gletscherstande

title_for_slug: gletscherstande

label: Web GIS Client des Kanton Uri. Die Daten können direkt via dem Portal in den unterschiedlichsten Datenformaten heruntergeladen werden.

spatial: Canton of Uri (UR)

fr: WMS-Dienst des Kanton Uri

de: WMS-Dienst des Kanton Uri

en: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

fr: Die abgebildeten Gletscherstände wurden in der Karte "Gletscherstände im Kanton Uri" im Buch "Geologie des Kantons Uri" (Hrsg.: Naturforschende Gesellschaft Uri) 2011 veröffentlicht. Die Karte enthält eine praktisch vollständige Bestandesaufnahme aller Moränenwälle im Kanton. Erfasst wurden nicht nur die Moränen des letzten Gletscherhochstandes um 1850 (Kleine Eiszeit), sondern auch Moänenablagerungen von Gletschervorstössen während verschiedener Kältephasen in den letzten 16'000 Jahren. Die unterschiedlichen Farben der eingezeichneten Moränen geben einen Hinweis auf das Alter der Ablagerungen und der gleichzeitige Gletschervorstösse in den einzelnen Tälern. Sämtliche Aufnahmen basieren auf Feldkartierungen in den Jahren 1976 bis 2010.

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.

value: Uri-Kt - Geocat Harvester

fr: Gletscherstände

de: Gletscherstände

en: Gletscherstände

it: Gletscherstände


jagdbanngebiete-kantonal-ur

geography

fr: Géographie

de: Geographie

en: Geography

it: Geografia

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

title_for_slug: jagdbanngebiete-kantonal-ur

fr: district-franc geodonnees-de-base

de: jagdbanngebiet geobasisdaten

en: game-reserve official-geodata

it: bandita-di-caccia geodati-di-base

label: geocat.ch permalink

state: active

type: dataset

format: WMS

format: WFS

fr: Dieser Datensatz enthält die Jagdbanngebiete des Kanton Uris (CH168) Die Jagdbanngebiet des Bundes können bei Bedarf über den Geodatenkatalog als WMS eingebunden werden.

geoinformation-kanton-uri

political_level: canton

kanton-uri

capacity: public

type: organization

approval_status: approved

fr: Jagdbanngebiete kantonal (UR)

de: Jagdbanngebiete kantonal (UR)

en: Jagdbanngebiete kantonal (UR)

it: Jagdbanngebiete kantonal (UR)


bodenubersichtskarte

title_for_slug: bodenubersichtskarte

fr: geodonnees type-de-sol sol sols carte-pedologique

de: geodaten bodenart boden boden bodenkarte

en: geodata soil-type soil soil soil-map

it: geodati tipo-di-suolo suolo suolo carta-del-suolo

label: Geobasisdatenkatalog

label: ThurGIS Shop

label: Open Government Data

spatial: Kanton Thurgau (TG)

de: Bodenübersichtskarte

de: Übersichtkarte mit typischen Böden und Bodengesellschaften sowie deren Eigenschaften in der Tiefe

kanton-thurgau

fr: Canton de Thurgovie

de: Kanton Thurgau

en: Canton of Thurgau

it: Cantone di Turgovia

de: Der Kanton Thurgau veröffentlicht hier schrittweise OGD-Datensätze der kantonalen Verwaltung.

value: TG-Kt - Geocat Harvester


en: pairwise-stand-comparisons biogeography biodiversity forest-management soil-impacts

en: headers

media_type: ascii, tab separated, also as excel table

en: Description of headers for data_wohlgemuth_etal_Neobiota_2022

format: SERVICE

en: references

media_type: XLS

en: Bibliography of all references cited in data_wohlgemuth_etal_Neobiota_2022.

format: XLS

media_type: CSV

en: Baseline data

format: CSV

en: Compiled data on the impacts of seven important NNTs (Acacia dealbata, Ailanthus altissima, Eucalyptus globulus, Prunus serotina, Pseudotsuga menziesii, Quercus rubra, Robinia pseudoacacia) on physical and chemical soil and biodiversity in Europe, and summarise commonalities and differences. A total of 107 publications considered, studies referred to biodiversity attributes and soil properties: 2804 lines and 30 rows.

biodiversity

display_name: biodiversity

biogeography

display_name: biogeography

forest-management

display_name: forest-management

pairwise-stand-comparisons

display_name: pairwise-stand-comparisons

soil-impacts

display_name: soil-impacts

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

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

key: guid

key: uri

value: Envidat Harvester

en: Impact of non-native tree species in Europe on soil properties and biodiversity: a review


maintainer: Anita Risch

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

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).

#References

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- Hodel M, Schütz M, Vandegehuchte ML, Frey B, Albrecht M, Busse MD, Risch AC. 2014. Does the aboveground herbivore assemblage influence soil bacterial community composition and richness in subalpine grasslands? Microb Ecol 68:584–95.
- Liu WT, Marsh TL, Cheng H, Forney LJ. 1997. Characterization of microbial diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S rRNA. Appl Environ Microbiol 63:4516–22. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC168770/
- Manning P, van der Plas F, Soliveres S, Allan E, Maestre FT, Mace G, Whittingham MJ, Fischer M. 2018. Redefining ecosystem multifunctionality. Nat Ecol Evol 2:427–36. https://doi.org/10.1038/s41559-017-0461-7
- Meola M, Lazzaro A, Zeyer J. 2014. Diversity, resistance and resilience of the bacterial communities at two alpine glacier forefields after a reciprocal soil transplantation. Environ Microbiol 16:1918–34. https://onlinelibrary.wiley.com/doi/abs/10.1111/1462-2920.12435
- Meyer ST, Koch C, Weisser WW. 2015. Towards a standardized Rapid Ecosystem Function Assessment (REFA). Trends Ecol Evol 30:390–7. http://www.sciencedirect.com/science/article/pii/S0169534715000968
- Norman R., Stucki JW. 1981. The determination of nitrate and nitrite in soil extracts by ultraviolet spectrophotometry. Soil Sci Soc Am J 45:347–53.
- Ochoa-Hueso R. 2016. Non-linear disruption of ecological interactions in response to nitrogen deposition. Ecology 87:2802–2814.
- Oostenbrink M. 1960. Estimating nematode populations by some selected methods. In: Sasser NJ, Jenkins WR, editors. Nematology. Chapel Hill, NC, USA: University of North Carolina Press. pp 85–101.
- R Core Team. 2016. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing
- Risch AC, Haynes AG, Busse MD, Filli F, Schütz M. 2013. The response of soil CO2 fluxes to progressively excluding vertebrate and invertebrate herbivores depends on ecosystem type. Ecosystems 16:1192–202.
- Risch AC, Schütz M, Vandegehuchte ML, Van Der Putten WH, Duyts H, Raschein U, Gwiazdowicz DJ, Busse MD, Page-Dumroese DS, Zimmermann S. 2015. Aboveground vertebrate and invertebrate herbivore impact on net N mineralization in subalpine grasslands. Ecology 96:3312–22.
- Schütz M, Risch AC, Achermann G, Thiel-Egenter C, Page-Dumroese DS, Jurgensen MF, Edwards PJ. 2006. Phosphorus translocation by red deer on a subalpine grassland in the Central European Alps. Ecosystems 9:624–633.
- Schütz M, Risch AC, Leuzinger E, Krüsi BO, Achermann G. 2003. Impact of herbivory by red deer (Cervus elaphus L.) on patterns and processes in subalpine grasslands in the Swiss National Park. For Ecol Manage 181:177–88.
- Vandegehuchte ML, van der Putten WH, Duyts H, Schütz M, Risch AC. 2017a. Aboveground mammal and invertebrate exclusions cause consistent changes in soil food webs of two subalpine grassland types, but mechanisms are system-speciﬁc. Oikos 126:212–23.
- Vandegehuchte ML, Raschein U, Schütz M, Gwiazdowicz DJ, Risch AC. 2015. Indirect short- and long-term effects of aboveground invertebrate and vertebrate herbivores on soil microarthropod communities. PLoS One 10:e0118679.
- Vandegehuchte ML, Schütz M, de Schaetzen F, Risch AC. 2017b. Mammal-induced trophic cascades in invertebrate food webs are modulated by grazing intensity in subalpine grassland. J Anim Ecol 86:1434–46.
- Vandegehuchte ML, Trivellone V, Schütz M, Firn J, de Schaetzen F, Risch AC. 2018. Mammalian herbivores affect leafhoppers associated with specific plant functional types at different timescales. Funct Ecol 32:545–55.
- Wirthner S, Frey B, Busse MD, Schütz M, Risch AC. 2011. Effects of wild boar (Sus scrofa L.) rooting on the bacterial community structure in mixed-hardwood forest soils in Switzerland. Eur J Soil Biol 47:296–302. http://dx.doi.org/10.1016/j.ejsobi.2011.07.003
- Zumsteg A, Luster J, Göransson H, Smittenberg RH, Brunner I, Bernasconi SM, Zeyer J, Frey B. 2012. Bacterial, Archaeal and Fungal Succession in the Forefield of a Receding Glacier. Microb Ecol 63:552–64. https://doi.org/10.1007/s00248-011-9991-8

