en: wolf soil-biodiversity biogeochemical-cycling carcass decomposition yellowstone detritus fungi bacteria carrion

en:
Site	ID for different carcasses across the Yellowstone Landscape
Location	Sampling location, either under carcass or control plot
Species	Indicating whether carcass was from bison or elk
Gender	Gener of carcass; F = female, M = male, ? = unknown
Lat	Latitude
Long	Longitude
DSK	Days since kill; in days
Soil.N	Soil N concentration (%)
Soil.C	Soil C concentration (%)
Veg.C	Vegetation C concentration (%); dominant grasses sampled
Veg.N	Vegetation N concentration (%); dominant grasses sampled
Veg.CtoN	Vegetation C to N ratio; dominant grasses sampled
Soil.resp	Soil respiration (g m-2 h-1)
soil.temp	Soil temperature (°C)
Soil.moist	Soil moisture (volumentric; %)
CRR	Cotton rotting rate
bacteria.richness	Bacteria richness
bacteria.diversity	Bacteria diverstiy
fungi.richness	Fungi richness
fungi.diversity	Fungi diversity
bacteria.abundance	Bacteria abundance
fungi.abundance	Fungi abundance

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

Paper Citation:

Risch, A.C., Frossard, A., Schütz, M., Frey, B., Morris, A.W., Bump, J.K. (accepted) Effects of elk and bison carcasses on soil microbial communities and ecosystem functions in Yellowstone, USA. (accepted). Functional Ecology
doi: ...

Methods
Study area and study sites
This study was conducted in YNP’s Northern Range (NR), located in north-western Wyoming and south-western Montana, USA (~44.9163° N, 110.4169° W). The NR expands over ~1000 km2 and features long cold winters and short dry summers. Grasslands and shrublands dominate the NR that is the home of large migratory herds of bison (winter counts 2017: ~3919 individuals; Geremia, Wallen, & White, 2017) and elk (~5349 individuals) as well as their main predators, approximately five packs of wolves with a total of 33 individuals (Smith et al., 2017). As part of a long-term research program within YNP, wolf predation has been studied since their reintroduction in 1995.
For our study, we received ground-truthed coordinates of bison and elk carcasses from winter 2016/17 (November 2016 through April 2017) from the YNP Wolf Project. Between June 20 and July 1, 2017, we visited 24 carcasses in total. At five sites, we could not sample as the carcasses were no longer found. In total we located remains (hairmats, rumen content, bones, teeth) of 19 adult male and female carcasses (7 bison, 12 elk; Supplementary Table 1). Live body weights of adult bison and elk are approximately 730 kg (male bison), 450 kg (female bison), 330 kg (male elk), and 235 kg (female elk, Meagher, 1973; Quimby & Johnson, 1951).
The kills and subsequent consumption happened between 34 and 173 days prior to our sampling (hereafter “days since kill”, DSK), for which we accounted in our statistics. Note that wolves and other scavengers consumed the soft tissue of the carcasses quickly, hence, there is close to no soft tissue left for decomposition as compared to an intact body left on the soil surface. The 19 carcass sites covered the extent of YNP’s NR, with both bison and elk carcasses showing similar distributions; elevation ranged from 1703 to 2884 m a.s.l. (Supplementary Fig 1 & Supplementary Table 1). The carcasses were all located in grassland or sage-brush shrubland, with or without sparsely scattered trees, and both bison and elk carcasses showed the same distribution of DSK. At each study site, we selected a reference plot (hereafter “control”) that was of comparable size, slope aspect and vegetation to the carcass location (hereafter “carcass”). The control was at least 10 m away (Danell, Berteaux, & Brathen, 2002; Melis et al., 2007) from the carcass itself to ensure the absence of potential direct and indirect carcass effects (paired design; (Bump, Webster, et al., 2009; Bump, Peterson, et al., 2009).

Ecosystem functions and soil properties
We randomly collected 50 g of mineral soil from three locations on both control and carcass plots to a depth of 5 cm with sterile techniques and gently mixed the material to obtain a composite sample. Half the soil sample was immediately bagged in plastic bags (whirl packs), stored in a cooler with ice packs (~5 ºC), sieved (2-mm) and frozen within 4-6 hours of collection to assess soil microbial communities. For this purpose, we extracted total genomic DNA from 0.5 g soil using the PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany). DNA concentrations were measured using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplifications of partial bacterial small-subunit ribosomal RNA genes (region V3–V4 of 16S rRNA) and fungal ribosomal internal transcribed spacers (region ITS2) were performed as described previously (Frey et al., 2016). Each sample consisting of 40 ng DNA was amplified in triplicate and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantified with the Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were sent to the Genome Quebec Innovation Center (Montreal, Canada) for barcoding using the Fluidigm Access Array technology and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA).
Quality control of bacterial and fungal reads was performed using a customized pipeline (Supplementary Table 2; Frey et al., 2016). Paired-ends reads were matched with USEARCH (Edgar & Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, & Alekseyev, 2013) and PCR primers were trimmed (allowing for 1 mismatch, read length >300 bp for 16S and >200 bp for ITS primers) using Cutadapt (M. Martin, 2011). Sequences were dereplicated and singleton reads removed prior to clustering into operational taxonomic units (OTUs) at 97% identity using USEARCH (Edgar, 2013). The remaining centroid sequences were tested for the presence of ribosomal signatures using Metaxa2 (Bengtsson-Palme et al., 2015) or ITSx (Bengtsson-Palme et al., 2013). Taxonomic assignments of the OTUs were obtained using Bayesian classifier (Wang, Garrity, Tiedje, & Cole, 2007) with a minimum bootstrap support of 60% implemented in mothur (Schloss et al., 2009) by querying the bacterial and fungal reads against the SILVA Release 128 (Quast et al., 2013) and UNITE 8.0 (Abarenkov et al., 2010) reference databases for 16S and ITS OTUs, respectively.
Abundances of the bacterial 16S rRNA gene and fungal ITS amplicon were determined by quantitative real-time PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) as described previously (Frossard et al., 2018). The same primers (without barcodes) and cycling conditions as for the sequencing approach were used for the 16S and ITS qPCR. Three standard curves per target region were obtained using tenfold serial dilutions of plasmids generated from cloned targets (Frey, Niklaus, Kremer, Lüscher, & Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight).
The other half of the soil sample was bagged in paper, dried to constant weight at 60°C, passed through a 2 mm sieve and analyzed for total C and N concentration with a CE Instruments NC 2100 soil analyzer (CE Elantech Inc., Lakewood NJ, USA). We also collected 20 mature and undamaged leaves of the dominant grass species growing on control and carcass sites, but taxa were not recorded. The plant material was dried at 60°C, finely ground till homogenized and also analyzed to obtain total C and N concentrations. Soil temperature (10 cm depth) was measured with a waterproof digital thermometer (Barnstead International, Dubuque IA, USA) at three locations each at the control and carcass site. Soil moisture (0 – 10 cm depth) was measured with time domain reflectometry (Field-Scout TDR-100; Spectrum Technologies, Plainfield IL, USA) at five randomly chosen points on control and carcass sites. We measured soil respiration at five randomly chosen points at both control and carcass sites with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA). For each measurement the soil chamber (15 cm high; 10 cm diameter) was tightly placed on the soil surface, after clipping plants to avoid measuring plant respiration or photosynthesis. Measurements were conducted over 120 s.
In addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter & Howson, 1977; Latter & Walton, 1988). Cotton cloth tensile strength loss (CTSL) is a measure of decomposition, and an index to express the combined effect of soil microclimatic, physical, chemical and biological properties on decomposition while accounting for OM quality (Latter & Walton, 1988; Risch, Jurgensen, & Frank, 2007; Withington & Sanford Jr., 2007). We placed five 20 cm wide x 13 cm long sheets of 100% unbleached cotton cloth (American Type SM 1/18’’, Warp: 34/1, Weft: 20/1, Weave plain, 29.5 picks/cm warp, 22 picks/cm weft, 237 g/m2; Daniel Jenny & Co., Switzerland;) at each carcass and control site vertically into the soil by making slits with a flat spade to a depth of 12 cm. We inserted each cloth with the spade, and then pushed the slit closed to assure tight contact with the soil. The cloths were retrieved after 18 to 27 days. After retrieval, the cloths were air-dried, remaining soil gently removed by hand, and 1.5 cm wide strips were cut at the 3.5-5.0 cm (top) and the 9-10.5 cm (bottom) soil depth. The strips were equilibrated at 50 % relative humidity and 20°C for 48 hours (climate chamber) prior to strength testing (Scanpro Awetron TH-1 tensile strength tester; AB Lorentzen and Wettre, Kista, Sweden). Cotton rotting rate (CRR) = (CTScontrol - CTSfinal/CTSfinal)1/3 * (365/t), where CTScontrol is the cotton tensile strength of a control cloth and CTSfinal the cotton tensile strength of the incubated sample, t is the incubation period in days. Control cloths were inserted into the ground and immediately retrieved to account for tensile strength loss associated with cloth insertion. We averaged the CRR of top and bottom strips for further analyses as no difference was found between the two. All sampling and cloth insertion took place between June 20 and July 1, 2017, cloths were retrieved between July 17 and 20, 2017. Soil respiration, average CRR, vegetation N concentration and vegetation C:N ratio are defined as ecosystem functions, soil C and N concentration, soil temperature and moisture as soil abiotic properties, and bacterial and fungal richness (number of taxa), diversity (Shannon) and abundance as soil biotic properties.

