maintainer: Anita Risch

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

en: Datafile

en:
ID: Plot ID composited of Fence & Treatment number (90 plots total)

Grassland: Name of the six subalpine grasslands where the exclosure setups were erected. In large grasslands (GRIM, MING, STAB), we established four hierarchical exclosure setups (two in short- and two in tall-grass vegetation), whereas in the small grasslands (BOTS, DADA, MARG) only one setup was erected per vegetation type. BOTS = Val dal Botsch, GRIM = Grimmels, MING = Val Mingér, STAB = Alp Stabelchod, DADA = Stabelchod da daint, MARG = Margunet

Fence: Number of exclosure setup (1 - 18). Note that uneven numbers (1,3,5,7,9,11,13,15,17) represent setups established on short-grass vegetation, even numbers (2,4,6,8,10,12,14,16,18) setups established in tall-grass vegetation
Vegetation	Vegetation type, 1 = short-grass vegetation, 2 = tall-grass vegetation; (9 replicate fences on each vegetation type)
Treatment	Progressive animal exclusion, numbers indicate which animals had access to the plot: 1 = Large/medium/small mammals, and invertebrates have access ("L/M/S/I"), 2 = Medium/small mammals, and invertebrates have access (“M/S/I”), 3 = Small mammals and invertebrates have access (“S/I”), 4 = Invertebrates have access (“I”), 5 = No animals have access (“None”)

Bulkden:	Fine fraction soil bulk density (g cm-3)

Soil.moist	: Soil moisture (%)

Soil.org.C: Rhizosphere soil C content (%)

pH: Soil pH

Nema.PC1: First principal component (PC1) of principal component analysis (PCA) of the soil nematode community composition

Nema.PC2: Second principal component (PC2) of principal component analysis (PCA) of the soil nematode community composition

Micro.PC1: 	First principal component (PC1) of principal component analysis (PCA) of the soil microbial community composition

Micro.PC2: Second principal component (PC2) of principal component analysis (PCA) of the soil microbial community composition

Abv.Inv.PC1: First principal component (PC1) of principal component analysis (PCA) of the aboveground-dwelling invertebrate community composition

Abv.Inv.PC2: Second principal component (PC2) of principal component analysis (PCA) of the aboveground-dwelling invertebrate community composition

Arthr.PC1: First principal component (PC1) of principal component analysis (PCA) of the soil arthropod community composition

Arthr.PC2: Second principal component (PC2) of principal component analysis (PCA) of the soil arthropod community composition

Plant.PC1: First principal component (PC1) of principal component analysis (PCA) of the plant community composition

Plant.PC2: Second principal component (PC2) of principal component analysis (PCA) of the plant community composition

Root.biomass: Root biomass (g m-2)

Micro.biomass	: Microbial biomass carbon (mg kg-1)

Plant.richness: Plant species richness

Plant.N: Plant tissue nitrogen content (%)

Soil.N.min: Soil net nitrogen mineralisation (kg ha-1 season)

Soil.resp: Soil carbon dioxide emissions (soil respiration; g m-2 h-1)

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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- R Core Team. 2016. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing
- Risch AC, Haynes AG, Busse MD, Filli F, Schütz M. 2013. The response of soil CO2 fluxes to progressively excluding vertebrate and invertebrate herbivores depends on ecosystem type. Ecosystems 16:1192–202.
- Risch AC, Schütz M, Vandegehuchte ML, Van Der Putten WH, Duyts H, Raschein U, Gwiazdowicz DJ, Busse MD, Page-Dumroese DS, Zimmermann S. 2015. Aboveground vertebrate and invertebrate herbivore impact on net N mineralization in subalpine grasslands. Ecology 96:3312–22.
- Schütz M, Risch AC, Achermann G, Thiel-Egenter C, Page-Dumroese DS, Jurgensen MF, Edwards PJ. 2006. Phosphorus translocation by red deer on a subalpine grassland in the Central European Alps. Ecosystems 9:624–633.
- Schütz M, Risch AC, Leuzinger E, Krüsi BO, Achermann G. 2003. Impact of herbivory by red deer (Cervus elaphus L.) on patterns and processes in subalpine grasslands in the Swiss National Park. For Ecol Manage 181:177–88.
- Vandegehuchte ML, van der Putten WH, Duyts H, Schütz M, Risch AC. 2017a. Aboveground mammal and invertebrate exclusions cause consistent changes in soil food webs of two subalpine grassland types, but mechanisms are system-speciﬁc. Oikos 126:212–23.
- Vandegehuchte ML, Raschein U, Schütz M, Gwiazdowicz DJ, Risch AC. 2015. Indirect short- and long-term effects of aboveground invertebrate and vertebrate herbivores on soil microarthropod communities. PLoS One 10:e0118679.
- Vandegehuchte ML, Schütz M, de Schaetzen F, Risch AC. 2017b. Mammal-induced trophic cascades in invertebrate food webs are modulated by grazing intensity in subalpine grassland. J Anim Ecol 86:1434–46.
- Vandegehuchte ML, Trivellone V, Schütz M, Firn J, de Schaetzen F, Risch AC. 2018. Mammalian herbivores affect leafhoppers associated with specific plant functional types at different timescales. Funct Ecol 32:545–55.
- Wirthner S, Frey B, Busse MD, Schütz M, Risch AC. 2011. Effects of wild boar (Sus scrofa L.) rooting on the bacterial community structure in mixed-hardwood forest soils in Switzerland. Eur J Soil Biol 47:296–302. http://dx.doi.org/10.1016/j.ejsobi.2011.07.003
- Zumsteg A, Luster J, Göransson H, Smittenberg RH, Brunner I, Bernasconi SM, Zeyer J, Frey B. 2012. Bacterial, Archaeal and Fungal Succession in the Forefield of a Receding Glacier. Microb Ecol 63:552–64. https://doi.org/10.1007/s00248-011-9991-8

ecosystem-coupling

display_name: ecosystem-coupling

ecosystem-functions

display_name: ecosystem-functions

ecosystem-multifunctionality

display_name: ecosystem-multifunctionality

exclusion

display_name: exclusion

grassland

display_name: grassland

grazing

display_name: grazing

invertebrates

display_name: invertebrates

subalpine

display_name: subalpine

swiss-national-park

display_name: swiss-national-park

vertebrates

display_name: vertebrates

envidat

en: EnviDat: the Environmental Data Portal

en: Ecosystem coupling and multifunctionality - exclosure experiment


wind-crust-formation-snowmicropen-data

maintainer: EnviDat Support

title_for_slug: wind-crust-formation-snowmicropen-data

en: wind-crust snow smp wind-packing snowmicropen

state: active

type: dataset

en: SMP data

media_type: Link

en: Links to the dataset

format: SERVICE

en: Processing scripts

media_type: application/zip

en: Contains the R-scripts used to process the SMPs

format: ZIP

mimetype: application/zip

en: README

media_type: text/plain

en: Describes the dataset in detail

format: TXT

mimetype: text/plain

en:
This dataset contains the SnowMicroPen (SMP) data from 38 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winters 2015/16 and 2016/17 and include more than 1000 SMP measurements.