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


national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia

geography

en: Geography

work

fr: Travail, rémunération

de: Arbeit, Erwerb

en: Work and income

it: Lavoro e reddito

de: geodaten jagd kontrolliertes-jagdgebiet

en: geodata hunting controlled-hunting-zone

de: Jagdreviere

de: Grenzen der Thurgauer Jagdreviere, inklusive der Adressen der Jagdaufseher

kanton-thurgau

en: Canton of Thurgau


maintainer: Thorsten Bartels-Rausch

en: alpine solution interface ozone snow arctic reaction antarctica chemistry heterogeneous brine metamorphism laboratory-study

en: Acetone data

media_type: dat

en:
intensity of the acetone-water-cluster and water-cluster (as derived by a chemical ionisation mass spectrometer) with time in the carrier gas downstream of packed bed flow tube holding the snow sample or the by-pass, respectively. Time "0" denotes the time when the mass spectrometer data acquisition was started. When the carrier
gas with acetone was passed via the packed bed flow tube, that is switched from by-pass to pass the snow, acetone concentration in the carrier gas rapidly dropped due to surface adsorption and slowly recovered as the adsorption sites saturate. From this, the surface area can be obtained.

en: data list

en: Lists file names, snow sample properties, and flow settings for each data-set.

en: Matlab Script to view and plot data

media_type: .m

en: Matlab Data

media_type: mat

en: All data as Matlab mat file

en: Readme file

media_type: application/pdf

en: This file shows data, code, and plots.

format: PDF

mimetype: application/pdf

en: Ozone data

en: Trend of ozone mixing ratio ( _O3mixingRatio_ppb_ with time ( _time_s_ ) as measured downstream of the snow sample during the experiments. Time "0" denotes the time when data acquisition with ozone analyser was started. And, ozone (_nOzone_molec_per_s_ ) loss for each experiment with duration of ozone exposure ( _duration_s_ ) of ozone exposure, as derived from the ozone trends shown above. Time "0" denotes the time when the carrier gas with ozone was passed via the snow sample.

en: Data Figure 1

en: Data of published Figure

en: Earth’s snow cover is very dynamic on diurnal time scales. The changes to the snow structure during this metamorphism have wide ranging impacts such as on avalanche formation and on the capacity of surface snow to exchange trace gases with the atmosphere. Here, we investigate the influence of dry metamorphism, which involves fluxes of water vapor, on the chemical reactivity of bromide in the snow. For this, the heterogeneous reactive loss of ozone at a concentration of 5-6E12 molecules cm-3 is investigated in artificial, shock-frozen snow samples doped with 6.2 uM sodium bromide and with varying metamorphism history. The oxidation of bromide in snow is one reaction initiating polar bromine releases and ozone depletions.

alpine

display_name: alpine

antarctica

display_name: antarctica

arctic

display_name: arctic

brine

display_name: brine

chemistry

display_name: chemistry

heterogeneous

display_name: heterogeneous

interface

display_name: interface

laboratory-study

display_name: laboratory-study

metamorphism

display_name: metamorphism

ozone

display_name: ozone

reaction

display_name: reaction

snow

display_name: snow

solution

display_name: solution

en: Data set on bromide oxidation by ozone in snow during metamorphism from laboratory study


kreiselkataster

geography

de: Geographie

en: Geography

mobility

fr: Mobilité et transports

de: Mobilität und Verkehr

en: Mobility and Transport

it: Mobilità e trasporti

title_for_slug: kreiselkataster

de: Kreisel

de: Beinhaltet alle Kreisel des Kantons Thurgau sowie wichtige Knoten und deren Bezeichnung.

kanton-thurgau

en: Canton of Thurgau

de: Kreiselkataster


greenland-shrubs-and-microclimate

maintainer: Nathalie Chardon

title_for_slug: greenland-shrubs-and-microclimate

en: arctic sdm validation climate field-data accessible-shrub-forest

en: Kangerluarsunnguaq

media_type: .csv

en:
 Collected August 2020 (PI: Nathalie Isabelle Chardon)

These data collected August 2020 (PI: Nathalie Isabelle Chardon). Please contact N. I. Chardon (nathalie.chardon_at_gmail.com) for individual quadrat (i.e. 1 m x 1 m) data, which are averaged at the 90 m spatial grid here.