Statistical analyses
Univariate analyses for ecosystem functions, soil biotic and abiotic properties
We tested whether individual ecosystem functions, soil biotic and abiotic properties differed between carcass and control (“Location”), bison and elk (“Species”) and days since kill (“DSK”). For this purpose, we used linear mixed effect models (LMM, “nlme” package v 3.1 – 131.1 in R v 3.4.4; Pinheiro, Bates, DebRoy, & Sarkar, 2018; R Core Team, 2019) with Location, Species, Location x Species and DSK as fixed effects. Site was included as random effect to account for the paired design. We developed a separate model for all dependent variables. All but bacterial richness, fungal richness, fungal diversity and vegetation N concentration were natural-log transformed to meet model assumptions. For each LMM, we calculated contrasts to assess the specific comparisons we were interested in with the “lsmeans” package v 2.27-62 (Lenth & Love, 2018): 1) carcass vs control, 2) carcass bison vs control bison, and 3) carcass elk vs control elk. We also tested whether we had differences between bison and elk carcasses or the sites where bison and elk were killed and included contrasts 4) carcass bison vs carcass elk and 5) control bison vs control elk.
We calculated the log response ratio (LRR = ln[carcass/control]) to obtain carcass effects for all variables for both species separately. LRR < 0 indicates higher value at control compared to carcass, LRR > 0 indicates higher values at carcass compared control. We used LRRs for visualization and to assess spatial patterns in carcass effects across YNP. For this purpose we calculated the Moran’s I statistic for each ecosystem function, soil biotic and abiotic property based on a latitude-longitude matrix with the “moran.test” function in the “spdep” package version 1.1-3 (Bivand et al., 2019).
Multivariate analyses
Rare OTUs, defined as OTUs with a low abundance of reads, were retained in multivariate methods because they only marginally influence these analyses (Gobet, Quince, & Ramette, 2010). Bray–Curtis dissimilarity matrices were generated based on square-root-transformed matrices. We used Principal Coordinate Analyses (PCoA) to assess how soil bacterial and fungal communities differed between control and carcass of bison and elk (“vegan” package v 2.5-4, Oksanen et al., 2019). We then extracted PCoA axes scores 1 and 2 and used LMM (“nlme” package) with Location, Species, Location x Species and DSK as fixed effects. Site was, again, included as random effect. We again calculated the contrasts as described above using the “lsmeans” package. We also assessed how ecosystem functions, and soil abiotic and biotic properties were related to the soil bacteria and fungi community structure associated with bison and elk control and carcasses using the “envfit” function in the “vegan” package (Oksanen et al., 2019).
Indicator species analyses were performed using the multipatt function implemented in the “indicspecies” package version 1.7.6 with 100000 permutations (De Caceres & Jansen, 2016). This step allowed to identify OTUs that led to changes in multivariate patterns between control and carcass of both bison and elk separately (De Cáceres, Legendre, & Moretti, 2010). The multipatt function uses a point biserial correlation coefficient statistical test. Indicator OTUs were defined as bacterial and fungal OTUs with more than 50 sequences, i.e., removing rare taxa and taxa with low abundances containing little indicator information (Rime et al., 2015) and that were significantly correlated with Location (p < 0.05, correlation coefficient > 0.3). A heatmap of these OTUs were generated with the vegan and ggplot2 packages. The indicator analyses were performed in R version 3.3.3 (R Core Team, 2017).

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

bacteria

display_name: bacteria

biogeochemical-cycling

display_name: biogeochemical-cycling

carcass

display_name: carcass

carrion

display_name: carrion

decomposition

display_name: decomposition

detritus

display_name: detritus

fungi

display_name: fungi

soil-biodiversity

display_name: soil-biodiversity

wolf

display_name: wolf

yellowstone

display_name: yellowstone

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: Elk and bison carcasses in Yellowstone, USA


maintainer: EnviDat Support

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

state: active

type: dataset

media_type: application/zip

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

format: ZIP

mimetype: application/zip

media_type: text/plain

format: TXT

mimetype: text/plain

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

catchment-data

display_name: catchment-data

meteorological-data

display_name: meteorological-data

rain-on-snow

display_name: rain-on-snow

snow-hydrology

display_name: snow-hydrology

snowpack

display_name: snowpack

education

fr: Education, science

de: Bildung, Wissenschaft

it: Formazione e scienza

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

it: EnviDat: il portale dei dati ambientali

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

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


maintainer: Elisabeth Hafner

en: hazard-mapping natural-hazard avalanche snow satellite

en: SPOT6 Avalanche Outlines 16 January 2019

media_type: SHP

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

format: SHP

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

The generation of the data is described in:

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

The data was comprehensivly validated in a subset area in

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

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

snow

display_name: snow

en: SPOT6 Avalanche outlines 16 January 2019


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

en: Data plot of the field station (NEF)

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Data plot of the forest station (NEB)

en: Site photo of the forest station (NEB)

media_type: image/jpeg

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

format: SERVICE

mimetype: image/jpeg

en: Field station data (NEF)

media_type: text/csv

en: Data file.

format: CSV

mimetype: text/csv

en: Site photo of the field station (NEF)

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

en: Forest station data (NEB)

en: Site aerial photo (Neunkirch)

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

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 Neunkirch in Switzerland where one station is located within a natural deciduous forest stand (NEB) with European beech (_Fagus sylvatica_; 160 yrs), sycamore maple (_Acer pseudoplatanus_; 160 yrs) and lime trees (_Tilia sp._; 110 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NEF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Neunkirch is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


maintainer: Yves Bühler

en: snow hazard-mapping hazard-indication-mapping release ramms avalanche

en: RAMMS simulations NHESS

en: Simulated maximum pressures for the scenarios frequent and extreme taking the forest layer into account

en: Potential Release  Area  NHESS

en: This folder contains the output of the different investigated algorithms plus the reference dataset, used for training and validation.

en:
This dataset contains the output and reference data published in the paper "Automated snow avalanche release area delineation - validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping" Yves Bühler, Daniel von Rickenbach, Andreas Stoffel, Stefan Margreth, Lukas Stoffel, Marc Christen (2018) Natural Hazards And Earth System Sciences.

Abstract:
Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche cadastre information, terrain analysis, climatological datasets and numerical modelling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such area-wide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-the-art numerical avalanche simulations.

In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche cadastre data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff mapped most avalanches in detail since many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference datasets. Because all tested algorithms do not provide meaningful delineation between individual potential release areas (PRA), we propose a new algorithm based on object-based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10 – 30 years return period) and extreme (100 – 300 years return period). This approach opens the door for large scale avalanche hazard indication mapping in all regions where high quality and resolution digital terrain models and snow data are available.

hazard-indication-mapping

display_name: hazard-indication-mapping

ramms

display_name: ramms

release

display_name: release

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: Automated Avalanche Release Area (PRA) Delineation Davos


en: biogas switzerland manure bioenergy review

en: Production de biogaz à partir d’engrais de ferme en Suisse

media_type: PDF

en: L'objectif de ce livre blanc est de fournir aux décideurs, aux administrations et aux parties prenantes les résultats de recherche les plus récents afin de promouvoir l'utilisation optimale de la bioénergie issue des engrais de ferme dans la transition énergétique suisse. A cette fin, les résultats du centre de compétence suisse pour la recherche en bioénergie - SCCER BIOSWEET - sont résumés et présentés dans un contexte plus large. Si rien d'autre n'est mentionné, les résultats se réfèrent à la Suisse et, dans le cas de la matière première, aux potentiels nationaux de biomasse.

