soil-respiration-exclosure-experiment

title_for_slug: soil-respiration-exclosure-experiment

en: ungulates grassland soil-respiration grazing subalpine

en: Soil respiration Switzerland

en:
ID: Fence number

Year	:Year of sampling

mean.CO2: soil respiration measured with PP-systems SRC-1 soil respriation chamber attached to a EGM-4 infrared gas analyser; units CO2 g m-2 h-1

Grassland: Grasslands where fences were errected. Note that on large grasslands (STAB, GRIM, MING) 2 pairs of fences were errected (2 in short, 2 in tall-grass vegetation), on small grasslands (DADA, MARG, BOTS) only one pair of fences was errected (1 short-grass; one-tallgrass vegetation)

Fence: Fence number; un-even numbers on short-grass vegetation (1,3,5,7,9,11,13,15,17), even numbers on tall-grass vegetation (2,4,6,8,10,12,14,16,18)

Vegetation:	1 = shortgrass vegetation, 2 = tall-grass vegetation (9 replicate fences on each vegetation type)

Treatment	: 1 = all herbivores grazing, 2 = large ungulates (red deer, chamois) excluded

en:
Location of data collection
The Swiss National Park (SNP) is located in the southeastern part of Switzerland, and covers an area of 170 km2, 50 km2 of which is forested, 33 km2 is occupied by alpine and 3 km2 by subalpine grasslands. Elevations range from 1350 to 3170 m a.s.l., and mean annual precipitation and temperature are 871 mm and 0.6°C measured at the Park’s weather station in Buffalora (1980 m a.s.l.) between 1960 and 2009 (MeteoSchweiz 2011). Founded in 1914, the SNP received minimal human disturbance for almost 100 years (no hunting, fishing, or camping, visitors are not allowed to leave the trails). Large (> 1 ha) homogeneous patches of short- and tall-grass vegetation characterize the subalpine grasslands. The average vegetation height of short-grass vegetation is 2 to 5 cm. Red fescue (Festuca rubra L.), quaking grass (Briza media L.) and common bent grass (Agrostis tenuis Sipthrob) are the predominating plant species in this vegetation type. Tussocks of evergreen sedge (Carex sempervirens Vill.) and mat grass (Nardus stricta L.) are predominant in the tall-grass vegetation, which averages 20 cm in vegetation height (Schütz and others 2006). Short-grass vegetation developed in areas where cattle and sheep rested (high nutrient input) during agricultural land-use (from 14th century until 1914); tall-grass vegetation developed in areas where cattle and sheep used to graze, but did not rest (Schütz and others 2003, 2006). Herbivores were shown to consume > 60% of the biomass in short-grass compared to < 20% in tall-grass vegetation (Schütz and others 2006). The herbivore community present in the SNP can be divided into four groups based on body size/weight: large [red deer (Cervus elaphus L.) and chamois (Rupricapra rupricapra L.); 30 - 150 kg], medium [marmot (Marmota marmota L.) and snow hare (Lepus timidus L.); 3 – 6 kg], and small vertebrate herbivores (small rodents: e.g. Clethrionomys spp., Microtus spp., Apodemus spp.; 30 – 100 g) as well as invertebrates (e.g. grasshoppers, caterpillars, cicadas, < 5 g).

Experimental design
We selected 18 subalpine grassland sites (9 short-grass, 9 tall-grass vegetation). The sites were spread across the entire park on dolomite parent material at altitudes of 1975 to 2300 meters. At each site we established an exclosure network (fences) in spring 2009 (early June), immediately after snowmelt. Each exclosure network consisted of a total of five 2 × 3 m sized plots that progressively excluded the different herbivores listed above (further labeled according to the herbivore guilds that had access to the respective plots “All”, “Marmot/Mice/Invertebrates”, “Mice/Invertebrates”, “Invertebrates”, “None”). The “All” treatment was thus accessible to all herbivores, was not fenced and was located at least 5 m away from a 2.1 m tall and 7 × 9 m main fence that enclosed the other four treatments. This fence was constructed of 10 × 10 cm wooden posts and electrical equestrian tape (AGRARO ECO, Landi, Bern, Switzerland; 20 mm width) mounted at 0.7 m, 0.95 m, 1.2 m, 1.5 m and 2.1 m above the ground that were connected to a solar charged battery (AGRARO Sunpower S250, Landi, Bern, Switzerland). We also mounted non-electrically charged equestrian tape at 0.5 m to help exclude deer and chamois, yet allow marmots and hares to enter safely. Within each main fenced area we randomly established four 2 × 3 m plots: (1) The “Marmot/Mice/Invertebrates” plot remained unfenced, thus, with the exception of red deer and chamois, all herbivores were able to access the plot, (2) The “Mice/Invertebrates” plot consisted of a 90 cm high electric sheep fence (AGRARO Weidezaunnetz ECO, Landi, Bern, Switzerland; mesh size 10 × 10 cm) connected to the solar panel and excluded all medium sized mammals (marmots, hares), but provided access for small mammals and invertebrates, (3) The “Invertebrates” plot provided access for invertebrates only and was surrounded by 1 m high metal mesh (Hortima AG, Hausen, Schweiz; mesh size 2 × 2 cm), (4) The “None” plot was surrounded by a 1 m tall mosquito net (Sala Ferramenta AG, Biasca, Switzerland; mesh size 1.5 × 2 mm) to exclude all herbivores. This plot was covered with a roof constructed of a wooden frame lined with mosquito mesh that was mounted on the wooden corner posts. We also treated this plot with a biocompatible insecticide (Clean kill original, Eco Belle GmbH, Waldshut-Tiengen, Germany) when needed to remove insects that might have entered during data collection or that hatched from the soil.

!!! The here published data set only contains data for “All”, and “Marmot/Mice/Invertebrates” (= ungulates excluded) plots !!!

Data collection
In-situ soil CO2 emissions were measured 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) on two randomly selected locations on one subplot within each of the 90 plots. For each measurement the soil chamber (15 cm high; 10 cm diameter) was placed on a permanently installed PVC collar (10 cm diameter) driven five centimeters into the soil at the beginning of the study (June 2009). The measurements were conducted between 0900 and 1700 hours every two weeks from early to early September 2009, 2010, 2011 and 2013. Freshly germinated plants growing within the PVC collars were removed prior to each measurement to avoid measuring plant respiration/photosynthesis. The two measurements collected per plot every two weeks were averaged.