The SMPs are organized per experiment. Each experiment subfolder contains the processed SMP profiles and some additional files.

Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.

This dataset is the basis of the following publication:

Sommer C.G., Lehning M., & Fierz C. (2017). Wind tunnel experiments: Saltation is necessary for wind-packing. Journal of Glaciology, 63(242), 950-958. doi:10.1017/jog.2017.53

smp

display_name: smp

snow

display_name: snow

snowmicropen

display_name: snowmicropen

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

it: EnviDat: il portale dei dati ambientali

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.

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Wind crust formation: SnowMicroPen data


maintainer: Nander Wever

en: precipitation-amount shortwave-radiation humidity snow-depth air-temperature longwave-radiation wind-direction wind-speed snow-ice-temperature

en: Version 1

media_type: SMET

en:
Deprecated version. Release notes:

First release of dataset.

en: SNOWPACK ini file (new syntax)

en: This ini-file can be used to run SNOWPACK simulations with the WFJ data set, using the new MeteoIO Filter, Generator and Interpolation syntax.

en: Version 6

en:
Deprecated version.

Release notes:

- Missing ISWR for 2009-09-30 and 2010-04-30 has been replaced by data from WFJ Gipfel

This dataset is the basis for the Weissfluhjoch dataset in ESM-SnowMIP (doi: http://dx.doi.org/10.16904/16).

en: Version 3

en:
Deprecated version. Release notes:

Bug fix: in version 2, the undercatch correction was not applied correctly

Bug fix: in version 2, snow lysimeter data was missing for extended periods of time

Update: dataset extended to 2016-01-02T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Undercatch correction

media_type: application/pdf

en: This document describes how the undercatch correction for the Weissfluhjoch measurement site was determined.

format: PDF

mimetype: application/pdf

en: SNOWPACK sno file

en: This sno-file can be used to run SNOWPACK simulations with the WFJ data set, using version 3.4.0 or higher.

en: Version 7 (current)

en:
Release notes:

Bug fix: in previous versions, snow lysimeter data from September 2010 onwards was erroneously reported for UTC+2, instead of UTC+1 (i.e., reported one hour later than should have been).

Update: dataset extended to 2017-07-11T00:30.

en: Version 5

en:
Deprecated version. Release notes:

Update: using the incoming and outgoing short- and longwave radiation data from the new processing chain from 2010 onwards, filling also data gaps in pre-2010 data.

Update: dataset extended to 2016-10-04T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 2

en:
Deprecated version. Release notes:

Bug fix: in v1, incoming radiation components were taken from WFJ_Gipfel station, instead of WFJ Versuchsfeld.

en: Version 4

en:
Deprecated version. Release notes:

Update: dataset extended to 2016-09-02T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: SNOWPACK ini file (old syntax)

en: This ini-file can be used to run SNOWPACK simulations with the WFJ data set, using version 3.4.0 or higher.

en: All versions

en:
Dataset of meteorological and snowpack measurements from the automatic weather station at Weissfluhjoch, Davos, Switzerland, suitable for driving snowpack models. The dataset contains standard meteorological measurements, and additionally snowpack runoff data from a snow lysimeter. Where possible, data is quality checked and missing data are replaced from backup sensors from the measurement site itself, or (in only a few cases) from the MeteoSwiss weather station at 470 m distance and 150 m above the measurement site.

Publication
Wever, N., Schmid, L., Heilig, A., Eisen, O., Fierz, C., and Lehning, M. Verification of the multi-layer SNOWPACK model with different water transport schemes. 2015. The Cryosphere. Volume 9. 2271-2293. http://dx.doi.org/10.5194/tc-9-2271-2015.

air-temperature

display_name: air-temperature

humidity

display_name: humidity

longwave-radiation

display_name: longwave-radiation

precipitation-amount

display_name: precipitation-amount

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

snow-ice-temperature

display_name: snow-ice-temperature

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

en: WFJ_MOD: Meteorological and snowpack measurements from Weissfluhjoch, Davos, Switzerland


maintainer: Peter Bebi

en: carbon-dioxide soil-temperature experiment vegetation climate-change

en: Project description

en: FACE DATASETS

en: FACE experimental design

media_type: PNG

format: PNG

en:
 Overview
High elevation ecosystems are important in research about environmental change because shifts in climate associated with anthropogenic greenhouse gas emissions are predicted to be more pronounced in these areas than in most other regions of the world. This project involves a Free Air CO2 Enrichment (FACE) and soil warming experiment located in a natural treeline environment near Davos, Switzerland (Stillberg, 2200 m a.s.l.). Elevated atmospheric CO2 concentrations (+200 ppm) were applied from 2001 to 2009, and a soil warming treatment (+4 K) was added in 2007 (ongoing). The combined CO2 enrichment and warming treatment reflects conditions expected to occur in this region in approximately 2050. A broad range of ecological and biogeochemical research is carried out as part of this environmental change project.

#Experimental setup
The experiment consists of 40 hexagonal 1.1 m2 plots, 20 with a Pinus mugo ssp. uncinata (mountain pine, evergreen) individual in the centre and 20 with a Larix decidua (European larch, deciduous) individual in the centre. A dense cover of understorey vegetation surrounds the tree in each plot, including the dominant dwarf shrub species Vaccinium myrtillus (bilberry), Vaccinium gaultherioides (group V. uliginosum agg., northern bilberry) and Empetrum nigrum ssp. hermaphroditum (crowberry) plus several herbaceous and non-vascular species.

At the beginning of the experimental period, the 40 plots were assigned to 10 groups of four neighbouring plots (two larch and two pine trees per group) in order to facilitate the logistics of CO2 distribution and regulation. Half of these groups were randomly assigned to an elevated CO2 treatment while the remaining groups served as controls and received no additional CO2. In spring 2007, one plot of each tree species identity was randomly selected from each of the 10 CO2 treatment groups and assigned a soil warming treatment, yielding a balanced design with a replication of five individual plots for each combination of CO2 level, warming treatment and tree species.