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

switzerland

display_name: switzerland


data-broedlin-cnp

title_for_slug: data-broedlin-cnp

en: phosphorus nitrogen mineralization forest stoichiometry dissolved-organic-matter soil drying-rewetting carbon

en: Data Broedlin-CNP

format: XLS

en: Data Broedlin-CNP-Release

en: Raw data of C-N-P mobilization experiment from Oi, Oe/Oa, and A horizons of beech forest soils along a nutrient availability gradient.

en: Mircocosm experiment to identify the individual patterns and controls of C, N, and P mobilization in soils under beech forests. Organic and mineral horizons sampled along a nutrient availability gradient in Germany were exposed to either permanent moist conditions or to dry spells in microcosms and quantified the release of inorganic and organic C, N, and P.

carbon

display_name: carbon

dissolved-organic-matter

display_name: dissolved-organic-matter

drying-rewetting

display_name: drying-rewetting

forest

display_name: forest

mineralization

display_name: mineralization

nitrogen

display_name: nitrogen

phosphorus

display_name: phosphorus

soil

display_name: soil

stoichiometry

display_name: stoichiometry

en: Data Broedlin CNP


maintainer: Christoph Marty

en: water-cycle climate-change-impacts hydrology snowfall snow

en: DORA Persistent URL

en: Synthesis report chapter

en:
This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is nothing other than a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff currently comes from snow melting and only 1% from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months.
Due to the great dependence on minus temperatures and precipitation, the snow cover reacts sensitively to temperatures above 0° Celsius and more or less precipitation. Due to climate change and the associated warming, the proportion of precipitation that falls as snow decreases measurably. In addition to this reduction in snowfall, the warmer temperatures also cause the snow cover to melt more quickly. The decline in snowfall has so far mainly affected lower altitudes, where winter temperatures often reach positive levels.
As climate change progresses, this trend is likely to continue and above all affect higher zones. Even at higher altitudes, the snow cover will then start later, melt away earlier and is increasingly no longer permanently present. This development will also have an effect on the water bodies. Today nival regimes, i.e. regimes shaped by snow, are shifting towards pluvial regimes, i.e. regimes dominated by rain. Overall, winter runoff increases, summer runoff decreases. By the end of the century, the proportion of runoff from snowmelt will decrease throughout Switzerland, albeit to a lesser extent than the proportion from glacier melt.

climate-change-impacts

display_name: climate-change-impacts

hydrology

display_name: hydrology

snow

display_name: snow

snowfall

display_name: snowfall

water-cycle

display_name: water-cycle

en: Snow and the water cycle in a changing climate


en: seilaplan open-source qgis-plugin geoinformation-system digital-planning wms-data swiss-geodata cable-yarding digital-elevation-model

en: View video on youtube!

en: WMS-Layer als Hintergrundkarten laden

en:
In order to digitally plan a cable line using the QGIS plugin ‘Seilaplan’, maps with various background information are helpful. In this tutorial we show you how to obtain maps that are helpful for cable line planning, for example a national map of Switzerland at different scales, the NFI vegetation height model or the NFI forest mix rate. For this we explain what WMS datasets are and how to integrate them into QGIS. No download of large data is needed for this, only a good internet connection. Please note that the tutorial language is German!

Link for the integration of WMS data: https://wms.geo.admin.ch/

Link to the description on the Swisstopo website:
https://www.geo.admin.ch/en/geo-services/geo-services/portrayal-services-web-mapping/web-map-services-wms.html

Link to the Seilaplan website: https://seilaplan.wsl.ch

**************************

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung sind verschiedene Hintergrundkarten hilfreich. In diesem Tutorialvideo zeigen wir, was WMS Daten sind und wie man diese in QGIS einbinden kann. Dafür müssen die Daten nicht heruntergeladen werden. Es braucht lediglich eine gute Internetverbindung. Für die Seillinienplanung hilfreiche Karten sind bspw. die Landeskarte der Schweiz in verschiedenen Massstäben, das Vegetationshöhenmodell LFI oder der Waldmischungsgrad LFI.

Link zur Einbindung der WMS Daten: https://wms.geo.admin.ch/

Link zur Beschreibung auf der Swisstopo Webseite: https://www.geo.admin.ch/de/geo-dienstleistungen/geodienste/darstellungsdienste-webmapping-webgis-anwendungen/web-map-services-wms.html

Link zur Seilaplan-Website: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

seilaplan

display_name: seilaplan

swiss-geodata

display_name: swiss-geodata

wms-data

display_name: wms-data

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

en: Seilaplan Tutorial: Load WMS layers as background maps


wind-lidar-davos-wolfgang

title_for_slug: wind-lidar-davos-wolfgang

en: wolfgangpass profiles lidar wind raclets

en: Wind LIDAR data Wolfgang

en: A Leosphere WidCube 100S was deployed at Davos Wolfang, measuring wind profiles as well as performing PPI and RHI scans. Time format is in UTC with highest time resolution of 5 seconds.

en: Scanning wind Lidar from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 200 m above ground to 8100 m. The time resolution is up to 5 seconds. The Lidar was measuring wind profiles but also performed plan position indicator (PPI) and range height indicator (RHI) scans.

lidar

display_name: lidar

profiles

display_name: profiles

raclets

display_name: raclets

wind

display_name: wind

wolfgangpass

display_name: wolfgangpass

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: Wind LIDAR Davos Wolfgang


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

media_type: TIFF

en: Mean diurnal range modified from CHELSA

format: TIFF

media_type: tif

en: Mean annual temperature modified from CHELSA

en: Precipitation seasonality from CHELSA

en: Soil clay content modified from SoilGrids

en: Soil pH modified from SoilGrids

en: Soil organic carbon content modified from SoilGrids

en: Precipitation of driest quarter modified from CHELSA

en: Frost change frequency modified from CHELSA

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

aridity

display_name: aridity

frost-change-velocity

display_name: frost-change-velocity

ground-temperature

display_name: ground-temperature

precipitation

display_name: precipitation

sdms

display_name: sdms

soil

display_name: soil

en: Environmental layers for SDM simulations (GDPlants)


label: Geometadaten Kanton Zürich: GeoLion

label: geocat.ch permalink

media_type: WMS

de: Waldareal im Kanton Zürich

format: WMS

name:
Amt für Landschaft und Natur
               - Abteilung Wald

geoinformation-kanton-zuerich

fr: Département de Géoinformation du Canton de Zurich

de: Geoinformation Kanton Zürich

en: Department of Geoinformation of the Canton of Zurich

it: Dipartimento della geoinformazione del Cantone di Zurigo

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

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

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

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

political_level: canton

value: ZH - Geocat Harvester

de: WMS Waldareal


agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

it: Agricoltura, selvicoltura

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

spatial: Kanton Zürich

de: Download via GIS-Browser

de: Der Waldentwicklungsplan des Kanton Zürich (kurz WEP) stellt für das gesamte Waldareal sicher, dass der Wald seine Funktionen nachhaltig erfüllen kann. Er ist als Planungsinstrument für alle Behörden von Kanton und Gemeinden verbindlich. Der WEP besteht aus einem Textteil und den drei Teilplänen Planungsgrundlagen, «Waldfunktionen und Besondere Ziel. Die besonderen Ziele werden soweit möglich und sinnvoll örtlich festgelegt, abgegrenzt und auf den Plänen dargestellt. Der Waldfunktionenplan stellt die Vorrangfunktion örtlich dar, während der Plan «Planungsgrundlagen» die vorhandenen verbindlichen Vorgaben übergeordneter Planungen oder eigentümerverbindlicher Sachpläne umfasst.

name:
Amt für Landschaft und Natur
             - Abteilung Wald

de: Waldentwicklungsplan


de: Waldentwicklungsplan Kanton Zürich: Waldfunktionen

de: WMS Waldentwicklungsplan: Waldfunktionen (OGD)


de: schutzwald sw gravitative-naturgefahren gerinnerelevanter-schutzwald tobelwalder grs schadenpotential schutzfunktion

de: Als Schutzwald werden jene Wälder bezeichnet, die Naturgefahren wie Schneerutsche, Steinschlag, Hangrutsche, Murgänge und Hochwasser verhindern oder zumindest reduzieren und damit Menschen oder erhebliche Sachwerte schützen.