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

id: sampling zone and unique 90 m grid identifier

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

date: sampling day.month.year

 Species presence [1] or absence [0] at grid level

alcr: _Alnus crispa_

bena: _Betula nana_

dila: _Diapensia lapponica_

emni: _Empetrum nigrum_

hahy: _Harimanella hypoides_

lopr: _Loiseleuria procumbens_

oxpa: _Oxycoccus palustre_

phca: _Phyllodoce caerulea_

rhod: _Rhododendron_ sp.

saar: _Salix arctica_

sagl: _Salix glauca_

sahe: _Salix herbacea_

vaul: _Vaccinium uliginosum_

drin: _Dryas integrifolia_

thpr: _Thymus praecox_

 Habitat characteristics [% cover]

shrub: all shrub species

gram: graminoids

forb: forbs

bryo: bryophytes

lich: lichens

litt: litter

bare: bare ground

stone: stones (> 2 cm)

org: organic crust

 Shrub species height (maximum vegetative height) [mm] and cover (estimated in 1x1 m quadrat) [%]

alcr_c: _Alnus crispa_ cover

alcr_h: _Alnus crispa_ height

bena_c: _Betula nana_ cover

bena_h: _Betula nana_ height

dila_c: _Diapensia lapponica_ cover

dila_h: _Diapensia lapponica_ height

emni_c: _Empetrum nigrum_ cover

emni_h: _Empetrum nigrum_ height

hahy_c: _Harimanella hypoides_ cover

hahy_h: _Harimanella hypoides_ height

lopr_c: _Loiseleuria procumbens_ cover

lopr_h: _Loiseleuria procumbens_ height

oxpa_c: _Oxycoccus palustre_ cover

oxpa_h: _Oxycoccus palustre_ height

phca_c: _Phyllodoce caerulea_ cover

phca_height: _Phyllodoce caerulea_ height

rhod_c: _Rhododendron_ sp. cover

rhod_h: _Rhododendron_ sp. height

saar_c: _Salix arctica_ cover

saar_h: _Salix arctica_ height

sagl_c: _Salix glauca_ cover

sagl_h: _Salix glauca_ height

sahe_c: _Salix herbacea_ cover

sahe_h: _Salix herbacea_ height

vaul_c: _Vaccinium uliginosum_ cover

vaul_h: _Vaccinium uliginosum_ height

drin_c: _Dryas integrifolia_ cover

drin_h: _Dryas integrifolia_ height

 Community characteristics

rich_pa: shrub species richness at grid level

rich: shrub species richness averaged across 3 1x1 m sampling quadrats per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on _Betula nana_ (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on _Salix glauca_ (i.e. summed cover of non-_S. glauca_ species) [% cover]

ht_cwm: maximum shrub canopy height (i.e. community weighted mean by abundance of maximum shrub height) [mm]

 Topography and terrain wetness (calculated from)

tcws: Tasseled Cap Wetness component (Sentinal images)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

 Downscaled climate (time series 1984 – 2013)

insoljja: average June-August insolation [MJ cm-2 yr-1]

precipmam: cumulative March-May precipitation [mm]

tempcont: yearly maximum – minimum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

precipjja: cumulative June-August precipitation [mm]

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

en: Nuup Kangerlua

en:
 Collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen)

These data collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen). Please contact J. Nabe-Nielsen (jnn_at_ecos.au.dk) to request access to data on all surveyed shrub species. Available here are only data used in Chardon et al. 2022.

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

year: sampling year

alt: average altitude of grid as recorded during field surveys [m]

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

 Species presence [1] or absence [0] at grid level

pa_bena: _Betula nana_

pa_sagl: _Salix glauca_

 Shrub species cover (calculated from pin hits in 0.7 x 0.7 m frame, based on total of 25 hits) [%]

cov_bena: _Betula nana_ cover

cov_sagl: _Salix glauca_ cover

 Downscaled climate (time series 1984 – 2013)

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

tempcont: yearly maximum – minimum temperature [ºC]

precipmam: cumulative March-May precipitation [mm]

precipjja: cumulative June-August precipitation [mm]

insoljja: average June-August insolation [MJ cm-2 yr-1]

 Topography and terrain wetness (calculated from)

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

tcws: Tasseled Cap Wetness component (Sentinal images)

 Community characteristics

rich: shrub species richness averaged across pin-point frames per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on Betula nana (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on Salix glauca (i.e. summed cover of non-_S. glauca_ species) [% cover]

en: GitLab Repository

media_type: .zip

en: Zip drive of GitLab repository contents (https://gitlab.com/nathaliechardon/gl_microclim).

en:
 Study Aim

We collected these data to alternatively train and validate high resolution (~ 90 m) Species Distribution Models (SDMs) and Species Abundance Models (SAMs) for _Betula nana_ L. (dwarf birch, Betulaceae) and _Salix glauca_ L. (grey willow, Salicaceae) in Southwest Greenland to assess how well such models can predict local-scale patterns.

 Data Description

Individual (presence-absence, abundance, maximum vegetative height) and community (species composition, maximum canopy height) shrub data for two fjords near Nuuk, Southwest Greenland. Also provided are corresponding downscaled climate data as well as calculated topographic and terrain wetness indicator variables.

 Nuup Kangerlua (Godthåbsfjord)

_Betula nana_ and _Salix glauca_ presence-absence, abundance, community species richness

 Kangerluarsunnguaq (Kobbefjord)

Shrub presence-absence, abundance, maximum vegetative height, community composition, maximum shrub canopy height

 Methods

 Field survey in Nuup Kangerlua

We conducted a stratified systematic plant survey along the length of Nuup Kangerlua (NK) fjord in Soutwesth Greenland (Fig. 1 in Chardon et al. 2022; following Nabe-Nielsen et al., 2017). At five distinct sites, we sampled along elevational gradients to collect data on presences, absences, abundance, and species composition of all woody species using a 0.7 x 0.7 m pin-point frame (Fig. 1e in Chardon et al. 2022). For model training, we converted these pin-point data to percent cover estimates based on the number of pins dropped (n = 25 per plot) and averaged them across the 119 spatio-climatic grids (see next section) corresponding to the plot locations (for details see Appendix S2 in Chardon et al. 2022).

 Field survey in Kangerluarsunnguaq

We conducted a random stratified plant survey in Kangerluarsunnguaq (K) fjord in Southwest Greenland. We used a preliminary Species Abundance Model trained with summed pin counts of _Betula nana_ in NK fjord (see Fig. S1.3 in Chardon et al. 2022) to stratify the ~ 27 x 17 km fjord landscape into low, medium, and high abundances classes. We randomly selected 90 x 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2 in Chardon et al. 2022). Within each grid, we sampled within three 1 m2 quadrats arranged in a randomly rotated equilateral triangle centered on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Fig. 1h in Chardon et al. 2022) to record woody species presences, absences, and composition, estimated percent cover, and measured maximum shrub species vegetatitve height. At every plot, we also visually scanned the area in a 20 m radius from the plot and recorded the presence of any additional shrub species to estimate grid-level species richness. As in NK fjord, we averaged these data at the grid level (for details see Appendix S2 in Chardon et al. 2022).

 Biotic variables

We calculated biotic microscale variables from the plant survey data collected in NK and K fjords. We calculated shrub species richness, diversity, and competition (i.e. sum of non-B. nana or non-S. glauca pin hits or percent cover). In K fjord, we also calculated canopy height as the community weighted mean (by abundance) of maximum vegetative shrub height.

