual%2Fqkndvi%2F
    Description: Global annual layers of the the kernel normalized difference vegetation index anomaly (qkndvi) at a 1/12° (~10km) spatial resolution.

de:
Resource Title: Standardised Precipitation Evapotranspiration Index (SPEI)
    Size: 209715200 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fglobal%2Fmonthly%2Fspei%2F
    Description: Global monthly layers of the Standardized Precipitation Evapotranspiration Index (SPEI) a 30 arcsec (~1km) spatial resolution

de:
Resource Title: Multiyear meteorological droughts (MYMD10) statistics
    Size: 928956 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/8670a317-ca6b-4bce-9191-428e2827184e/resource/fa3d92e1-1eb0-478c-900d-dca818e3b4ff/download/mymd10_stats.csv
    Description: Multiyear meteorological droughts (MYMD10) statistics

Statistics of the multiyear meteorological droughts, containing the ID, duration, area, and empirical ranking of the droughts, and the anomalies in kNDVI

de:
Resource Title: CHELSA-drought-indices Technical Documentation
    Size: 335096 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/8670a317-ca6b-4bce-9191-428e2827184e/resource/a4ec699c-1e4a-45ab-8b7c-ccdf09c6e828/download/chelsa_file_specification_drought_indicies.pdf
    Description: Technical documentation document including the variable names, units, scaling, offset, and background.

de:
Resource Title: Multiyear meteorological droughts (MYMD10)
    Size: 107930 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fglobal%2Fannual%2Fmymd10%2F
    Description: Global annual layers of the multiyear meteorological droughts (MYMD) at a 1/12° (~10km) spatial resolution

de:
Resource Title: Multiyear meteorological droughts (MYMD)
    Size: 368640 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fglobal%2Fannual%2Fmymd%2F
    Description: Global annual layers of the multiyear meteorological droughts (MYMD) at a 1/24° (~5km) spatial resolution

de:
Resource Title: Standardized Precipitation Index (SPI)
    Size: 209715200 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fglobal%2Fmonthly%2Fspi%2F
    Description: Global monthly layers of the Standardized Precipitation Index (SPI) a 30 arcsec (~1km) spatial resolution

de:
Resource Title: Multiyear meteorological droughts (MYMD) statistics
    Size: 785961 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/8670a317-ca6b-4bce-9191-428e2827184e/resource/21046cb1-0a1c-4890-8025-aefb5d9f3f1d/download/mymd_stats.csv
    Description: Statistics of the multiyear meteorological droughts, containing the ID, duration, area, and empirical ranking of the droughts.

de:
Resource Title: CHELSA-drought-indices Technical Documentation
    Size: 257611 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/8670a317-ca6b-4bce-9191-428e2827184e/resource/3149a573-b55c-499c-9efa-2cbe920bc483/download/chelsa_file_specification_drought_indicies.pdf
    Description: Technical documentation document including the variable names, units, scaling, offset, and background.