#Data
Soil and air conditions have been monitored closely throughout the study period, with most measurements made during the combined CO2 x warming experiment (2007-2009).

carbon-dioxide

display_name: carbon-dioxide

climate-change

display_name: climate-change

experiment

display_name: experiment

soil-temperature

display_name: soil-temperature

vegetation

display_name: vegetation

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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: FACE: Stillberg CO2 enrichment and soil warming study


en: nfi-photos perception-of-ecosystem-services online-questionnaire-survey adults-and-teenagers visual-attractiveness

en: Questionnaire French

media_type: .pdf

en: Dataset in csv-format

media_type: text/csv

format: CSV

mimetype: text/csv

en: SPSS-Dataset

en: Questionnaire German

media_type: PDF

en: Questionnaire Italian

en: Codebook to SPSS-file

en: This questionnaire survey was conducted as an online survey and aimed at investigating the relationship between physical forest characteristics, visual attractiveness of forest and the perception of ecological and cultural ecosystem services in urban forests. Each participant was shown 6 photos out of a pool of 50 photos taken from the Swiss National Forest Inventory (NFI) database. Physical forest characteristics were derived from the photos. The study was conducted as part of the "WaMos meets LFI" (WML) project.

adults-and-teenagers

display_name: adults-and-teenagers

nfi-photos

display_name: nfi-photos

online-questionnaire-survey

display_name: online-questionnaire-survey

perception-of-ecosystem-services

display_name: perception-of-ecosystem-services

visual-attractiveness

display_name: visual-attractiveness

en: Relationship between physical forest characteristics, visual attractiveness and perception of ecosystem services in urban forests


maintainer: Sven-Eric Hopf

en: iap forest monitoring ash-dieback fraxinus

en: Monitoring of ash trees as part of the Intercantonal Forest Observation Programme

en: Packgroun information on data for the publication: Spread and Severity of Ash Dieback in Switzerland – Tree Characteristics and Landscape Features Explain Varying Mortality Probability, (Klesse et al. 2021, Front. For. Glob. Change 4:645920., doi: 10.3389/ffgc.2021.645920)

en:
In 2013, the Institute for Applied Plant Biology (IAP) started a monitoring programme to study the development and the spatial variation of the ash dieback disease with the aim to find some partially resistant European ash trees (Fraxinus excelsior).
We collaborate as co-authors for the publication: Spread and Severity of Ash Dieback in Switzerland - Tree Characteristics and Landscape Features Explain Varying Mortality Probability (Klesse et al. 2021 in frontiers)

ash-dieback

display_name: ash-dieback

forest

display_name: forest

fraxinus

display_name: fraxinus

iap

display_name: iap

monitoring

display_name: monitoring


en: catchment-data snow-hydrology snowpack meteorological-data rain-on-snow

en: The file contains all data needed to run simulations with the snow cover model SNOWPACK.

en: Meteorological data used to run SNOWPACK for 58 catchments in the Swiss Alps. The data consists of a 2 km grid of "virtual meteorological stations" for each catchment. It was used to simulate snow cover processes during rain-on-snow events, therefore meteorological data of each catchment contains at least one rain-on-snow event. Further information can be found in the attached readme.txt and in Würzer & Jonas et al. (2017), currently under review in Hydrological Processes.

catchment-data

display_name: catchment-data

meteorological-data

display_name: meteorological-data

rain-on-snow

display_name: rain-on-snow

snow-hydrology

display_name: snow-hydrology

snowpack

display_name: snowpack

education

en: Education and science

en: Meteorological data for investigation of rain-on-snow events in 58 catchments in Switzerland


maintainer: Anita C. Risch

en: mineralization global nutrient-network soil grassland soil-chemistry soil-biology worldwide

en:
site.code: Site code

Continent: Continent

sampleID: Sample ID

nn.latitude: Lattitude

nn.longitude: Longitude

bioclim.T.ann: Mean annual temperature (°C)

bioclim.T.var: Temperature variability (°C)

bioclim.T.q.wet: Temperature of the wettest quarter (°C)

bioclim.T.q.dry: Temperature of the driest quarter (°C)

bioclim.T.q.warm: Temperature of the warmest quarter (°C)

bioclim.P.ann: Mean annual preciptiation (mm)

bioclim.P.q.wet: Precipitation of the wettest quarter (mm)

ch.pH: pH

ch.WSK.vol: Water holding capacity (vol%)

ch.bulkdensity.gcm3: Bulk density (g cm-3)

ch.porespace: Pore space (vol%)

ch.sand.perc: Soil texture: sand (%)

ch.silt.perc: Soil texture: silt (%)

ch.clay.perc: Soil texture: clay (%)

ch.NO3start.mgg: NO3 at the beginning of the incubation (mgkg)

ch.Ntot.perc: Total soil N (%)

ch.Ctot.perc: Total soil C (%)

ch.Corg.perc: Soil organic C (%)

ch.netNminfield.mgkgd: Realised soil net N mineralisation (mg kg-1 d-1)

ch.netNminlab.mgkgd: Potential soil ne N mineralisation (mg kg-1 d-1)

archaea.cpg.log: Ammonia oxidising archaea (archaeal amoA gene copies g-1 soil)

bacteria.cpg.log: Ammonia oxidising bacteria (bacterial amoA gene copies g-1 soil)

micr.biom: Microbial biomass (ug g-1 soil)

ch.CtoN: C to N ratio

ch.netNnitriffield.mgkgd.sqrt: Realised soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniffield.mgkgd.sqrt: Realised soil net ammonification, transformed (mg kg-1 d-1)

ch.netNnitriflab.mgkgd.sqrt: Potential soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniflab.mgkgd.sqrt: Potential soil net ammonifiation, transformed (mg kg-1 d-1)

format: XLS

en:
This dataset contains all data on which the following publication below is based.

Paper Citation:

Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Schütz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2

Please cite this paper together with the citation for the datafile.

