maintainer: Matthias Haeni

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

en: Data plot of the field station (OTF)

media_type: application/pdf

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

en: Forest station data (OTB)

en: Data file.

en: Field station data (OTF)

en: Site photo of the field station (OTF)

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

en: Site photo of the forest station (OTB)

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

en: Site aerial photo (Othmarsingen)

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

en: Data plot of the forest station (OTB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Othmarsingen in Switzerland where one station is located within a natural deciduous forest stand (OTB) with European beech (_Fagus sylvatica_; 120-140 yrs) and lime trees (_Tilia sp._; 120-140 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, OTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Othmarsingen is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

envidat

en: EnviDat: the Environmental Data Portal

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


en: soil-water-balance temperature growing-season-length relative-humidity precipitation growing-degree-days solar-radiation

state: active

type: dataset

en: CHELSA-BIOCLIM+

media_type: geotiff

en: Global extented set of bioclimatic variables at different temporal (e.g. monthly, annual, long term normals) resolution in Geotiff format.

format: GeoTIFF

en: CHELSA-BIOCLIM+ Technical Documentation

media_type: PDF

en: Technical documentation document including the variable names, units, scaling, offset, and background.

format: PDF

en: A multitude of physical and biological processes on which ecosystems and human societies depend are governed by climatic conditions. Understanding how these processes are altered by climate change is central to mitigation efforts. Based on mechanistically downscaled climate data, we developed a set of climate-related variables at yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We created gridded data for near-surface relative humidity (hurs), cloud area fraction (clt), near-surface wind speed (sfcWind), vapour pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), climate moisture index (cmi), and site water balance (swb), at a monthly temporal and 30 arcsec spatial resolution globally starting 1980 until 2018. At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981-2010, 2011-2040, 2041-2070, and 2071-2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models. Time-series variables showed high accuracy when validated against observations from meteorological stations. Climatological normals were also highly correlated to observations although some variables showed notable biases, e.g., snow cover days (scd). Together, the data sets presented here allow improving our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world’s ecosystems and associated services to societies.

growing-degree-days

display_name: growing-degree-days

growing-season-length

display_name: growing-season-length

soil-water-balance

display_name: soil-water-balance

solar-radiation

display_name: solar-radiation

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution


maintainer: Albrecht von Boetticher

en: natural-hazard murgang dberisintermixing finite-volumes debris-flow barrier-impact-simulation shallow-landslide hangmuren interfoam

en: These are the installation instructions for debrisInterMixing4

mimetype: application/pdf

media_type: application/zip

en:
This version of debrisInterMixing is based on OpenFOAM Version 4.x and was tested on Ubuntu 18.04.1 LTS.
Please follow the instructions given in ReadMe_debrisInterMixing4.pdf for installation.

format: ZIP

mimetype: application/zip

en: debrisInterMixing for OF-Version 6

en: Installation Instructions for debrisInterMixing-6

en:
Here the updated versions of debrisInterMixing are provided for download. The first OpenFoam-compatible Version 2.3.x are available as supplement to v. Boetticher, A., Turowski, J. M., McArdell,W. B., Rickenmann, D., Hürlimann, M., Scheidl, C., and Kirchner, J. W.: DebrisInterMixing-2.3: A Finite Volume solver for three dimensional debris flow simulations based on two calibration parameters. Part two: model validation with experiments. Geoscientific Model Development, 10, 11: 3963-3978. doi: 10.5194/gmd-10-3963-2017.
DebrisInterMixing is a Volume-of-Fluid based Finite Volume code that accounts for shear-thinning sensitive shares of fine sediment suspension together with pressure-sensitive components of the gravel grains within debris flow mixtures. All model properties can be derived from a material sample except for a grid-sensitive calibration parameter.
For more information, please contact albrecht.vonboetticher@wasserbau.ch.

barrier-impact-simulation

display_name: barrier-impact-simulation

dberisintermixing

display_name: dberisintermixing

debris-flow

display_name: debris-flow

finite-volumes

display_name: finite-volumes

hangmuren

display_name: hangmuren

interfoam

display_name: interfoam

murgang

display_name: murgang

natural-hazard

display_name: natural-hazard

shallow-landslide

display_name: shallow-landslide

en: Three-dimensional debris flow simulation tool debrisInterMixing


maintainer: Marcia Phillips

en: ritigraben permos borehole permafrost meteo-station

en: DATA ACCESS

media_type: SMET

format: SERVICE

en: Meterological station at the [Ritigraben permafrost borehole](http://www.envidat.ch/dataset/rit1) (RIT_0102) in canton Valais, Switzerland. The station is located at 2690 m asl on a flat site.

borehole

display_name: borehole

meteo-station

display_name: meteo-station

permafrost

display_name: permafrost

permos

display_name: permos

ritigraben

display_name: ritigraben

envidat

en: EnviDat: the Environmental Data Portal

en: RIT2: Meteorological station at Ritigraben borehole site


maintainer: EnviDat Support

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

en: Planning intentios

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

development-priorities

display_name: development-priorities

europe

display_name: europe

spatial-information

display_name: spatial-information

strategic-plans

display_name: strategic-plans

urban-region

display_name: urban-region

en: Planning intentions in strategic plans of European urban regions


en: weissfluhjoch longwave-radiation incoming outgoing shortwave-radiation

en: Parameter file

media_type: text/plain

format: TXT

mimetype: text/plain

en: Metadata

en: Processing doc

en:
Corrected incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch summit, Davos, CH.