 Climate variables

We computed high resolution temperature, precipitation, and insolation for local scale data for the study area by statistically downscaling climate time series (1982 - 2013) from the monthly CHELSA data (Karger et al. 2017). We downscaled these data from 30 arc sec (~ 400 m at the latitude of our study) to our target grid size of ~ 90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1 (Howat et al., 2014, 2015). We then calculated 30-year averages of the climate parameters: average summer (June – August) maximum temperature, yearly maximum temperature, yearly minimum temperature, temperature continentality (yearly max. - min. temperatures), cumulative Spring (March – May) precipitation, cumulative summer precipitation, and average summer incident solar radiation (henceforth, insolation) (for calculation details see Appendices S2, S3 in Chardon et al. 2022 and Appendix S2 in von Oppen et al. 2021).

 Topography and terrain wetness indicator variables

We calculated several topographic and terrain wetness indices at a local scale. We derived slope, aspect, and the SAGA wetness index (hereafter TWI; Boehner et al., 2002; Boehner and Selige, 2006) from the GIMP DEM. TWI is a measure of how ‘wet’ an area is, based on water drainage from the surrounding landscape. We also calculated the tasseled cap wetness component (hereafter TCW, Crist and Cicone 1984) from satellite images (for details see Appendices S2, S3 in Chardon et al. 2022) as an alternative measure of wetness.

 Computer code

Attached as zip file and available on GitLab (https://gitlab.com/nathaliechardon/gl_microclim)

 Third-party data

Data used to calculate climate, topography, and terrain wetness indicator variables are publicly available (see Appendix S2 in Chardon et al. 2022 for all data references).

accessible-shrub-forest

display_name: accessible-shrub-forest

climate

display_name: climate

field-data

display_name: field-data

sdm

display_name: sdm

validation

display_name: validation

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Greenland shrubs and microclimate


territory

de: Raum und Umwelt

en: Territory and environment

de: larmempfindlichkeitsstufe geobasisdaten

fr: WMS-Dienst des Kanton Uri

de: WMS-Dienst des Kanton Uri

en: WMS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

fr: Ab der öffentlichen Auflage werden gemäss Lärmschutz-Verordnung (LSV, SR 814.41) für alle Nutzungszonen Lärmempfindlichkeitsstufen dargestellt. Es werden die Empfindlichkeitsstufen keine ES, ES I, ES II, ES III und ES IV unterschieden. Ausserdem können Teile von Nutzungszonen der ES I und der ES II der nächst höheren Stufe zugeordnet werden, wenn sie mit Lärm vorbelastet sind.

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.


en: species-abundance-distributions arthropods insects diversity sdms birds snails urban-ecology species-richness-models spiders urbanization

media_type: TXT

en: contains the raw data to perform the species richness models of the hypercommon species

format: TXT

en: contains the raw data to perform species distribution models, only for common species

en: abundance and occurrence data of the 1446 species studied, used to investigate species-abundance distributions and species-occurrence distributions

en: contains the raw data to perform the species richness models of the rare species

en: contains the raw data to perform the species richness models of all species

en:
Richness, site occurrence and abundance data of bees, beetles, birds, hoverflies, net-wingeds, true bugs, snails, spiders, milipides, wasps collected in the city of Zurich using different sampling techniques, and the environmental variables for each sampling site.
Data are provided on request to contact person against bilateral agreement.

arthropods

display_name: arthropods

birds

display_name: birds

diversity

display_name: diversity

insects

display_name: insects

sdms

display_name: sdms

snails

display_name: snails

species-abundance-distributions

display_name: species-abundance-distributions

species-richness-models

display_name: species-richness-models

spiders

display_name: spiders

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization

education

de: Bildung, Wissenschaft

envidat

de: EnviDat: das Umwelt-Datenportal

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.

en: Ecological properties of urban ecosystems. Biodiversity dataset of Zurich


culture

fr: Culture, médias, société de l'information, sport

de: Kultur, Medien, Informationsgesellschaft, Sport

en: Culture, media, information society, sport

it: Cultura, media, società dell'informazione, sport

energy

fr: Energie

de: Energie

en: Energy

it: Energia

geography

en: Geography

fr: WFS-Dienst des Kanton Uri

fr: Die Versorgungsgebiete bezeichnen und bestimmen die Netzgebiete der im Kanton Uri tätigen Stromnetzbetreiber gemäss Stromversorgungsgesetz (StromVG) Ein Netzgebiet ist die räumliche Ausdehnung des Stromnetzes des jeweiligen Netzbetreibers, an dem Endverbraucher angeschlossen sind. Ziel der Netzzuteilung ist die vollständige Abdeckung des gesamten Kantonsgebiets ohne verwaiste Gebiete. Aus rechtlicher Sicht ist die Netzzuteilung eine Konzession die den Netzbetreiber berechtigt und verpflichtet, das jeweilige Netzgebiet mit Strom zu versorgen. Private Haushalte verwenden die niedrigste Spannungsebene (Netzebene 7) und industrielle Verbraucher höhere Spannungsebenen (Netzebene 3 und 5).

fr: Stromversorgungssicherheit: Netzebene 5 (UR)


abwasseranlagen

fr: Géographie

territory

fr: Territoire et environnement

title_for_slug: abwasseranlagen

de: leitungskataster geobasisdaten

en: pipeline-cadastre official-geodata

it: catasto-delle-condotte geodati-di-base

it: WMS-Dienst des Kanton Uri

fr: In diesem Datensatz werden alle Leitungen für verschmutztes und nicht verschmutztes Abwasser erfasst (Art. 26. Kantonales Umweltschutzgesetz, KUG). Weiter enthält dieser Datensatz Abwasserreinigungsanlagen, Versickerungsanlagen und Sonderbauwerke.

fr: Abwasseranlagen

de: Abwasseranlagen

en: Abwasseranlagen

it: Abwasseranlagen


maintainer: EnviDat Support

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

en: Forest station data (SCB)

en: Data file.

en: Site aerial photo (Schänis)

media_type: JPEG

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

en: Field station data (SCF)

media_type: text/csv

mimetype: text/csv

en: Site photo of the field station (SCF)

en: Site photo of the field station from above (viewing downwards the slope).

en: Data plot of the field station (SCF)

media_type: PDF

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

en: Site photo of the forest station (SCB)

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

en: Further details on the data repository

en: Meta data documentation.