We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/

global

display_name: global

grassland

display_name: grassland

mineralization

display_name: mineralization

nutrient-network

display_name: nutrient-network

soil

display_name: soil

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide

envidat

en: EnviDat: the Environmental Data Portal

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: climate-change-impact uncertainty rain-on-snow cryosphere snow-water-equivalent natural-climate-variability

en: climate realizations

en: The dataset contains for three variables (snow water equivalent, surface water input and liquid precipitation) 50 realizations of current and future climate periods for two time horizons (mid end end of century), two emission senarions (RCP 4.5 and 8.5) and 10 climate model chains (all EUR11 chains within CH2018). To quantify natural climate variability for projections of snow conditions and resulting rain-on-snow (ROS) flood events, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 x 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The data was extracted in 2021 from original model output.

climate-change-impact

display_name: climate-change-impact

cryosphere

display_name: cryosphere

natural-climate-variability

display_name: natural-climate-variability

snow-water-equivalent

display_name: snow-water-equivalent

uncertainty

display_name: uncertainty

en: Multiple realizations of daily snow water equivalent, surface water input and liquid precipitation projections for mid- and late-century


maintainer: Thorsten Bartels-Rausch

en: arctic snow cryosphere polar laboratory-study antartic sea-salt-aerosol

en: Experimental results of uptake coefficient vs. temperature and ozone concentration

en:
Data to Figure 3: Measured uptake coefficients as a function of temperature at 5E12 molecules per cm3 ozone concentration.

Data to Figure 6: Measured uptake coefficients at different temperatures as a function of gas- phase ozone concentration.

en: Experimental Results: Uptake coefficients vs. temperature and ozone concentration

en:
* Final Data to Figure 3: Uptake coefficients as a function of temperature at 5E12 molecules per cm3 ozone concentration.
* Final Data to Figure 6: Uptake coefficients at different temperatures as a function of gas- phase ozone concentration.

The average ozone uptake coefficients of the three cycles between the bypass and the flow tube per repeat is taken as the mean uptake coefficient for each experiment. Gamma was corrected for gas phase diffusion limitation following the Cooney–Kim– Davis (CKD) method52 (see supporting material of publication for details).

en: The reaction of ozone with bromide in polar regions results in the formation of reactive bromide species with impacts on ozone budget and the oxidative capacity of the lower atmosphere. Here, we present a data investigating the temperature dependence of bromide oxidation by ozone using a coated wall flow tube reactor coated with an aqueous mixture of citric acid and sodium bromide, a proxy for sea salt aerosol in snow or the free troposphere. Thus study shows the effect of of organic species at relatively mild temperatures between the freezing point and eutectic temperature as typical for Earth's cryosphere.

antartic

display_name: antartic

arctic

display_name: arctic

laboratory-study

display_name: laboratory-study

polar

display_name: polar

sea-salt-aerosol

display_name: sea-salt-aerosol

snow

display_name: snow

en: Kinetic experiments on the oxidation of bromide by ozone from 289-245 K.


en: strategic-plans urban-region development-priorities spatial-information europe

en: Planning intentios

en:
The present dataset is part of the report titled Gradinaru S.R., Hersperger A.M., Schmid F. (2021). Deriving Planning Intentions from written planning documents. Report on CONCUR Project- From plans to land change: how strategic spatial planning contributes to the development of urban regions.
The data corresponds to the data collected as part of the DPI Method for deriving all PIs contained in a plan (open coding) as detailed in section 4 of the report. The method involved reading the plans to break down of information in meaningful discrete “incidents” or planning intentions. To identify the planning intentions, the starting points were represented by a) the structuring of the plans in chapters and sub chapters and b) the themes that the plans addressed. Thus, the collected information was not grouped according to pre-defined categories of planning intentions, but rather put together as a list of intentions as revealed by each plan. As a result, we provide, for each case study, a document (named [Urban region name] PI as defined in the plan) which contains:
	Date when the information was filled in.
	Name of the urban region and analysed strategic spatial plan .
	A list of all planning intentions contained in a plan, with each PI being addresses as follows:
	Name of PI as it appears in the plan
	Translated name of the PI (i.e. short name for easy understanding of the meaning)
	Explanation regarding the meaning of the PI
	Why the PI is considered a priority for the urban region
	Spatial information on the PI (text and cartographic representations).
In total, 14 documents are available, one for each case study. Documents contain up to 20 pages of information extracted from the plans together with explanations and notes taken during plan reading.

development-priorities

display_name: development-priorities

europe

display_name: europe

spatial-information

display_name: spatial-information

strategic-plans

display_name: strategic-plans

urban-region

display_name: urban-region

education

en: Education and science

en: Planning intentions in strategic plans of European urban regions


en: biological-control population-genetics simple-sequence-repeats multilocus-genotypes switzerland trichopria-drosophilae drosophila-suzukii nuclear-microsatellite-markers

en: Multilocus genotypes (biallelic coding for 21 nuclear microsatellites) of Trichopria drosophilae described in Gugerli et al. Agrarforschung Schweiz 2019. Missing data denoted as '999'. Population codes (PopNo) are the following: 1, Vezia; 3, Contone ; 5, Davesco; 6, Corteglia; 7, Mezzana; 8, Stabio; 9, Gordola; 10, Sementina; 12, Brusata di Novazzano; 14, Monteggio; 16, Giubiasco; 17, ComoBP; 18, VD; 19, Weil_D; 20, Dossenheim; 21, BioPlanet_Como. Coordinates (CoordCH_E, CoordCH_N) refer to the Swiss coordinates system CH1903.

en: Table comprising all pertinent information for multiplex PCRs of species-specific nuclear microsatellite makers for Trichopria drosophilae, comprising all 21 newly developed markers, details on full sequences with GenBank accession numbers, primer sequences (including labeling and pig-tails), and repeat motives. PCRs with respective annealing temperatures (AnnealingTemp) refer to Type-It Multiplex PCR Kit (Qiagen, Hilden, Germany).

en:
 Nuclear microsatellite markers and genotype data for _Trichopria ddrosophilae_

This data set comprises (i) the characteristics of a set of 21 species-specific nuclear microsatellites for PCR amplification in _Trichopria drosophilae_ (ii) and genotype data for samples collected in southern Switzerland (Canton of Ticino), with few reference samples from Canton of Vaud, southern Germany, and northern Italy (lab-reared population).
Markers were developed by Ecogenics GmbH, Balgach (Switzerland), using MiSeq Nano 2x250 v2 format (on a mix of 10 individuals). Multiplex PCR assays for multilocus genotyping were established by Ecological Genetics (WSL Birmensdorf), and population genetic analyses are found in Gugerli et al., Agrarforschung Schweiz 2019.

