en:
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).
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en: Ecosystem coupling and multifunctionality - exclosure experiment

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

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ecosystem-functions

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grazing

display_name: grazing

invertebrates

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en:
Naturally, large parts of the Swiss Plateau are characterised by wetlands
and meandering rivers. That this is no longer the case today is the result
of centuries of efforts to obtain dry land. But how did this process take
place? What were the relevant actors and what were their motivations? And
what can be said about the ecological consequences of this development?
In a research project on the history of wetlands in Switzerland since 1700,
we conducted (a) a historical analysis of the development of land use in
wetlands and the actors involved, (b) a historical-cartographic
reconstruction of wetland extent since 1850 and (c) an evaluation of
ecological effects of changes in wetlands on various organisms groups.
The series of GIS layers on wetland history stem from the second part of
the project. The area reconstruction is based on digitized and homogenized
signatures from national map series, as they have been available since
about 1850. Details about the digitalization process and the homogenization
procedures applied ("Rekonstruktionen") are included in Stuber & Bürgi
2019.
Book Citation:
 - Stuber M, Bürgi M (2019) Vom «eroberten Land» zum Renaturierungsprojekt. Geschichte der Feuchtgebiete in der Schweiz seit 1700. "Bristol Schriftenreihe", Band 59. Haupt Verlag, Bern, Stuttgart, Wien. 262 Seiten.

en: History of wetlands in Switzerland since 1850

en: environmental---history wetlands historical-carthography switzerland environmental--policy ecology

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

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

format: ZIP

ecology

display_name: ecology

environmental---history

display_name: environmental---history

environmental--policy

display_name: environmental--policy

historical-carthography

display_name: historical-carthography

switzerland

display_name: switzerland

wetlands

display_name: wetlands


forest-canopy-layer-code

en:
R script for large-scale mapping of forest canopy layering. The approach combines tree-based and area-based methods, using airborne laser scanning point clouds together with individual tree detection (ITD) information (tree height and position) to distinguish between single- and multi-layered forests. The output is a raster layer at 10m x 10m resolution, encoded as follows:
1: single-layered (class 1): Both point cloud and ITD approaches classify the forest as single-layered.
2: probably single-layered (class 2): ITD classifies the forest as single-layered, while the point cloud suggests multi-layered.
3: probably multi-layered (class 3): ITD classifies the forest as multi-layered, while the point cloud suggests single-layered.
4: multi-layered (class 4): Both point cloud and ITD approaches classify the forest as multi-layered.
Next to the R script, we also provide an example data set. The LAZ files in the example dataset are from the nationwide ALS data and are provided by the Federal Office of Topography swisstopo in accordance with its open data policy (swisstopo, 2022a, 2019). The detected individual trees were identified using Dalponte and Coomes' (2016) algorithm on a spike-free vegetation height model (cell size 0.5m). The provided forest mask corresponds to the NFI forest layer definition by Waser et al. (2015). For details and mentioned references, we refer to the related publication by Bast et al. (under revision; JAG).

en: Forest Canopy Layer Code

en: mountain-forest itd protective-forest forest-management forest lidar vhm

title_for_slug: forest-canopy-layer-code

forest

display_name: forest

forest-management

display_name: forest-management

itd

display_name: itd

lidar

display_name: lidar

mountain-forest

display_name: mountain-forest

protective-forest

display_name: protective-forest

vhm

display_name: vhm


en: This dataset contains code and data to reproduce the figures and analyses in the article 'Environmental DNA metabarcoding reveals biodiversity recovery in tropical forest restoration across multiple landscapes' in Ecosphere journal. In this study, we analysed environmental DNA data from water and soil samples collected across gradients of forest regrowth at six sites in Colombia to assess the recovery of mammal and soil biodiversity associated with forest restoration.

en: Data and code underlying 'Environmental DNA metabarcoding reveals biodiversity recovery in tropical forest restoration across multiple landscapes'

en: forest-restoration soil-biodiversity biodiversity-assessment colombia environmental-dna mammals

en:
Resource Title: Analysis Data and Code.zip
    Size: 15005123 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/18ce28d1-0fa3-4f97-9690-78697dd39683/resource/275e7eac-1898-48ed-8c91-e3d2ee840782/download/analysis-data-and-code.zip
    Description: A zipped folder containing the scripts and data necessary to reproduce the analyses described in the study. Further instructions are provided in scripts/ReadMe.txt

en:
Resource Title: Raw Data.zip
    Size: 1610612736 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/18ce28d1-0fa3-4f97-9690-78697dd39683/resource/b97d2608-f0d8-4544-a9a6-024f43b1d55d/download/raw-data.zip
    Description: Paired-end reads (specified by the R1 and R2 suffixes) for each individual sample. The reads have been demultiplexed using cutadapt but primers remain present in the sequences.

media_type: application/zip

biodiversity-assessment

display_name: biodiversity-assessment

colombia

display_name: colombia

environmental-dna

display_name: environmental-dna

forest-restoration

display_name: forest-restoration

mammals

display_name: mammals

soil-biodiversity

display_name: soil-biodiversity


aerosol-data-weissfluhjoch

en:
Aerosol properties were measured between February 8 and March 31 2019  at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (- 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.
Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 &956;m) and GT-526S (0.3 – 5 &956;m), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM&8321;, PM&8322;,&8325; and PM&8321;&8320; (assumptions: particle density: 1.65 g cm&713;&179;, refractive index: 1.5+i0).
Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 &956;m and ~20 &956;m by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cm&713;&179;).
Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cm&713;&179;).
Coriolis &956; and LINDA: A microbial air sampler (Coriolis &956;, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 &956;m were drawn with an air flow rate of up to 300 l min&8254;&185; into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.