chelsa

display_name: chelsa

drought

display_name: drought

extreme-events

display_name: extreme-events

megadrought

display_name: megadrought

multi-year-drought

display_name: multi-year-drought

potential-evapotranspiration

display_name: potential-evapotranspiration

precipitation

display_name: precipitation


en:
The global biodiversity crisis driven by anthropogenic pressures significantly threatens marine ecosystems functioning. The rate of climate change and the impacts of anthropogenic pressures outpacing the capabilities of our observation tools, stresses the need to develop new methods to assess these rapid modifications. Environmental DNA (eDNA; DNA traces released by organisms) metabarcoding has emerged as a non-invasive method that has been widely developed over the last decade.  Thanks to a large spatio-temporal coverage,  high detection of rare species and its time and cost effectiveness, eDNA metabarcoding represents a promising biomonitoring tool. However, capturing fish diversity using eDNA requires a high-quality genetic reference database, which we are currently still lacking. For the South European Atlantic shelf area, we estimated that only 41% of the fish species present were recorded in the available eDNA reference databases. Improving reference databases can notably rely on opportunistic sampling enabling the reporting of sequences for new species. Therefore, the data provided here consists of barcoding 95 species of ray-finned and cartilaginous fishes over the 12S mitochondrial DNA gene. We generated 168 12S barcodes from fishes that were sampled in the Bay of Biscay (Northeast Atlantic, France) between 2017 and 2019. We also provided the “Teleo” barcode associated with a specific 12S region for each individual. In addition to the sequences, we provided for each individual the taxonomy, the details associated with the barcode (Genbank accession number, chromatograms), a photograph, as well as 5 ecomorphological measures and 11 life-history traits. These traits document several functions such as dispersion, diet, habitat use, and position in the food web. Furthermore, we provided the metadata of each sampling site (date, station, sampling hour, gear, latitude, longitude, depth) and environmental variables measured in situ (conductivity, salinity, water temperature, water density, air temperature).
This data set is highly valuable to improve the Northeast Atlantic eDNA genetic database, thus helping to better understand the effects of environmental forcing in the Bay of Biscay, a transition zone housing mixed assemblages of boreal, temperate and subtropical fish species susceptible to display variability in functional traits to adapt to changing conditions.

en: barcoding atlantic-ocean actinopterygii teleo-primers environmental-dna mitochondrial-dna 12s

envidat

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: ATLFISHREF A 12S mitochondrial reference dataset for metabarcoding Atlantic Fishes frequently caught during scientific surveys in the Bay of Biscay

de:
Resource Title: Sequences_Info_&_Functional_Traits_Fish_BoB.csv
    Size: 36388 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/1d2d5111-0c08-4549-b238-1345c550d580/resource/99fe96a8-79b9-4645-9fc1-0582de470b42/download/sequences_info__functional_traits_fish_bob.csv
    Description: This file contains the metadata associated with each 12 s mitochondrial sequence.

de:
Resource Title: ATL_FISHREF_12S_mitochondrial_tree_156sequences.pdf
    Size: 983790 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/1d2d5111-0c08-4549-b238-1345c550d580/resource/1833a744-7ab8-4a2b-ba58-eabbc7dc671f/download/atl_fishref_12s_mitochondrial_tree_156.pdf
    Description: Mitochondrial tree based on the 12s sequences obtained for altantic fish.
This data represent the maximum likelihood phylogenetic tree allowing to assess 1) the clustering of the different sequences generated in
this study and their closest sequences recovered through a MegaBLAST on GenBank, 2) the robustness of the clades and 3) the
phylogenetic dissimilarities through the branch length of the tree among the different sequences. This tree was built with IQtree2
using the best substitution model found by modelFinder. The robustness of the node was evaluated using 1000 ultra-fast bootstrap
(node information indicated on the right of the /) and SH-aLRT procedures (node information indicated on the left of the /), using a hillclimbing nearest neighbor interchange (NNI) search to reduce the risk of overestimating branch supports with ultra-fast Bootstrap
caused by too severe model violations.

de:
Resource Title: ATL_FISHREF_12S_mitochondrial_sequences.fasta
    Size: 119690 bytes
    Format: text/x-fasta
    URL: https://www.envidat.ch/dataset/1d2d5111-0c08-4549-b238-1345c550d580/resource/240862cf-df17-4390-9a12-64ed43b8a0bc/download/atl_fishref_12s_mitochondrial_sequences.fasta
    Description: This file contains the 12s mitochondrial sequences for the Atlantic fishes collected on a French scientific survey (Evhoe) for stock assessment.

de:
Resource Title: ATL_FISHREF_Teleo_barcode.fasta
    Size: 17598 bytes
    Format: text/x-fasta
    URL: https://www.envidat.ch/dataset/1d2d5111-0c08-4549-b238-1345c550d580/resource/d61fa932-931b-43cb-8bc4-62ee54d54874/download/atl_fishref_teleo_barcode.fasta
    Description: This file contains the Teleo Barcode for the Atlantic fishes collected during a French scientific survey (Evhoe) for stock assessment.