biological-control

display_name: biological-control

drosophila-suzukii

display_name: drosophila-suzukii

multilocus-genotypes

display_name: multilocus-genotypes

nuclear-microsatellite-markers

display_name: nuclear-microsatellite-markers

population-genetics

display_name: population-genetics

simple-sequence-repeats

display_name: simple-sequence-repeats

switzerland

display_name: switzerland

trichopria-drosophilae

display_name: trichopria-drosophilae

envidat

en: EnviDat: the Environmental Data Portal

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: Nuclear microsatellite markers for Trichopria drosophilae, parasitoid wasp on Drosophila suzukii


en: frost-change-velocity aridity soil sdms precipitation ground-temperature

media_type: tif

en: Precipitation of driest quarter modified from CHELSA

en: Precipitation seasonality from CHELSA

media_type: TIFF

en: Mean diurnal range modified from CHELSA

format: TIFF

en: Mean annual temperature modified from CHELSA

en: Soil pH modified from SoilGrids

en: Soil clay content modified from SoilGrids

en: Soil organic carbon content modified from SoilGrids

en: Frost change frequency modified from CHELSA

en: The dataset contains seven environmental layers (average annual temperature, aridity [annual precipitation divided by annual potential evapotranspiration], frost change frequency, precipitation in the driest quarter, mean diurnal temperature range, and precipitation seasonality) modified from CHELSA (https://chelsa-climate.org/) and three soil layers (soil organic matter content, pH water, and clay content) modified from SoilGrids (https://soilgrids.org/).

aridity

display_name: aridity

frost-change-velocity

display_name: frost-change-velocity

ground-temperature

display_name: ground-temperature

precipitation

display_name: precipitation

sdms

display_name: sdms

soil

display_name: soil

en: Environmental layers for SDM simulations (GDPlants)


maintainer: Axel Volkwein

en: natural-hazard rockfall field-tests impact-testing field-data experiments mass-movements experiment field-test rockfall-experiments

en: Acceleration measurements

en: The zip file contains five excel files, one for each test conducted. The first sheet contains the raw data and a data set that went through a low-pass filter. The sheet "Allgemein" contains calibration factors for the sensors and time details of a test. The sheets "DiaDaten" and "DiaDatenGefiltert" show the measurements over time. The block and slab kinetics are determined in the sheets "ZeitintegrationBlock" and "ZeitintegrationPlatte". The sheet "VergleichBeschleunigungen" shows the influence of the low-pass-filter and a running average filter on the block accelerations.

en: Raw data (high speed videos, acceleration measurements, laboratory tests)

en:
This file contains

* the high speed videos showing the concrete blocks impacting the gallery roof.
* the raw data of the acceleration measurements
* the data of Tiefbauamt Graubünden containg the tension tests of gallery reinforcment, ME-testing the cushion layer and the tests on concrete samples.

en: Video analysis

en: The zip contains five Excel-files that in turn contain the results of the performed video analyses. The sheet "Daten" contains the pixels of different positions on the blocks that were tracked by the software "Kinovea". The sheet "Auswertung" determines the block kinetics based on the pixel data over time.

en: Five full-scale field tests were conducted with concrete blocks weighting between 800 and 3200 kg being dropped onto the roof of a gallery structure made from reinforced concrete. The impacts were recorded using high-speed video and acceleration measurements at the falling blocks. The dataset contains the raw data as well as the analyses of the block trajectories, i.e. kinetics and dynamics. Setup of the measurements and the analyses conducted are published in Volkwein, A. "Durchführung und Auswertung von Steinschlagversuchen auf eine Stahlbetongalerie", WSL-Berichte, Heft 68, 2018.

experiment

display_name: experiment

experiments

display_name: experiments

field-data

display_name: field-data

field-test

display_name: field-test

field-tests

display_name: field-tests

impact-testing

display_name: impact-testing

mass-movements

display_name: mass-movements

natural-hazard

display_name: natural-hazard

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

education

en: Education and science

en: Rockfall gallery testing Parde 2016


weather-station-davos-wolfgang

maintainer: Jörg Wieder

title_for_slug: weather-station-davos-wolfgang

en: relative-humidity pressure raclets temperature weather

en: Weather Station Davos Wolfgang

en: The data file contains date and time (YYYY-MM-DD hh:mm:ss) in UTC, temperature (°C), relative humidity (%), pressure (hPa) at 10 m above ground and above sea level. The data was averaged over 10 minutes.

en: The dataset contains weather parameters measured at Davos Wolfgang (LON: 9.853594, LAT: 46.835577).

pressure

display_name: pressure

raclets

display_name: raclets

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

weather

display_name: weather

envidat

en: EnviDat: the Environmental Data Portal

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: snow-distribution airborne-laser-scanning snow-depth forest-structure canopy-height

en: Terrain elevation maps

en: Digital elevation model computed from snow-off flights for each of the field areas Davos, Engadine and Grand Mesa

en: Snow depth maps

en: Snow depth maps from two snow-on flights at each of the three field areas Davos (March 20th and 31st 2017), Engadien (March 20th and 31st, 2017) and Grand Mesa (February 8th and 16th, 2017)

en: Canopy height maps

en: Canopy height maps of each of the three field areas Davos, Engadine and Grand Mesa

en: Readme

en:
This dataset includes snow depth, canopy height and terrain elevation maps of forest stands in the Grisons (CH) and at Grand Mesa (CO,USA) derived from airborne lidar. Data were acquired i) within a pilot mission of NASA's Airborne Snow Observatory in the Swiss Alps in March 2017 and ii) during NASA’s SnowEx campaign at Grand Mesa in February 2017. Snow depth maps are available for two dates separated by approx.1 week, and include an area of ca. 0.5km2 for each of the three sites Davos, Engadine and Grand Mesa.
All data were presented and analyzed in the publication 'Revisiting Snow Cover Variability and Canopy Structure within Forest Stands: Insights from Airborne Lidar Data' (Mazzotti et al., 2019, WRR, doi: 10.1029/2019WR024898). This publication must be cited when using this dataset.