Please acknowledge the funding of the study: funded by the Swiss National Science Foundation, SNF grant-no 31003A_122009/1 to Anita C. Risch, Martin Schütz and Flurin Filli

grassland

display_name: grassland

grazing

display_name: grazing

soil-respiration

display_name: soil-respiration

subalpine

display_name: subalpine

ungulates

display_name: ungulates

en: Soil respiration - exclosure experiment


maintainer: EnviDat Support

en: jupyter geospatial gis buffer roads density

state: active

type: dataset

media_type: ipynb

format: SERVICE

en: Road Density Calculation (Python Notebook) online

media_type: url

media_type: application/zip

en: Road data from OSM for the surroundings of Menzberg, Switzerland

format: ZIP

mimetype: application/zip

en: Plots

en: Random plot locations

en:
This dataset contains the road and plot data used for the geospatial analysis example showcased in "Fostering Open Science at WSL with the EnviDat Environmental Data Portal", a contribution to the 5th Open Source Geospatial Research and Education Symposium (OGRS), 2018.
The example uses Jupyter Notebook to calculate road densities in the neighbourhood of sample plot locations with Python. Road data were extracted from OpenStreetMap, while the point data (sample plots) were generated manually.

buffer

display_name: buffer

density

display_name: density

geospatial

display_name: geospatial

gis

display_name: gis

jupyter

display_name: jupyter

roads

display_name: roads

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

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Dataset for OGRS 2018 publication


maintainer: Frank Techel

en: european-alps warning-regions avalanche-forecast danger-level snow

en: shape file warning regions Alps

en: Forecast avalanche danger level

media_type: text/csv

en: contains the highest forecast danger level (dangerLevelMax) for each day and warning region

format: CSV

mimetype: text/csv

en:
This dataset contains the data used in the publication by Techel et al., 2018 _Spatial consistency and bias in avalanche forecasts - a case study in the European Alps_ (Nat Haz Earth Syst Sci).
For details on the data please refer to this publication.
The dataset contains the following:
- shape files for the warning regions in the Alps
- highest forecast danger level for each warning region and day

avalanche-forecast

display_name: avalanche-forecast

danger-level

display_name: danger-level

european-alps

display_name: european-alps

snow

display_name: snow

warning-regions

display_name: warning-regions

en: Forecast avalanche danger level European Alps 2011 - 2015


maintainer: M. Carol Resch

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

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

format: XLS

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: 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: 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: 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:
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

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

en: Grassland restoration: nematodes and plant communities


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

en: Predictors

media_type: .tiff

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

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

en: Protected areas

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

en: Taxonomic and functional metrics in the community plots

media_type: .txt

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

en: Variable importance, model evaluation, partial dependence plots

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

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

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

anthophila

display_name: anthophila

beta-diversity

display_name: beta-diversity

biodiversity

display_name: biodiversity

climate

display_name: climate

community-indices

display_name: community-indices

elevation-gradient

display_name: elevation-gradient

functional-diversity

display_name: functional-diversity

hymenoptera

display_name: hymenoptera

predictive-modelling

display_name: predictive-modelling

protected-areas

display_name: protected-areas

resource-availability

display_name: resource-availability

switzerland

display_name: switzerland

urbanization

display_name: urbanization

wild-bee

display_name: wild-bee

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: Data on wild bee taxonomic and functional diversity in Switzerland


radiosondes

maintainer: Jörg Wieder

title_for_slug: radiosondes

en: radiosonde holoballoon wind pressure relative-humidity wind-direction raclets temperature

en: Radiosond Template Files

en:
The "template" files contain standard parameters, i.e. ambient pressure (mbar and Pa), temperature (°C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), as well as additional information, e.g. time (UTC and local time), dew point (°C), mixing ration (g/kg), density (kg m³), altitude (m MSL), height above ground (ft and m AGL), and geographic coordinates (longitude, latitude). If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosond Sounding Files

en:
The "soundings" radiosond data contains information on ambient pressure (mbar), temperature (C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), and are ordered with respect to the height above ground (m AGL) with a step size of 20 m. The first line of each file idicates the geographic location and the start time (in UTC) of the ascent. If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosondes (Windsond, Sparv Embedded AB) were started in Davos Wolfgang (LON: 9.853594, LAT: 46.835577) to report height profiles of pressure, relative humidity and temperature at specific days. In addition to regular launches of radiosondes, sondes were attached to [HoloBalloon](https://www.envidat.ch/group/clouds-in-situ-raclets) to report the ambient conditions of the in-situ measurements. Further profiles of meteorological measures were recorded at [HoloGondel](https://www.envidat.ch/group/clouds-in-situ-raclets) which was installed at the gondola moving between Gotschnaboden and Gotschnagrat at 2285 m a.s.l.

holoballoon

display_name: holoballoon

pressure

display_name: pressure

raclets

display_name: raclets

radiosonde

display_name: radiosonde

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

wind

display_name: wind

wind-direction

display_name: wind-direction

en: Radiosondes


en: landolt soil-ph soil-moisture ecological-maps switzerland ecological-indicator-values soil-mapping plants

en: Predictors

en: Predictors generated and used in Descombes et al. (2020)

en: Description

en: Description of the predictors provided in this dataset

en:
Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland.

This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection ("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"). The excel file (xlsx) provides a short description of the raster layers.

Paper Citation:

Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph


restoring-grassland-multifunctionality

title_for_slug: restoring-grassland-multifunctionality

en: ecosystem-multifunctionality bacteria grassland soil-biodiversity mineralization nematodes biodiversity fungi restoration plants

en: Functional diversity above_belowground fauna

media_type: XLSX

en: Please note that EMF, MF.below, MF.abio and Soil.hetero have been corrected.

en: Please note that C.sto and Corg have been corrected.

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

Resch, M. C., Schütz, M., Buchmann, N., Frey, B., Graf, U., van der Putten, W. H., Zimmermann, S., Risch, A. C. 2021. Evaluating long-term success in grassland restoration – an ecosystem multifunctionality approach. Ecological Applications 31, e02271.

 Study area

The study was conducted in the Canton of Zurich, Switzerland, in and around two nature reserves Eigental and Altläufe der Glatt (47°27’ to 47°29’ N, 8°37’ to 8°32’ E, 417 to 572 m a.s.l.). All studied grasslands were located with a radius of approximately 4 km. Average monthly temperatures range from 0.7 ± 2.0 °C (January) to 19.0 ± 1.5 °C (July), and monthly precipitation range from 60 ± 42 mm (January) to 118 ± 46 mm (July [maxima]; 1989-2017; MeteoSchweiz 2018). In our study, we focused on semi-dry and semi-wet oligo- to mesotrophic grasslands characterized by high plant species richness and groundwater fluctuations throughout the year (Delarze et al. 2015, see also Resch et al. 2019).

 Experimental design and sampling

A large-scale restoration experiment to expand and reconnect isolated remnants of species-rich grasslands was initiated in the nature reserve Eigental in 1990. Twenty hectares of adjacent intensive grasslands were chosen for restoration. In 1995, three restoration methods of increasing intervention intensities were implemented. The goal of all three methods was to lower the availability of soil nutrients and hence, facilitate ecosystem development towards the targeted nutrient-poor grasslands. These methods were: Harvest only (hay harvest twice a year), Topsoil (removal of the nutrient-rich topsoil), and Topsoil+Propagules (topsoil removal combined with the application of hay from target vegetation). Plant biomass harvest (once a year in late summer/early autumn) commenced in Topsoil and Topsoil+Propagules five years after the soils were removed and is still ongoing today. We measured restoration success by comparing the three restoration methods with intensively managed (Initial) and semi-natural grasslands (Target) 22 years after restoration. Initial grassland sites share the same agricultural history as the restored sites: mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes (Resch et al. 2019). Target sites were the sites from which hay for seeding the Topsoil+Propagules sites was collected. Soil conditions (i.e., soil types, soil texture) were comparable to those found in the restored grasslands (Resch et al. 2019). Additionally, Target sites were selected to represent a variety of semi-natural grasslands, including semi-dry to semi-wet conditions. In Target grasslands, biomass is harvested once a year in late summer or early autumn. Eleven 5 m x 5 m (25 m2) plots were randomly established in each of the five treatments (in total 55 plots; for a detailed map see Neff et al. 2020). An additional 2 m x 2 m (4 m2) subplot was randomly established at least 2 m away from each 25 m2 plot for destructive sampling. Data sampling took place between June and September 2017.

