maintainer: Anita Risch

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

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

en: Ecosystem coupling and multifunctionality - exclosure experiment


maintainer: Mathias Bavay

en: dew-point-temperature wind-speed air-pressure treeline snow-depth relative-humidity precipitation-sum soil-temperature stillberg shortwave-radiation air-temperature wind-direction

state: active

type: dataset

media_type: application/zip

en: The Davos Stillberg meteorological data (Stillberg_meteo_v1.1.zip) consists of timeseries from five meteorological stations operated at the Stillberg treeline ecological research site. Metadata are provided in the folder 'Stillberg_meteo_metadata' and the data in the folder 'Stillberg_meteo_data' with subfolders for the three quality levels 'raw_standardized', ‘raw_edited’, and ‘filtered’.

format: ZIP

en: The Davos Stillberg meteorological data (Stillberg_meteo_v1.zip) consists of timeseries from five meteorological stations operated at the Stillberg treeline ecological research site. Metadata are provided in the folder 'Stillberg_meteo_metadata' and the data in the folder 'Stillberg_meteo_data' with subfolders for the three quality levels 'raw_standardized', ‘raw_edited’, and ‘filtered’.

en:
 Background information
The Stillberg ecological treeline research site is located in the transition zone between the relatively humid climate of the Northern Alps and the continental climate of the Central Alps. In 1975, 92,000 seedlings of the high-elevation conifer species *Larix decidua* Mill. (European larch), *Pinus cembra* L. (Cembran pine), and *Pinus mugo* ssp. *uncinata* (DC.) Domin (mountain pine) were systematically planted across an area of 5 hectares along an elevation gradient of about 150 metres, with the aim to develop ecologically, technically, and economically sustainable afforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Alongside the ecological long-term monitoring of the afforestation, several meteorological stations have recorded local meteorological conditions at the Stillberg research site. Here, we provide the Davos Stillberg meteorological timeseries of five stations from 1975 (01-01-1975), the year of the afforestation establishment, until the end of the year 2022 (31-12-2022).

 Station description
The five meteorological stations were all installed at the same location (46°46′25.015″N 9°52′01.792″E) at 2090 m a.s.l., in the lower part of the afforestation area. In general, the five stations were operated sequentially (Stillberg_meteo_metadata_stations_v1.csv). However, there are some overlapping time periods when more than one station was operated in parallel. The stations have recorded environmental parameters, such as air and soil temperature, dew point temperature, air pressure, relative humidity, wind direction and velocity, radiation, precipitation, and snow depth (Stillberg_meteo_metadata_parameters_v1.csv). The meteorological measurements were recorded hourly from 1975 until 1996 and have been recorded in 10-minute intervals since 1997.

 Data description
We processed the Davos Stillberg meteorological timeseries with the MeteoIO meteorological data pre-processing library (Bavay & Egger, 2014). Data files are provided for each station and quality level separately and named according to the station (see ‘Stillberg_meteo_metadata_stations_v1.csv’). From the raw data in their original formats, we generated three data quality levels: raw standardized (folder ‘raw_standardized’), edited (folder ‘raw_edited’) and filtered (folder ‘filtered’). The processing level is indicated in the headers of the data files. The whole processing protocol is described in a set of human-readable configuration files that are used by MeteoIO to generate the required data quality levels. This improves long-term reproducibility (Bavay et al., 2022), as the data could be regenerated in the future, even using a completely different software, to account for additional data points or to introduce new data corrections. The first quality level (raw standardized) is generated by parsing the original data files and interpreting them in order to convert all data points to a common format and meteorological parameter naming scheme, while excluding unreadable or duplicated data lines. The generated data files are derivatives of CSV files, with a standardised header that contains the metadata that are necessary to interpret and use the data (use metadata) and to populate a data index (search metadata). The latter is a textual implementation of the Attribute Convention for Data Discovery (ACDD) metadata standard (Attribute Convention for Data Discovery 1-3, 2022). The second quality level (edited) builds on the raw data by performing low-level data editing, such as removing some data periods that are known to be unusable (often based on maintenance records or anecdotal evidence) or applying undocumented calibration factors (for example, when there seems to be an obvious offset on a measured parameter for a period between two documented maintenance operations). The third quality level is generated by applying statistical filters on the data (per station and per meteorological parameter) to exclude presumably wrong values. We did not perform gap filling, as no single strategy could be relied upon that would work best for all possible data usage scenarios.

air-pressure

display_name: air-pressure

air-temperature

display_name: air-temperature

dew-point-temperature

display_name: dew-point-temperature

precipitation-sum

display_name: precipitation-sum

relative-humidity

display_name: relative-humidity

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

soil-temperature

display_name: soil-temperature

stillberg

display_name: stillberg

treeline

display_name: treeline

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

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: Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l.


maintainer: EnviDat Support

en: land-use-transformations land-use-simulations digital-zoning-plans land-use-change spatial-planning

format: SERVICE

en: Legend simulated maps

en:
Table of Content: 1. General context of the data set "LUSzoning”; 2. Background and aims of the study using the data set LUSzoning; 3. The data set LUSzoning.
1. General context of the data set "LUSzoning".
The data set "LUSzoning" stands for Land-use simulations integrating zoning regulations in Spanish functional urban areas. The data set has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2021. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, digital zoning plans) into quantitative land-change modelling approaches at the urban regional level.
2.	Background and aims of the study using the data set “LUSzoning”.
As part of the CONCUR project, a specific task was to integrate planning spatial policies in land-change modelling. Planning can be implemented in modelling using either hard or gradual restrictions. Different studies have addressed the inclusion of spatial planning policies in land-use change modelling. However, the integration of zoning constraints is generally established as hard or Boolean-based restrictions (e.g., whether urban development is allowed or not), while not accounting for the spatial heterogeneity or gradual characteristics within planning zones (e.g., whether planning regulations allow low, medium or high urban density), though these could improve real patterns simulations in urban areas. We assume Spanish General Zoning plans were suitable to explore the integration of planning into land-change modelling as soft constrains because they define land-use intensities in the buildable zoning areas. In light of the above considerations, the overall aim of the study was to model urban land-use changes using a multi-scenario approach that integrates digitized zoning plans for the Functional Urban Areas (FUAs) of Madrid, Barcelona, Valencia, and Zaragoza. The following specific objectives were addressed: i) to analyse the role of planning by defining three future scenarios that integrate digitized zoning plans and one scenario that assumes almost no planning intervention; ii) to introduce zoning constraints that reflect different degrees of urban densities; iii) to generate a transferable spatially-explicit modelling framework to integrate planning into land-use change simulations.
Four future land-use demands scenarios were defined for the FUAs. Storylines were created considering probable development scenarios related to zoning plans, current Spanish legislation and sustainability goals defined along two axes: a high market-oriented vs. high planning-intervention axis, and an axis of short-term economic growth vs. long-term sustainable growth. The sustainable development scenario (S1) is characterized by low gross floor area (GFA) growth that is limited to areas that are currently under development according to zoning plans. The business-as-usual scenario (S2) is characterized by medium GFA growth in the range of on-going trends. The strong development scenario (S3) is characterized by high GFA growth rates. Growth is restricted to buildable areas without urbanization project designated in zoning plans. The unrestricted development scenario (S4) prioritizes a high degree of market liberalization characterized by high GFA growth that surpasses population demands. S4 follows a rapid economic growth pattern with almost no planning intervention.
3.	 The data set “LUSzoning”.
The dataset includes 16 .asc raster layers providing the simulated land-uses under four defined scenarios for Barcelona, Madrid, Valencia and Zaragoza Functional Urban Areas (FUAs) for 2030. The simulated raster layers were created using CLUMondo simulation framework and have a spatial resolution of 30m. The .asc layers name include the name of the FUA and scenario number. For example, the output from simulating the urban growth for the city of Zaragoza under Scenario 2 is named “Zaragoza_S2.tif”. Furthermore, a .txt file named “Legend.txt” includes the numeric value of the land-use and the category of land-use that represents to interpret the .asc raster layers. The name of the land-use classes is a reclassification of the Urban Atlas 2012 land-use classes within the four Spanish FUAs analyzed.