de: Schutzwald


de: Die Objekte des WNB-Inventars weisen besondere (seltene) standortgebundene Naturwerte von kantonaler Bedeutung auf. Meist liegt eine seltene Waldgesellschaft oder ein Lebensraum einer seltenen Pflanze oder eines seltenen Tieres vor. Mit der Aufnahme einer solchen Waldfläche ins Inventar zeigt die Behörde, dass sie den Standort aus kantonaler Sicht als naturkundlich bedeutsam beurteilt und dass der kartierte Naturwert erhalten bleiben muss. Insgesamt weist das Inventar 881 Objekte mit total 1’425 Teilflächen und einer Gesamtfläche von 6’185 ha aus – 12 % der Zürcher Waldfläche. Das Inventar stellt eine wichtige Grundlage für die Umsetzung des Naturschutzgesamtkonzeptes (RRB vom 20. Dezember 1995) im Wald dar. 1. Im WNB lassen sich 2 Objektzustände (vgl. Attribut KAT_CODE) unterschieden: Zum Zeitpunkt der Inventaraufnahme bereits bestehende geschützte Teilflächen, wie: SVO Waldschutzzonen IV A, Inventarobjekte von nationaler Bedeutung oder vertraglich gesicherte kantonale Waldreservate 2. neu ausgeschiedene Teilflächen mit seltenen Naturwerten ohne formellen Schutz: - seltene Waldgesellschaften (selten gemäss definierten Flächenkriterien), - seltene Pflanzen und Tiere (Rote Liste) und deren Lebensräume, - besondere, seltene Biotopstrukturen (z.B. Feuchtgebiete, Felspartien, Rutschflächen, wilde Bachtobel) - Bestände mit urwaldähnlichen Strukturen (z.B. unzugängliche, lange Zeit unbewirtschaftet gebliebene Gebiete), sowie - seltene kulturbedingte Waldformen (z.B. Eichenwälder). Nicht standortgebundene Naturwerte wie Altholzinseln und Laubmischwälder (auch wenn sie z.B. für den Vogelschutz wichtig sind), verbreitete Waldgesellschaften oder intensiv bewirtschaftete Waldformen (z.B. aufgewertete oder aufzuwertende Waldränder, Mittelwälder, Niederwälder) werden im WNB nicht ausgewiesen. Ausnahme: die Bestände sind gleichzeitig Lebensraum einer seltenen und/oder bedrohten Art.

de: Inventar der Waldstandorte von naturkundlicher Bedeutung (WNB)


media_type: WCS

de: Der WCS Vegetationshöhe beschreibt die Höhe der Vegetation (Bäume, Büsche, etc.) im Kanton Zürich. Symbology: Stretched, 3-50, Color Ramp, Apply Gamma Stretch 3-50

format: WCS

de: WCS Vegetationshöhe


de: Schutzwaldausscheidung, festgesetzt am 14. Juli 2008 (enthält Schutzwald 1. und 2. Priorität). Als Schutzwald werden diejenigen Wälder bezeichnet, welche Naturgefahren wie Schneerutsche, Steinschlag, Hangrutsche, Murgänge und Hochwasser vermindern. Die Schutzwirkung des Waldes beruht darauf, dass die Wurzeln den Boden zusammenhalten, die Bäume dem Boden Wasser entziehen, die Baumkronen Niederschläge zurückhalten und die Ablagerung von Schnee günstig beeinflussen.

geoinformation-kanton-zuerich

de: Geoinformation Kanton Zürich

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

de: WMS Schutzwald


maintainer: M. Carol Resch

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

en: Nematode families

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

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

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

en: Plant community composition (plant species)

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

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

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

en: Nematode feeding types

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

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

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

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

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

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

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

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

en: Nemadotes and vegetation

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

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

X: X-Coordinate

Y: Y-Coordinate

Ri.veg: Total number of plant species

Div.veg: Shannon diversity of plant species

Ri.nem: Total number of nematode families

Div.nem: Shannon diversity of nematode families

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Tillage: last time of tillage

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

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

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

pH: soil pH (CaCl2)

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

en: Coordinates of one research plot were wrong. This was corrected in the current version of the file.

en: Nematode colonizer-persister classes

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

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

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

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

Paper Citation:
 > Resch, M.C., Schütz, M., Graf, U., Wagenaar, R., van der Putten, W.H., Risch, A.C. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56: 1782-1793.

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

 Methods
 Study area and experimental settings
The study was conducted in a nature reserve (Eigental: 47° 27’ to 47° 29’ N, 8° 37’ E, 461 to 507 m a.s.l.) that is located on the Swiss Central plateau close to Zurich airport (Canton Zurich, Switzerland). The mean annual temperature in this area ranges from 8.9 to 10.6 °C, mean annual precipitation from 910 to 1260 mm [10-year average (2007-2017); MeteoSchweiz, 2018]. The main soil types are calcaric to gleyic Cambisol and Gleysols. The reserve was established in 1967 to protect small remnants of oligotrophic semi-natural grasslands (roughly 12 ha). The plant community can be characterized as Molinion and Mesobromion (semi-wet to semi-dry), depending on the site-specific groundwater level and slope inclination (Delarze, Gonseth, Eggenberg, & Vust, 2015). These remnants represent species-rich islands in an otherwise intensively managed agricultural landscape. Semi-natural grasslands covered an area of 60,000 ha in the Canton Zurich in 1939, however, by 2005 only roughly 600 ha remained (Baudirektion Kanton Zürich, 2007). In 1990, the government of Canton Zurich decided to enlarge the nature reserve Eigental. The goal was to incorporate eleven patches of 20 ha adjacent intensively farmed land and transform these patches into semi-natural grasslands. The patches had a different agricultural history, ranging from permanent (no tillage for >50 years) to temporary grassland (as part of crop rotation; last tillage <5 years). On all freshly integrated patches fertilization was stopped in 1992 and from then on biomass was harvested three times a year and removed. After 5 years without noticeable effects on vegetation composition, the Nature Conservation Agency of Canton Zurich decided to increase the restoration efforts. In 1995, a large-scale experiment was initialized to evaluate if certain treatments can facilitate restoration within a reasonable timeframe of 5 to 10 years after treatment implementation.
The three restoration treatments used were:
i. “Harvest only”: Plots are being mowed two to three times a year and the biomass is removed.
ii. “Topsoil”: Topsoil was removed to a depth of 10 to 20 cm, depending on the depth of the O and A horizon, in four randomly selected areas within each of the eleven patches in late autumn 1995. The size of each topsoil removal area depended on individual patch size and was between 2700 and 7000 m2.
iii. “Topsoil+Propagules”: Propagules from target vegetation were added on half of the area where topsoil was removed, using fresh, seed-containing hay originating from a mixture of semi-dry to semi-wet species-rich grasslands of local provenance (within a radius of 7 km). Hay applications were conducted twice in 1995 and 1996. Repeated applications were chosen to account for the low quantity of available plant material per transfer, since area ratio between receptor and donor sites was roughly 1:1. In addition, hand-collected propagules from 15 selected target species of regional provenance (within a radius of 30 km) were equally applied in 1996 and 1997.
“Topsoil” and “Topsoil+Propagules” plots are mowed once a year, and the biomass is removed. Mowing on these plots started five years after the treatment was implemented.
Eleven permanent plots of 5 m x 5 m were randomly established in each treatment to monitor the vegetation development. The experiment was complemented with 11 control plots that represent the initial state of intensively managed grasslands, further referred to as “Initial”, and 11 control plots that represent the targeted state of donor sites for “Topsoil+Propagules”, further referred to as “Target”. Consequently, the experiment consists of 55 plots (5 treatments x 11 replicates). Management of intensively used grasslands includes mowing and fertilizing (manure) between two to five times a year, as well as different tillage regimes (no tillage for >50 years; last time of tillage <5 years).