Vegetation properties

All plant species were identified within the 25 m2 plots (nomenclature: Lauber and Wagner 1996) in mid-June 2017 (in total 250 species). Vegetation structure and plant biomass were assessed diagonally on a transect of 2 m x 10 cm within the 25 m2 plot in early July 2017. We measured the maximum and mean height of the vegetation at the start, middle and end of the transect and calculated the standard deviation of these measures to describe vegetation structural heterogeneity (Schuldt et al. 2019). Thereafter, biomass was clipped on the entire transect to 1 cm height, sorted into five functional groups (graminoids, forbs, legumes, litter, and woody species), dried at 60 °C for 48 h, and weighed (Meyer et al. 2015).

 Aboveground arthropods

Aboveground arthropods were sampled at two locations in each 25 m2 plot in early July 2017 (see also Neff et al. 2020). Briefly, two cylindrical baskets (50 cm diameter, 67 cm height; woven fabric) were thrown simultaneously from outside the plot into two opposite corners. A closable mosquito mesh sleeve was mounted to the top of the baskets and an integrated metal ring at the bottom was fixed to the ground with metal stakes to assure that insects could not escape. A suction sampler (Vortis, Burkhard Manufacturing Co. Ltd., Hertfordshire, England) was then inserted into one of the baskets through the opening of the sleeve and the plot was “vacuumed" twice for 105 seconds with a 30 seconds break. The collected animals were immediately transferred into 70% ethanol. Arthropods were sorted and assigned to 23 taxonomic groups. Holometabolic larvae were lumped into one category while hemimetabolic larvae were grouped separately from adults in the respective taxonomic rank. We used mean values of individuals per plot for total abundance. Aboveground arthropod richness was defined by the number of different taxa to lowest taxonomic level (in total 23 taxa). All taxa were assigned to one of five trophic levels: 1) primary producers, 2) primary consumers, 3) secondary consumers, 4) tertiary consumers, and 5) quaternary consumers.

 Belowground fauna

Sampling of all belowground fauna took place in mid-July 2017. Earthworms were sampled in two 30 cm x 30 cm x 20 cm soil monoliths at two opposite corners of the 25 m2 plot (opposite to aboveground arthropod sampling). The excavated soil monolith was broken by hand, all earthworms collected and immediately transferred in a 4% formaldehyde solution. Thereafter, earthworm individuals were identified to species level (in total 10 taxa; Christian and Zicsi 1999) and species assigned to three functional groups (Bouché 1977).
To assess soil arthropod communities, we randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) in each 4 m2 subplot with a slide hammer corer lined with a plastic sleeve (AMS Samplers, American Falls, Idaho, USA). Soil arthropods were extracted using Berlese-Tullgren funnels (3 mm mesh), starting the day of sampling and lasting 14 days. Individuals were stored in 70% ethanol. Soil arthropods were assigned to 41 taxonomic groups and 4 feeding types. Holometabolic and hemimetabolic larvae were treated as previously described for aboveground arthropods. Belowground arthropod richness refers to the 41 taxonomic groups.

For soil nematode sampling, we randomly collected eight soil cores of 2.2 cm diameter (Giddings Machine Company, Windsor, CO, USA) within each 4 m2 subplot to a depth of 12 cm. The eight cores were combined, gently homogenized, placed in coolers, kept at 4 °C and transported to the laboratory at NIOO in Wageningen (NL) within one week after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink 1960) and prepared for morphological identification and quantification as described by Resch et al. (2019). Nematodes were identified to family level (39 taxa) according to Bongers (1988), assigned to 17 functional groups, 5 feeding types and 5 colonizer-persister (C-P) classes (Yeates et al. 1993, Bongers 1990, Resch et al. 2019).

We randomly collected two more soil cores (2.2 cm diameter x 12 cm depth) within each 4 m2 subplot to determine soil microbial communities. Again, the soil cores were combined, homogenized, placed in coolers and transported to the laboratory at WSL in Birmensdorf (Switzerland) where the metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNeasy PowerMax Soil Kit (Quiagen, Hilden, NRW, GER) according to the manufacturer`s instructions. PCR amplification of the V3-V4 region of the prokaryotic small-subunit (16S) and the ribosomal internal transcribed spacer region (ITS2) of eukaryotes was performed with 1 ng of template DNA utilizing PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates and pooled. The pooled amplicons were sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA). Quality filtering, clustering into operational taxonomic units (OTUs) and taxonomic assignment were performed as described by Frey et al. (2016) and Adamczyk et al. (2019). We used a customised pipeline largely based on UPARSE (Edgar 2013) implemented in USEARCH v. 9.2 (Edgar 2010). After discarding singletons of dereplicated sequences, clustering into OTUs with 97% sequence similarity was performed (Edgar 2013). Quality-filtered reads were mapped on the filtered set of centroid sequences. Taxonomic classification of prokaryotic and fungal sequences was conducted querying against most recent versions of SILVA (v.132, Quast et al. 2013) and UNITE (v.8, Nilsson et al. 2018). Only taxonomic assignments with confidence rankings equal or higher than 0.8 were accepted (assignments below 0.8 set to unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as eukaryotic OTUs assigned other than fungi were removed prior to data analysis. In addition, prokaryotic and fungal datasets were filtered to discard singletons and doubletons. Thereafter, OTU abundance matrices were rarefied to the lowest number of sequences per community, to normalize the total number of reads and achieve parity between samples (Prokaryota: 29,843 reads; Fungi: 26,690 reads). Finally, prokaryotic and fungal observed richness (number of OTUs) were estimated (Prokaryota: 14,010 OTUs; Fungi: 5,813 OTUs). For prokaryotes, we distinguished five and for fungi six functional types based on lowest taxonomic resolution (Nguyen et al. 2016, Tedersoo et al. 2014). Belowground taxon richness was defined by the total number of earthworm, arthropod, nematode, fungi, and prokaryote taxa assigned to lowest taxonomic level. Finally, all belowground taxa were assigned to the same five trophic levels as the aboveground arthropods.