Paper Citation:
 > _Giulia Mazzotti; William Ryan Currier; Jeffrey S. Deems; Justin M. Pflug; Jessica D. Lundquist; Tobias Jonas (2019). Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data. Water Resources Research, 55, 6198– 6216, [doi: 10.1029/2019WR024898](https://doi.org/10.1029/2019WR024898)._

airborne-laser-scanning

display_name: airborne-laser-scanning

canopy-height

display_name: canopy-height

forest-structure

display_name: forest-structure

snow-depth

display_name: snow-depth

snow-distribution

display_name: snow-distribution

en: ALS-based snow depth and canopy height maps from flights in 2017 (Grisons, CH and Grand Mesa, CO)


automatic-detection-of-avalanches

maintainer: Matthias Heck

title_for_slug: automatic-detection-of-avalanches

en: avalanche-activity snow monitoring seismic hidden-markov-model

en: Results and Scripts

en: This zip package contains the classification results, coherence results, reference data set, python scripts used for the classification and the start and end times for the pre-processed continuous seismic data. A further readme file is also include for more detailed information.

en: This dataset contains the results obtained by an automatic classification using hidden Markov models of a continuous seismic dataset. To avoid long computational times, we reduced the seismic data using pre-processing step. The start and end times of the windows used for the classification are also included in this dataset. Furthermore, an avalanche reference data set is included and the python scripts used to perform the processing steps and the classification.

avalanche-activity

display_name: avalanche-activity

hidden-markov-model

display_name: hidden-markov-model

monitoring

display_name: monitoring

seismic

display_name: seismic

snow

display_name: snow

education

en: Education and science

en: Automatic detection of avalanches


en: lee-side-flow-field preferential-deposition tls lidar alpine3d

en: Dataset

media_type: ZIP

en:
The data presented here corresponds to the publication "A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation" (Gerber et al., 2017), which investigates an eddy-like structure in the vicinity of the Sattelhorn in the Dischma valley (Davos Switzerland) and its influence on snow accumulation. The dataset contains:

* Alpine3D: Alpine3D snow depth grids (25 m resolution) for two simulations with and without snow redistribution.
* ARPS: 10 ARPS simulations (25 m horizontal resolution) with different model setups (wind direction, wind speed, stability).
* LiDAR: Processed LiDAR PPI (D2_PPI_1h) and RHI (D2_cross_1h) across the valley with a hourly resolution for the period 27 October 2015 01:00 - 29 October 2015c 21:00 (spatial resolution: 25 m).
* meteostations-dischma: Meteorological station data of two meteorological stations in the Dischma valley with 10 minute resolution for the period 28 October 2015 - 30 October 2015.
* TLS: Snow depth change data (m) between 28 October 2015 and 30 October 2015 based on terrestrial laser scans.

For more details about the simulation and observation data, see Gerber et al., 2017.

Publication: Gerber et al., 2017: A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation, submitted to JGR - Atmospheres.

lee-side-flow-field

display_name: lee-side-flow-field

lidar

display_name: lidar

preferential-deposition

display_name: preferential-deposition

tls

display_name: tls

envidat

en: EnviDat: the Environmental Data Portal

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: DISCHMEX - Observations and simulations of the close-ridge small-scale atmospheric flow field and snow accumulation at Sattelhorn, Dischma valley, Davos, Switzerland.


radar-wind-profiler-davos-wolfgang

maintainer: Maxim Hervo

title_for_slug: radar-wind-profiler-davos-wolfgang

en: wind-direction raclets wolfgangpass radar wind-speed

en: Radar Wind Profiler Data

en: A Vaisala Lap3000 was installed at Davos Wolfgang to record wind profiles. The time format is number of days fom January 0, 0000 in UTC with a 10 minute resolution.

en: The RADAR wind profiler from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 2171 m above sea level to 11079 m, with a temporal resolution of 10 minutes.

raclets

display_name: raclets

radar

display_name: radar

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wolfgangpass

display_name: wolfgangpass

en: RADAR Wind profiler Davos Wolfgang


en: anthophila community-indices hymenoptera wild-bee switzerland climate protected-areas urbanization functional-diversity resource-availability biodiversity predictive-modelling elevation-gradient beta-diversity

en: Variable importance, model evaluation, partial dependence plots

media_type: .txt

en: The compress file containes a set of txt files with the variable importance, the model evaluation metrics and the partial dependence plots of each of the responses used (alpha and beta taxonomic and functional diversity metrics; community weighted means of 8 functional traits)

en: Protected areas

media_type: .tiff

en: Protected areas considered, divided in three groups and rasters: protected areas sensu stricto, protected areas sensu lato and all protected areas

en: Taxonomic and functional metrics in the community plots

en:
Response data used for the modeling.
Lat: latitude community plot
Lon: longitude community plot
Richness: species richness
Shannon: Shannon diversity index
FDis: functional dispersion
TOP: Trait Onion Peeling (functional richness)
TED: Trait Even Distribution (functional evenness)
LCBD_taxo: local community contributions to taxonomic beta diversity
LCBD_fun: local community contributions to functional beta diversity
ITD: community weighted means inter-tegula distance
feeding_specialization: community weighted means feeding specialization
tong_length: community weighted means tongue length
phenostart: community weighted means start of the phenology
phenoduration: community weighted means duration of the phenoloy
cleptoparasite: community weighted means cleptoparasites
belowgound: community weighted means proportion belowground
solitary: community weighted means proportion solitary

en: Predictors

en:
The compress file includes the rster files of the predictors used in the analyses, including the climate, resource availability, land-use and beekeeping intensity metrics.

The raster on the climate contains the PCA axes on the CHELSA variables.
The raster on resource availability contains the PCA axes on plant communities
The raster on land-use contains the land-use composition at 2500 m.
The raster on beekeeping intensity contains thedensity of hives within 2500 m

en:
Raw data supporting the paper "Countrywide wild bee taxonomic and functional diversity reveal a spatial mismatch between alpha and beta-diversity facets across multiple ecological gradients". It contains taxonomic and functional metrics in 3343 community-plots distributed across Switzerland. The calculated metrics are:
- Alpha taxonomic community metrics: species richness and Shannon diversity
- Alpha functional community metrics: Functional richness (using the Trait Onion Peeling index, TOP), functional eveness (using the Trait Even Distribution index, TED) and the functional dispersion.
- Community weighted means of 8 functional traits
- The local community contributions on the functional and taxonomic beta diversity (LCBD).