en: Data plot of the forest station (SCB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Schänis in Switzerland where one station is located within a natural mixed forest stand (SCB) with European beech (_Fagus sylvatica_; 130-150 yrs), European silver fir (_Abies alba_; 130-150 yrs) and European ash (_Fraxinus excelsior_; 130-150 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, SCF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Schänis is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

education

fr: Education, science

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Schänis, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1998 onwards


schutzinventar-landschaft

geography

de: Geographie

en: Geography

it: Geografia

territory

en: Territory and environment

title_for_slug: schutzinventar-landschaft

en: WMS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

fr: Der Datensatz zeigt die schutzwürdigen Landschaften. Das Kantonale Schutzinventar hat gemäss kNHG Art. 17 hinweisenden Charakter. Es ist nicht abschliessend und wird laufend nachgeführt. Für die vollständige Anzeige des Schutzinventars müssen noch die beiden Layer "Schutzinventar: Kultur" und "Schutzinventar: Natur" dazugeladen werden. Link zu den Inventarlisten des Kantons Uri

fr: Schutzinventar Landschaft

de: Schutzinventar Landschaft

en: Schutzinventar Landschaft

it: Schutzinventar Landschaft


schutzinventar-natur

territory

it: Territorio e ambiente

title_for_slug: schutzinventar-natur

fr: geotope zone-alluviale bas-marais haut-marais cours-deau geodonnees-de-base

de: geotop auengebiet flachmoor hochmoor fliessgewasser geobasisdaten

en: geotope alluvial-site swamp raised-bog watercourse official-geodata

it: geotopo zona-golenale palude torbiera-alta corso-dacqua geodati-di-base

fr: Der Datensatz zeigt die schutzwürdigen Objekte (Gebiete, Einzelobjekte) im Bereich Natur. Das Kantonale Schutzinventar hat gemäss kNHG Art. 17 hinweisenden Charakter. Es ist nicht abschliessend und wird laufend nachgeführt. Für die Anzeige des vollständigen Schutzinventars müssen noch die beiden Layer "Schutzinventar: Kultur" und "Schutzinventar: Landschaft" dazugeladen werden. Link zu den Inventarlisten des Kantons Uri

fr: Schutzinventar Natur

de: Schutzinventar Natur

en: Schutzinventar Natur

it: Schutzinventar Natur


gemeindliche-planungszonen-ur

territory

de: Raum und Umwelt

title_for_slug: gemeindliche-planungszonen-ur

de: nutzungsplanung geobasisdaten

en: land-use-planning official-geodata

fr: WMS-Dienst des Kanton Uri

fr: Planungszonen bezwecken, den Erlass oder die Änderung von Nutzungsplänen für ein genau bezeichnetes Gebiet zu sichern (Art. 56 Planungs- und Baugesetz [PBG]). Innerhalb der Planungszone darf nichts unternommen werden, was die Nutzungsplanung erschweren könnte (Art. 59 Abs. 1 PBG).

fr: Gemeindliche Planungszonen (UR)

de: Gemeindliche Planungszonen (UR)

en: Gemeindliche Planungszonen (UR)

it: Gemeindliche Planungszonen (UR)


culture

fr: Culture, médias, société de l'information, sport

en: Culture, media, information society, sport

energy

fr: Energie

en: Energy

de: WMS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

de: Die Versorgungsgebiete bezeichnen und bestimmen die Netzgebiete der im Kanton Uri tätigen Stromnetzbetreiber gemäss Stromversorgungsgesetz (StromVG) Ein Netzgebiet ist die räumliche Ausdehnung des Stromnetzes des jeweiligen Netzbetreibers, an dem Endverbraucher angeschlossen sind. Ziel der Netzzuteilung ist die vollständige Abdeckung des gesamten Kantonsgebiets ohne verwaiste Gebiete. Aus rechtlicher Sicht ist die Netzzuteilung eine Konzession die den Netzbetreiber berechtigt und verpflichtet, das jeweilige Netzgebiet mit Strom zu versorgen. Private Haushalte verwenden die niedrigste Spannungsebene (Netzebene 7) und industrielle Verbraucher höhere Spannungsebenen (Netzebene 3 und 5).

fr: Stromversorgungssicherheit: Netzebene 3 (UR)


grundwasserschutzzonen-ur

title_for_slug: grundwasserschutzzonen-ur

de: grundwasserschutzareal geobasisdaten

fr: WFS-Dienst des Kanton Uri

fr: Grundwasserschutzzonen dienen dazu, Trinkwassergewinnungsanlagen und das Grundwasser unmittelbar vor seiner Nutzung als Trinkwasser vor Beeinträchtigungen zu schützen. Sie sind um die im öffentlichen Interesse liegenden Grundwasserfassungen auszuscheiden, d.h. um alle Fassungen, deren Wasser den Anforderungen der Lebensmittelgesetzgebung entsprechen muss, sowie um Grundwasser-Anreicherungsanlagen. Die Grundwasserschutzzonen sind das wichtigste Instrument des nutzungsorientierten, planerischen Grundwasserschutzes. Grundwasserschutzzonen werden gegliedert in: Zone S1 (Fassungsbereich), Zone S2 (Engere Schutzzone), Zone S3 (Weitere Schutzzone)

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.

fr: Grundwasserschutzzonen (UR)

de: Grundwasserschutzzonen (UR)

en: Grundwasserschutzzonen (UR)

it: Grundwasserschutzzonen (UR)


culture

de: Kultur, Medien, Informationsgesellschaft, Sport

energy

de: Energie

geography

fr: Géographie

territory

fr: Territoire et environnement

fr: Die Versorgungsgebiete bezeichnen und bestimmen die Netzgebiete der im Kanton Uri tätigen Stromnetzbetreiber gemäss Stromversorgungsgesetz (StromVG) Ein Netzgebiet ist die räumliche Ausdehnung des Stromnetzes des jeweiligen Netzbetreibers, an dem Endverbraucher angeschlossen sind. Ziel der Netzzuteilung ist die vollständige Abdeckung des gesamten Kantonsgebiets ohne verwaiste Gebiete. Aus rechtlicher Sicht ist die Netzzuteilung eine Konzession die den Netzbetreiber berechtigt und verpflichtet, das jeweilige Netzgebiet mit Strom zu versorgen. Private Haushalte verwenden die niedrigste Spannungsebene (Netzebene 7) und industrielle Verbraucher höhere Spannungsebenen (Netzebene 3 und 5).

fr: Stromversorgungssicherheit: Netzebene 7 (UR)


statische-waldgrenzen-projektiert-ur

title_for_slug: statische-waldgrenzen-projektiert-ur

fr: Ab der öffentlicher Auflage zeigt der Layer: Gemäss der Kantonalen Waldverordnung (KWV), Artikel 11, ist entlang von Bauzonen, die an Wald grenzen oder in Zukunft grenzen sollen und in Gebieten, in denen nach dem kantonalen Richtplan eine Zunahme des Waldes verhindert werden soll, eine Waldfeststellung vorzunehmen. Der kantonale Richtplan schreibt vor, dass nebst den Waldgrenzen entlang der Bauzone auch entlang von landwirtschaftlichen Nutzflächen (Hügelzone bis Bergzone 4), die an Wald grenzen, eine statische Waldgrenze festgelegt werden soll. Die statische Waldgrenze stellt dabei eine definitive Trennlinie zwischen dem Wald und den Bauzonen und zwischen dem Wald und den landwirtschaftlichen Nutzflächen dar.