digital-zoning-plans

display_name: digital-zoning-plans

land-use-change

display_name: land-use-change

land-use-simulations

display_name: land-use-simulations

land-use-transformations

display_name: land-use-transformations

spatial-planning

display_name: spatial-planning

en: LUSzoning: Land-use simulations integrating zoning regulations in Spanish functional urban areas


modeling-snow-failure-with-dem

title_for_slug: modeling-snow-failure-with-dem

en: snow snow-micro-mechanics snow-failure snow-avalanche discrete-element-modelling

en: Python file

en: Python file to reproduce the figures in Bobillier et al. (2019)

mimetype: text/x-python

en: Read-me

media_type: application/pdf

en: The read-me file contains a description of all the data provided in the zip file.

format: PDF

mimetype: application/pdf

en: Dataset

en: The zip file contains the data underlying the figures presented in Bobillier et al. (2019). The figures can be reproduced with the python file, which is also provided.

mimetype: application/zip

en: This data set includes the modeling results described in the research article by Bobiller et al. (2020). All the figures in the article can be reproduced with the data provided.

discrete-element-modelling

display_name: discrete-element-modelling

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-micro-mechanics

display_name: snow-micro-mechanics

en: Modeling snow failure with DEM


maintainer: Elisabeth Hafner

en: avalanche satellite natural-hazard hazard-mapping snow

en: SPOT6 Avalanche Outlines 16 January 2019

en: Shapefile of the manually mapped avalanche outlines. Please only use this data together with the example key document provided with this dataset and within the limitations of the dataset described in the publications.

format: SHP

en:
Outlines of 6'041 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 16 January 2019. The dataset was acquired following a period with very high avalanche danger. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping16012019.pdf)

The generation of the data is described in:

Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.

The data was comprehensivly validated in a subset area in

Hafner, E.D.; Techel, F.; Leinss, S.; Bühler, Y., 2021: Mapping avalanches with satellites - evaluation of performance and completeness. Cryosphere, 15, 2: 983-1004. doi: 10.5194/tc-15-983-2021

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

educ

en: Education, culture and sport

en: SPOT6 Avalanche outlines 16 January 2019


disdrometer-data-gotschnagrat

title_for_slug: disdrometer-data-gotschnagrat

en: hydrometeors disdrometer visibility raclets precipitation

en: Disdrometer Data Gotschnagrat

en: The zip folder contains one netCDF file per day for Gotschnagrat from February 11 to March 27 2019 with a temporal resolution of 30 seconds (time in UTC). The naming convention for the files is Gotschna_YYYYMMDD.nc and the files include the following: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

en: A laser optical disdrometer (Parsivel² , OTT Hydromet) was deployed at Gotschnagrat (LON: 9.849, LAT: 46.859) to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms. The dataset contains information on precipitation amount and type for the period of February 11 to March 27 2019 at Gotschnagrat.

disdrometer

display_name: disdrometer

hydrometeors

display_name: hydrometeors

precipitation

display_name: precipitation

raclets

display_name: raclets

visibility

display_name: visibility

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: avalanche remote-sensing reliability avalanche-mapping avalanche-size-estimation

en: AvalancheReliabilityData

en: The .zip contains all data used to conduct the reliability study as well as the results, a detailed description of the included files is contained in the included pdf. All geospatial data is provided in the Swiss Coordinate System: CH1903+ / LV95 (EPSG 2056). For any use of the data the proper citation provided is obligatory!

mimetype: application/x-zip-compressed

en:
This dataset contains the data used and collected to investigate the reliability of avalanche size estimates and avalanche outlines (see related publications). Namely this is

 Avalanche size estimates

* the 10 pictures depicting avalanches
* the survey results from the 170 participants

 Avalanche outlines mapped from oblique photographs
* the pictures of the six avalanches
* the avalanche outlines mapped by the 10 participants
* the reference mapped from the georeferenced photographs

 Avalanche outlines from remotely-sensed imagery
* the orthophotos in the four different resolutions (2m, 1m, 50cm, 25cm)
* the area of interest (AoI) delimiting the research area
* the avalanche mappings in each resolution for all five participants
* the shade masks used to separate illuminated and shaded areas

The acquisition of the orthophotos has been partially supported by the Swiss National Science Foundation (SNF; Grant N°200021_172800).

avalanche-mapping

display_name: avalanche-mapping

avalanche-size-estimation

display_name: avalanche-size-estimation

reliability

display_name: reliability

remote-sensing

display_name: remote-sensing

en: Data reliability study: avalanche size estimates and outlines


maintainer: Yohann Chauvier

en: cluster background-data target-group independent-dataset plant-species survey-effort covariate-correction random-stratified-sampling environmental-stratification point-process-model

en: Non-copyright data, scripts, diversity outputs

en:
Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence-only SDMs that often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence-only models.

Innovation: Recent methods correct for observer bias by using covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1’900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps.

Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as bias covariate (Target-BCC) displayed most realistic modelled species distributions, with a clear positive correlation (r≃0.5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.

background-data

display_name: background-data

cluster

display_name: cluster

covariate-correction

display_name: covariate-correction

environmental-stratification

display_name: environmental-stratification

independent-dataset

display_name: independent-dataset

plant-species

display_name: plant-species

point-process-model

display_name: point-process-model

random-stratified-sampling

display_name: random-stratified-sampling

survey-effort

display_name: survey-effort

target-group

display_name: target-group

en: Novel methods to correct for observer and sampling bias in presence-only species distribution models


maintainer: Giulia Mazzotti

en: snow-distribution forest-structure snow-energy-balance forest-snow snow-modelling

en: Snow depth data open sites

en: Contains all snow detph data accquired at open sites nearby the forest stands during the same period.

format: XLS

en: Coordinates data

en: Includes coordinate information of each surveyed point (7 x 276 = 1932 forest points, 2 open areas)

en: Meteo data to drive FSM2 model

en: Includes meteorological driving data used to run the forest snow model FSM2, processed and parsed in text files that match the format required by FSM2. Separate files are available for each field area (Laret, Drusatscha and Ischlag) and season (2012/13, 2013/14, 2014/15). Each file includes data for the period from October 1st to May 31st at hourly resolution.

en: Snow depth data forest sites

en: Contains all snow depth data acquired at all forest sites during winters 2012/13, 2013714 and 2014/15.

en: Grid point nomenclature

en: The grid structure of each forest site and the respective nomenclature applied to label individual points within each grid are outlined here.  Each point marks the location of snow depth measurements and co-located canopy structure parameters.

en: Canopy data

en: Includes all canopy structure metrics computed at all surveyed locations

en:
This dataset contains all snow, canopy and meteorological data presented and used in the publication: Mazzotti, G., Essery, R., Moeser, D. & Jonas T. (2020) 'Resolving spatial variability of forest snow using an energy-balance model with a 1-layer canopy'. Water Resources Research, https://doi.org/10.1029/2019WR026129.
This publication must be cited when using this dataset.

forest-snow

display_name: forest-snow

forest-structure

display_name: forest-structure

snow-distribution

display_name: snow-distribution

snow-energy-balance

display_name: snow-energy-balance

snow-modelling

display_name: snow-modelling

educ

en: Education, culture and sport

en: Snow depth, canopy structure and meterorological datasets from the Davos area, Switzerland, Winters 2012/13-2014/15, used for high-resolution forest snow modelling


en: davos longwave-radiation weisfluhjoch shortwave-radiation radiation

en: DATA ACCESS

en: ASRB radiation station at the Weissfluhjoch versuchsfeld.

en: Parameter file

media_type: text/plain

en: Description of ASRB parameters.

format: TXT

mimetype: text/plain

en: Metadata

en: Processing doc

en: Description of processing code that was ported to R by P. Frei (2016).

en:
Incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch research site, Davos, CH. The experimental site at the Weissfluhjoch (WFJ, 46.83 N, 9.81 E) is located at an altitude of 2540 m in the Swiss Alps near Davos. During the winter months, almost all precipitation falls as snow at this altitude. As a consequence, a continuous seasonal snow cover builds up every winter, with a maximum snow height ranging from 153–366 cm over the period 1934–2012. The measurement site is located in an almost flat part of a southeast oriented slope.

 References
1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019.
2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.

davos

display_name: davos

longwave-radiation

display_name: longwave-radiation

radiation

display_name: radiation

shortwave-radiation

display_name: shortwave-radiation

weisfluhjoch

display_name: weisfluhjoch

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: ASRB_WFJVF: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos


regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

de: notariate notariatskreise grundbuchkreise konkursamtskreise

label: geocat.ch permalink

fr: WFS für Open-Government-Data des Kantons Zürich (OGD ZH WFS)

format: WFS

fr: Administrative Einteilungen

de: Administrative Einteilungen

en: Administrative Einteilungen

it: Administrative Einteilungen

format: WMS

fr: Zuständigkeitsgebiete der Notariate, Grundbuch- und Konkursämter

spatial: Kanton Zürich

geoinformation-kanton-zuerich

fr: Département de Géoinformation du Canton de Zurich

de: Geoinformation Kanton Zürich

en: Department of Geoinformation of the Canton of Zurich

it: Dipartimento della geoinformazione del Cantone di Zurigo

fr:
Le département de géomatique est le centre de compétence du system d'information géographique du canton de Zurich.
Nous coordinons l'aquisition des géodonnées d'intérêt commun sur le territoire cantonal et les mettons à disposition des
secteurs public et privé et des particuliers.

en:
The department of geoinformation is the geospatial competency center of the Canton of Zurich.
We coordinate the acquisition of geodata within the cantonal territory and provide public, private, and government sectors with data of outstanding quality.

de: Die Abteilung Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Zürich. Wir versorgen Gesellschaft, Wirtschaft und öffentliche Verwaltung mit qualitativ hochstehenden Geodaten und Informationen über den Kanton Zürich.

it: La divisione della geoinformazione è il centro d'eccellenza del Cantone di Zurigo per dati georeferenziati. Mettiamo a disposizione dati geografici e informazioni di alta qualità per l'economia, la società e la pubblica amministrazione su tutto il cantone.

political_level: canton

value: ZH - Geocat Harvester

fr: Notariats-, Grundbuch- und Konkursamtskreise


de: gemeinden gemeindegrenzen administrative grenzen hoheitsgrenzen generalisiert

de: WFS für Open-Government-Data des Kantons Zürich (OGD ZH WFS)

fr: Diese Daten sind mittels Generalisierung der Grenzverläufe aus den Gemeinden 1:5000 abgeleitet und eignen sich für Darstellungen im A4-Format (z.B. 1:260'000). Bezug der GIS-Formate über den Download-Dienst. Grafik-Formate (Illustrator, SVG, JPEG, PDF u.a.) können hier in der Dokumentation unter "Datengrundlage", "Dokumentation (HTML)" heruntergeladen werden. Es stehen ArcMap-Vorlagen zur Verfügung.

fr: Gemeindegrenzen für A4-Darstellungen

de: Gemeindegrenzen für A4-Darstellungen

en: Gemeindegrenzen für A4-Darstellungen

it: Gemeindegrenzen für A4-Darstellungen


de: gemeinden gemeindegrenzen administrative grenzen hoheitsgrenzen

fr: Gemeindegrenzen in Übersichtsplangenauigkeit. Es stehen drei Datensätze zur Verfügung: Gemeinden: Offizieller Verlauf der Gemeindegrenzen, d.h. die Grenze der Stadt Zürich verläuft durch den Zürichsee, die Grenzen der übrigen Zürichsee- und der Greifenseegemeinden verlaufen entlang der Seeufer. Gemeinde-, Bezirks- und Kantonsgrenzen: Anhand dieser Linienversion können für kartografische Zwecke Gemeinde-, Bezirks- und Kantonsgrenzen sowie Grenzen auf Seeflächen unterschieden werden. Gemeinden mit Berücksichtigung von Seen: In dieser Variante sind Zürich-, Greifen-, Pfäffiker-, Chatzen- und Türlersee voll ausgespart.