en: Aerosol Data Weissfluhjoch

en: aerosol ice-nucleation inp particles raclets linda

title_for_slug: aerosol-data-weissfluhjoch

en:
Resource Title: OPC-N3 Dataset Weissfluhjoch
    Size: 54387041 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67ab6458-99fd-4f2e-b707-20c8dcf52109/resource/3932aae8-fa08-4dd7-bb1c-35cdfe3859bf/download/opc-n3_weissfluhjoch.csv
    Description: Particle concentrations in a size range of 0.35 μm to 40 μm were measured with an OPC-N3 (Alphasense, Inc.) in a resolution of 20 seconds (time in UTC). PM₁, PM₂,₅ and PM₁₀ were directly derived.

format: CSV

media_type: text/csv

en:
Resource Title: LINDA Dataset Weissfluhjoch
    Size: 191898 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/67ab6458-99fd-4f2e-b707-20c8dcf52109/resource/6451c23d-b1ec-4100-8f78-ba35611e540f/download/raclets_inp_wfj.nc
    Description: The LINDA file contains information on temperature (in °C) dependent frozen fractions, number of Ice Nuleating Particles (INP, per standard liter) and differential INP concentrations (per standard liter and Kelvin).

en:
Resource Title: SMPS Dataset Weissfluhjoch
    Size: 32316159 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/67ab6458-99fd-4f2e-b707-20c8dcf52109/resource/585a81d2-1d89-4453-b9b0-49176e48359d/download/smps_weissfluhjoch.nc
    Description: The SMPS file contains aerosol data (particle concentration, total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) in a size range between 12 and 460 nm (electrical mobility diameter) as well as instrument characteristics (sample temperature, pressure, relative humidity, mean free path and gas viscosity) in a temporal resolution of 2 minutes, with time in UTC, measured at Weissfluhjoch.

en:
Resource Title: APS Dataset Weissfluhjoch
    Size: 98624420 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/67ab6458-99fd-4f2e-b707-20c8dcf52109/resource/c7846a5d-4a0f-462b-bddc-594bfb6a578b/download/aps_weissfluhjoch.nc
    Description: The APS file contains aerosol data (total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) for aerodynamic diameters between 0.5 μm and ~20 μm as well as instrument characteristics, in a temporal resolution of 20 seconds, with time in UTC, measured at Weissfluhjoch.

en:
Resource Title: OPC-GT526S Dataset Weissfluhjoch
    Size: 11552644 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67ab6458-99fd-4f2e-b707-20c8dcf52109/resource/6e0ab260-4ec2-4b08-b485-63bbed6b4433/download/opc-gt526s_weissfluhjoch.csv
    Description: Particle counts were recorded in six size channels (0.3, 0.5, 0.8, 1.5, 2.5 and 5 μm) in a resolution of 20 seconds (time in UTC) by a particle counter (GT-526S, Met One Instruments, Inc.).

aerosol

display_name: aerosol

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

linda

display_name: linda

particles

display_name: particles

raclets

display_name: raclets


en:
This repository contains extensive data for the European Alps:
   - Observations of ~3,500 plant species
   - Climate (1-km), soil (100-m) and land cover predictors (1km); current and future scenarios (28 CMIP6-GCMs, 2 land cover change and 3 dispersal scenarios i.e., unlimited, no and realistic vegetation dispersal)
   - Flora migration rates (categorical) and ecological preferences (continuous indicator values)
   - Regional maps of barriers to migration and water bodies at 100-m resolution
   - Sampling effort, distance to roads and cities predictors at 100-m resolution
   - Present and future abundances over the study region at 1-km resolution (~2,000 species)
   - Present and future multifaceted and uniqueness of the European Alps' Flora at  1-km resolution
   - Present and future conservation recommendations at 1-km resolution (26 current and future strategies)
   - Phylogenetic data and functional traits of ~2,000 plants (raw data and classification trees)
   - All scripts, data and plots used for the analyses, including a singularity container (mini-linux) to run them

en: Present and future multifaceted plant diversity, uniqueness and conservation in the European Alps

en: land-use-change global-change zonation taxonomic-diversity functional-diversity migclim plants phylogenetic-diversity european-alps conservation species-distribution-models beta-diversity

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

envi

en: Environment

en:
Resource Title: Updated SDM outputs
    Size: 10242439935 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/7b947fcf-73a0-46a7-be03-804797c9c3d7/resource/4838348a-6050-4774-83f1-cab5efe7b244/download/sdm_outputs_updated.zip
    Description: Refined scenarios of plants distribution in the European Alps

en:
Resource Title: Data Access
    Size: 85899345920 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.371%2F
    Description:

beta-diversity

display_name: beta-diversity

conservation

display_name: conservation

european-alps

display_name: european-alps

functional-diversity

display_name: functional-diversity

global-change

display_name: global-change

land-use-change

display_name: land-use-change

migclim

display_name: migclim

phylogenetic-diversity

display_name: phylogenetic-diversity

plants

display_name: plants

species-distribution-models

display_name: species-distribution-models

taxonomic-diversity

display_name: taxonomic-diversity

zonation

display_name: zonation


en:
The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat.
Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

en: Large GIS raster data derived from Natural Earth Data (Cross Blended Hypso with Shaded Relief and Water)

en:
Resource Title: map preview 0.5MB
    Size: 482223 bytes
    Format: image/jpeg
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.68/gisdata/tiny_map_preview.jpg
    Description:

format: jpeg

en:
Resource Title: Bulk Data Access
    Size: 111669149696 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.68%2F
    Description: Link to the S3 bucket where all the data resources can be downloaded in bulk

gis

display_name: gis

large-files

display_name: large-files

natural-earth

display_name: natural-earth

qgis

display_name: qgis


en:
This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format.
The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication:
_Schürholt, K., Kowalski, J., Löwe, H.; Elements of future snowpack modeling - Part 1: A physical
instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_
The code and potential updates can be accessed directly through git via:
https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics

en: A numerical solver for heat and mass transport in snow based on FEniCS

en: snow pattern-formation python numerical-model vapor-transport

numerical-model

display_name: numerical-model

pattern-formation

display_name: pattern-formation

python

display_name: python

snow

display_name: snow

vapor-transport

display_name: vapor-transport


en:
This dataset contains the input data generated for the pseudo-global warming (PGW) experiments used in the study of the March 2022 Antarctic heatwave with the CRYOWRF model. It includes:
- PGW delta fields derived from CMIP6 simulations, used to perturb ERA5 reanalysis data for past and  future storyline experiments.
- Model configuration files required to reproduce the CRYOWRF simulations.
These resources allow researchers to reproduce the experimental setup and generate model outputs consistent with the results presented in the study.