 Soil chemical and physical properties, soil nitrogen mineralization

We randomly collected three 5 cm diameter x 12 cm depth soil samples in each 4 m2 subplot with a slide hammer corer (AMS Samplers, American Falls, Idaho, USA), pooled them and then made two subsamples. One was field-fresh and stored at 3 °C until analysis, the other was dried for 48 h at 60 °C and passed through a 4 mm mesh. From the dried sample, we measured soil pH potentiometrically in 0.01 M CaCl2 (soil:solution ratio=1:2; 30 minutes equilibration time). Total and organic carbon content were measured on fine-ground samples (≤ 0.5 mm) by dry combustion using a CN analyzer NC 2500 (CE Instruments, Wigan, United Kingdom). Inorganic carbon of samples with a pH > 6.5 was removed with acid vapor prior to analysis of organic carbon (Walthert et al. 2010). We calculated total soil carbon (C) storage after correcting its content for soil depth, stone content and density of fine earth (see below). Exchangeable cations were determined on another 5 g dry soil sample with 50 mL unbuffered 1 M NH4Cl solution (soil:solution ratio=1:10, end-over-end shaker for 1.5 hours) and measured by an ICP-OES (Optima 7300 DV, Perkin-Elmer, Waltham, Massachusetts, USA). Thereafter, cation exchange capacity (CEC) was calculated as the sum of exchangeable cations and protons (and expressed as mmolc per 1 kg soil) and used to describe nutrient retention capacity in our plots. Concentrations of exchangeable protons were calculated as the difference between total and Al-induced exchangeable acidity as determined by the KCl-method (Thomas 1982).

Ammonium (NH4+) and nitrate (NO3−) were extracted from a 20 g fresh subsample with 80 mL 1M KCl for 1.5 hours on an end-over-end shaker and filtered through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnemühle FineArt GmbH, Dassel, Germany). NH4+ concentrations were determined colorimetrically by automated flow injection analysis (FIAS 300, Perkin-Elmer, Waltham, Massachusetts, USA). NO3− concentrations were measured colorimetrically according to Norman and Stucki (1981). Potential soil net nitrogen (N) mineralization was assessed during an 8-week incubation period under controlled moisture (60% of field capacity), temperature (20 °C) and light conditions (dark) in the laboratory. We weighed duplicate samples of fresh soil equivalent to 8 g dry soil (24 h at 104 °C) into 50 mL Falcon tubes. Soil samples were extracted for NH4+ and NO3− at the beginning and after eight weeks as described above. Soil net N mineralization was calculated as the difference between the inorganic nitrogen (NH4+ and NO3−) before and after the incubation (Hart et al. 1994), corrected for the total incubation time and represented per day values expressed as mg N kg-1 soil d-1.

To assess soil physical properties, we randomly collected one undisturbed soil core per 4 m2 subplot (5 cm diameter, 12 cm depth) in a steel cylinder that fit into the slide hammer (AMS Samplers, American Falls, Idaho, USA). The cylinder was capped in the field to avoid disturbance. We then measured field capacity in the laboratory. For this purpose, the cylinder and soil therein were saturated in a water bath and drained on a sand/silt-bed with a suction corresponding to 60 cm hydrostatic head. The moist soil was dried at 105 °C to constant weight. Field capacity was calculated by dividing the mass of water by the total mass of wet soil contained at 60 cm hydrostatic head and used to describe water holding capacity. Thereafter, samples were passed through a 4 mm mesh. Fine-earth and skeleton fractions were weighed separately to assess bulk soil density (fine-earth plus skeleton), density of fine earth, and proportion of skeleton. Particle density was determined with the pycnometer method (Blake and Hartge 1986), and total porosity and proportion of fine pores were calculated (Danielson and Sutherland 1986). Clay, silt, and sand contents were quantified with the sediment method (Gee and Bauder 1986).

Surface and soil temperature (12 cm depth, water-resistant digital pocket thermometer; IP65, H-B Instrument, Trappe, Pennsylvania, USA) as well as volumetric soil moisture content (12 cm depth, time domain reflectometry; Field-Scout TDR 300, Spectrum Technologies, Aurora, Illinois, USA) were measured at five random locations within the 4 m2 subplots every month from June to September. We calculated the standard deviation of each temperature and moisture measure over four months to describe seasonal variations. Slope inclination was determined at plot-level via GPS measurements (GPS 1200, Leica Geosystem, Heerbrugg, Switzerland) and categorized into slope gradient classes according to FAO standards (1990). Thickness of the topsoil horizon (equivalent to Ah or Aa horizon) was determined at one soil monolith (30 x 30 x 30 cm3) per 4 m2 subplot in cm and rounded to next integer.

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Bongers, T. 1990. The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia 83:14-19.

Bouché, M. B. 1977. Strategies lombriciennes. Ecological Bulletins 25:122-132.

Christian, E., and A. Zicsi. 1999. Ein synoptischer Bestimmungsschlüssel der Regenwürmer Österreichs (Oligochaeta: Lumbricidae). Die Bodenkultur 50:121-131.

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Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensräume der Schweiz: Ökologie ‐ Gefährdung ‐ Kennarten, Ott Verlag, Bern.

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Meyer, S. T., C. Koch, and W. W. Weisser. 2015. Towards a standardized rapid ecosystem function assessment (REFA). Trends in Ecology and Evolution 30:390-397.

Neff, F., M. C. Resch, A. Marty, J. Rolley, M. Schütz, A. C. Risch, and M. M. Gossner. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands – a functional approach. Ecological Applications 0:e02133

Nguyen, N.H., Z. W. Song, S. T. Bates, S. Branco, L. Tedersoo, J. Menke, J. S. Schilling, and P. G. Kennedy. 2016. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology 20:241–248.

Nilsson, R. H., K.-H. Larsson, A. F. S. Taylor, J. Bengtsson-Palme, T. S. Jeppesen, D. Schigel, P. G. Kennedy, K. Picard, F. O. Glöckner, L. Tedersoo, I. Saar, U. Kõljalg, and K. Abarenkov. 2018. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research 47:259–264.

Norman, R. J., and J. W. Stucki. 1981. The Determination of Nitrate and Nitrite in Soil Extracts by Ultraviolet Spectrophotometry. Soil Science Society of America Journal 45:347-353.

Oostenbrink, M. 1960. Estimating nematode populations by some selected methods. Pages 81-101 in J. J. Sasser, and W. R. Jenkins, editors. Nematology. Univ. of North Carolina Press, Chapel Hill.

Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies, and F. O. Glöckner. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41:590-596.

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

Schuldt, A., A. Ebeling, M. Kunz, M. Staab, C. Guimarães-Steinicke, D. Bachmann, N. Buchmann, W. Durka, A. Fichtner, F. Fornoff, W. Härdtle, L. R. Hertzog, A-N. Klein, C. Roscher, J. Schaller, von G. Oheimb, A. Weigelt, W. Weisser, C.Wirth, J. Zhang, H. Bruelheide, and N. Eisenhauer. 2019. Multiple plant diversity components drive consumer communities across ecosystems. Nature Communications 10:1460.