en: WFS für Open-Government-Data des Kantons Zürich (OGD ZH WFS)

fr: Die GIS-ZH-Gemeindegrenzen werden aus den Grenzen der amtlichen Vermessung (GIS-ZH Nr. 405) aufbereitet. Die Benutzerversionen sind topologisch bereinigt, Kreisbögen sind aufgelöst, und Gemeindegrenzen an Seeufern orientieren sich an den Uferlinien.

fr: Gemeindegrenzen

de: Gemeindegrenzen

en: Gemeindegrenzen

it: Gemeindegrenzen


historische-gemeindegrenzen

title_for_slug: historische-gemeindegrenzen

de: gemeinden gemeindegrenzen administrative grenzen hoheitsgrenzen historisch

it: WFS für Open-Government-Data des Kantons Zürich (OGD ZH WFS)

fr: Fast jedes Jahr erfolgen zwischen den Gemeinden des Kantons Grenzregulierungen, und in jüngerer Zeit fanden Gemeindefusionen statt. Die Gemeindegrenzen lassen sich anhand dieses Datensatzes bis ins Jahr 2001 zurückverfolgen. Er basiert auf den Gemeindegrenzdaten GIS-ZH Nr. 95. Ältere Stände sind in Übersichtsplangenauigkeit (1:5000). Anleitung unter Datengrundlage, Bemerkungen beachten!

fr: Historische Gemeindegrenzen

de: Historische Gemeindegrenzen

en: Historische Gemeindegrenzen

it: Historische Gemeindegrenzen


generalisierte-gemeindegrenzen

title_for_slug: generalisierte-gemeindegrenzen

fr: Diese Daten sind mittels Generalisierung der Grenzverläufe aus den Gemeinden 1:5000 abgeleitet und eignen sich für Darstellungen im Massstabsbereich ca. 1:100'000 bzw. für Format ca. A1.

fr: Generalisierte Gemeindegrenzen

de: Generalisierte Gemeindegrenzen

en: Generalisierte Gemeindegrenzen

it: Generalisierte Gemeindegrenzen


soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

regi

fr: Régions et villes

label: Les services sont exposés au travers de l'API REST d'ArcGIS Server. Interfaces supportées : REST - SOAP - WMS - WFS

label: "Lien vers le géoportail SITG pour prévisualisation de la donnée"

fr: SERVICE DE TELECHARGEMENT

de: SERVICE DE TELECHARGEMENT

en: SERVICE DE TELECHARGEMENT

it: SERVICE DE TELECHARGEMENT

fr: Les données vectorielles sont disponibles sous forme de fichiers.zip, incluant la donnée dans le format choisi, sa fiche de métadonnée (.pdf) ainsi que les conditions générales d'utilisation (.pdf). Sauf exception, les formats suivant sont proposés: SHAPE (ESRI) - Geodatabase-File (ESRI) - GML - KML - CSV

fr: Localisation des emplacements destinés à l'installation ponctuelle de stands à caractère politique, syndical et d'information sur le domaine public de la Ville de Genève.

spatial: Genève

fr: SITG - Système d'information du territoire à Genève

de: SITG - Genfer Geoinformationssystem

en: SITG - The territorial information system of Geneva

it: SITG - Sistema informativo territoriale di Ginevra

fr: Le Système d’Information du Territoire à Genève (SITG) est un organisme fondé sur un réseau de partenaires publics ayant pour but de coordonner, centraliser et diffuser les géodonnées relatives au territoire genevois, produites dans le cadre de leurs missions.

de: Das Genfer Geoinformationssystem (SITG) ist eine Organisation zur Koordination, Zentralisierung und Bereitstellung sämtlicher Geodaten, die durch verschiedene öffentliche Einrichtungen im Rahmen ihrer Missionen produziert werden.

en: The territorial information system of Geneva (SITG) is an organization based on a network of public partners aiming to coordinate, centralize and distribute the geodata of the territory of Geneva.

it: Il Sistema Informativo Territoriale di Ginevra (SITG) è un'organizzazione basata su una rete di partner pubblici con lo scopo di coordinare, centralizzare e pubblicare i dati spaziale del territorio di Ginevra, prodotti come parte delle loro missioni.

value: SITG - Geocat Harvester

fr: VILLE GE - EMPLACEMENTS DESTINES AUX STANDS POLITIQUES, SYNDICAUX ET D'INFORMATION


heal

fr: Santé

de: Gesundheit

en: Health

it: Salute

de: Regionen und Städte

de: Les données vectorielles sont disponibles sous forme de fichiers.zip, incluant la donnée dans le format choisi, sa fiche de métadonnée (.pdf) ainsi que les conditions générales d'utilisation (.pdf). Sauf exception, les formats suivant sont proposés: SHAPE (ESRI) - Geodatabase-File (ESRI) - GML - KML - CSV

fr: Secteur des établissements médico-sociaux (EMS)

fr: SECTEURS DES ETABLISSEMENTS MEDICO-SOCIAUX


itineraires-pour-convois-exceptionnels

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: itineraires-pour-convois-exceptionnels

en: Les données vectorielles sont disponibles sous forme de fichiers.zip, incluant la donnée dans le format choisi, sa fiche de métadonnée (.pdf) ainsi que les conditions générales d'utilisation (.pdf). Sauf exception, les formats suivant sont proposés: SHAPE (ESRI) - Geodatabase-File (ESRI) - GML - KML - CSV

fr: Les itinéraires pour convois exceptionnels sont des routes pouvant être empruntées par des convois exceptionnels, c'est à dire des transports de charges dont le poids et les dimensions dépassent les valeurs limites légales fixées par l'ordonnance sur les règles de la circulation routière (OCR).