fr: Statische Waldgrenzen projektiert (UR)

de: Statische Waldgrenzen projektiert (UR)

en: Statische Waldgrenzen projektiert (UR)

it: Statische Waldgrenzen projektiert (UR)


abwasseranlagen-darstellung-eigentum

geography

de: Geographie

title_for_slug: abwasseranlagen-darstellung-eigentum

de: leitungskataster geobasisdaten

en: pipeline-cadastre official-geodata

fr: Dieser Datensatz hat den gleichen Inhalt wie der Datensatz "34-UR_Abwasseranlagen". Die Abwasserleitungen sind gemäss dem Attribut "Eigentum" verschiedenfarbig dargestellt.

fr: Abwasseranlagen Darstellung Eigentum

de: Abwasseranlagen Darstellung Eigentum

en: Abwasseranlagen Darstellung Eigentum

it: Abwasseranlagen Darstellung Eigentum


fr: etat-des-eaux cours-deau

de: gewasserzustand fliessgewasser

en: water-status watercourse

it: stato-delle-acque corso-dacqua

url: https://geo.ur.ch/?layers=Gewässerzustand Ökomorphologie DÜFUR GEP

fr: WMS-Dienst des Kanton Uri

fr: Anhand der Ökomorphologie wird hier der Zustand der Urner Fliessgewässer dargestellt. Ab dem Massstab 1:10'000 werden dabei auch Bauwerke und Abstürze ersichtlich. Ab dem Massstab 1:100'000 sind zusätzlich die 40 Messstellen, an welchen Daten für das Projekt DÜFUR (Dauerüberwachung der Fliessgewässer in den Urkantonen und Luzern) erhoben werden, dargestellt und jeweils mit dem Standortdatenblatt verlinkt. Die Farbcodierung der Punkte beruht nur auf die Beurteilung des äusseren Aspekts - pflanzlicher Bewuchs. Weiter sind auch die GEP-Punkte abgebildet.


waldabstandslinien-projektiert-ur

territory

de: Raum und Umwelt

title_for_slug: waldabstandslinien-projektiert-ur

fr: deboisement alignement planification-de-lutilisation-du-sol geodonnees-de-base

de: waldabstand baulinie nutzungsplanung geobasisdaten

en: forest-clearance building-line land-use-planning official-geodata

it: disboscamento linea-di-edificazione pianificazione-dellutilizzazione geodati-di-base

fr: Ab der öffentlichen Auflage zeigt der Layer mit den Waldabstandslinien im Nutzungsplan der Waldabstand gegenüber der festgelegten Waldgrenze, in Abweichung zum generellen Waldabstand von 20 Metern gemäss Artikel 93 Absatz 1 des Planungs- und Baugesetzes (PBG; RB 40.1111), festgelegt. Sie entsprechen einer Baulinie Wald gemäss Artikel 48 Absatz 2 PBG.

fr: Waldabstandslinien projektiert (UR)

de: Waldabstandslinien projektiert (UR)

en: Waldabstandslinien projektiert (UR)

it: Waldabstandslinien projektiert (UR)


gewasserraum-nutzungsplanung-ur

geography

en: Geography

territory

en: Territory and environment

title_for_slug: gewasserraum-nutzungsplanung-ur

fr: alignement geodonnees-de-base

de: baulinie geobasisdaten

en: building-line official-geodata

it: linea-di-edificazione geodati-di-base

de: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

fr: Der Gruppenlayer Gewässerraum bildet die Codes 321.Gewässerraumzone, 322.Gewässer, 525./5252 Gewässerraumzone überlagert und 712.Baulinie Gewässer aus den Nutzungsplanungsdaten ab.

fr: Gewässerraum (Nutzungsplanung) (UR)

de: Gewässerraum (Nutzungsplanung) (UR)

en: Gewässerraum (Nutzungsplanung) (UR)

it: Gewässerraum (Nutzungsplanung) (UR)


larmempfindlichkeitsstufen-ur

geography

fr: Géographie

territory

fr: Territoire et environnement

title_for_slug: larmempfindlichkeitsstufen-ur

de: larmempfindlichkeitsstufe geobasisdaten

en: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

fr: Gemäss Lärmschutz-Verordnung (LSV, SR 814.41) sind für alle Nutzungszonen Lärmempfindlich-keitsstufen zu erfassen. Es werden die Empfindlichkeitsstufen keine ES, ES I, ES II, ES III und ES IV unterschieden. Ausserdem können Teile von Nutzungszonen der ES I und der ES II der nächst höheren Stufe zugeordnet werden, wenn sie mit Lärm vorbelastet sind.

fr: Lärmempfindlichkeitsstufen (UR)

de: Lärmempfindlichkeitsstufen (UR)

en: Lärmempfindlichkeitsstufen (UR)

it: Lärmempfindlichkeitsstufen (UR)


nutzungsplanung-ur

geography

it: Geografia

title_for_slug: nutzungsplanung-ur

fr: affectation-primaire planification-de-lutilisation-du-sol plan-de-zones espace-reserve-aux-eaux geodonnees-de-base

de: grundnutzung nutzungsplanung zonenplan gewasserraum geobasisdaten

en: primary-use land-use-planning zoning-plan space-provided-for-waters official-geodata

it: utilizzazione-di-base pianificazione-dellutilizzazione piano-delle-zone spazio-riservato-alle-acque geodati-di-base

fr: Der Nutzungsplan (kantonal / regional und kommunal) ordnet die räumlichen Sachverhalte in genereller Weise: die Nutzung des Bodens, die Erschliessung, den Schutz usw., indem er eigentümerverbindlich und parzellenscharf Art und Mass der Nutzung festlegt (Art. 14 RPG).

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.

fr: Nutzungsplanung (UR)

de: Nutzungsplanung (UR)

en: Nutzungsplanung (UR)

it: Nutzungsplanung (UR)


statische-waldgrenzen-ur

geography

de: Geographie

territory

it: Territorio e ambiente

title_for_slug: statische-waldgrenzen-ur

it: WFS-Dienst des Kanton Uri

fr: Gemäss der Kantonalen Waldverordnung (KWV), Artikel 11, ist entlang von Bauzonen, die an Wald grenzen oder in Zukunft grenzen sollen und in Gebieten, in denen nach dem kantonalen Richtplan eine Zunahme des Waldes verhindert werden soll, eine Waldfeststellung vorzunehmen. Der kantonale Richtplan schreibt vor, dass nebst den Waldgrenzen entlang der Bauzone auch entlang von landwirtschaftlichen Nutzflächen (Hügelzone bis Bergzone 4), die an Wald grenzen, eine statische Waldgrenze festgelegt werden soll. Die statische Waldgrenze stellt dabei eine definitive Trennlinie zwischen dem Wald und den Bauzonen und zwischen dem Wald und den landwirtschaftlichen Nutzflächen dar.