Tedersoo, L., M. Bahram, S. Põlme, U. Kõljalg, N. S. Yorou, R. Wijesundera, L. Villarreal Ruiz, A. M. Vasco-Palacios, P. Q. Thu, A. Suija, M. E. Smith, C. Sharp, E. Saluveer, A. Saitta, M. Rosas, T. Riit, D. Ratkowsky, K. Pritsch, K. Põldmaa, M. Piepenbring, C. Phosri, M. Peterson, K. Parts, K. Pärtel, E. Otsing, E. Nouhra, A. L. Njouonkou, R. H. Nilsson, L. N. Morgado, J. Mayor, T. W. May, L. Majuakim, D. J. Lodge, S. See Lee, K.-H. Larsson, P. Kohout, K. Hosaka, I. Hiiesalu, T. W. Henkel, H. Harend, L.-D. Guo, A. Greslebin, G. Grelet, J. Geml, G. Gates, W. Dunstan, C. Dunk, R. Frenkhan, L. Dearnaley, A. De Kesel, T. Dang, X. Chen, F. Buegger, F. Q. Brearley, G. Bonito, S. Anslan, S. Abell, and K. Abarenkov. 2014. Global diversity and geography of soil fungi. Science 346:1256688.

Thomas, G.W. 1982. Exchangeable cations. Pages 159-165 in A. L. Page, R. H. Miller, and D. R. Keeney, editors. Methods of Soil Analysis: Part 2—Chemical and microbiological properties. Soil Science Society of America (SSSA) Inc., Madison.

Walthert, L., U. Graf, A. Kammer, J. Luster, D. Pezzotta, S. Zimmermann, and F. Hagedorn. 2010. Determination of organic and inorganic carbon, δ13C, and nitrogen in soils containing carbonates after acid fumigation with HCl. Journal of Plant Nutrition and Soil Sciences 173:207-216.

Yeates, G. W., T. Bongers, R. G. M. de Goede, D. W. Freckman, and S. S. Georgieva. 1993. Feeding habits in soil nematode families and genera – an outline for soil ecologists. Journal of Nematology 25:315-331.

bacteria

display_name: bacteria

biodiversity

display_name: biodiversity

ecosystem-multifunctionality

display_name: ecosystem-multifunctionality

fungi

display_name: fungi

grassland

display_name: grassland

mineralization

display_name: mineralization

nematodes

display_name: nematodes

restoration

display_name: restoration

soil-biodiversity

display_name: soil-biodiversity

education

en: Education and science

en: Restoring grassland multifunctionality


en: erosion model snow sediment-transport mars

media_type: text/plain

en: Datasets of Figure 2

format: TXT

mimetype: text/plain

en: Datasets of Figure 4a

en: Datasets of Figure 3b

en: Datasets of Figure 3a

en: Datasets of Figure 4b

en:
The files contain the datasets used to produce Figures 2, 3, and 4 of the manuscript ([doi: 10.1002/2016GL071822](http://dx.doi.org/10.1002/2016GL071822)).

 Manuscript Abstract:
Despite being the main sediment entrainment mechanism in aeolian transport, granular splash is still poorly understood. We provide a deeper insight into the dynamics of sand and snow ejection with a stochastic model derived from the energy and momentum conservation laws. Our analysis highlights that the ejection regime of uniform sand is inherently different from that of heterogeneous sand. Moreover, we show that cohesive snow presents a mixed ejection regime, statistically controlled either by energy or momentum conservation depending on the impact velocity. The proposed formulation can provide a solid base for granular splash simulations in saltation models, leading to more reliable assessments of aeolian transport on Earth and Mars.

erosion

display_name: erosion

mars

display_name: mars

model

display_name: model

sediment-transport

display_name: sediment-transport

snow

display_name: snow

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: Energy- and momentum-conserving model of splash entrainment in sand and snow saltation


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

media_type: ascii x, y, z

en: Legend simulated maps

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

digital-zoning-plans

display_name: digital-zoning-plans

land-use-change

display_name: land-use-change

land-use-simulations

display_name: land-use-simulations

land-use-transformations

display_name: land-use-transformations

spatial-planning

display_name: spatial-planning

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


maintainer: Amt für Abfall, Wasser, Energie und Luft des Kantons Zürich, Abteilung Luft, Klima und Strahlung - Sektion Monitoring

de: luft lufttemperaturen

label: LoRa-Datenbeschrieb

de: LoRa-Sensor-Messwerte 2021/08

de: LoRa-Sensor-Messwerte 2021/07

de: LoRa-Sensor-Messwerte 2020/06

de: LoRa-Sensor-Messwerte 2022/11

de: LoRa-Sensor-Messwerte 2021/06

de: LoRa-Sensor-Messwerte 2019/09

de: LoRa-Sensor-Messwerte 2019/08

de: LoRa-Sensor-Messwerte 2020/07

de: LoRa-Sensor-Messwerte 2022/06

de: LoRa-Sensor-Messwerte 2019/10

de: LoRa-Sensor-Messwerte 2021/10

de: LoRa-Sensor-Messwerte 2020/02

de: LoRa-Sensor-Messwerte 2021/01

de: LoRa-Sensor-Messwerte 2021/11

de:
Seit Frühjahr 2019 betreibt das AWEL des Kantons Zürich ein Messnetz aus rund 40 Lufttemperatur und -feuchte Sensoren (LoRa). Die Sensor-Standorte befinden sich hauptsächlich im städtischen Raum, verdichtet in der Stadt Zürich, aber auch in weiteren Gemeinden im Kanton Zürich. Ziel des Messnetzes ist es, Messdaten für Stadtklima-Analysen bereitzustellen, damit Aspekte des Lokalklimas und Klimawandels stärker sichtbar werden und in der Raumplanung und Stadtentwicklung berücksichtigt werden können. Mit Hilfe dieser Daten lassen sich u.a. die Auswirkungen von Hitzewellen im städtischen Raum beschreiben. Das Messnetz wird durch das AWEL betrieben, unter Beteiligung der Stadt Zürich; die weiteren Standort-Gemeinden sind über die Messaktivität informiert.
Der hier verfügbare Datensatz wird täglich aktualisiert und umfasst alle, für die Standorte gültigen Messwerte im 10 Minuten Mittel, inklusive entsprechender Standortinformationen. Weitere Angaben zu Datensatz-Struktur und verwendeten Sensoren sind im Datensatz-Beschrieb zu finden.

awel-kanton-zuerich

de: Amt für Abfall, Wasser, Energie und Luft Kanton Zürich

de: Das Amt für Abfall, Wasser, Energie und Luft (AWEL) des Kantons Zürich hat den Auftrag, der Bevölkerung im Kanton Zürich eine gesunde, sichere und attraktive Umwelt zu bieten. Zu den Themen Abfall, Wasser, Energie und Luft stellt das AWEL Fachleuten und Interessierten Informationen zur Verfügung.

de: Lufttemperatur und Luftfeuchte LoRa-Sensor-Messwerte


maintainer: Robert Pazúr

en: remote-sensing switzerland cropland sentinel-2 vegetation-indices machine-learning habitat agriculture grassland phenology

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.5)

media_type: JS

en: The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland. Compare to the previous version the newer version includes the shadow masking procedure which might be important in the mountainous areas.