en: Storylines of the March 2022 heatwave event at Concordia - East Antarctica

en: climate-change antarctica event-attribution heatwave atmospheric-rivers extreme-events era5 cmip6

en:
Resource Title: CRYOWRF_ANT_20220315_ConfigFiles.tar
    Size: 912259096 bytes
    Format: application/x-tar
    URL: https://www.envidat.ch/dataset/79b7c457-e689-49bd-8330-29b0508d2b2c/resource/7f77d116-cd3b-4eec-a6d6-061b0d7b43f6/download/cryowrf_ant_20220315_configfiles.tar
    Description: Model configuration files required to reproduce the CRYOWRF simulations over Antarctica.

en:
Resource Title: PGW_deltas.tar
    Size: 3701853962 bytes
    Format: application/x-tar
    URL: https://www.envidat.ch/dataset/79b7c457-e689-49bd-8330-29b0508d2b2c/resource/9936c23c-dc86-4eaa-8a6f-5a49cffb338c/download/pgw_deltas.tar
    Description: PGW delta fields derived from CMIP6 simulations, used to perturb ERA5 reanalysis data for future storyline experiments.

antarctica

display_name: antarctica

atmospheric-rivers

display_name: atmospheric-rivers

climate-change

display_name: climate-change

event-attribution

display_name: event-attribution

extreme-events

display_name: extreme-events

heatwave

display_name: heatwave


carabid-beetles-in-forests

en:
Carabidae data from all historic up to the recent projects (21.10.2019) of WSL, collected with various methods in forests of different types.
Version 2 ('FIDO_global_extract 2019-11-22_18-11-24 WSL-Forest-Carabidae') contains additional data field PROJ_FALLENBEZEICHNUNG.
Data are provided on request to contact person against bilateral agreement.

en: Carabid beetles in forests

en: running-beetles carabidae trapping coleoptera forest insects arthropoda

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.

title_for_slug: carabid-beetles-in-forests

en:
Resource Title: FIDO_global_extract 2019-10-21_15-36-33 WSL-Forest-Carabidae.sql
    Size: 7054 bytes
    Format: application/x-sql
    URL: https://www.envidat.ch/dataset/9e84cac9-f613-48ee-b784-4839e2e4d66b/resource/c1ac034b-97ff-40af-bf44-7980f4862773/download/fido_global_extract-2019-10-21_15-36-33-wsl-forest-carabidae.sql
    Description: Data query used on Fauna Database to retrieve the submitted data.

en:
Resource Title: FIDO_global_extract 2019-11-22_18-11-24 WSL-Forest-Carabidae.txt
    Size: 26421343 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/9e84cac9-f613-48ee-b784-4839e2e4d66b/resource/31809c51-86a7-4d35-8b2f-8979c105598f/download/fido_global_extract-2019-11-22_18-11-24-wsl-forest-carabidae.txt
    Description: Tab-delimited data of the according sql query.

format: TXT

media_type: text/plain

en:
Resource Title: FIDO_global_extract 2019-10-21_15-36-33 WSL-Forest-Carabidae.txt
    Size: 26257912 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/9e84cac9-f613-48ee-b784-4839e2e4d66b/resource/59e9fccd-cc64-41c4-80a4-25f947d3a0d6/download/fido_global_extract-2019-10-21_15-36-33-wsl-forest-carabidae.txt
    Description: Data extract in TAB-delimted format. First row is header, first column is counter.

en:
Resource Title: FIDO_global_extract 2019-11-22_18-11-24 WSL-Forest-Carabidae.sql
    Size: 7228 bytes
    Format: application/x-sql
    URL: https://www.envidat.ch/dataset/9e84cac9-f613-48ee-b784-4839e2e4d66b/resource/2ed9a413-7520-49ce-88fd-4558f80ff952/download/fido_global_extract-2019-11-22_18-11-24-wsl-forest-carabidae.sql
    Description: SQL query used to extract the data from fauna database.