 Nematode and plant sampling
Soil nematodes were sampled in 2 m x 2 m plots, randomly established at least 2 m away from the vegetation plots. We collected eight soil cores with a 2.2 cm diameter soil core sampler (Giddings Machine Company, Windsor, CO, USA) to a depth of 12 cm (representing the majority of the plant rooting system) in each plot at the beginning of July 2017. The eight cores within each replicate plot were combined, gently homogenized, placed in coolers and transported to the laboratory of NIOO in Wageningen, the Netherlands, within one week. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink, 1960) and concentrated, resulting in 6 mL nematode solution. The nematode solution was subdivided into three subsamples, two for morphological identification and quantification, and one for molecular work (not used in this study). For morphological identification and quantification, nematodes were heat-killed at 90 °C and fixed in 4 % formaldehyde solution (final volume 10 mL per subsample). All nematodes in 1 mL of formaldehyde solution were counted, and a minimum of 150 individuals per 1 mL sample (or all if less nematodes were present) were identified to family level using Bongers (1988). We then extrapolated the numbers of each nematode taxa identified to the entire sample and expressed them per 100 g dry soil for further analyses.
We calculated number of nematode taxa and Shannon diversity and assessed nematode community composition. In addition, we classified the nematode taxa into feeding types (herbivores, bacterivores, fungivores, omni-carnivores), structural and functional guilds (Table S4). Structural guilds assign nematode taxa according to life-history traits into five colonizer-persister (C-P) classes, ranging from one (early colonizers of new resources) to five (persisters in undisturbed habitats; Bongers 1990). C-P classes can be categorized as indicators for nutrient-enriched (C-P1), stressed (C-P2) and structured (C-P3 + C-P4 + C-P5) soil conditions (Ferris, Bongers, & de Goede, 2001). Functional guilds assign nematode taxa according to their C-P classification combined with their feeding habits (Ferris, Bongers, & de Goede, 2001). Based on the structural and functional guild classification we calculated five additional indices to assess soil nutrient status, disturbance and food web characteristics using NINJA (Sieriebriennikov, Ferris, & de Goede, 2014). 1) The Maturity index indicates the degree of different environmental perturbations (e.g., tillage, nutrient enrichment, pollution) and is used to monitor colonization and subsequent succession after disturbances (Bongers, 1990). 2) The ratio between the Plant Parasite (C-P of herbivorous nematodes only) to Maturity index is used to monitor the recovery of disturbed habitats incorporating information of life-history traits for all feeding types (Bongers, van der Meulen, & Korthals, 1997). 3) The Enrichment index indicates nutrient-enriched soils and agricultural management practices (Ferris, Bongers, & de Goede, 2001). 4) The Structure index provides information about the succession stage of the soil food web and therefore correlates with the degree of maturity of an ecosystem (Ferris, Bongers, & de Goede, 2001). 5) The Channel index provides information about the predominant decomposition pathways, where higher values stand for a higher proportion of energy transformed through the slow fungal decomposition channel (Ferris, Bongers, & de Goede, 2001). In addition, the Structure and Enrichment indices can be displayed in a biplot where nematode assemblages are plotted along a structure (x-axis) and enrichment (y-axis) trajectory (increasing index values). Each biplot quadrat reflects different levels of disturbance, soil nutrient pools and decomposition pathways (Ferris, Bongers, & de Goede, 2001).
The plant surveys were conducted on the 25 m2 permanent plots in June 2017. Plant species cover was visually assessed according to the semi-quantitative cover-abundance scale of Braun-Blanquet (1964; nomenclature: Lauber & Wagner, 1996). We calculated number of species and Shannon diversity, and assessed plant community composition. We also counted the number of target species (all species recorded in the eleven target plots plus propagules of species applied by hand, resulting in a total of 143 species) and categorized plant species into species of concern based on their red list status in Switzerland as well as their protection status in Switzerland and the Canton Zurich (Moser, Gygax, Bäumler, Wyler, & Palese, 2002). Furthermore, we calculated indicator values for soil moisture and soil nutrients for each species according to Landolt et al. (2010).

 References
Baudirektion Kanton Zürich (2007). 10 Jahre Naturschutz-Gesamtkonzept für den Kanton Zürich 1995-2005 – Stand der Umsetzung. Zürich: Baudirektion Kanton Zürich.
Bongers, T. (1988). De nematoden van Nederland. Utrecht: Stichting Uitgeverij Koninklijke Nederlandse Natuurhistorische Vereniging.
Bongers, T. (1990). The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia, 83, 14-19. doi:10.1007/BF00324627
Bongers, T., van der Meulen, H., & Korthals, G. (1997). Inverse relationship between the nematode maturity index and plant parasite index under enriched nutrient conditions. Applied Soil Ecology, 6, 195-199. doi:10.1016/S0929-1393(96)00136-9
Braun-Blanquet, J. (1964). Pflanzensoziologie, Grundzüge der Vegetationskunde (3rd ed.). Wien: Springer.
Delarze, R., Gonseth, Y., Eggenberg, S., & Vust, M. (2015). Lebensräume der Schweiz: Ökologie - Gefährdung - Kennarten (3rd ed.). Bern: Ott.
Ferris, H., Bongers, T., & de Goede, R.G.M. (2001). A framework for soil food web diagnostics: extension of the nematode faunal analysis concept. Applied Soil Ecology, 18, 13-29. doi:10.1016/S0929-1393(01)00152-4
Landolt, E., Bäumler, B., Erhardt, A., Hegg, O., Klötzli, F., Lämmler, W., … Wohlgemuth, T. (2010). Flora indicativa. Ecological indicator values and biological attributes of the Flora of Switzerland and the Alps (2nd ed.). Bern: Haupt.
Lauber, K., & Wagner, G. (1996). Flora Helvetica. Flora der Schweiz. Bern: Haupt.
MeteoSchweiz (2018). Klimabulletin Jahr 2017, Zürich: MeteoSchweiz.
Moser, D., Gygax, A., Bäumler, B., Wyler, N., & Palese, R. (2002) Rote Liste der gefährteten Farn- und Blütenpflanzen der Schweiz. Bern: BUWAL.
Oostenbrink, M. (1960). Estimating nematode populations by some selected methods. In N.J. Sasser & W.R. Jenkins (Eds.), Nematology (pp. 85-101). Chapel Hill: University of North Carolina Press.
Sieriebriennikov, B., Ferris, H., & de Goede, R.G.M (2014). NINJA: An automated calculation system for nematode-based biological monitoring. European Journal of Soil Biology, 61, 90-93. doi:10.1016/j.ejsobi.2014.02.004

diversity

display_name: diversity

grassland

display_name: grassland

long-term-experiment

display_name: long-term-experiment

nematodes

display_name: nematodes

restoration

display_name: restoration

topsoil-removal

display_name: topsoil-removal

vegetation

display_name: vegetation

education

de: Bildung, Wissenschaft

en: Education and science

envidat

de: EnviDat: das Umwelt-Datenportal

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.

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: Grassland restoration: nematodes and plant communities


wms-waldentwicklungsplan-ogd

title_for_slug: wms-waldentwicklungsplan-ogd

de: Waldentwicklungsplan

geoinformation-kanton-zuerich

en: Department of Geoinformation of the Canton of Zurich

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

de: WMS Waldentwicklungsplan (OGD)


media_type: WFS

de: Waldareal im Kanton Zürich

format: WFS

de: WFS Waldareal


de: Dieser WMS beinhaltet das Inventar der Waldstandorte von naturkundlicher Bedeutung, kurz WNB genannt. Die Objekte des WNB-Inventars weisen besondere (seltene) standortgebundene Naturwerte von kantonaler Bedeutung auf. Meist liegt eine seltene Waldgesellschaft oder ein Lebensraum einer seltenen Pflanze oder eines seltenen Tieres vor. Mit der Aufnahme einer solchen Waldfläche ins Inventar zeigt die Behörde, dass sie den Standort aus kantonaler Sicht als naturkundlich bedeutsam beurteilt und dass der kartierte Naturwert erhalten bleiben muss

de: WMS Inventar der Waldstandorte von naturkundlicher Bedeutung (WNB)


de: Waldentwicklungsplan Kanton Zürich: Besondere Ziele

de: WMS Waldentwicklungsplan: Besondere Ziele (OGD)


agriculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

geography

de: Geographie

en: Geography

territory

de: Raum und Umwelt

en: Territory and environment

de: forstkreis wald forstrevier

de: Download via GIS-Browser

de: Die Forstkreise regeln die territoriale Zuständigkeit des kantonalen Forstdienstes im Kanton Zürich. Die den Forstkreisen vorstehenden Kreisforstmeister sind die primären Ansprechpartner bei Fragen im Wald. Mit Verfügung der Volkswirtschaftsdirektion vom 4.6.2002 wurde die Forstkreiseinteilung ab 1.9.2002 neu festgelegt. Die Grenzen der Forstkreise verlaufen entlang von Gemeindegrenzen und entsprechen grundsätzlich jenen der Planungsregionen. Abweichend von diesem Einteilungskriterium wird im Gebiet des Bachsertales auf die Bezirksgrenze zurückgegriffen, da die Planungsregion Unterland sehr viele Gemeinden umfasst. Auch im Bereich des Flughafens Zürich-Kloten, wird von der Einteilung nach Planungsregionen abgewichen, damit alle an den Flughafen angrenzenden Gemeinden dem gleichen Forstkreis zugeordnet sind.

de: Forstkreise


de: waldgrenzen waldfeststellung

de: Das Bundesgesetz über den Wald (Waldgesetz, WaG) schreibt in Art. 13 vor, dass der Wald gegenüber den Nutzungszonen mittels einer Waldfeststellung abgegrenzt und die enstprechende Abgrenzung in den Nutzungsplänen einzutragen ist. Neue Bestockungen ausserhalb dieser Waldgrenzen gelten nicht als Wald (-> statische Waldgrenze). Wenn Nutzungspläne revidiert oder angepasst werden und sich die tatsächlichen Verhältnisse wesentlich geändert haben, können diese Waldgrenzen im Waldfeststellungsverfahren überprüft und angepasst werden.