fr: ITINERAIRES POUR CONVOIS EXCEPTIONNELS

de: ITINERAIRES POUR CONVOIS EXCEPTIONNELS

en: ITINERAIRES POUR CONVOIS EXCEPTIONNELS

it: ITINERAIRES POUR CONVOIS EXCEPTIONNELS


equipements-et-espaces-publics

regi

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

title_for_slug: equipements-et-espaces-publics

it: Les données vectorielles sont disponibles sous forme de fichiers.zip, incluant la donnée dans le format choisi, sa fiche de métadonnée (.pdf) ainsi que les conditions générales d'utilisation (.pdf). Sauf exception, les formats suivant sont proposés: SHAPE (ESRI) - Geodatabase-File (ESRI) - GML - KML - CSV

fr: Le service de l'observation du territoire de l'office de l'urbanisme (DT) publie les géodonnées relatives aux espaces publics et aux équipements publics du canton. Ces nouvelles géodonnées remplacent entièrement la couche des espaces verts/publics et celle des équipements, précédemment éditées par la direction du plan directeur cantonal. La fusion de ces deux couches distinctes a été motivée par la complémentarité thématique et souvent géographique entre les géodonnées liées aux équipements et aux espaces publics. A noter que cette nouvelle couche unique, dont la structuration a été totalement révisée, est parfois tributaire d'interprétations et par conséquent de choix de catégorisation. De ce fait, les géodonnées contenues dans cette couche, ainsi que leur structuration sont susceptibles d'évoluer ou d'être enrichies par de nouvelles catégories et doivent donc être uniquement considérées à titre informatif. Les données sont organisées selon les principaux volets décrits ci-dessous. ÉQUIPEMENTS - Administration - Cimetières - Cultes - Culture - Enseignement - Santé & social - Sports & loisirs - Transports ESPACES PUBLICS - Parcs - Espaces verts - Places - Espaces piétonniers - Mails ou promenades - Rues piétonnes - Plages Responsable des données liées aux espaces publics : Pierre STOENESCO pierre.stoenesco@etat.ge.ch +41 22 546 73 55 Responsable des données liées aux équipements publics : Marc JUGE marc.juge@etat.ge.ch +41 22 546 73 54 A terme, les espaces publics de proximité seront également géoréférencés.

fr: EQUIPEMENTS ET ESPACES PUBLICS

de: EQUIPEMENTS ET ESPACES PUBLICS

en: EQUIPEMENTS ET ESPACES PUBLICS

it: EQUIPEMENTS ET ESPACES PUBLICS


agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

envi

it: Ambiente

fr: La forêt est utilisée par la société de différentes manières et à diverses intensités. La fonction Coeur_Usages définit les intensités acceptables ou planifiées. Les activités sont RÉGULÉES dans les aires dans lesquelles la protection du milieu forestier est prioritaire comme dans les réserves ; elles sont TOLEREES dans la majorité des peuplements ; elles peuvent être AMENAGEES dans les endroits dans lesquels des infrastructures ont été créés à cet effet ; elles peuvent enfin revêtir un caractère ACCRU sur des surfaces limitées : - ACCRU PARTICULIER : dédiées à des activités particulières limitant les autres usages (comme le tir à l'arc, le vélo de descente, le lasergame, etc.) ; - ACCRU GÉNÉRAL : dans lesquels la pression du public est simplement plus forte (places de pique-nique, zones de liberté pour chiens, etc.). La forêt exerce différentes catégories de fonctions en relation avec la biodiversité: coeur_nature. Les RESERVES sont des espaces au bénéfice d'une protection légale renforcée ; les NOYAUX NATURE ont une surface et une profondeur capables d'héberger la faune sauvage alors que les RELAIS NATURE sont plutôt des lieux de passage. La production de bois est l'une des fonctions de base demandée à la forêt. Certaines surfaces, les SANCTUAIRES, sont exclues de cette fonction et aucun bois n'y est récolté. Dans d'autres, seul le bois abattu pour des objectifs de biodiversité est VALORISE. Dans le reste de la forêt la PRODUCTION est assurée dans le cadre d'une sylviculture proche de la nature.

name: Office cantonal de l'agriculture et de la nature

fr: PLAN DIRECTEUR FORESTIER - FONCTION DE COEUR : USAGE, NATURE ET PRODUCTION


locaux-de-vote

regi

fr: Régions et villes

title_for_slug: locaux-de-vote

fr: SERVICE DE TELECHARGEMENT

fr: Les données vectorielles sont disponibles sous forme de fichiers.zip, incluant la donnée dans le format choisi, sa fiche de métadonnée (.pdf) ainsi que les conditions générales d'utilisation (.pdf). Sauf exception, les formats suivant sont proposés: SHAPE (ESRI) - Geodatabase-File (ESRI) - GML - KML - CSV

fr: Locaux de vote, localisés sur leur adresse. Chaque local de vote est associé à un arrondissement électoral (voir couche GEO_VOTATION_PL)

fr: LOCAUX DE VOTE

de: LOCAUX DE VOTE

en: LOCAUX DE VOTE

it: LOCAUX DE VOTE


bien-fonds-darbres-dornements

title_for_slug: bien-fonds-darbres-dornements

fr: Cette couche consiste à adapter le classement de l'USSP (Union Suisse des Services des Parcs) concu pour l'échelle communale, à l'échelle cantonale. Le Canton de Genève, en tant que canton-ville, rend cette adaptation d'échelle plus simple: le type de zone peut être défini par les zones d'affectations en vigueur. Les utilisateurs peuvent utiliser cette couche pour calculer la situation du bien-fonds pour les calculs des valeurs compensatoires lors d'abattage lié à un projet de construction. Référence normes de l'USSP de 1974.

fr: SITG - Système d'information du territoire à Genève

fr: Le Système d’Information du Territoire à Genève (SITG) est un organisme fondé sur un réseau de partenaires publics ayant pour but de coordonner, centraliser et diffuser les géodonnées relatives au territoire genevois, produites dans le cadre de leurs missions.

fr: BIEN-FONDS D'ARBRES D'ORNEMENTS

de: BIEN-FONDS D'ARBRES D'ORNEMENTS

en: BIEN-FONDS D'ARBRES D'ORNEMENTS

it: BIEN-FONDS D'ARBRES D'ORNEMENTS


educ

fr: Éducation, culture et sport

it: Istruzione, cultura e sport

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

fr: Carte des secteurs géographiques (régions) de la direction des autorisations de construire

fr: REGIONS DE LA DIRECTION DES AUTORISATIONS DE CONSTRUIRE


agglo-adresses

envi

fr: Environnement

title_for_slug: agglo-adresses

fr: AGGLO - ADRESSES GEOREFERENCEES DANS LE PERIMETRE DE L'AGGLOMERATION Cette classe d'objet contient les adresses genevoises, vaudoises et francaises des départements voisins (Ain, Jura, Haute-Savoie) qui sont situées dans le périmètre de l'agglomération (AGGLO). Les adresses genevoises proviennent de la Direction de l'information du territoire - Mensuration officielle Les adresses vaudoises sont fournies par l'Administration Fédérale (geo.admin.ch). Pour les adresses francaises, la source des données est la "Base des Adresses Nationales Ouvertes (BANO)" établie par OpenStreetMap France à partir entre autres de la BAN co-éditée par La Poste et l'IGN. Cette donnée est libre d'accès (licence ODbL). A noter qu'elle n'est pas complétement exhaustive.. Plus d'infos sur : - France : https://www.data.gouv.fr/fr/datasets/base-d-adresses-nationale-ouverte-bano/ - Vaud : https://data.geo.admin.ch/ch.bfs.gebaeude_wohnungs_register/CSV/VD/VD.zip

fr: AGGLO - ADRESSES

de: AGGLO - ADRESSES

en: AGGLO - ADRESSES

it: AGGLO - ADRESSES


bases-dambulances

heal

fr: Santé

title_for_slug: bases-dambulances

fr: Bases d'ambulances du canton de Genève.