The dataset also includes the following:
- The used predictors to model the spatial distribution of the community metrics (climate PCA, vegetation PCA, land-use metrics, beekeeping intensity).
-The three types of protected areas, defined according to the protective measures.
- The model evaluation, variable importance and partial dependece data.

anthophila

display_name: anthophila

beta-diversity

display_name: beta-diversity

biodiversity

display_name: biodiversity

climate

display_name: climate

community-indices

display_name: community-indices

elevation-gradient

display_name: elevation-gradient

functional-diversity

display_name: functional-diversity

hymenoptera

display_name: hymenoptera

predictive-modelling

display_name: predictive-modelling

protected-areas

display_name: protected-areas

resource-availability

display_name: resource-availability

switzerland

display_name: switzerland

urbanization

display_name: urbanization

wild-bee

display_name: wild-bee

en: Data on wild bee taxonomic and functional diversity in Switzerland


en: amphibians landscape conservation wetlands occupancy-models

en: Readme

en: Description of methods and datasets.

en: Model code

media_type: Rmd

en: This R markdown file contains all code and descriptions necessary to load and format the provided data and to fit the dynamic occupancy models described in the associated publication, including JAGS code for the fitted models.

en: Model code, description

media_type: HTML

en: This is the output of the R markdown file that provides JAGS model code and describes how to format the provided data and fit the dynamic occupancy models described in the associated publication, for a quick overview.

format: HTML

en: Covariate data 1

media_type: CSV

en: This file contains covariate data that does not change over time for 856 ponds: the region the pond is in, whether the pond is old or new (constructed since 1991), elevation, water surface area, fluctuations of the water table (yes, no), the percent forest cover in 100m radius around the pond, the area or large (>6m wide) roads in 1km radius around the pond.

en: Covariate data 2

en: This file contains covariate data that changes over time for 856 ponds in 21 years (long format): whether or not a pond exists in year _t_, the age of constructed ponds in year _t_, structural connectivity of the pond in year _t_.

en: Observation data

en: This file contains detection/non-detection data for the 12 amphibian species between 1999 and 2019 for 3 annual visits. Detectable denotes whether or not a visit j was made to pond i in year t. ObservedID gives a unique ID for the observer collecting the data during visit j to pond i in year t.

en: Original raw data

media_type: XLSX

en: This file contains the original raw monitoring data as provided by the canton of Aargau. Access is restricted and only considered upon request and in consultation with the canton of Aargau.

en:
In the canton of Aargau, hundreds of new ponds have been constructed since the 1990s to benefit declining amphibian populations. This dataset consists of monitoring data for all 12 pond-breeding amphibian species in the canton of Aargau from 1999 to 2019 in 856 ponds, and environmental variables that describe the ponds and the landscape surrounding the ponds. Species observation data is detection/non-detection data from repeat visits during survey years, during which all potentially suitable ponds in an area were surveyed. Environmental variables describing the ponds are whether the pond has been newly constructed since 1991 or not, pond age (if constructed), elevation a.s.l., the water surface area, and whether the water table fluctuates or not. Environmental variables describing the surroundings of the ponds are the percent area of forest within a circular buffer of radius 100m around the pond, the area of large (width ≥6m) roads within a circular buffer of radius 1km around the pond, and structural connectivity.

The canton of Aargau is the owner of the monitoring data; the original datafile is only disclosed upon request and in consultation with the canton of Aargau.

The edited dataset contains cleaned observation data for the 12 amphibian species, as well as compiled and edited covariate data and code to fit dynamic occupancy models.

amphibians

display_name: amphibians

conservation

display_name: conservation

landscape

display_name: landscape

occupancy-models

display_name: occupancy-models

wetlands

display_name: wetlands

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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: Amphibian observation and pond data (Aargau, Switzerland)


en: relative-humidity climate-change meteo long-term swiss-forest-lab meteorology temperature precipitation wind-speed par

en: Data plot of the forest station (LAB)

en: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en: Data plot of the field station (LAF)

en: Site aerial photo (Lausanne)

media_type: image/jpeg

en: Site aerial photo from both Lausanne stations. Source: Google. 2016. Site aerial photo from both Lausanne stations. Retrieved November 11, 2016, from https://www.google.ch/maps

mimetype: image/jpeg

en: Site photo of the forest station (LAB)

en: Site photo of the fforest station from the side.

en: Field station data (LAF)

en: Data file.

en: Forest station data (LAB)

en: Site photo of the field station (LAF)

en: Site photo of the field station from the side.

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Lausanne in Switzerland where one station is located within a natural mixed forest stand (LAB) with European beech (_Fagus sylvatica_; 160-170 yrs), European silver fir (_Abies alba_; 160-170 yrs) and Norway spruce (_Picea abies_; 160-170 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, LAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lausanne is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

en: Lausanne, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards


en: python consistent cryowrf antarctica aws

media_type: .zip

en: Scripts to preprocess (and download for some databases) atmospheric weather station data. Furthermore, a list of stations and a map can be created to have an overview.

en:
There are many sources providing atmospheric weather station data for the Antarctic continent. However, variable naming, timestamps and data types are highly variable between the different sources. The published python code intends to make processing of different AWS sources from Antarctica easier. For all datasets that are taken into account variables are renamed in a consistent way. Data from different sources can then be handled in one consistent python dictionary.

The following data sources are taken into account:

* AAD: Australian Antarctic Division (https://data.aad.gov.au/aws)
* ACECRC: Antarctic Climate and Ecosystems Cooperative Research Centre by the Australian Antarctic Division
* AMRC: Antarctic Meteorological Research Center (ftp://amrc.ssec.wisc.edu/pub/aws/q1h/)
* BAS: British Antarctic Survey (ftp://ftp.bas.ac.uk/src/ANTARCTIC_METEOROLOGICAL_DATA/AWS/; https://legacy.bas.ac.uk/met/READER/ANTARCTIC_METEOROLOGICAL_DATA/)
* CLIMANTARTIDE: Antarctic Meteo-Climatological Observatory by the italian National Programme of Antarctic Research (https://www.climantartide.it/dataaccess/index.php?lang=en)
* IMAU: Institute for Marine and Atmospheric research Utrecht (Lazzara et al., 2012), https://www.projects.science.uu.nl/iceclimate/aws/antarctica.ph
* JMA: Japan Meteorological Agency (https://www.data.jma.go.jp/antarctic/datareport/index-e.html)
* NOAA: National Oceanic and Atmospheric Administration (https://gml.noaa.gov/aftp/data/meteorology/in-situ/spo/)
* Other/AWS_PE: Princess Elisabeth (PE), KU Leuven, Prof. N. van Lipzig, personal communication
* Other/DDU_transect: Stations D-17 and D-47 (in transect between Dumont d’Urville and Dome C, Amory, 2020)
* PANGAEA: World Data Center (e.g. König-Langlo, 2012)

Important notes

* Information about data sources is available. Some downloading scripts are included in the provided code. However, users should make sure to comply with the data providers terms and conditions.
* Given changing download options of the differnent institutions the above links may not permanently work and data has to be retrieved by the user of this dataset.
* No quality control is applied in the provided preprocessing software - quality control is up to the user of the datasets. Some dataset are quality controlled by the owner.

Acknowledgements
--------------------------
We thank all the data providers for making the data publicly available or providing them upon request. Full acknowledgements can be found in Gerber et al., submitted.