arthropoda

display_name: arthropoda

carabidae

display_name: carabidae

coleoptera

display_name: coleoptera

forest

display_name: forest

insects

display_name: insects

running-beetles

display_name: running-beetles

trapping

display_name: trapping


en:
This data was collected at Mizuho Station (44.315°E, 70.71°S, 2230 m a.s.l.), East Antarctica, from **30 September to 22 November 2000** during the 41st Japanese Antarctic research expedition. Vertical profiles of the horizontal mass flux and particle number size distribution of drifting and blowing snow were measured in the lowest 10 m of the atmosphere using four **snow particle counters** (SPCs). Three 3-dimensional **ultrasonic anemometers** were deployed in the lowest 25 m of the atmosphere to record the wind velocity components and sonic temperature at a high frequency of 100 Hz. Additionally, an **automatic weather station** provided air temperature, relative humidity, air pressure, shortwave and longwave downward and upward radiation fluxes, surface temperature, snow temperatures at depths of 0.1 and 0.4 m below the surface, wind speed, and wind direction. The data is complemented by **weather observations** performed twice to eleven times per day by the Japanese Meteorological Agency.
Most of the time, the lowest SPC was situated at a height of 0.05 or 0.1 m above the snow surface. During certain periods called profile runs, the height of the lowest SPC was systematically varied between 0.02 and 0.2 m to increase the vertical resolution of the measured profiles. The other SPC's were installed at fixed heights of 1.1, 3.1, and 9.6 m. During each profile run, the lowest SPC was kept at a specific height for approximately 10 min before changing the height. If the wind speed remained approximately constant throughout a profile run, the data was used to compute the average snow-transport profile in that period. In total, 24 of such average profiles were obtained. A part of these SPC measurements are discussed in Nishimura and Nemoto (2005).
The ultrasonic anemometers were placed at heights of 0.3, 1, and 25 m. However, the height of the lowest ultrasonic anemometer was changed several times in the measurement period and ranged from 0.05 to 0.32 m. On top of that, 19 short periods (each lasting 15 to 160 min) were used to systematically vary the height of the lowest ultrasonic anemometer and thus increase the vertical resolution of the measured wind speed profile. Occasionally, drifting and blowing snow particles perturbed the ultrasonic measurement signal and electrically-charged particles caused an electric charge of the anemometers, leading to artifacts such as spikes and dropouts in the measured time series. These artifacts were largely removed and replaced by NaN, using the statistical spike removal algorithm of Mauder et al. (2013). However,  the artifact removal was sometimes incomplete for the lowest anemometer because of very intense snow transport close to the surface. While temporal averages are barely affected by the artifacts, the high-frequency data of the lowest anemometer should be used carefully when computing turbulent fluxes using the eddy-covariance method. Further important explanations are provided in the file Readme.pdf in data resource '(a) Metadata'.
References
- Mauder, M., Cuntz, M., Drüe, C., Graf, A., Rebmann, C., Schmid, H.P., Schmidt, M., Steinbrecher, R. (2013). A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements. Agric For Meteorol 169:122–135. https://doi.org/10.1016/j.agrformet.2012.09.006
- Nishimura, K., Nemoto M. (2005). Blowing snow at Mizuho station, Antarctica. Phil. Trans. R. Soc. A. 363: 1647–1662. https://doi.org/10.1098/rsta.2005.1599

en: Profile measurements of snow transport and micrometeorology at Mizuho Station in Antarctica

en: blowing-snow drifting-snow meteorological-measurements atmospheric-turbulence antarctica

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: (i) Sonic data 6
    Size: 1503238554 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.540/i_Sonic_data_6.zip
    Description: Ultrasonic anemometer data in the period from 13 to 18 November 2000.

en:
Resource Title: (b) SPC time series
    Size: 149946368 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.540/b_SPC_time_series.zip
    Description: Data from the snow particle counters with 1-s intervals.

en:
Resource Title: (k) Wind speed profiles
    Size: 302490 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.540/k_Wind_speed_profiles.zip
    Description: Average vertical profiles of horizontal wind speed based on the ultrasonic anemometer data.

en:
Resource Title: (d) Sonic data 1
    Size: 1825361101 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.540/d_Sonic_data_1.zip
    Description: Ultrasonic anemometer data in the period from 30 September to 11 October 2000.

en:
Resource Title: (f) Sonic data 3
    Size: 1932735283 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.540/f_Sonic_data_3.zip
    Description: Ultrasonic anemometer data in the period from 21 to 30 October 2000.

en:
Resource Title: (e) Sonic data 2
    Size: 1932735283 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.540/e_Sonic_data_2.zip
    Description: Ultrasonic anemometer data in the period from 11 to 21 October 2000.

en:
Resource Title: (g) Sonic data 4
    Size: 1932735283 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.540/g_Sonic_data_4.zip
    Description: Ultrasonic anemometer data in the period from 30 October to 6 November 2000.

en:
Resource Title: (j) Spike percentage
    Size: 831078 bytes
    Format: text/csv
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.540/j_Spike_percentage.csv
    Description: Percentage of spikes removed from the ultrasonic anemometer data per 5-min interval.

en:
Resource Title: (h) Sonic data 5
    Size: 1825361101 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.540/h_Sonic_data_5.zip
    Description: Ultrasonic anemometer data in the period from 6 to 13 November 2000.

en:
Resource Title: (c) SPC average profiles
    Size: 7235174 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.540/c_SPC_average_profiles.zip
    Description: Average vertical profiles of the horizontal snow mass flux, particle number size distribution, and mean and standard deviation of the particle diameter.

en:
Resource Title: (a) Metadata
    Size: 113832 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.540/a_Metadata.zip
    Description: General and specific metadata for all files.

en:
Resource Title: (m) Weather observations
    Size: 16589 bytes
    Format: text/csv
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.540/m_Weather_observations.csv
    Description: Weather elements and phenomena observed by the Japanese Meteorological Agency.

en:
Resource Title: (l) AWS data
    Size: 736870 bytes
    Format: text/csv
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.540/l_AWS_data.csv
    Description: Automatic weather station data with 10-min intervals.

atmospheric-turbulence

display_name: atmospheric-turbulence

blowing-snow

display_name: blowing-snow

drifting-snow

display_name: drifting-snow

meteorological-measurements

display_name: meteorological-measurements


en:
The Jorat is one of the largest continously forested areas in the Swiss Plateau. On a forested area of 778 ha, the Parc Naturel du Jorat (PNJ), a periurban parc, has been established in 2022. To document the initial state of the forest within the perimeter of the PNJ, a sample plot inventory (SPI) was carried out on 132 sample plots (SP) in winter 2021/22.
This dataset contains results from this sample plot inventory. It consists of the following files:
- results_trees.csv: Results for or living and dead trees.
- results_regeneration.csv: Results for trees with DBH - 7.0 cm, assessed in three height classes.
- results_lying_deadwood.csv: Results for lying deadwood, assessed no three line transects
- results_trems.csv: Results for occurence of tree related microhabitats (TreMs)
- results_habitat_trees.csv: Results for occurence / densities of trees carrying at least one TreM or with a DBH -= 80 for living trees or -= 36 cm for dead trees respectively.
- lookup.csv: Contains lookup tables which describe the respective results in-depth.
- data_description.pdf: Briefly describes the datasets mentioned above.