fr: Statische Waldgrenzen (UR)

de: Statische Waldgrenzen (UR)

en: Statische Waldgrenzen (UR)

it: Statische Waldgrenzen (UR)


waldabstandslinien-ur

title_for_slug: waldabstandslinien-ur

fr: WMS-Dienst des Kanton Uri

fr: Mit Waldabstandslinien wird im Nutzungsplan der Waldabstand gegenüber der festgelegten Waldgrenze, in Abweichung zum generellen Waldabstand von 20 Metern gemäss Artikel 93 Absatz 1 des Planungs- und Baugesetzes (PBG; RB 40.1111), festgelegt. Sie entsprechen einer Baulinie Wald gemäss Artikel 48 Absatz 2 PBG.

fr: Waldabstandslinien (UR)

de: Waldabstandslinien (UR)

en: Waldabstandslinien (UR)

it: Waldabstandslinien (UR)


nutzungsplanung-projektiert-ur

title_for_slug: nutzungsplanung-projektiert-ur

fr: affectation-primaire planification-de-lutilisation-du-sol plan-de-zones geodonnees-de-base

de: grundnutzung nutzungsplanung zonenplan geobasisdaten

en: primary-use land-use-planning zoning-plan official-geodata

it: utilizzazione-di-base pianificazione-dellutilizzazione piano-delle-zone geodati-di-base

fr: Ab der öffentlichen Auflage wird in diesem Layer der Nutzungsplan projektiert gemäss Datensatz 73B dargestellt.

fr: Nutzungsplanung projektiert (UR)

de: Nutzungsplanung projektiert (UR)

en: Nutzungsplanung projektiert (UR)

it: Nutzungsplanung projektiert (UR)


grundwasserschutzareal-ur

territory

de: Raum und Umwelt

title_for_slug: grundwasserschutzareal-ur

de: grundwasserschutzareal geobasisdaten

fr: WFS-Dienst des Kanton Uri

fr: Grundwasserschutzareale sind speziell ausgeschiedene Gebiete, in welchen der Schutz des unterirdischen Gewässers im Hinblick auf eine künftige Grundwasserbewirtschaftung (Nutzung oder Anreicherung) vorsorglich sichergestellt werden soll. In diesen Arealen dürfen keine Bauten und Anlagen erstellt werden, welche die künftige Bewirtschaftung beeinträchtigen können.

fr: Grundwasserschutzareal (UR)

de: Grundwasserschutzareal (UR)

en: Grundwasserschutzareal (UR)

it: Grundwasserschutzareal (UR)


neophyten

agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

it: Agricoltura, selvicoltura

geography

fr: Géographie

territory

fr: Territoire et environnement

title_for_slug: neophyten

de: WMS-Dienst des Kanton Uri

fr: Standorte von gebietsfremden Pflanzen (Neophyten) im Kanton Uri.

fr: Neophyten

de: Neophyten

en: Neophyten

it: Neophyten


waldentwicklungsplan

title_for_slug: waldentwicklungsplan

de: WFS-Dienst des Kanton Uri

fr: Der Waldentwicklungsplan (WEP) Uri ist als Sachplan Wald das forstliche Planungsinstrument auf kantonaler Ebene. Er dient der Sicherstellung der öffentlichen Interessen am Wald und ist ein Führungsinstrument des kantonalen Forstdienstes. Planungseinheit ist das gesamte Waldareal des Kantons. Er gibt Aufschluss über die an die Wälder gestellten Ansprüche und umschreibt die Entwicklungsabsichten und Bewirtschaftungsgrundsätze für die kommenden Jahre.

fr: Waldentwicklungsplan

de: Waldentwicklungsplan

en: Waldentwicklungsplan

it: Waldentwicklungsplan


abwasseranlagen-darstellung-nutzungsart

culture

fr: Culture, médias, société de l'information, sport

de: Kultur, Medien, Informationsgesellschaft, Sport

en: Culture, media, information society, sport

it: Cultura, media, società dell'informazione, sport

energy

fr: Energie

de: Energie

en: Energy

it: Energia

geography

en: Geography

title_for_slug: abwasseranlagen-darstellung-nutzungsart

de: leitungskataster geobasisdaten

en: pipeline-cadastre official-geodata

it: catasto-delle-condotte geodati-di-base

fr: Dieser Datensatz hat den gleichen Inhalt wie der Datensatz "34-UR_Abwasseranlagen". Die Abwasserleitungen sind gemäss dem Attribut "Nutzungsart" verschiedenfarbig dargestellt.

fr: Abwasseranlagen Darstellung Nutzungsart

de: Abwasseranlagen Darstellung Nutzungsart

en: Abwasseranlagen Darstellung Nutzungsart

it: Abwasseranlagen Darstellung Nutzungsart


geography

de: Geographie

it: Geografia

territory

en: Territory and environment

fr: WMS-Dienst des Kanton Uri

en: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

fr: Zur Steuerung des Schutzraumbaus und Zuweisungsplanung gemäss ZSV, SR 520.11; Art. 74, sind Beurteilungsgebiete festzulegen. Es gelten die Weisungen des Bundesamtes für Bevölkerungsschutz (BABS).

name: Amt für Bevölkerungsschutz und Militär Kt. Uri

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.


hochwasseruberlastkorridoren

title_for_slug: hochwasseruberlastkorridoren

fr: Bei den Hochwasserüberlastkorridoren werden Gebiete angezeigt, welche bei einem Hochwasserereignis infolge Überlast eines Fluss- oder Bachgerinnes oder bewusst als Entlastungs- oder Retentionsräume überschwemmt werden können. Bei den hier dargestellten Gebieten handelt es sich um Hochwasserüberlastkorridore bis zu einem 300-jährlichen Hochwasserereignis. Dies bedeutet, ein solches Ereignis tritt mit einer Wahrscheinlichkeit von einem Mal in 300 Jahren auf. Die bezeichneten Korridore und Retentionsräume werden von den Gemeinden in ihrer kommunalen Nutzungsplanung z.B. durch Baulinien oder bestimmten Zonenvorschriften berücksichtigt.

fr: Hochwasserüberlastkorridore

de: Hochwasserüberlastkorridore

en: Hochwasserüberlastkorridore

it: Hochwasserüberlastkorridore


zonenplane

territory

it: Territorio e ambiente

title_for_slug: zonenplane

de: baulinie eigentumsbeschrankungoreb grundeigentum nutzungsplanung raumplanung zonenplan

spatial: Kanton Luzern

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

de: Zonenpläne regeln die zulässigen Nutzungen im Gebiet einer Gemeinde.