References
---------------
Amory, C. (2020). “Drifting-snow statistics from multiple-year autonomous measurements in Adélie Land, East
Antarctica”. The Cryosphere, 1713–1725. doi: 10.5194/tc-14-1713-2020

Gerber, F., Sharma, V. and Lehning, M.: CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB, JGR - Atmospheres, submitted.

König-Langlo, G. (2012). “Continuous meteorological observations at Neumayer station (2011-01)”. Alfred Wegener
Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, doi: 10.1594/PANGAEA.
775173

antarctica

display_name: antarctica

aws

display_name: aws

consistent

display_name: consistent

cryowrf

display_name: cryowrf

python

display_name: python

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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: Preprocessing Antarctic Weather Station (AWS) data in python


maintainer: Matthias Haeni

en: meteorology relative-humidity temperature par wind-speed long-term meteo swiss-forest-lab precipitation climate-change

en: Data plot of the field station (ISF)

en: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en: Site aerial photo (Isone)

en: Site aerial photo from both Isone stations. Source: Google. 2016. Site aerial photo from both Isone stations. Retrieved November 11, 2016, from https://www.google.ch/maps

en: Data plot of the forest station (ISB)

en: Forest station data (ISB)

en: Site photo of the forest station (ISB)

en: Site photo of the forest station from below (viewing upwards the slope).

en: Site photo of the field station (ISF)

en: Field station data (ISF)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Isone in Switzerland where one station is located within a natural broad-leaved forest stand (ISB) with European beech (_Fagus sylvatica_; 70-100 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, ISF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Isone is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

en: Isone, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards


en: European Snow Booklet unpublished metadata

en: The entire station metadata of all European institutions that contributed data is stored in METADATA_unpublished.xlsx. This file, however, cannot be accessed, since not all institutions gave permission to publish their station metadata. If you wish to access the metadata, please send a request to marty@slf.ch stating clearly why you want to access these metadata. NOTE: in this file ONLY station metadata (Lat/Lon/Altitude) is given, NO measured snow depth data.

en: European Snow Booklet public metadata

en:
The core of the European Snow Booklet (ESB), the country reports of 38 European countries, are based on information and metadata on snow measurements collected through a comprehensive survey, the ESB Questionnaire, between August 2017 and March 2018. The station metadata (for example location, elevation, measurement start date) from each country was provided by one or several institutions responsible for snow observations. By answering the questionnaire the contacts agreed that the information and metadata provided could be presented in the ESB. For cases where institutions indicated that information on station locations could be made publicly available, the metadata for the variables snow depth (HS), depth of snowfall (HN) and water equivalent of the snow cover (SWE), is additionally provided here for download (METADATA.xlsx).
Please note that not all institutions gave permission to publish their station metadata. This metadata has to be requested from the non-personal institution contacts provided in the METADATA.xlsx file. In some cases, however, no institutional contact is available and thus the contact of the country representative who filled in the questionnaire is used.
For current, updated station metadata we refer to the (non-personal) institutional contacts provided in METADATA.xlsx and/or the institution webpages. Note, however, that we cannot guarantee for completeness of the dataset for all countries because operational networks are prone to changes.

en: List of authors and list of the editorial board of the European Snow Booklet

en:
Here you can find the list of all lead authors of the different chapters of the European Snow Booklet (ESB).
In addition a list of the editorial board that reviewed the ESB is given.

en: COST European Snow Booklet (ESB) - DORA Link

en: DORA Link of the COST European Snow Booklet (ESB).

en: COST European Snow Booklet (ESB)

en: Please find here the web version of the COST European Snow Booklet (ESB).

en:
The European Snow Booklet (ESB) is a book of reference for snow measurements that has been produced through collaboration with many European snow practitioners and snow scientists in the framework of the European Cooperation in Science and Technology (COST) Action ES1404 “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction (HarmoSnow)”. The ESB provides a unique collection of information about operational snow observations in the European countries and the methods used to perform basic measurements of snow on the ground: snow depth (HS), depth of snowfall (HN), water equivalent of the snow cover (SWE) and presence of snow on the ground (PSG). Information and station metadata (for example location, elevation) for these basic snow variables were collected through a comprehensive survey, the ESB questionnaire between August 2017 and March 2018. Numerous institutions of 38 European countries provided detailed information describing the status of the operational snow observations and the methods used at the time of the survey. Based on the information provided, a country report was written for each European country. Similarities and differences among the countries, that is, the choice of snow variables to be measured, the measurement principles applied, the number of stations, or the spatial and elevational station distribution are pointed out. Thus the collection of country reports demonstrates the relevance of snow measurements for each country. Thus, the intention of the ESB is to foster better knowledge transfer regarding snow measurements between the snow science and operational communities and to improve the communication of information to the general public.

For detailed information on the European countries, we refer to the ESB, which can be downloaded here (envidat.ch). Please note that the ESB is not a living document and information and station metadata are from August 2017 till March 2018, except for Latvia (metadata updated in December 2018).

depth-of-snowfall

display_name: depth-of-snowfall

harmonisation-of-snow-measurements

display_name: harmonisation-of-snow-measurements

snow-depth

display_name: snow-depth

snow-measurement-methods

display_name: snow-measurement-methods

water-equivalent-of-the-snow-cover

display_name: water-equivalent-of-the-snow-cover

education

en: Education and science

en: European Snow Booklet – an Inventory of Snow Measurements in Europe


maintainer: Loïc Pellissier Pellissier

en: functional-traits alps plant-herbivore-interactions community-indices elevation environmental-gradients herbivores-communities

en: Orthoptera surveys

media_type: text/plain

en: Species inventories of Orthoptera

en: Study sites

en: Coordinates and elevation of the study sites

en: Vegetion surveys

en: Species inventories of plants

en: Plant traits

en: Plant functional traits (SLA, LDMC, carbon and nitrogen content, punch strenght; averaged by species)

en: Orthoptera traits

en: Orthoptera functional traits (incisive mandibular strength averaged by species)

en: Nematode surveys

en: Surveys of soil nematode along elevation

en: Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017.

alps

display_name: alps

community-indices

display_name: community-indices

elevation

display_name: elevation

environmental-gradients

display_name: environmental-gradients

functional-traits

display_name: functional-traits

herbivores-communities

display_name: herbivores-communities

plant-herbivore-interactions

display_name: plant-herbivore-interactions

envidat

en: EnviDat: the Environmental Data Portal

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: Above- and below-ground herbivore communities along elevation