en: Sample plot inventory data form the Parc Naturel du Jorat

en: swiss-plateau forest-in temperate-forests beech forest-reserves

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:
Resource Title: Results habitat trees
    Size: 1098 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/5f308438-a867-4aa0-8e2d-1e8d1f65c423/download/results_habitat_trees.csv
    Description: The file results_habitat_trees.csv contains results for the number of habitat trees.

format: XLS

en:
Resource Title: Results TreMs
    Size: 9840 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/93a5dba0-ae68-482d-bb87-5f5c62538cf3/download/results_trems.csv
    Description: The file results_trems.csv contains results for the density of TreMs per ha.

en:
Resource Title: Results regeneration
    Size: 6112 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/db868a5f-c640-4346-ac88-10e1b7052dba/download/results_regeneration.csv
    Description: The file results_regeneration.csv contains results of the regeneration survey.

en:
Resource Title: Results trees
    Size: 494778 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/008f44a9-3aa7-4cb6-a69c-f11bbd63c8e7/download/results_trees.csv
    Description: The file results_trees.csv contains results of living, standing and lying and dead standing
trees as well as for stumps (trees found on the plot but broken / cut below 1.3 m).

en:
Resource Title: Lookup
    Size: 9034 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/6e728238-c52f-4b72-ae3f-a5109aefbd72/download/lookup.csv
    Description: The file lookup.csv contains description for the values shown in the 5 result tables.

en:
Resource Title: Data description
    Size: 102082 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/f3201ed0-992c-4d3d-a460-1fa1ce4478a0/download/description_envidat.pdf
    Description: This document briefly describes the datasets.

format: PDF

media_type: application/pdf

en:
Resource Title: Results lying deadwood
    Size: 2436 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/c8f653da-6b07-4949-a7c7-19f9ae1b51e0/download/results_lying_deadwood.csv
    Description: The file results_lying_deadwood.csv contains results of the lying deadwood survey.

beech

display_name: beech

forest-in

display_name: forest-in

forest-reserves

display_name: forest-reserves

swiss-plateau

display_name: swiss-plateau

temperate-forests

display_name: temperate-forests


en:
This data set provides estimates of annual fresh water fluxes related to sea-ice formation from ocean freezing and snow-ice formation, sea-ice melting, lateral transport of sea ice in the Southern Ocean over the period 1982 to 2008.It is derived from a mass balance calculation of local sea-ice volume change and divergence from satellite data and sea-ice reconstructions. The mass balance is calculated on a daily basis and fluxes are then integrated over the entire year, where a year is defined from March to February of the next year (i.e. from March 1982 to February 2009). This approach combines multiple products of sea-ice concentration (Cavalieri & Parkinson, 2008;Comiso, 1986; Meier et al., 2013), sea-ice thickness (Kurtz & Markus, 2012; Massonnet et al., 2013; Worby et al., 2008), and sea-ice drift (Fowler et al., 2013; Kwok 2005; Schwegmann et al., 2011). For a detailed description of the method see Haumann et al. (2016). The data set is derived to estimate large-scale (regional to basin-scale) fluxes on an annual basis. Our confidence is reduced on a grid cell basis, such as for single coastal polynyas, where the method and underlying data induce large, unknown uncertainties.
_Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues._
Related publication: http://www.nature.com/nature/journal/v537/n7618/full/nature19101.html (doi:10.1038/nature19101)
Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues.

en: Antarctic sea-ice freshwater fluxes associated with freezing, transport, and melting

en: freezing transport physical-oceanography sea-ice-divergence salt sea-ice mass-balance satellite freshwater-flux melting forcing polar sea-ice-volume salinity antartic cryospheric-science southern-ocean climate-change

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: AnIceFlux_v01_annual_1982-2008.nc
    Size: 1493303 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/b16a0901-3abe-4da0-b61e-cdb80c59ba63/resource/5de37dd3-e6a7-41b4-b97a-21b14734b861/download/aniceflux_v01_annual_1982-2008.nc
    Description: Data in NetCDF4 CF-1.6 format

en:
Resource Title: Explanatory Document
    Size: 379128 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/b16a0901-3abe-4da0-b61e-cdb80c59ba63/resource/a2d8430f-dc92-4a03-b3d6-6661b553bdd1/download/aniceflux_v01.pdf
    Description:

en:
Resource Title:  AnIceFlux_v01_clim_mean_1982-2008.nc
    Size: 260945 bytes
    Format: application/x-netcdf
    URL: https://www.envidat.ch/dataset/b16a0901-3abe-4da0-b61e-cdb80c59ba63/resource/6a315e6d-4c92-4f85-b131-92fd87853b65/download/aniceflux_v01_clim_mean_1982-2008.nc
    Description: Data in NetCDF4 CF-1.6 format

en:
Resource Title: Link to Related Publication
    Size: 1 bytes
    Format: application/pdf
    URL: http://www.nature.com/articles/nature19101.epdf?author_access_token=dtLqMQ3Ss6p16tymoagae9RgN0jAjWel9jnR3ZoTv0PH2BR1NrVw3zxcEnl7ViU5pKp8iHAlc_ntQx82EiRMqFRuGOR0VueOUVoGWzy8lSzML6YUBUMMZ5f4LYCdK8Ca
    Description:

antartic

display_name: antartic

climate-change

display_name: climate-change

cryospheric-science

display_name: cryospheric-science

forcing

display_name: forcing

freezing

display_name: freezing

freshwater-flux

display_name: freshwater-flux

mass-balance

display_name: mass-balance

melting

display_name: melting

physical-oceanography

display_name: physical-oceanography

polar

display_name: polar

salinity

display_name: salinity

salt

display_name: salt

satellite

display_name: satellite

sea-ice

display_name: sea-ice

sea-ice-divergence

display_name: sea-ice-divergence

sea-ice-volume

display_name: sea-ice-volume

southern-ocean

display_name: southern-ocean

transport

display_name: transport


en:
This dataset contains all data on which the following publication below is based.
Paper Citation:
Risch Anita C., Zimmermann, Stefan, Moser, Barbara, Schütz, Martin, Hagedorn, Frank, Firn, Jennifer, Fay, Philip A., Adler, Peter B., Biederman, Lori A., Blair, John M., Borer, Elizabeth T., Broadbent, Arthur A.D., Brown, Cynthia S., Cadotte, Marc W., Caldeira, Maria C., Davies, Kendi F., di Virgilio, Augustina, Eisenhauer, Nico, Eskelinen, Anu, Knops, Johannes M.H., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Prober, Suzanne M., Seabloom, Eric W., Siebert, Julia, Silveira, Maria L. , Speziale, Karina L., Stevens, Carly J., Tognetti, Pedro M., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties. Global Change Biology
Please cite this paper together with the citation for the datafile.
We assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth’s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).