geoinformation-kanton-zuerich

de: Geoinformation Kanton Zürich

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

de: ÖREB-Kataster - vereinfachtes Datenmodell ZH - Statische Waldgrenzen


de: wald waldareal pnf bestockte-flachen waldperimeter

de: Der Wald des Kantons Zürich wurde im Rahmen der "Periodischen Nachführung der Amtlichen Vermessung" (PNF) 2017/2018 aktualisiert. Der davon abgeleitete Geodatensatz Waldareal umfasst grössere bestockte Flächen innerhalb der Kantonsgrenzen. Diese sind an den Gemeindegrenzen unterteilt. Kleinere unbestockte Flächen (< 800 m2) sowie Bäche und Waldwege gehören zum Waldareal. Grössere Gewässer und Durchgangsstrassen werden ausgespart oder unterteilen es. Bestockungen < 800 m2 (sog. Feldgehölze) sind nicht mehr im Geodatensatz enthalten. Eine Nachführung der Geodaten ist grundsätzlich nur bei Rodungen oder Rodungsersatz geplant oder wenn nachgewiesenermassen offensichtliche Fehler vorliegen und wird ggf. vom ARE, Fachstelle Kataster durchgeführt. Das Waldareal hat keine direkte Rechtswirkung, bildet aber die Grundlage für die gemeindeweise Festsetzung der Statischen Waldgrenzen im Kanton Zürich, welche zusammen mit der kantonalen Nutzungsplanung in Angriff genommen werden.

de: Waldareal


de: naturwaldreservate sonderwaldreservate nutzungsverzicht langfristiger-schutz mcpfe walddynamik

de: Naturwaldreservate im Kanton Zürich sind durch Verträge langjährig gesicherte Waldflächen, in denen weitestgehend auf Pflegeeingriffe verzichtet wird (Typ MCPFE1_2), um sie den natürlichen Alterungs- und Zerfallsprozessen zu überlassen. Dadurch erhöht sich der Alt- und Totholzanteil und die Strukturvielfalt, welche spezialisierten Tier- und Pflanzenarten Lebensraum bietet. Sonderwaldreservate (Typ: MCPFE1_3) lassen gezielte Eingriffe zur Biodiversitätsförderung zu. Sie sind entweder langjährig vertraglich gesichert oder als Waldteile mit Reservatscharakter durch überkommunale Schutzanordnungen geschützt.

de: Waldreservate


agriculture

de: Land- und Forstwirtschaft

geography

de: Geographie

de: wald vegetation hohe vegetationshohe

de: Im Rahmen des Projektes "Kanton ZH - WND Inventar WNB Umsetzung Modul 1" wurde 2014 durch die Nategra GmbH im Auftrag der Fachstelle Naturschutz und der Abt. Wald des Kantons Zürich auf Basis von Fernerkundungsdaten (LiDAR) unter Abzug von Gebäudedaten eine Geodatenebene Vegetationshöhe hergeleitet.

de: Normalisiertes Digitales Oberflächenmodell Vegetation (nDOM Veg) - 2014


maintainer: Bundesamt für Energie BFE

fr: Contenu des bassins d'accumulation, dimanche 24:00 heures, bulletin hebdomadaire

it: Grado di riempimento dei bacini di accumulazione, domenica alle ore 24, rapporto settimanale livello di accumulazione

author: Bundesamt für Energie BFE

media_type: CSV

media_type: SERVICE

fr:
Ce relevé, élaboré chaque dimanche à 24h00, recense le contenu (en GWh et en %) des bassins d'accumulation suisses dans 4 régions différentes.
Le taux de remplissage des bassins d'accumulation est relevé dans le cadre de la Statistique de l'électricité, qui fait partie de la statistique publique de la Suisse (base légale: LSF).

de: Der Füllungsgrad der Speicherseen, Sonntag 24h, Wochenbericht Speicherinhalt gibt Auskunft über den Speicherinhalt der Schweizer Speicherseen für vier Regionen in GWh und als Füllgrad in % jeweils am Sonntag 24h auf wöchentlicher Basis. Der Füllungsgrad der Speicherseen wird im Rahmen der Elektrizitätsstatistik erhoben, welche Teil der öffentlichen Statistik der Schweiz ist (gesetzliche Grundlage: BStatG).

statistical-basis

fr: Bases statistiques

de: Statistische Grundlagen

en: Statistical basis

it: Basi statistiche

national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia

energy

fr: Energie

de: Energie

en: Energy

it: Energia

territory

de: Raum und Umwelt

bundesamt-fur-energie-bfe

fr: Office fédéral de l'énergie OFEN

de: Bundesamt für Energie BFE

en: Swiss Federal Office of Energy SFOE

it: Ufficio federale dell'energia UFE

fr: L'Office fédéral de l'énergie (OFEN) est le centre de compétences du Département fédéral de l'environnement, des transports, de l'énergie et de la communication (DETEC) pour les questions liées à l'approvisionnement en énergie et à son utilisation.

de: Das Bundesamt für Energie (BFE) ist das Kompetenzzentrum für Fragen der Energieversorgung und der Energienutzung im Eidgenössischen Departement für Umwelt, Verkehr, Energie und Kommunikation (UVEK).

en: The Swiss Federal Office of Energy (SFOE) is the country's competence centre for issues relating to energy supply and energy use at the Federal Department of the Environment, Transport, Energy and Communications (DETEC).

it: L'Ufficio federale dell'energia (UFE) è il centro di competenza in materia di approvvigionamento e di impiego dell'energia in seno al Dipartimento federale dell'ambiente, dei trasporti, dell'energia e delle comunicazioni (DATEC).

political_level: confederation


smiley-geschwindigkeitsanzeigen

mobility

fr: Mobilité et transports

de: Mobilität und Verkehr

en: Mobility and Transport

it: Mobilità e trasporti

public-order

fr: Ordre et sécurité publics

de: Öffentliche Ordnung und Sicherheit

en: Public order and security

it: Ordine pubblico e sicurezza pubblica

title_for_slug: smiley-geschwindigkeitsanzeigen

fr: vitesse securite-routiere

en: speed road-safety

it: velocita sicurezza-stradale

spatial: Kanton Basel-Stadt (BS)

de: Geodaten-Shop

de: Öffentlich zugänglicher Geodaten-Downloaddienst des Kantons Basel-Stadt

de: WMS BS

de: Die derzeit im WMS BS verfügbaren Datenebenen entsprechen, bis auf wenige Ausnahmen, den Geodaten, welche auch in MapBS dargestellt sind. Der WMS BS ist kostenlos nutzbar und bietet Zugriff auf öffentliche sowie beschränkt öffentliche Geodaten, wobei gegebenenfalls Benutzername und Passwort benötigt werden (Zugangsdaten von MapBS).

de: WFS BS

de: Die derzeit im WFS BS verfügbaren Datenebenen entsprechen, bis auf wenige Ausnahmen, den Geodaten, welche auch in MapBS dargestellt sind. Der WFS BS ist kostenlos nutzbar und bietet Zugriff auf öffentliche sowie beschränkt öffentliche Geodaten, wobei gegebenenfalls Benutzername und Passwort benötigt werden (Zugangsdaten von MapBS).

de: Die Smiley-Geschwindigkeitsanzeigen dienen der Kantonspolizei Basel-Stadt zur Erhöhung der Verkehrssicherheit an der betreffenden Örtlichkeit. Die Wechselanzeige von der aktuell gefahrenen Geschwindigkeit und Smiley-Symbol (Lob oder Tadel) ohne Repression weist auf freundliche Weise auf ein allfälliges Fahrverhalten hin. Durch diese Selbstkontrolle soll das Fahrverhalten positiv beeinflusst, die vorgeschriebene Geschwindigkeit besser eingehalten und die Aufmerksamkeit der Verkehrsteilnehmenden erhöht werden. Mit den Geräten können zudem Verkehrsdaten anonym erfasst werden. Die Geschwindigkeitsanzeigen stehen nicht in einem Zusammenhang mit Ordnungsbussen oder einer strafrechtlichen Verfolgung.

kanton-basel-stadt

fr: Canton de Bâle-Ville

de: Kanton Basel-Stadt

en: Canton of Basel-City

it: Cantone di Basilea-Città

de: Der Kanton Basel-Stadt veröffentlicht nach und nach Open Government-Datensätze von kantonalen Ämtern und Dienststellen. Die kantonale Fachstelle für Open Government Data ist als Koordinationsorgan zuständig für die Unterstützung der datenpublizierenden Verwaltungsstellen.