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.5)

media_type: geotiff

format: GeoTIFF

en: (current version) Cropland and grassland map of Switzerland (v.1.5)

en: Cropland, grassland, and shrubland map of Switzerland (v1.0)

media_type: TIFF

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; (3) shrubland; other LC classes are masked out from the map

format: TIFF

en: Cropland and grassland map of Switzerland  (v.1.0)

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; other LC classes are masked out from the map

en: (current version) Cropland, grassland, and shrubland map of Switzerland (v.1.5)

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6  (v1.0)

en:
!!! Please check out the newer version in the repository !!!
Mask of unsealed surfaces of Switzerland based on 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.0)

en: !!! Please check out the newer version in the repository !!! The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.0)

en: !!! Please check out the newer version in this repository !!! Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.7.

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6 (v1.5)

en: Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6.

en: We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland, sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.

agriculture

display_name: agriculture

cropland

display_name: cropland

habitat

display_name: habitat

machine-learning

display_name: machine-learning

phenology

display_name: phenology

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

vegetation-indices

display_name: vegetation-indices

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: Cropland and grassland map of Switzerland based on Sentinel-2 data


en: decision-making ecosystem-protection hydropower gis integral-water-management

en: Indicators

media_type: text/markdown

en: The indicators capture supporting ecosystem functions.

media_type: SHP

format: SHP

en:
 A spatial dataset and tool to simultaneously assess hydropower potential and ecological potential of the Swiss river network (Version 2016)

 Introduction
The steadily growing demand for energy and the simultaneous pursuit of decarbonisation are increasing interest in the expansion of renewable energies worldwide. In Switzerland, various funding projects have been launched to promote technologies in the field of renewable energies and their application as quickly as possible. With the introduction of a funding instrument in 2009, the number of projects submitted to produce renewable energies increased rapidly. The applications for small hydropower plants (≤ 10 MW) were correspondingly numerous. However, the assessment of the environmental impact and its comparison with hydropower importance is still not standardized.
To provide a basis for decision-making, a methodology was developed to determine the overall hydropower potential of a region. A detailed assessment of each river reach, and the systematic and holistic assessment of small hydropower projects at a regional scale are combined here. The assessment of a river reach is conducted at the river space (i.e., the river and adjacent areas) and at the surrounding landscape level. The HYDROpot_integral methodology was developed as part of Carol Hemund's dissertation (2012) at the University of Bern. It allows the evaluation of river reaches holistically, regarding ecological, social, economic and cultural criteria. As a second part of the overall project, the theoretical hydropower (or hydraulic) potential was calculated for the entire river network, which complemnets the spatial assessment. In particular, it is possible to classify river reaches into those that are more suitable for hydropower production (=”use”) and those that are more suitable for protection.

 Material and method
The HYDROpot_Integral method was developed and tested on the basis of cantonal and national data (Hirschi et al. 2013). The method relies on 73 geodata sets. This holistic assessment is the key element of the entire assessment procedure. Its aim is to quantify the importance of the ecosystem functions in terms of services. The river network (GWN07) is divided into reaches of about 450m and for each reach two study units are defined. The river space (RS) records the ecosystem functions of the water body and the nearby riparian area. The length of the RS is 315 m on average in Switzerland and a maximum of 450 m, whereas the width is based on the FOEN definition (BWG 2001: 18f) and varies between 7-107 m. The surrounding landscape (SLS) is the second survey unit that records the ecosystem functions of the surrounding area over a range of 21 m to 321 m. The SLS is calculated over three times the RS width. The length of the SLS is identical to the length of the RS. The ecosystem functions are divided into three types: regulating (service A), cultural (service B) and provisioning (service C) functions. Accordingly, the assessment of the functions is divided into three parts and three values are assigned to each river reach. The more functions there are and the greater their performance, the higher these values are and the more important the corresponding functions are. Hence, these values quantify the importance of the ecosystem functions and the ecological, cultural and economic ecosystem services of each river reach. The concatenation of ecosystem services results in a value (ABC) that can occur in 125 different versions due to the chosen five-level value scale; i.e. each digit of the three-digit number sequence can be assigned a value between 1 and 5. Each of the 125 combinations, and thus each river reach, has its own characteristics determined by the assessments of the three function types. To record the suitability, the combinations are ranked according to their ecological, cultural and economic ecosystem services. These rules mean that the combination that is most suitable for hydropower production at minimum cost in terms of ecological and cultural ecosystem services and has a high economic potential is ranked first; rank 125 indicates the highest ecological and cultural ecosystem services and the lowest economic services and is therefore most suitable for protection. A river reach that is excluded from hydropower use due to legislation, a so-called priority reach, is given rank 126 from the outset and specially marked. A more detailed description of the methods can be found in Hirschi et al. 2013 [Link]. The dataset presented here presents the latest state of the HYDROpot_integral methodology applied at the national level. Only national data that is easily accessible was used in the preparation of the dataset. The cantonal data, such as renaturation and revitalization, would have to be requested by each canton individually and was excluded here. The nationwide value synthesis was made with R.
A list of data sources can be found here [link to text file]
A list of all parameters can be downloaded here [link to PDF and text files]

 Dataset description
Data is presented as a single shapefile. It contains the river network and all assessment results obtained with HYDROpot_Integral.

 Changes in the methodology compared to the original method (Hirschi et. al 2013)

 * RS_A11 Ecomorphology: recorded for the whole of Switzerland and zero values equated with NA;
individual cantons such as Zug and St. Gallen have no mapped values according to the modular
concept of the federal government, Valais and Graubünden only the main valleys, Ticino and Fribourg not
completely (BAFU 2009).
 * RS_A14 Renaturation and revitalization data: not centrally available at the time of data collection.
centrally available, therefore values in GR were equated with NA.
 * RS_A15 Dilution ratio at wastewater treatment plants (WWTPs) for discharges: Zero values
equal to NA.
 * RS_A20 Water flow: use WASTA (2013) with hydroelectric power plants (> 300 kW) under
Federal control and dams serving hydroelectricity (Dam, as of 2013).
 * RS_C05 Synoptic hazard maps: are cantonally managed at the time of data collection,
Values in GR are marked with a 5 so that the systematics in the decision tree is not affected.
is affected.
 * Water quality (RS_A15, RS_A16, RS_A17, RS_A18, RS_A19): for the evaluation of the function type.
A Nature, it is important whether the median of the five values is less than or equal to 3 in total. This
evaluation is based on the decision tree for evaluating GR (Hirschi et al. 2013:22).
Therefore, an evaluation of the station data is made where critical and possible river segments
with poor quality (median less than 3) exist. Only two longer and one short
sections in Switzerland receive a lower median than 3 for water quality.
 * SLS_B06 Visibility: For 99 percent of the river segments (30,733 of 31,062) in the canton of
Bern (2015 reduced version), the landscape area is considered to be visible. Due to this high number
of sections, a large number of viewpoints in the layer of Swisstopo and
the computation time and computability in ArcGIS, the landscape area is classified as generally viewable
(equal to 1).
16 Method
Additional indicators were added (see Appendix B.2):
 * SLS_A21 Dissection
 * SLS_A22 Forest areas
 * SLS_B03 Hiking trails
 * SLS_B10 Residential and vacation homes
 * SLS_B11 Tourist infrastructure
 * SLS_C01 Landfill
 * SLS_C03 Infrastructure
 * SLS_C05 Industry
 * SLS_C06 Agricultural land
Not to be added, although present to some extent:
 * SLS_B06 Cultural assets of national importance: here, too, the calculability of the visibility analysis is
for the whole of Switzerland is limited
 * SLS_A15 Legally binding protection and land use planning: the individual river sections are not
clearly designated, i.e. no geodata exist
The following data are also not supplemented, as they are cantonal data:
 * SLS_A10 Cantonal nature reserves
 * SLS_A16 Forest reserves
 * SLS_A17 Cantonal inventories and contractually protected areas