de:
Resource Title: Metadata_EVHOE_2017_2018_2019.csv
    Size: 25259 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/1d2d5111-0c08-4549-b238-1345c550d580/resource/ec48f037-0cc7-444e-a73a-15383a2e6d0b/download/metadata_evhoe_2017_2018_2019.csv
    Description: This file contains the metadata associated with each scientific survey

de:
Resource Title: ATL_FISHREF_chromatograms_&_pictures.zip
    Size: 1481604020 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/1d2d5111-0c08-4549-b238-1345c550d580/resource/06c3ab11-43b8-4983-943d-e851cfd275bc/download/atl_fishref_chromatograms__pictures_updated_29-08-24-1.zip
    Description:

actinopterygii

display_name: actinopterygii

atlantic-ocean

display_name: atlantic-ocean

barcoding

display_name: barcoding

environmental-dna

display_name: environmental-dna

mitochondrial-dna

display_name: mitochondrial-dna

teleo-primers

display_name: teleo-primers


en: Data include SOC stocks at 556 old-growth Swiss forest sites from the Swiss forest soil inventory including environmental variables (elevation, mean annual precipitation, mean annual temperature, net primary production from MODIS, soil properties, forest types, measured SOC stocks (organic layers and mineral soil up to 100 cm depth) and stocks simulated by the Yasso20-model.

en: forest-topsoil som-storage soil-organic-carbon soil-carbon-model soil-chemistry

en: Soil organic carbon drivers in the Swiss forest soil inventory

envi

de: Umwelt

forest-topsoil

display_name: forest-topsoil

soil-carbon-model

display_name: soil-carbon-model

soil-chemistry

display_name: soil-chemistry

soil-organic-carbon

display_name: soil-organic-carbon

som-storage

display_name: som-storage


en: The dataset contains time series of water levels, precipitation measured in the two sub-catchments of the Erlenbach catchment and its vicinity during summer and autumn 2021, as well as flowing drainage network lengths calculated for these areas using the CEASE method developed by the authors. Detailed description of the dataset is provided in the documentation.

en: data drainage drainage-network dynamics catchment-data catchment water-level erlenbach precipitation dataset channel

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: EnviDat: the Environmental Data Portal

en: Short-term Drainage Density Dynamics Dataset for the Erlenbach Catchment

de:
Resource Title: short-term_drainage_density_dynamics_dataset.csv
    Size: 22884800 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/509baa5e-b955-4bb9-a928-80fbccc0f610/resource/b0b19958-af5e-49bf-8ab1-b10191d7d950/download/short-term_drainage_density_dynamics_dataset.csv
    Description:

de:
Resource Title:  short-term_drainage_density_dynamics_dataset_documentation.pdf
    Size: 3718100 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/509baa5e-b955-4bb9-a928-80fbccc0f610/resource/37aaedcc-8217-4229-9bf3-49fe7e06d925/download/short-term_drainage_density_dynamics_dataset_documentation.pdf
    Description:

de:
Resource Title: short-term_drainage_density_dynamics_dataset_documentation.docx
    Size: 1135026 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/509baa5e-b955-4bb9-a928-80fbccc0f610/resource/a90d31fa-4d8e-4bf1-a650-3c0f8ce12ae4/download/short-term_drainage_density_dynamics_dataset_documentation.docx
    Description:

catchment

display_name: catchment

catchment-data

display_name: catchment-data

channel

display_name: channel

data

display_name: data

dataset

display_name: dataset

drainage

display_name: drainage

drainage-network

display_name: drainage-network

dynamics

display_name: dynamics

erlenbach

display_name: erlenbach

water-level

display_name: water-level


en: Data and code for the publication entitled "Large Eddy Simulation of Near-Surface Boundary Layer Flow over Patchy Snow". The dataset contains variables about the simulation ("constant_and_system.zip" covering files for the simulation setup and "LES_cut_yNormal_every_8th_timestep.nc" the simulation output). Furthermore, the dataset covers eddy covariance data at multiple heights that is used for comparison and validation in the publication.