en: Anthropogenic change and soil net N mineralization

en: fertilization global-change nitrogen ammonification nitrification grassland mineralization herbivore nutrient-network climate soil

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.

ammonification

display_name: ammonification

climate

display_name: climate

fertilization

display_name: fertilization

global-change

display_name: global-change

grassland

display_name: grassland

herbivore

display_name: herbivore

mineralization

display_name: mineralization

nitrification

display_name: nitrification

nitrogen

display_name: nitrogen

nutrient-network

display_name: nutrient-network

soil

display_name: soil


en:
This dataset contains all data on which the following publication below is based.
Paper Citation:
 - Resch, M.C., Schütz, M., Graf, U., Wagenaar, R., van der Putten, W.H., Risch, A.C. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56: 1782-1793.
Please cite this paper together with the citation for the datafile.
 Methods
 Study area and experimental settings
The study was conducted in a nature reserve (Eigental: 47° 27’ to 47° 29’ N, 8° 37’ E, 461 to 507 m a.s.l.) that is located on the Swiss Central plateau close to Zurich airport (Canton Zurich, Switzerland). The mean annual temperature in this area ranges from 8.9 to 10.6 °C, mean annual precipitation from 910 to 1260 mm [10-year average (2007-2017); MeteoSchweiz, 2018]. The main soil types are calcaric to gleyic Cambisol and Gleysols. The reserve was established in 1967 to protect small remnants of oligotrophic semi-natural grasslands (roughly 12 ha). The plant community can be characterized as Molinion and Mesobromion (semi-wet to semi-dry), depending on the site-specific groundwater level and slope inclination (Delarze, Gonseth, Eggenberg, & Vust, 2015). These remnants represent species-rich islands in an otherwise intensively managed agricultural landscape. Semi-natural grasslands covered an area of 60,000 ha in the Canton Zurich in 1939, however, by 2005 only roughly 600 ha remained (Baudirektion Kanton Zürich, 2007). In 1990, the government of Canton Zurich decided to enlarge the nature reserve Eigental. The goal was to incorporate eleven patches of 20 ha adjacent intensively farmed land and transform these patches into semi-natural grasslands. The patches had a different agricultural history, ranging from permanent (no tillage for -50 years) to temporary grassland (as part of crop rotation; last tillage -5 years). On all freshly integrated patches fertilization was stopped in 1992 and from then on biomass was harvested three times a year and removed. After 5 years without noticeable effects on vegetation composition, the Nature Conservation Agency of Canton Zurich decided to increase the restoration efforts. In 1995, a large-scale experiment was initialized to evaluate if certain treatments can facilitate restoration within a reasonable timeframe of 5 to 10 years after treatment implementation.
The three restoration treatments used were:
i. “Harvest only”: Plots are being mowed two to three times a year and the biomass is removed.
ii. “Topsoil”: Topsoil was removed to a depth of 10 to 20 cm, depending on the depth of the O and A horizon, in four randomly selected areas within each of the eleven patches in late autumn 1995. The size of each topsoil removal area depended on individual patch size and was between 2700 and 7000 m2.
iii. “Topsoil+Propagules”: Propagules from target vegetation were added on half of the area where topsoil was removed, using fresh, seed-containing hay originating from a mixture of semi-dry to semi-wet species-rich grasslands of local provenance (within a radius of 7 km). Hay applications were conducted twice in 1995 and 1996. Repeated applications were chosen to account for the low quantity of available plant material per transfer, since area ratio between receptor and donor sites was roughly 1:1. In addition, hand-collected propagules from 15 selected target species of regional provenance (within a radius of 30 km) were equally applied in 1996 and 1997.
“Topsoil” and “Topsoil+Propagules” plots are mowed once a year, and the biomass is removed. Mowing on these plots started five years after the treatment was implemented.
Eleven permanent plots of 5 m x 5 m were randomly established in each treatment to monitor the vegetation development. The experiment was complemented with 11 control plots that represent the initial state of intensively managed grasslands, further referred to as “Initial”, and 11 control plots that represent the targeted state of donor sites for “Topsoil+Propagules”, further referred to as “Target”. Consequently, the experiment consists of 55 plots (5 treatments x 11 replicates). Management of intensively used grasslands includes mowing and fertilizing (manure) between two to five times a year, as well as different tillage regimes (no tillage for -50 years; last time of tillage -5 years).
 Nematode and plant sampling
Soil nematodes were sampled in 2 m x 2 m plots, randomly established at least 2 m away from the vegetation plots. We collected eight soil cores with a 2.2 cm diameter soil core sampler (Giddings Machine Company, Windsor, CO, USA) to a depth of 12 cm (representing the majority of the plant rooting system) in each plot at the beginning of July 2017. The eight cores within each replicate plot were combined, gently homogenized, placed in coolers and transported to the laboratory of NIOO in Wageningen, the Netherlands, within one week. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink, 1960) and concentrated, resulting in 6 mL nematode solution. The nematode solution was subdivided into three subsamples, two for morphological identification and quantification, and one for molecular work (not used in this study). For morphological identification and quantification, nematodes were heat-killed at 90 °C and fixed in 4 % formaldehyde solution (final volume 10 mL per subsample). All nematodes in 1 mL of formaldehyde solution were counted, and a minimum of 150 individuals per 1 mL sample (or all if less nematodes were present) were identified to family level using Bongers (1988). We then extrapolated the numbers of each nematode taxa identified to the entire sample and expressed them per 100 g dry soil for further analyses.