de: Bases d'ambulances du canton de Genève.

en: Bases d'ambulances du canton de Genève.

it: Bases d'ambulances du canton de Genève.

fr: BASES D'AMBULANCES

de: BASES D'AMBULANCES

en: BASES D'AMBULANCES

it: BASES D'AMBULANCES


soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

educ

de: Bildung, Kultur und Sport

en: Education, culture and sport

regi

fr: Régions et villes

tran

fr: Transports

fr: SERVICE DE TELECHARGEMENT

de: SERVICE DE TELECHARGEMENT

en: SERVICE DE TELECHARGEMENT

it: SERVICE DE TELECHARGEMENT

fr: Les données vectorielles sont disponibles sous forme de fichiers.zip, incluant la donnée dans le format choisi, sa fiche de métadonnée (.pdf) ainsi que les conditions générales d'utilisation (.pdf). Sauf exception, les formats suivant sont proposés: SHAPE (ESRI) - Geodatabase-File (ESRI) - GML - KML - CSV

fr: Le principe de base consiste à superposer une couche vecteurs récente sur une image d'un plan raster plus ancien et à les comparer. La digitalisation des parcelles Grange à été obtenue par la superposition de la couche CAD_PARCELLE_MENSU (2004) sur le plan raster GRANGE (1896-1911) et par comparaison des objets entre eux, selon la méthode régressive, toutes les parcelles présentant la même géométrie que celles issues de la couche CAD_PARCELLE_MENSU ont été récupérées depuis celle-ci et celles exprimant une différence partielle ou totale ont été digitalisées sur la base du plan raster GRANGE (1896-1911). Cette couche a été créée en 2004 par Monsieur Sylvain Ponserre et Madame Sokhna N'Diaye dans le cadre d'une convention de stage passée avec le département de Géographie de l'Université de Genève pour le Certificat complémentaire en Géomatique. Echelle: 0.04 km

name: Service de l'inventaire des monuments d'art et d'histoire

it: SITG - Sistema informativo territoriale di Ginevra

fr: PARCELLES GRANGE (1896-1911)

de: PARCELLES GRANGE (1896-1911)

en: PARCELLES GRANGE (1896-1911)

it: PARCELLES GRANGE (1896-1911)


agri

fr: Agriculture, pêche, sylviculture et alimentation

regi

de: Regionen und Städte

fr: Le Pôle métropolitain du Genevois francais, la Région de Nyon et l'Etat de Genève se sont associé pour disposer d'une cartographie de l'Infrastructure Écologique (IE) à l'échelle du Grand Genève et de chacun de leur périmètre respectif. Cette IE, élaborée par GE21, est définie comme un réseau d'aires (semi-)naturelles connectées permettant une priorisation des objectifs de conservation de la nature. Elle apporte des données essentielles dans le cadre de diverses démarches engagées sur le territoire du Grand Genève, notamment: - Étude environnementale stratégique de la démarche VTT (Vision Transfrontalière Territoriale) - AMI Zéro Artificialisation Nette dont le Pôle métropolitain du Genevois francais est lauréat - Infrastructure Écologique du Canton de Genève (Stratégies biodiversité suisse et cantonale) Les données de diagnostic de base sont accessible en téléchargement via cette fiche et consultables sur le site https://arcg.is/1vf1KH0. Les données sont spatialement structurées en hexagones d'environ 540 m2 et contiennent les indicateurs suivants: - Diagnostic Biodiversité - Composition - Services Écosystémiques - Connectivité - Structure

fr: SITG - Système d'information du territoire à Genève

de: SITG - Genfer Geoinformationssystem

en: SITG - The territorial information system of Geneva

fr: Le Système d’Information du Territoire à Genève (SITG) est un organisme fondé sur un réseau de partenaires publics ayant pour but de coordonner, centraliser et diffuser les géodonnées relatives au territoire genevois, produites dans le cadre de leurs missions.

de: Das Genfer Geoinformationssystem (SITG) ist eine Organisation zur Koordination, Zentralisierung und Bereitstellung sämtlicher Geodaten, die durch verschiedene öffentliche Einrichtungen im Rahmen ihrer Missionen produziert werden.

en: The territorial information system of Geneva (SITG) is an organization based on a network of public partners aiming to coordinate, centralize and distribute the geodata of the territory of Geneva.

it: Il Sistema Informativo Territoriale di Ginevra (SITG) è un'organizzazione basata su una rete di partner pubblici con lo scopo di coordinare, centralizzare e pubblicare i dati spaziale del territorio di Ginevra, prodotti come parte delle loro missioni.

fr: AGGLO - INFRASTRUCTURE ECOLOGIQUE - DIAGNOSTIC


forets-protectrices

it: Regioni e città

envi

fr: Environnement

title_for_slug: forets-protectrices

de: Les données vectorielles sont disponibles sous forme de fichiers.zip, incluant la donnée dans le format choisi, sa fiche de métadonnée (.pdf) ainsi que les conditions générales d'utilisation (.pdf). Sauf exception, les formats suivant sont proposés: SHAPE (ESRI) - Geodatabase-File (ESRI) - GML - KML - CSV

fr: Forêts de protection contre les dangers naturels tels que l'érosion, les glissements et les chutes de pierres - validées par l'Office fédérale de l'environnement

fr: FORETS PROTECTRICES

de: FORETS PROTECTRICES

en: FORETS PROTECTRICES

it: FORETS PROTECTRICES


educ

fr: Éducation, culture et sport

regi

en: Regions and cities

envi

de: Umwelt

en: Les données vectorielles sont disponibles sous forme de fichiers.zip, incluant la donnée dans le format choisi, sa fiche de métadonnée (.pdf) ainsi que les conditions générales d'utilisation (.pdf). Sauf exception, les formats suivant sont proposés: SHAPE (ESRI) - Geodatabase-File (ESRI) - GML - KML - CSV