en: heat-exchange eddy-covariance-measurements patchy-snow large-eddy-simulation atmosphere

en: Large Eddy Simulation of Near-Surface Boundary Layer Dynamics over Patchy Snow

envi

en: Environment

de:
Resource Title: ect2_0_9m.nc
    Size: 3150709 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/3189403b-73f7-4a31-a617-c2e386413e2e/resource/f6aff78c-c606-4a5b-9f1a-f0e591bf7c7c/download/ect2_0_9m.nc
    Description: Eddy-covariance data at the ECT2 at a height of 0.9m above the surface. Sensor: Campbell Scientific IRGASON

de:
Resource Title: constant_and_system.zip
    Size: 1002786643 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/3189403b-73f7-4a31-a617-c2e386413e2e/resource/6d6fc5be-becc-4341-b47e-173991df7bb5/download/constant_and_system.zip
    Description: Namelists, mesh, and forcing data to run the OpenFoam simulations

de:
Resource Title: LES_cut_yNormal_every_8th_timestep.nc
    Size: 3010449345 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/3189403b-73f7-4a31-a617-c2e386413e2e/resource/ea769320-b022-462b-a12e-6b6ed71001ac/download/les_cut_ynormal_every_8th_timestep.nc
    Description: slice through the LES simulation domain along the flow (normal to y-direction). The netCDF-file contains data from every 8th simulation time step for all cells in the slice

de:
Resource Title: 0.zip
    Size: 3754 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/3189403b-73f7-4a31-a617-c2e386413e2e/resource/8b17d2f1-9302-4dc3-9612-09b8a3af2f56/download/0.zip
    Description: Namelists for the OpenFOAM simulation containing the boundary conditions

de:
Resource Title: ect2_slow_data.nc
    Size: 470562 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/3189403b-73f7-4a31-a617-c2e386413e2e/resource/e7e0071c-ffef-486c-b4e3-04181b815a2c/download/ect2_slow_data.nc
    Description: slow data (every 5s) from the ECT2. Interesting for this study is the air temperature recorded with a ventilated thermometer

de:
Resource Title: irdata_03_to_05.nc
    Size: 4794915257 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/3189403b-73f7-4a31-a617-c2e386413e2e/resource/9e8a4cc3-601d-492b-88d5-9e19acd1e8ea/download/irdata_03_to_05.nc
    Description: Data recorded using the IR screen setup. It is used for the figure comparing intermittent advection pattern

de:
Resource Title: aws.csv
    Size: 503544 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3189403b-73f7-4a31-a617-c2e386413e2e/resource/d95d1854-ba11-4073-a263-1a927bd95b55/download/aws.csv
    Description: Data from the automatic weather station. Record interval 10min

de:
Resource Title: ect2_1_9m.nc
    Size: 1910441 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/3189403b-73f7-4a31-a617-c2e386413e2e/resource/30c56a0e-b6d2-415b-b3d9-ae67b47bb888/download/ect2_1_9m.nc
    Description: Eddy-covariance data from the ECT2 at a height of 1.9m above the surface. Sensor: Campbell Scientific CSAT3B

de:
Resource Title: ect1_1_2m.nc
    Size: 3148676 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/3189403b-73f7-4a31-a617-c2e386413e2e/resource/a258d029-96fa-4500-bd6f-3cd7dfd82d7b/download/ect1_1_2m.nc
    Description: Eddy-covariance data from the ECT1 at a height of 1.2m above the surface. Sensor: Campbell Scientific IRGASON

de:
Resource Title: les_namelists.zip
    Size: 3826 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/3189403b-73f7-4a31-a617-c2e386413e2e/resource/329d415f-a54d-4710-94d1-6af2cb6ed088/download/les_namelists.zip
    Description:

de:
Resource Title: ect2_2_9m.nc
    Size: 1901989 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/3189403b-73f7-4a31-a617-c2e386413e2e/resource/c5971083-bedb-4be3-8b34-5ae41ec06c62/download/ect2_2_9m.nc
    Description: Eddy-covariance data recorded at the ECT2 at 2.9m above the surface. Sensor: Campbell Scientific CSAT3B

atmosphere

display_name: atmosphere

eddy-covariance-measurements

display_name: eddy-covariance-measurements

heat-exchange

display_name: heat-exchange

large-eddy-simulation

display_name: large-eddy-simulation

patchy-snow

display_name: patchy-snow