decision-making

display_name: decision-making

ecosystem-protection

display_name: ecosystem-protection

gis

display_name: gis

hydropower

display_name: hydropower

integral-water-management

display_name: integral-water-management

education

en: Education and science

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: forest longwave-radiation shading fractional-snow shortwave-radiation

en: Sky-view fraction along the cablecar transect

en: Data is used and described in the following paper: Malle et al. (2019). Shading by Trees and Fractional Snow Cover control the Subcanopy Radiation Budget. Journal of Geophysical Research. DOI:10.1029/2018JD029908.

en: Radiation Data 18.04.2019

en:
Spatiotemporal incoming/outgoing shortwave and longwave radiation as well as sunlit and shaded-snow view fractions below an evergreen forest stand in the Swiss alps. Data was collected on 18th of April 2018 along a 48m long, heterogeneous forest stand under full snow cover conditions.

The presented data is used and further described in the following paper: Malle et al. (2019). Shading by Trees and Fractional Snow Cover control the Subcanopy Radiation Budget. Journal of Geophysical Research Atmospheres. DOI:10.1029/2018JD029908.

en: Radiation Data 25.4.2018

en:
Spatiotemporal incoming/outgoing shortwave and longwave radiation as well as sunlit and shaded-snow view fractions below an evergreen forest stand in the Swiss alps. Data was collected on 25th of April 2018 along a 48m long, heterogeneous forest stand under partial snow cover conditions.

The presented data is used and further described in the following paper: Malle et al. (2019). Shading by Trees and Fractional Snow Cover control the Subcanopy Radiation Budget. Journal of Geophysical Research Atmospheres. DOI:10.1029/2018JD029908.

en: Seasonal sub-canopy radiation data

en:
Seasonal, spatially aggregated incoming and outgoing shortwave radiation below an evergreen forest stand collected with a cable car system.

The presented data is used and further described in the following paper: Malle et al. (2019). Shading by Trees and Fractional Snow Cover Control the Subcanopy Radiation Budget. Journal of Geophysical Research Atmospheres. DOI:10.1029/2018JD029908.

en: This data set consists of incoming and outgoing short- and longwave radiation as well as sunlit-snow-view-fraction as described in the JGR-Atmospheres paper "Shading by trees and fractional snow cover control the sub-canopy radiation budget", by Malle et al. (2019). Data was collected along a 48m long, heterogeneous forest transect between January and June 2018 close to Davos, Switzerland.

forest

display_name: forest

fractional-snow

display_name: fractional-snow

longwave-radiation

display_name: longwave-radiation

shading

display_name: shading

shortwave-radiation

display_name: shortwave-radiation


en: sea-salt-aerosol snow arctic antarctica ice

en: Fig 1F

en: Data to Figure 1F

en: Fig 1B_2

en: Individual data to mean shown in Figure 1B

en: Fig 1D

en: Data to Figure 1D

en: Fig 1B

en: Data in Figure 1B

en: Fig 1B_1

en: Fig 1E

en: Data to Figure 1E

en: Fig 1F_1

en: Individual data to mean shown in Figure 1F

en: Read Me

media_type: application/pdf

en: Description of the data tables

format: PDF

mimetype: application/pdf

en: Fig 1F_2

en: This table lists all data files in chronological order. "SampleTemp" refers to the reading of the PT-1000 at the side of the sample holder. "cellPressure" is the water vapour pressure during the measurements. From this, in presence of ice, the temperature of the ice sample can be calculated which is given in the column "iceTemp". "dT" is the difference, or temperature offset, between sensor's reading and the ice samples temperature. The offset is due to the sensor being mounted away from the cooled spot of the sample were it experiences heating from warmer parts of the sample holder. The off-set is used to calibrate the temperature sensor and the resulting temperature during the experiments is given in the column "temperature". "relHum" is the relative humidity above (supercooled) liquid and "concBrine" is the concentration of a NaCl solution in mol/l in equilibrium with ice at the experimental temperature. "Area" is the integrated PE signal.

en: Table 2

en: This table lists all NEXAFS data files in chronological order with PE carbon to oxygen signal intensity ratios -from which the atomic ratio were calculated in the paper - before ("preCtoO") and after ("postCtoO") each NEXAFS measurement. Also listed is the change in Na ("lossSodi"), O ("lossOxy"), Cl ("lossChlor"), and C ("lossCarb") signal intensity after the NEXAFS measurement compared to prior to the NEXAFS measurement.

en: Fig 1C

en: Data to Figure 1C

en: Fib 1B_3

en: Fig 1A

en: Laboratory experiments are presented on the phase change at the surface of sodium chloride – water mixtures at temperatures between 259 K and 240 K. High selectivity to the upper few nanometres of the frozen solution – air interface is achieved by using electron yield near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. We present the NEXAFS spectrum of the hydrohalite.

en: Data set on interfacial supercooling and the precipitation of hydrohalite in frozen NaCl solutions by X-ray absorption spectroscopy


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

en: Wetlands 1850 - 2010 (shapefiles)

en:
 History of wetlands - GIS-Layers
 Location of wetlands based on signatures in topographic maps
* Basis 1850: original measuring table sheets Dufour created between the years 1834 and 1863.
* Basis 1900/ Basis 1950: The Siegfried maps were updated and published at regular intervals between 1870 and 1949. This long period of time made it possible to reconstruct wetlands for two periods based on the Siegfried Maps: the first edition (hereinafter referred to as "1900") and the last edition ("1950").
* Basis 2010: Map sheets from the National Map from 2006 to 2011 were used for the most recent time period of the reconstruction.
 Location of wetlands due to reconstructions
The reconstruction **must not** be interpreted in the sense of a surface-accurate separation, but shows without claim to location accuracy the approximate extent of the areas that would have corresponded to the mapping criteria for wetlands used in today's topographic maps around 1850, 1900 and 1950. The basics can be found in Stuber & Bürgi 2019, chapter 5.4.

en:
Naturally, large parts of the Swiss Plateau are characterised by wetlands
and meandering rivers. That this is no longer the case today is the result
of centuries of efforts to obtain dry land. But how did this process take
place? What were the relevant actors and what were their motivations? And
what can be said about the ecological consequences of this development?