fr: Carte des bâtiments construit entre 1919 et 1975. L'architecture à Genève 1919-1975, est un ouvrage en deux volumes publié en 1999 et réédité en 2005 par l'office du patrimoine et des sites (OPS) du département de l'urbanisme (DU), à l'époque département de l'aménagement, de l'équipement et du logement (DAEL), qui a rencontré un succès considérable tant dans les milieux professionnels qu'auprès du grand public. Lauréat du prix INTERASSAR, il est devenu une référence qui contribue significativement à l'information et à la sensibilisation des Genevois à l'égard de leur patrimoine bâti du XXe siècle. L'originalité de l'ouvrage, L'architecture à Genève 1919-1975, a été de proposer un double regard sur la production architecturale des deux générations qui édifièrent la Genève du XXe siècle, celle des pionniers de la modernité des années 1920-1930 et celle de la rénovation urbaine et de l'aménagement du territoire à grande échelle d'après-guerre. D'une part, cette publication aborde, à partir de l'inventaire rigoureux réalisé par l'Institut de théorie et d'histoire de l'architecture de l'École polytechnique fédérale de Lausanne sous la direction du professeur Jean-Marc Lamunière, la question de l'exemplarité des réalisations architecturales et urbanistiques ayant marqué la période considérée. D'autre part, elle propose une lecture descriptive des oeuvres retenues à laquelle le recul historique confère une dimension analytique et critique. L'architecture à Genève 1919-1975 (2 volumes sous coffret avec carte) © République et Canton de Genève, direction du patrimoine et des sites DAEL Auteurs: Isabelle Charollais, Jean-Marc Lamunière, Michel Nemec Editeur. Payot, 1999, ISBN 2-601-03232-4 (réédition 2005) 387 pages et 910 pages /12 x 18 cm broché Méthode d'acquisition: La digitalisation des bâtiments a été obtenue à partir de la couche BATIMENTS HORS-SOL et a été créée en 2013 par Madame Anne-Marie Viaccoz-de Noyers (IMAH).

fr: L'ARCHITECTURE A GENEVE 1919 - 1975

de: L'ARCHITECTURE A GENEVE 1919 - 1975

en: L'ARCHITECTURE A GENEVE 1919 - 1975

it: L'ARCHITECTURE A GENEVE 1919 - 1975


soci

fr: Population et société

ener

fr: Énergie

regi

fr: Régions et villes

en: Environment

it: Les données vectorielles sont disponibles sous forme de fichiers.zip, incluant la donnée dans le format choisi, sa fiche de métadonnée (.pdf) ainsi que les conditions générales d'utilisation (.pdf). Sauf exception, les formats suivant sont proposés: SHAPE (ESRI) - Geodatabase-File (ESRI) - GML - KML - CSV

fr: Le cadastre technique du sous-sol (CTSS) a pour but de recenser et reporter l'ensemble des objets des canalisations, des constructions et des accessoires à la construction, occupant le sous-sol privé et public de facon permanente, et ne figurant ni dans le cadastre officiel, ni dans les cadastres spécifiques des services publics. Le but du cadastre technique est donc de permettre une meilleure maîtrise du sous-sol et de renseigner utilement les requérants et professionnels sur son occupation. Liste des objets répertoriés dans cette classe d'entités: - zone d'influence - zone de pieux de fondation - zone de micro-pieux - autres

name: Direction de l'information du territoire

fr: CADASTRE TECHNIQUE DU SOUS-SOL (CTSS) - GEOTECHNIQUE SURFACE


envi

it: Ambiente

fr: SERVICE DE TELECHARGEMENT

fr: Les données vectorielles sont disponibles sous forme de fichiers.zip, incluant la donnée dans le format choisi, sa fiche de métadonnée (.pdf) ainsi que les conditions générales d'utilisation (.pdf). Sauf exception, les formats suivant sont proposés: SHAPE (ESRI) - Geodatabase-File (ESRI) - GML - KML - CSV

fr: L'Infrastructure Ecologique genevoise (IE) découle de l'objectif 11 d'Aichi défini dans le Plan Stratégique mondial (CBD, 2010), repris par la confédération et la SBG30. Il s'agit de mettre en périmètre protégé 17% de zone terrestre de haute qualité biologique. Toutefois, pour assurer la protection de la biodiversité cantonale, ces hotspots ne se suffisent pas. En effet, selon le Forum Biodiversité Suisse, 13 % de zone supplémentaire sont encore nécessaires pour assurer une mise en réseau cohérente, mais aussi la fonctionnalité des écosystèmes et fournir des services écosystémique (SE) tout en participant à la prospérité économique, sociale et environnementale de la population. La donnée présentée ici est dérivée des couches FFP_IE_DIAGNOSTIC_BIODIVERSITE, FFP_IE_STRUCTURE, FFP_IE_CONNECTIVITE, et FFP_IE_SERVICE_ECOSYSTEMIQUES. Il s'agit de leur conversion d'un format raster vers un format vecteur puis de leur compilation dans une unique couche. Les polygones sont de forme hexagonale de 50m de hauteur pour faciliter la visualisation et représentent la valeur moyenne observée dans les couches originales. Les informations contenues dans la couche sont variés et traitent de la biodiversité de facon multidimensionnelle afin d'en estimer la qualité. Sont compilés des informations d'habitat et de richesse spécifique (pilier composition), de structure des écosystèmes (pilier structure), de fonction des écosystèmes (pilier connectivité) et de service écosystémique (pilier service écosystémique). Des valeurs faibles par pilier indiquent que la zone n'est plus fonctionnelle pour le pilier en question. A l'inverse des valeurs élevées (supérieures à 70 sur 100) indiquent que la zone est de grande importance pour le pilier en question. Les zones de valeurs intermédiaires n'ont pas moins d'intérêt puisqu'elles peuvent être à l'interface entre des zones de haute qualité et venir les dégrader risquerait de compromettre la fonctionnalité des écosystèmes. Cette aggrégation d'informations facilite ainsi la compréhension du diagnostic de la biodiversité et de son état de santé.