value: BS - Geocat Harvester

de: Smiley-Geschwindigkeitsanzeigen


kommunale-nutzungsplanung

geography

de: Geographie

title_for_slug: kommunale-nutzungsplanung

fr: protection-de-la-nature corps-executif risque-naturel planification-de-lespace-physique amenagement-du-paysage commune planification-regionale eaux-geographie cadastre amenagement-du-territoire bois polluant-sonore plan planification

de: naturschutz behorde naturgefahr raumplanung landschaftsplanung gemeinde regionalplanung gewasser kataster flachennutzungsplanung wald larmbelastung plan planung

spatial: Kanton Bern

fr: Lien vers le téléchargement de données (Zip)

de: Link zum Daten-Download (Zip)

fr: Thème du cadastre RDPPF L'ordonnance classe les jeux de géodonnées de base communales qui relèvent du domaine des plans d'affectation dans le cadastre des restrictions de droit public à la propriété foncière (cadastre RDPPF). Les thèmes du cadastre RDPPF suivantes se trouvent dans le géoproduit NUPLA: - Plans d'affectation communales (OGéo ID 73C), - Degré de sensibilité au bruit (OGéo ID 145), - Distances par rapport à la forêt communales (OGéo ID 159C). Les espaces réservés aux eaux déterminés par les communes se trouvent aussi dans le géoproduit (couches 'UEGEWRF: Espaces réservés aux eaux (surfaces)' et 'UEGEWRL: Espaces réservés aux eaux (lignes)'). **Le jeu de données est en construction et n'est pas encore disponible pour l'ensemble du canton de Berne.**

de: ÖREB-Katasterthema Die kommunalen Geobasisdatensätze im Bereich Nutzungsplanung sind gemäss der Verordnung über den Kataster der öffentlich-rechtlichen Eigentumsbeschränkungen (ÖREBKV) Bestandteile des ÖREB-Katasters. Folgende ÖREB-Katasterthemen sind in diesem Geoprodukt NUPLA enthalten: - Kommunale Nutzungsplanung (GeoIV ID 73C), - Lärmempfindlichkeitsstufen (GeoIV ID 145), - Kommunale Wald-Baulinien (GeoIV ID 159C). Enthalten sind auch die von den Gemeinden festgelegten Gewässerräume (Ebenen 'UEGEWRF: Gewässerraumflächen' und 'UEGEWRL: Gewässerraumlinien'). **Der Datensatz befindet sich im Aufbau und ist noch nicht flächendeckend für den ganzen Kanton Bern vorhanden. **

fr: Office de l'information géographique du canton de Berne

de: Amt für Geoinformation des Kantons Bern

fr: L'Office de la géoinformation est le centre de compétence pour l'information spatiale dans le canton de Berne. Les géodonnées cantonales peuvent être utilisées gratuitement, sous réserve des conditions d'utilisation. Le Géoportail du canton de Berne est le point d'entrée central pour les cartes interactives, les géodonnées et les géoservices de l'administration cantonale.

de: Das Amt für Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Bern. Die kantonalen Geodaten sind unter Einhaltung der Nutzungsbedingungen kostenlos nutzbar. Das Geoportal des Kantons Bern ist der zentrale Einstiegspunkt für interaktive Karten, Geodaten und Geodienste der kantonalen Verwaltung.

value: Bern-Kt - Geocat Harvester

fr: Plans d'affectation communales

de: Kommunale Nutzungsplanung


wald-flora-und-fauna-des-kantons-bern

title_for_slug: wald-flora-und-fauna-des-kantons-bern

fr: desserte gibier chasse animal-sauvage gestion-des-forets conseil vegetation comportement-animal routes-forestieres bois trafic-motorise espace-vital gestion-forestiere

de: erschliessung wild jagd wildtier waldbewirtschaftung gutachten vegetation tierverhalten waldstrassen wald motorfahrzeugverkehr lebensraum forstwirtschaft

fr: Service d'image

de: Darstellungsdienst

fr: Le service représente les données de base du canton de Berne selon la géocatégorie G conforme à eCH-0166. Il comprend la carte de base de la desserte des forêts, l'expertise des dégâts du gibier, les grands prédateurs, l'information sur la fôret, le modèle de hauteur de végétation forêt (LIDAR), le plan des routes forestières ainsi que les zones de gestion du gibier.

de: Der Dienst stellt Geobasisdatensätze des Kantons Bern der Geokategorie G gemäss eCH-0166 dar. Er enthält die Datensätze Grossraubtiere, Grundlagenkarte Walderschliessung, Vegetationshöhenmodell Wald und Wildschadengutachten, Waldinformation, Waldstrassenplan sowie Wildräume.

fr: Forêt, flore et faune du canton de Berne

de: Wald, Flora und Fauna des Kantons Bern


en: terrestiral-laserscanning depth-of-snowfall terrestrial-laser-scans antarctica gridded-data

en: Scan from 4 January 2019, 1 mm resolution

media_type: ascii x, y, z

en: Scan from 2 January 2019, 1 mm resolution

en: Scan from 11 January 2019, 1 mm resolution

en: Scan from 27 December 2018, 10 cm resolution

en: Scan from 4 January 2019, 10 cm resolution

en: Scan from 27 December 2018, 1 mm resolution

en: Scan from 21 December 2018, 1 mm resolution

en: Scan from 21 December 2018, 10 cm resolution

en: Scan from 11 January 2019, 10 cm resolution

en: Scan from 2 January 2019, 10 cm resolution

en:
Surface topography maps (spatial extent: 400 m x 400 m) obtained at approximately 300 m from the top of the Hammarryggen Ice Rise in Dronning Maud Land, East Antarctica, using a Riegl VZ-6000 Terrestrial Laser Scanner (TLS). Scans were obtained on 5 days in the 2018-2019 Austral summer: on December 21 and 27, and January 2, 4 and 11. By using reflectors installed on bamboo poles, scans were registered with respect to the reflectors, such that the difference between two successive scans reveals the spatial patterns of erosion and deposition of snow. On each scan day, we used multiple scan positions to create one combined point cloud. After applying an octree filter on the point cloud, a 3D surface was obtained. For each day, the dataset contains a 1 mm and a 10 cm octree filter resolution file, only including points in a 400 m x 400 m area centered around the scan positions.

Notes:

* All files in the dataset are in the same coordinate system. However, this coordinate system is arbitrary (i.e., not related to any global coordinate system).

* From the installed reflectors, 4 reflectors could be used over the full period. The scan accuracy is generally higher within the area enclosed by the reflectors.

* The scans from January 2 were found to have exhibited small tilt during the scan and are of lesser accuracy.

* By walking along fixed corridors, disturbance of the snow was limited.

antarctica

display_name: antarctica

depth-of-snowfall

display_name: depth-of-snowfall

gridded-data

display_name: gridded-data

terrestiral-laserscanning

display_name: terrestiral-laserscanning

terrestrial-laser-scans

display_name: terrestrial-laser-scans

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.

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: Terrestrial laser scans on Hammarryggen Ice Rise, Dronning Maud Land, East Antarctica


amtliche-vermessung-vereinfacht

geography

fr: Géographie

en: Geography

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

title_for_slug: amtliche-vermessung-vereinfacht

fr: cadastre planification

de: kataster planung

fr: Le géoproduit mensuration officielle simplifiée (MOPUBE) contient les données relatives à la structure de propriété et à l'utilisation du sol dans le canton de Berne. Ces données sont les plus précises disponibles actuellement. Elles se prêtent bien à un fond de plan pour la représentation d'informations thématiques. Le géoproduit mensuration officielle simplifiée est mis à jour chaque semaine ; il n'a pas de portée légale. Il constitue un sous-ensemble des données complètes de la mensuration officielle (MO). Il contient les données géométriques et les attributs des objets décrivant la propriété et l'utilisation du sol. Ces données sont à la base de la plupart des applications SIG. Elles ont été élaguées des informations plus détaillées n'intéressant principalement que les spécialistes de la mensuration cadastrale. Ce jeu de données comprend des extraits des couches d'information suivantes: points fixes, couverture du sol, objets divers, nomenclature, biens-fonds, divisions administratives et techniques de la mensuration officielle. La structure du geoproduit correspond à celle du modèle de données de la Confédération. Il a été toutefois complété avec des exigences cantonales.