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

fr: La géoinformation du canton de Lucerne est responsable de la collecte, du traitement et de la gestion des données à référence spatiale. Elle dirige, coordonne et surveille les travaux de la géoinformation et de la mensuration officielle.

en: Geoinformation of the Canton of Lucerne is responsible for the collection, processing and management of spatial data. It manages, coordinates and monitors the work of geoinformation and of the cadastral surveying.

de: Die Geoinformation des Kantons Luzern ist für die Erhebung, Verarbeitung und Verwaltung von raumbezogenen Daten zuständig. Sie leitet, koordiniert und überwacht die Arbeiten der Geoinformation und der amtlichen Vermessung.

it: La Geoinformation del Cantone di Lucerna è responsabile della raccolta, dell'elaborazione e della gestione dei dati territoriali. Gestisce, coordina e monitora il lavoro della geoinformazione e della misurazione ufficiale.

value: LU-Kt - Geocat Harvester

de: Zonenpläne


agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

territory

de: Raum und Umwelt

de: baulinie eigentumsbeschrankungoreb nutzungsplanung wald zonenplan

de: Zeigt die statischen Waldgrenzen aufgrund einer formellen Waldfeststellung durch die Dienststelle Landwirtschaft und Wald (lawa).

de: Statische Waldgrenzen


schutzinventar-kultur

culture

de: Kultur, Medien, Informationsgesellschaft, Sport

geography

fr: Géographie

population

fr: Population

de: Bevölkerung

en: Population

it: Popolazione

title_for_slug: schutzinventar-kultur

fr: isos zone geodonnees-de-base

de: isos zone geobasisdaten

en: isos zone official-geodata

it: isos zona geodati-di-base

de: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

fr: Der Datensatz zeigt die schutzwürdigen Objekte (Gebiete, Einzelobjekte) im Bereich Kultur. Das Kantonale Schutzinventar hat gemäss kNHG Art. 17 hinweisenden Charakter. Es ist nicht abschliessend und wird laufend nachgeführt. Rechtsverbindliche Angaben sind bei der Fachstelle Heimatschutz und Denkmalpflege erhältlich. Die Archäologischen Zonen sind nicht Bestandteil des Schutzinventars und werden lediglich zur Information aufgeführt. Für die Anzeige des vollständigen Schutzinventars müssen noch die beiden Layer "Schutzinventar: Natur" und "Schutzinventar: Landschaft" dazugeladen werden. Link zu den Inventarlisten des Kantons Uri

fr: Schutzinventar Kultur

de: Schutzinventar Kultur

en: Schutzinventar Kultur

it: Schutzinventar Kultur


geography

de: Geographie

territory

fr: Territoire et environnement

fr: carte-des-dangers danger

de: gefahrenkarte gefahren

en: hazard-map hazard

it: carta-dei-pericoli pericoli

spatial: Canton du Jura (JU)

fr: Accès aux données / fichiers de téléchargement

format: ZIP

protocol: OGC:service WMS

fr: Le Cadastre des événements relatifs aux dangers naturels est une application contenant tous les événements naturels ayant causés des dégâts dans le canton. Cette application, dénommée CadEve, reprend la plupart des informations de l'application StorMe, développée par la Confédération. Elle contient des données historiques ainsi que des données sur les derniers événements. Elle est régulièrement mise à jour. Un dossier photo peut compléter des informations sur l'ampleur de l'événement, sa périodicité, le phénomène mis en cause, etc. Les données de CadEve sont exportées régulièrement dans 3 couches de données ponctuelles, à savoir : les crues, les glissements de terrain et les chutes de pierre. Une 4 ème couche de polygone complète l'information en fournissant une extension géographique approximative de l'événement. Un hyperlien sur une fiche PDF permet d'avoir accès à l'ensemble des données stockées dans l'application.

canton-du-jura

fr: Canton du Jura

de: Kanton Jura

en: Canton of Jura

it: Canton Giura

fr: Le canton du Jura publie sur Open Data Swiss différents types de données de l'administration cantonale qui répondent au principe de l'Open Government Data.

en: The canton of Jura publishes on Open Data Swiss various types of data of the cantonal administration that meets the principle of Open Government Data.

de: Der Kanton Jura veröffentlicht auf Open Data Swiss verschiedene Arten von Daten der kantonalen Verwaltung, die dem Prinzip von Open Government Data entsprechen.

it: Il Cantone del Giura pubblica su Open Data Swiss vari tipi di dati dell'amministrazione cantonale che soddisfano il principio degli Open Government Data.

value: JU-Kt - Geocat Harvester


differentes-offres-touristiques

culture

fr: Culture, médias, société de l'information, sport

en: Culture, media, information society, sport

it: Cultura, media, società dell'informazione, sport

title_for_slug: differentes-offres-touristiques

fr: zone-dhabitation population

de: wohnzone bevolkerung

en: residential-zone human-population

it: zona-di-abitazione popolazione-umana

fr: Regroupe différentes offres touristiques, tel que : - Bureau d'accueil touristique de Jura Tourimse - Camping - Golf - Musée - Site de tir à l'arc - Vitraux

fr: Différentes offres touristiques


en: spatial-modelling basic-services nature-conservation urban-morphology urban-sprawl land-use-and-cover-change forest-gain planning-outcome forest-persistence built-up-classification topography urban-compactness

media_type: geotiff

en: Native forest cover gains from 2005 to 2015

format: GeoTIFF

media_type: SHP

en: Ecological-Economic Zoning (EEZ)

format: SHP

en: infill

en: New built-up pixels classified as infill

en: Increase in sanitation service

en: Increase in waste collection service

en: slope

en: Slope °

en: Permanent housing density increase

en: Descricao dos dados em portiuguês

en: Portugues description

en: Population density per pixel (change per year)

en: Variables description

en: Table of content. Variables description, source, and other important information

en: Axial

en: New built-up pixels classified as axial

en: Mean income in Reais (R$) per census sector (change per year)

en: Topographic Index Position (TPI)

en: Urban persistence: Built-up 2005 = Built-up 2015

en: Increase in Normalized Difference Vegetation Index

en: isolated

en: New built-up pixels classified as isolated

media_type: .shp

en: Master plan Ubatuba (zones)

en: Master plan Ihabela (zones)

en:
Basic education in % per census sector (change per
year)

en: Increase in clean water provision service

en: Native forest cover persistence from 2005 to 2015

en: Human Development Index (HDI) per municipality (change per year)

en:
The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation.

We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality.

1)	Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase

We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software.

Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015).

To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication).

The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of > 0.2 NDVI to represent an improvement in forest quality.

2)	Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population:

The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication).

3)	Topographic drivers

To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).

basic-services

display_name: basic-services

envidat

fr: EnviDat: le portail de données environnementales

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.

en: Spatially explicit data to evaluate spatial planning outcomes in a coastal region in São Paulo State, Brazil


geography

fr: Géographie

en: Geography

territory

en: Territory and environment

fr: eaux eaux-geographie

de: gewasser gewasser

en: waters water-geographic

it: acque acque-geografia

fr: Cette couche indique les rejets au cours d'eau identifié dans le cadre des PGEE et évalue leur impact dans le milieu récepteur. Une première campagne de mesures a été réalisée en 2008. Une mise à jour est en cours depuis 2020.

canton-du-jura

en: Canton of Jura

en: The canton of Jura publishes on Open Data Swiss various types of data of the cantonal administration that meets the principle of Open Government Data.

fr: PGEE_Impacts des rejets aux cours d'eau