We calculated number of nematode taxa and Shannon diversity and assessed nematode community composition. In addition, we classified the nematode taxa into feeding types (herbivores, bacterivores, fungivores, omni-carnivores), structural and functional guilds (Table S4). Structural guilds assign nematode taxa according to life-history traits into five colonizer-persister (C-P) classes, ranging from one (early colonizers of new resources) to five (persisters in undisturbed habitats; Bongers 1990). C-P classes can be categorized as indicators for nutrient-enriched (C-P1), stressed (C-P2) and structured (C-P3 + C-P4 + C-P5) soil conditions (Ferris, Bongers, & de Goede, 2001). Functional guilds assign nematode taxa according to their C-P classification combined with their feeding habits (Ferris, Bongers, & de Goede, 2001). Based on the structural and functional guild classification we calculated five additional indices to assess soil nutrient status, disturbance and food web characteristics using NINJA (Sieriebriennikov, Ferris, & de Goede, 2014). 1) The Maturity index indicates the degree of different environmental perturbations (e.g., tillage, nutrient enrichment, pollution) and is used to monitor colonization and subsequent succession after disturbances (Bongers, 1990). 2) The ratio between the Plant Parasite (C-P of herbivorous nematodes only) to Maturity index is used to monitor the recovery of disturbed habitats incorporating information of life-history traits for all feeding types (Bongers, van der Meulen, & Korthals, 1997). 3) The Enrichment index indicates nutrient-enriched soils and agricultural management practices (Ferris, Bongers, & de Goede, 2001). 4) The Structure index provides information about the succession stage of the soil food web and therefore correlates with the degree of maturity of an ecosystem (Ferris, Bongers, & de Goede, 2001). 5) The Channel index provides information about the predominant decomposition pathways, where higher values stand for a higher proportion of energy transformed through the slow fungal decomposition channel (Ferris, Bongers, & de Goede, 2001). In addition, the Structure and Enrichment indices can be displayed in a biplot where nematode assemblages are plotted along a structure (x-axis) and enrichment (y-axis) trajectory (increasing index values). Each biplot quadrat reflects different levels of disturbance, soil nutrient pools and decomposition pathways (Ferris, Bongers, & de Goede, 2001).
The plant surveys were conducted on the 25 m2 permanent plots in June 2017. Plant species cover was visually assessed according to the semi-quantitative cover-abundance scale of Braun-Blanquet (1964; nomenclature: Lauber & Wagner, 1996). We calculated number of species and Shannon diversity, and assessed plant community composition. We also counted the number of target species (all species recorded in the eleven target plots plus propagules of species applied by hand, resulting in a total of 143 species) and categorized plant species into species of concern based on their red list status in Switzerland as well as their protection status in Switzerland and the Canton Zurich (Moser, Gygax, Bäumler, Wyler, & Palese, 2002). Furthermore, we calculated indicator values for soil moisture and soil nutrients for each species according to Landolt et al. (2010).
 References
Baudirektion Kanton Zürich (2007). 10 Jahre Naturschutz-Gesamtkonzept für den Kanton Zürich 1995-2005 – Stand der Umsetzung. Zürich: Baudirektion Kanton Zürich.
Bongers, T. (1988). De nematoden van Nederland. Utrecht: Stichting Uitgeverij Koninklijke Nederlandse Natuurhistorische Vereniging.
Bongers, T. (1990). The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia, 83, 14-19. doi:10.1007/BF00324627
Bongers, T., van der Meulen, H., & Korthals, G. (1997). Inverse relationship between the nematode maturity index and plant parasite index under enriched nutrient conditions. Applied Soil Ecology, 6, 195-199. doi:10.1016/S0929-1393(96)00136-9
Braun-Blanquet, J. (1964). Pflanzensoziologie, Grundzüge der Vegetationskunde (3rd ed.). Wien: Springer.
Delarze, R., Gonseth, Y., Eggenberg, S., & Vust, M. (2015). Lebensräume der Schweiz: Ökologie - Gefährdung - Kennarten (3rd ed.). Bern: Ott.
Ferris, H., Bongers, T., & de Goede, R.G.M. (2001). A framework for soil food web diagnostics: extension of the nematode faunal analysis concept. Applied Soil Ecology, 18, 13-29. doi:10.1016/S0929-1393(01)00152-4
Landolt, E., Bäumler, B., Erhardt, A., Hegg, O., Klötzli, F., Lämmler, W., … Wohlgemuth, T. (2010). Flora indicativa. Ecological indicator values and biological attributes of the Flora of Switzerland and the Alps (2nd ed.). Bern: Haupt.
Lauber, K., & Wagner, G. (1996). Flora Helvetica. Flora der Schweiz. Bern: Haupt.
MeteoSchweiz (2018). Klimabulletin Jahr 2017, Zürich: MeteoSchweiz.
Moser, D., Gygax, A., Bäumler, B., Wyler, N., & Palese, R. (2002) Rote Liste der gefährteten Farn- und Blütenpflanzen der Schweiz. Bern: BUWAL.
Oostenbrink, M. (1960). Estimating nematode populations by some selected methods. In N.J. Sasser & W.R. Jenkins (Eds.), Nematology (pp. 85-101). Chapel Hill: University of North Carolina Press.
Sieriebriennikov, B., Ferris, H., & de Goede, R.G.M (2014). NINJA: An automated calculation system for nematode-based biological monitoring. European Journal of Soil Biology, 61, 90-93. doi:10.1016/j.ejsobi.2014.02.004

en: Grassland restoration: nematodes and plant communities

en: diversity vegetation nematodes restoration topsoil-removal grassland long-term-experiment

en:
Resource Title: Nematode families
    Size: 28500 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c0d0f316-cf15-405f-aca8-bf5167724fbe/resource/82bd6601-d124-4e6d-9508-696b79afe53a/download/nematodes_community.composition_family.level.xlsx
    Description: Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

column 3-41: Nematode abundance (individuals per 100g dry soil) per family; nomenclature according to Bongers 1988

en:
Resource Title: Nematode colonizer-persister classes
    Size: 14565 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c0d0f316-cf15-405f-aca8-bf5167724fbe/resource/b7005b87-699b-4ff1-8c59-82c49ae96d5e/download/nematodes_community.composition_colonizer.persister.level.xlsx
    Description: Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