de: Das Geoprodukt Amtliche Vermessung vereinfacht (MOPUBE) enthält die genauesten, verfügbaren Daten über Grund und Boden des Kantons Bern. Es eignet sich optimal  insbesondere die Bodenbedeckung und Liegenschaften  als Kartenhintergrund für die Darstellung thematischer Informationen. Das Geoprodukt Amtliche Vermessung vereinfacht wird wöchentlich aktualisiert; es besitzt keine Rechtswirkung. Es enthält eine Teilmenge der Daten der Amtlichen Vermessung (AV), namentlich diejenigen geometrischen und attributiven Daten zu Grund und Boden, die von den meisten GIS-Anwendern benötigt werden. Weitergehende Informationen, die vorwiegend Vermessungsspezialisten interessieren, werden weggelassen. Der Geodatensatz umfasst Auszüge aus den Informationsebenen Fixpunkte, Bodenbedeckung, Einzelobjekte, Nomenklatur, Liegenschaften, administrative und technische Einteilungen der Amtlichen Vermessung. Das Geoprodukt entspricht von der Struktur her dem Datenmodell «MOpublic» des Bundes. Es ist jedoch mit einigen kantonalen Anforderungen ergänzt worden.

fr: Mensuration officielle simplifiée

de: Amtliche Vermessung vereinfacht


parzellennummern

geography

de: Geographie

title_for_slug: parzellennummern

de: grundbuch eigentumsordnung kataster

fr: Lien vers le téléchargement de données (Zip)

fr: Le produit Numéros de parcelles contient les numéros géoréférencés de parcelles (bien-fonds ainsi que les droits distincts et permanents) du canton de Berne. Par le fait que les numéros de parcelles soient connues par leurs coordonnées (y et x), DIPANU permet de rechercher des parcelles dans le canton de Berne.

de: Das Produkt Parzellennummern beinhaltet die georeferenzierten Nummern von Grundstücken (Liegenschaften und Selbstständige, dauernde Rechte) im Kanton Bern. Dadurch, dass die Grundstücknummern mit ihren Koordinaten (y und x) abgelegt sind, dient DIPANU als Suchinstrument von Grundstücken innerhalb des Kantons Bern.

fr: Numéros de parcelles

de: Parzellennummern


gruda-av-export

title_for_slug: gruda-av-export

fr: batiment registre-foncier droit-de-propriete cadastre nomenclature

de: gebaude grundbuch eigentumsordnung kataster nomenklatur

de: Link zum Daten-Download (Zip)

fr: Export des données sur les biens-fonds, les bâtiments, désignation de la situation et la couverture du sol de la banque de données des biens-fonds GRUDA-MO. L'export correspond au modèle de données du téléchargement de données issues de GRUDA-MO La correspondance des attributs du modèle de données au géoproduit est décrite dans le fichier [GRUDAEXP_Attribute.pdf](<https://geofiles.be.ch/geoportal/pub/lpi/GRUDAEXP_Attribute.pdf>).

de: Daten-Export zu Grundstücken, Gebäuden, Lagebezeichnung und Bodenbedeckung aus der Grundstückdatenbank GRUDA-AV. Der Export entspricht dem Datenmodell des Datenablads aus GRUDA-AV Die Zuordnung der Attribute des Datenmodells zum Geoprodukt ist in der Datei [GRUDAEXP_Attribute.pdf](<https://geofiles.be.ch/geoportal/pub/lpi/GRUDAEXP_Attribute.pdf>) beschrieben.

fr: Office de l'information géographique du canton de Berne

de: Amt für Geoinformation des Kantons Bern

fr: L'Office de la géoinformation est le centre de compétence pour l'information spatiale dans le canton de Berne. Les géodonnées cantonales peuvent être utilisées gratuitement, sous réserve des conditions d'utilisation. Le Géoportail du canton de Berne est le point d'entrée central pour les cartes interactives, les géodonnées et les géoservices de l'administration cantonale.

de: Das Amt für Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Bern. Die kantonalen Geodaten sind unter Einhaltung der Nutzungsbedingungen kostenlos nutzbar. Das Geoportal des Kantons Bern ist der zentrale Einstiegspunkt für interaktive Karten, Geodaten und Geodienste der kantonalen Verwaltung.

fr: GRUDA MO Export

de: GRUDA AV Export


stand-der-amtl-vermessung

title_for_slug: stand-der-amtl-vermessung

fr: Sur la base du fichier cantonal Limites communale 1:5'000, les données relatives aux périmètres approuvé et en les périmètres en cours de la mensuration officielle du canton de Berne, depuis lintroduction du CC en 1910, ont été saisies dans un même fichier AMO. Le fichier AMO montre létat actuel de la mensuration officielle dans le canton de Berne et permet den exploiter les données.

de: Im Datensatz AMO sind auf der Grundlage des Kantonsdatensatzes Gemeindegrenzen 1:5'000 für den Kanton Bern die anerkannten Bearbeitungsperimeter und die sich in Bearbeitung befindenden Bearbeitungsperimeter der amtlichen Vermessung erfasst, welche nach Einführung des ZGB im Jahr 1910 bearbeitet wurden resp. bearbeitet werden. Der Datensatz AMO zeigt den heutigen Stand der amtlichen Vermessung im Kanton Bern.

fr: Etat actuel de la mensuration officielle

de: Stand der amtl. Vermessung


gebaudeadressen

title_for_slug: gebaudeadressen

fr: batiment liste-de-diffusion

de: gebaude adressenliste

fr: Le fichier GEBADR contient les adresses postales des bâtiments, adresses spatialisées et enregistrées dans la base de données de la mensuration officielle. Il contient aussi les bâtiments souterrains. En règle générale, une adresse de bâtiment se compose des éléments suivants : un nom de rue, ou de place, ou un lieu dénommé; un numéro de maison; le numéro postal d'acheminement (NPA) et la localité. Les bâtiments annexes (garages, remises, etc.) ne possèdent pas tous un numéro de maison ; ils sont désignés par la mention « nn » (qui veut dire non numéroté). L'objectif à moyen terme est d'attribuer aussi à ces bâtiments annexes un numéro de maison univoque. Cette désignation se fera en collaboration avec les communes bernoises.

de: Der Datensatz GEBADR enthält die postalischen Gebäudeadressen aller ober- und unterirdischen Gebäude, welche in der amtlichen Vermessung erfasst sind. Eine Gebäudeadresse setzt sich im Normalfall aus folgenden Elementen zusammen: Strassenname (oder Platz, benanntes Gebiet), Hausnummer, Postleitzahl und Ortschaft. Nebengebäude (z.B. Garagen, Ökonomiegebäude) verfügen teilweise nicht über eine eigene Hausnummer und sind deshalb mit der Bezeichnung 'nn' (= nicht nummeriert) bezeichnet. Es wird angestrebt, in Zusammenarbeit mit den bernischen Gemeinden künftig auch diese bisher nicht nummerierten Gebäude mit einer eindeutigen Hausnummer zu kennzeichnen.

fr: Adresses des bâtiments

de: Gebäudeadressen


kantonaler-wasserbauplan

geography

fr: Géographie

territory

fr: Territoire et environnement

de: Raum und Umwelt

title_for_slug: kantonaler-wasserbauplan

de: raumplanung wasserbau

spatial: Kanton Bern (reduziert)

fr: Le géoproduit WABAUPLA contient les géodonnées de base suivantes: - Plan d'aménagement des eaux, zones inondables (OGéo ID 109-BE).

de: Das Geoprodukt WABAUPLA enthält folgende Geobasisdaten: - Wasserbauplan, Überflutungsgebiete (GeoIV ID 109-BE)

fr: Plan cantonal d'aménagement des eaux

de: Kantonaler Wasserbauplan


geography

de: Geographie

fr: structure-batie document alimentation-en-eau eau-substance responsabilite deversement-des-eaux-usees eau-usee

de: bauwerk dokument wasserversorgung wasser zustandigkeit abwasserbeseitigung abwasser

fr: Lien vers le téléchargement de données (Zip)

fr: Le géoproduit SOELWA comprend la version numérisée de la GICP, qui a été autorisée par l'OED en qualité d'autorité directrice dans le cadre d'une procédure de droit public. Il s'agit d'une restriction de droit public à la propriété foncière qui sera ensuite traitée par la commune à partir de 2023 et publiée comme géoproduit dans le cadastre RDPPF.

de: Das Geoprodukt SOELWA umfasst die digitale/digitalisierte Version der öffentlich-rechtlichen Sicherungen von öffentlichen Leitungen (SöL), die vom AWA im Rahmen eines UeO-Verfahrens in Funktion der Leitbehörde genehmigt wurden. Dabei handelt es sich um eine öffentlich-rechtliche Eigentumsbeschränkung und wird ab 2023 gemeindeweise aufgearbeitet und als Geoprodukt als auch im ÖREB publiziert.

name: Amt für Wasser und Abfall - Siedlungswasserwirtschaft / Trinkwasser und Abwasser

fr: Procédure de garantie d’implantation des conduites publiques (GICP)

de: Öffentlich-rechtliche Sicherungen von öffentlichen Leitungen (SöL)