In a research project on the history of wetlands in Switzerland since 1700,
we conducted (a) a historical analysis of the development of land use in
wetlands and the actors involved, (b) a historical-cartographic
reconstruction of wetland extent since 1850 and (c) an evaluation of
ecological effects of changes in wetlands on various organisms groups.

The series of GIS layers on wetland history stem from the second part of
the project. The area reconstruction is based on digitized and homogenized
signatures from national map series, as they have been available since
about 1850. Details about the digitalization process and the homogenization
procedures applied ("Rekonstruktionen") are included in Stuber & Bürgi
2019.

Book Citation:
 > Stuber M, Bürgi M (2019) Vom «eroberten Land» zum Renaturierungsprojekt. Geschichte der Feuchtgebiete in der Schweiz seit 1700. "Bristol Schriftenreihe", Band 59. Haupt Verlag, Bern, Stuttgart, Wien. 262 Seiten.

ecology

display_name: ecology

environmental---history

display_name: environmental---history

environmental--policy

display_name: environmental--policy

historical-carthography

display_name: historical-carthography

wetlands

display_name: wetlands

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: History of wetlands in Switzerland since 1850


ist-daten-actual-data

maintainer: Specialist office Open Data

title_for_slug: ist-daten-actual-data

fr: donnees-reelles retard ponctualite

de: ist-daten ist kundeninformation punktlichkeit bpuic verspatung

en: punctuality actual-data delay

it: dati-effettivi ritardo puntualita

label: Terms of use

label: Cookbook

label: Github (openTdataCH)

spatial: Switzerland

media_type: CSV

mimetype: CSV

fr:
ATTENTION: En raison de problèmes de réseau le 9.11.2022, les "données réelles" ne sont disponibles que dans une mesure très limitée pour cette journée.

The actual service provided is displayed. The final forecast is used where no actual data are available.

de:
ACHTUNG: Aufgrund einer Netzwerkstörung am 9.11.2022 sind für diesen Tag Ist-Daten nur in sehr beschränktem Umfang vorhanden.

The actual service provided is displayed. The final forecast is used where no actual data are available.

en:
ATTENTION: Due to network problems on 9.11.2022, „actual data“ is available only to a very limited extent for this day.

The actual service provided is displayed. The final forecast is used where no actual data are available.

it:
ATTENZIONE: A causa di problemi di rete il 9.11.2022, i "dati reali" sono disponibili solo in misura molto limitata per questo giorno.

The actual service provided is displayed. The final forecast is used where no actual data are available.

fr:
La distance effectivement parcourue est calculée. Les dernières prévisions sont utilisées en l’absence de données effectives.
Archives de données reelles:https://opentransportdata.swiss/de/ist-daten-archiv/

en:
The actual service provided is displayed. The final forecast is used where no actual data are available. The "quality" is shown in the appropriate Status fields.
Actual data Archive:
https://opentransportdata.swiss/de/ist-daten-archiv/

de:
Es wird die effektiv gefahrene Leistung ausgegeben. Dort wo es keine real vorhandenen Daten gibt, wird die letzte Prognose verwendet.
Ist-Daten Archiv: https://opentransportdata.swiss/de/ist-daten-archiv/

it:
Viene resa nota l’effettiva distanza percorsa. Dove non sono disponibili i dati reali, si utilizza l’ultima previsione.
Archivio dati reale:https://opentransportdata.swiss/de/ist-daten-archiv/

oevch

fr: Secrétariat des tâches du système d’information à la clientèle (CFF SA)

de: Geschäftsstelle Systemaufgaben Kundeninformation (SBB AG)

en: Business office of Customer Information System Tasks (SBB CFF FFS)

it: Servizio commerciale Attività di sistema Informazione alla clientela (FFS SA)

fr: Le Secrétariat des tâches du système d’information à la clientèle (SKI) publie pour le compte de l’OFT les données de l’ensemble des transports publics de Suisse.

de: Die Geschäftsstelle Systemaufgaben Kundeninformation (SKI) veröffentlicht im Auftrag des BAV die Daten des gesamten öffentlichen Verkehrs der Schweiz

en: Working on behalf of the Swiss Federal Office of Transport (FOT), the Customer Information System Tasks (SKI) business office publishes data on all public transport in Switzerland

it: Il servizio commerciale Attività di sistema Informazione alla clientela (SKI) pubblica, per conto dell’UFT, i dati di tutto il trasporto pubblico in Svizzera.


maintainer: Jana von Freyberg

en: stable-water-isotopes solutes snowmelt-lysimeter snowmelt-rate snowpack-outflow

en: The dataset contains volumes, solutes and stable water isotopes of snowpack outflow at daily resolution. 10-minute data are provided of the measurements taken by three snowmelt lysimeter systems at spatially-distributed locations.

en:
This data contain volumes, solutes and isotopes of snowpack outflow measured by a snowmelt lysimeter system at three locations in the southern Alp catchment, situated Central Switzerland. The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l.
The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (snowpack outflow volumes) at daily intervals from several sampling locations. Additionally, the data measured by the snowmelt lysimeter system are provided in 10-minute resolution.

snowmelt-lysimeter

display_name: snowmelt-lysimeter

snowmelt-rate

display_name: snowmelt-rate

snowpack-outflow

display_name: snowpack-outflow

solutes

display_name: solutes

stable-water-isotopes

display_name: stable-water-isotopes

en: Daily data of the volumes, solutes and isotopes in snowpack outflow measured at three locations in the southern Alp catchment


en: snow-water-equivalent snow-ice-temperature snow-density snow-stratigraphy weisfluhjoch

en: WFJ Snow Profiles 1999-2014

en:
Biweekly manual snow profiles from Weissfluhjoch, Davos, Switzerland.
Version: 2015-09-14T16:25

en: README

media_type: TXT

en: README file giving use metadata.

en: Dataset of manual bi-weekly snow profiles from Weissfluhjoch, Davos, Switzerland. Typical snow profile measurements and observations are included (temperature, density, grain size, grain type, hardness, wetness), following the guidelines of the The International Classification for Seasonal Snow on the Ground (ICSSG) [Fierz, C., Armstrong, R.L., Durand, Y., Etchevers, P., Greene, E., McClung, D.M., Nishimura, K., Satyawali, P.K. and Sokratov, S.A. 2009. The International Classification for Seasonal Snow on the Ground. IHP-VII Technical Documents in Hydrology N°83, IACS Contribution N°1, UNESCO-IHP, Paris].

snow-density

display_name: snow-density

snow-ice-temperature

display_name: snow-ice-temperature

snow-stratigraphy

display_name: snow-stratigraphy

snow-water-equivalent

display_name: snow-water-equivalent

weisfluhjoch

display_name: weisfluhjoch

en: Manual bi-weekly snow profiles from Weissfluhjoch, Davos, Switzerland