CP1.a: C-P (colonizer-persister) class 1 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to enrichment indicators according to Ferris, Bongers, & de Goede 2001
CP2.a: C-P (colonizer-persister) class 2 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to stress indicators according to Ferris, Bongers, & de Goede 2001
CP3.a: C-P (colonizer-persister) class 3 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990
CP4.a: C-P (colonizer-persister) class 4 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990
CP5.a: C-P (colonizer-persister) class 5 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

en:
Resource Title: Nematode feeding types
    Size: 20512 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c0d0f316-cf15-405f-aca8-bf5167724fbe/resource/f782653d-25d4-45c6-bf9a-64d921bba0cc/download/nematodes_community.composition_feedingtype.level.xlsx
    Description: Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

He.ecto.a: Herbivorous ectoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

He.endo.a: Herbivorous endoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ba.a: Bacterivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Fu.a: Fungivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Om.a: Omnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ca.a: Carnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

en:
Resource Title: Plant community composition (plant species)
    Size: 64984 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c0d0f316-cf15-405f-aca8-bf5167724fbe/resource/783ee905-958a-4e63-b1eb-6de2dc5b44e1/download/plant_community.composition_species.level.xlsx
    Description: Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

column 3-252: Plant abundance per species (in total 252 plant species) expressed as cover percentage estimation according to van der Maarel (vdM 1979; 7 classes: 1-7); nomenclature  according to Lauber & Wagner (1996)

en:
Resource Title: Correction_Nematodes_vegetation_20200131.xlsx
    Size: 39601 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c0d0f316-cf15-405f-aca8-bf5167724fbe/resource/f13545be-edc5-41fd-86db-0d8a3ea9c16b/download/correction_nematodes_vegetation_20200131.xlsx
    Description: Coordinates of one research plot were wrong. This was corrected in the current version of the file.

en:
Resource Title: Nemadotes and vegetation
    Size: 34575 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c0d0f316-cf15-405f-aca8-bf5167724fbe/resource/f6b57e7e-0d1c-4701-bd0a-695af2744f75/download/nematodes_vegetation.xlsx
    Description: Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

X: X-Coordinate

Y: Y-Coordinate

Ri.veg: Total number of plant species

Div.veg: Shannon diversity of plant species

Ri.nem: Total number of nematode families

Div.nem: Shannon diversity of nematode families

abund: Total nematode abundance (individuals per 100g dry soil)

MI: Maturity index according to Bongers 1990; calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

PPI: Plant parasite index according to Bongers 1990; calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

PPI.MI: Plant parasite to Maturity index according to Bongers, van der Meulen, & Korthals 1997; calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

CI: Channel index according to Ferris, Bongers, & de Goede 2001 (%); calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

EI: Enrichment index according to Ferris, Bongers, & de Goede 2001 (%); calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

SI: Structure index according to Ferris, Bongers, & de Goede 2001 (%); calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

He.a: Herbivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

He.ecto.a: Herbivorous ectoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

He.endo.a: Herbivorous endoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ba.a:  Bacterivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Fu.a: Fungivorous  nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Om.a: Omnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ca.a: Carnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Om.Ca.a	Sum of omnivorous and carnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014
CP1.a: C-P (colonizer-persister) class 1 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to enrichment indicators according to Ferris, Bongers, & de Goede 2001

CP2.a: C-P (colonizer-persister) class 2 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to stress indicators according to Ferris, Bongers, & de Goede 2001

CP3.a: C-P (colonizer-persister) class 3 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

CP4.a: C-P (colonizer-persister) class 4 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

CP5.a: C-P (colonizer-persister) class 5 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

CP3-5.a: Sum of C-P3, C-P4 and C-P5 nematode abundance (individuals per 100g dry soil) equivalent to structure indicators according to Ferris, Bongers, & de Goede 2001

protect.sp.pro: plant species categorized into species of concern based on their red list status in Switzerland as well as their protection status in Switzerland and the Canton Zurich (Moser, Gygax, Bäumler, Wyler, & Palese, 2002)

tar.sp: Number of target species (all species recorded in the eleven target plots plus propagules of species applied by hand, resulting in a total of 143 species)

Indi.moist: Plant indicator values for soil moisture according to Landolt et al. 2010

Indi.nutri: Plant indicator values for soil nutrients according to Landolt et al. 2010

Management: level of mowing and manuring frequency (35-40 kg N ha-1 application-1); intensity1 = mowing once and no manure; intensity2 = 2-3 times mowing and no manure; intensity3 = 2-3 times mowing and manuring; intensity4 = 4-5 times mowing and manuring

Tillage: last time of tillage

Land.use.history: for Initial plots = recent agricultural use; for all restored sites = agricultural use before restoration
Mowing	earliest date of biomass harvesting (excluding “Initial”) based on contracts between farmers and the Nature Protection Agency of Zurich

Soil.type: based on soil mapping survey 1991 (before restoration)

Soil.texture: determined by the sediment method (Gee & Bauder 1986) and classified into soil textural classes according to FAO (1990)

pH: soil pH (CaCl2)

Slope: slope gradient classes according to FAO (1990; %)

diversity

display_name: diversity

long-term-experiment

display_name: long-term-experiment

nematodes

display_name: nematodes

restoration

display_name: restoration

topsoil-removal

display_name: topsoil-removal

vegetation

display_name: vegetation