fr: SYNTHESE INFRASTRUCTURE ECOLOGIQUE A 50 METRES


cadastre-forestier

agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

title_for_slug: cadastre-forestier

fr: La forêt couvre environ 2'950 hectares de territoire cantonal. Elle est répartie entre les collectivités publiques (55 %) et les privés (45 %). Fait unique en Suisse, la forêt genevoise a depuis longtemps été introduite dans les plans de zones sous l'appellation zone de bois et forêts. Or, cet état de droit n'est, en de nombreux endroits, plus compatible avec la réalité de la nature des lieux; des diminutions ont été opérées (défrichements) et des accrues sont apparues (boisements compensatoires et évolution naturelle), correspondant à la définition légale de la forêt. Les incidences de cette évolution sont d'autant plus importantes que la législation fédérale oblige la séparation stricte entre forêt et zone de construction. La mise à jour régulière vis-à-vis des zones de construction est garantie par une reconnaissance formelle de la nature forestière et par un levé de la lisière par un géomètre accompagnant l'inspecteur cantonal des forêts.

fr: SITG - Système d'information du territoire à Genève

fr: Le Système d’Information du Territoire à Genève (SITG) est un organisme fondé sur un réseau de partenaires publics ayant pour but de coordonner, centraliser et diffuser les géodonnées relatives au territoire genevois, produites dans le cadre de leurs missions.

fr: CADASTRE FORESTIER

de: CADASTRE FORESTIER

en: CADASTRE FORESTIER

it: CADASTRE FORESTIER


envi

fr: Environnement

fr: L'érosion des surfaces agricoles et viticoles doit être évitée pour des raisons d'ordre économique et écologique. Comme la quantité de sol formée chaque année est faible, des pertes de l'ordre d'une tonne par hectare amènent déjà une diminution irréversible du volume d'enracinement pour les plantes. Lorsque les phénomènes érosifs se répètent fréquemment, la capacité du sol à remplir ses fonctions (production, rétention de l'eau, filtration et adsorption de substances toxiques) diminue, et ceci d'autant plus que les pertes sont importantes et que l'épaisseur du sol est faible. La carte du risque d'érosion représente pour les sols cultivés du canton de Genève (grandes cultures, vignes et herbages permanents) la probabilité que la fertilité du sol soit menacée.

fr: CARTE DU RISQUE D'EROSION DES SOLS CULTIVES


ville-fortifications

soci

fr: Population et société

educ

fr: Éducation, culture et sport

it: Istruzione, cultura e sport

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: ville-fortifications

fr: Cette couche représente les anciennes fortifications de Genève, ainsi que les cours d'eaux principaux et certains éléments de voirie, à différentes périodes (1477, 1568, 1630, 1697, 1728). Les anciennes fortifications de la Vieille-Ville de Genève ontété vectorisées en objets. Ce projet est basé sur le travail effectué pour l'ouvrage "Genève, ville forte", le troisième tome réalisé par le service de l'Inventaire des monuments d'art et d'histoire du canton de Genève (IMAHGe). Pour chaque entité, le type de structure ainsi que des dates ont notamment été attribuées. Ces dernières représentent les dates de construction et de destruction de l'objet. Ce travail relève dune interprétation et représente létat des connaissances actuelles.

fr: VILLE - FORTIFICATIONS

de: VILLE - FORTIFICATIONS

en: VILLE - FORTIFICATIONS

it: VILLE - FORTIFICATIONS


regi

fr: Régions et villes

fr: SERVICE DE TELECHARGEMENT

fr: Les données vectorielles sont disponibles sous forme de fichiers.zip, incluant la donnée dans le format choisi, sa fiche de métadonnée (.pdf) ainsi que les conditions générales d'utilisation (.pdf). Sauf exception, les formats suivant sont proposés: SHAPE (ESRI) - Geodatabase-File (ESRI) - GML - KML - CSV

fr: Inventaire des établissements locaux présentant un intérêt historique.

fr: RECENSEMENT DES ETABLISSEMENTS PUBLICS - REPU


fr: La digitalisation des points d'eau Grange a été obtenue par l'identification minutieuse sur le plan raster de 1896-1911 des fontaines, des puits et des pompes. Cette identification a été complétée par la superposition des couches BILLON_EAU_POTABLE et CEARD_EAU_POTABLE sur le plan raster de Grange et par comparaison des objets entre eux selon la méthode régressive. Ainsi, tous les points d'eau présentant la même géométrie que ceux issus des couches CEARD_EAU_POTABLE ou BILLON_EAU_POTABLE ont été récupérées depuis celles-ci et ceux exprimant une différence partielle ou totale ont été digitalisés sur la base du plan Grange. Les symboles retenus sont le triangle pour les fontaines, la sphère pour les puits et le carré pour les pompes. Cette couche a été créée en 2013 par Monsieur Abou Ba dans le cadre d'une convention de stage passée avec le département de Géographie de l'Université de Genève pour le Certificat complémentaire en Géomatique.

fr: SITG - Système d'information du territoire à Genève

fr: POINTS D'EAU GRANGE (1896-1911)

de: POINTS D'EAU GRANGE (1896-1911)

en: POINTS D'EAU GRANGE (1896-1911)

it: POINTS D'EAU GRANGE (1896-1911)


agri

fr: Agriculture, pêche, sylviculture et alimentation

envi

fr: Environnement

fr: L'inventaire forestier de Genève a été effectué en 2004. Ses objectifs sont les suivants: - Fournir les données de base et de contrôle pour la gestion pérenne des massifs boisés (par exemple la structure des classes d'âge, la répartition des essences,les volumes de bois et la production de bois, etc.) - Fournir les données de base et de contrôle pour la gestion pérenne des différentes fonctions assignées à la forêt (présence de la faune, lumière au sol, départs d'érosion, images forestières, dégâts humains, etc.) Les surfaces inventoriées sont délimitées par le tracé du cadastre forestier. Les surfaces de moins de 2 hectares ont été éliminées automatiquement et les cordons boisés trop étroits ont été enlevés manuellement. L'inventaire est basé sur la méthode d'échantillonnage systématique par placettes temporaires. Il s'agit d'une grille calée sur les coordonnées nationales (une placette pour deux hectares et d'un rayon allant de 9 à 13 mètres en fonction de la densité des tiges). 1597 placettes ont été inventoriées pour 3000 hectares de forêt. Le précédent inventaire a eu lieu en 1985.

fr: Le Système d’Information du Territoire à Genève (SITG) est un organisme fondé sur un réseau de partenaires publics ayant pour but de coordonner, centraliser et diffuser les géodonnées relatives au territoire genevois, produites dans le cadre de leurs missions.

fr: INVENTAIRE FORESTIER 2004 - QUANTITE

de: INVENTAIRE FORESTIER 2004 - QUANTITE

en: INVENTAIRE FORESTIER 2004 - QUANTITE

it: INVENTAIRE FORESTIER 2004 - QUANTITE