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

incoming

display_name: incoming

longwave-radiation

display_name: longwave-radiation

outgoing

display_name: outgoing

shortwave-radiation

display_name: shortwave-radiation

weissfluhjoch

display_name: weissfluhjoch

education

en: Education and science

envidat

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

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


maintainer: Frank Hagedorn

en: beech seasons nitrogen phosphorus soil-temperature fertilization forest-topsoil zero-tension-lysimeters dissolved-organic-phosphorus nutrient-fluxes

media_type: XLSX

format: XLS

en:
Data on dissolved organic and inorganic phosphorus and nitrogen concentrations in leachates and their corresponding
fluxes from the litter layer, the Oe/Oa horizon, and the A horizon of two German beech forest sites.
Leachate samples were taken in April 2018, July 2018, October 2018, Feb./Mar. 2019, and July 2019
with zero-tension lysimeters after artificial irrigation. Soil samples were taken in July 2019.
For more details please refer to the publication.

beech

display_name: beech

dissolved-organic-phosphorus

display_name: dissolved-organic-phosphorus

fertilization

display_name: fertilization

forest-topsoil

display_name: forest-topsoil

nitrogen

display_name: nitrogen

nutrient-fluxes

display_name: nutrient-fluxes

phosphorus

display_name: phosphorus

seasons

display_name: seasons

soil-temperature

display_name: soil-temperature

zero-tension-lysimeters

display_name: zero-tension-lysimeters

en: Phosphorus and nitrogen leaching from beech forest soils


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

media_type: tif

en: Precipitation of driest quarter modified from CHELSA

media_type: TIFF

en: Soil organic carbon content modified from SoilGrids

format: TIFF

en: Soil pH modified from SoilGrids

en: Mean diurnal range modified from CHELSA

en: Frost change frequency modified from CHELSA

en: Soil clay content modified from SoilGrids

en: Precipitation seasonality from CHELSA

en: Mean annual temperature modified from CHELSA

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

aridity

display_name: aridity

frost-change-velocity

display_name: frost-change-velocity

ground-temperature

display_name: ground-temperature

precipitation

display_name: precipitation

sdms

display_name: sdms

soil

display_name: soil

envidat

en: EnviDat: the Environmental Data Portal

en: Environmental layers for SDM simulations (GDPlants)


weather-station-klosters

maintainer: Maxime Hervo

title_for_slug: weather-station-klosters

en: raclets wind klosters precipitation weather temperature

en: Weather Station Klosters

en: All in one weather station measuring temperature (°C), humidity (%), wind (m s‾ ¹) and precipitation type. The time format is: number of days from January 0, 0000, UTC.

en: A weather station (Lufft WS600) measured meteorological parameters at Klosters (LON: 9.880413, LAT: 46.869019). Detailed information on the specifications can be found [here](https://www.lufft.com/products/compact-weather-sensors-293/ws600-umb-smart-weather-sensor-1832/productAction/outputAsPdf/).

klosters

display_name: klosters

raclets

display_name: raclets

temperature

display_name: temperature

weather

display_name: weather

wind

display_name: wind

education

en: Education and science

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

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: 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: 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 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: 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


maintainer: Yves Bühler

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

en: Potential Release  Area  NHESS

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

en: RAMMS simulations NHESS

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

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

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

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

avalanche

display_name: avalanche

hazard-indication-mapping

display_name: hazard-indication-mapping

hazard-mapping

display_name: hazard-mapping

ramms

display_name: ramms

release

display_name: release

snow

display_name: snow

envidat

en: EnviDat: the Environmental Data Portal

en: Automated Avalanche Release Area (PRA) Delineation Davos


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

en: climate realizations

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

climate-change-impact

display_name: climate-change-impact

cryosphere

display_name: cryosphere

natural-climate-variability

display_name: natural-climate-variability

rain-on-snow

display_name: rain-on-snow

snow-water-equivalent

display_name: snow-water-equivalent

uncertainty

display_name: uncertainty

education

en: Education and science

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


en: precipitation prevah discharge low-flow soil-moisture drought-2018 hydrological-simulations

en: Precipitation

en: Simulated precipitation for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

en: Shapefile of 307 catchments in Switzerland

en: Shapefile of 307 catchments for which simulations of precipitation, discharge, soil moisture, and low flow are provided. The OBJECTID entry corresponds to the data columns in the textfiles of the simulated data provided.

en: Discharge

en: Simulated discharge for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

en: Low-flow storage

en: Simulated low-flow storage for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

en: Soil moisture

en: Simulated plant-available soil moisture for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

en: The dataset provides simulated 1) precipitation, 2) discharge, 3) soil moisture, and 4) low-flow simulations for 307 medium-sized catchments in Switzerland for the period 1981-2018. The data were simulated using the hydrological model PREVAH in its gridded-version. The simulated time series are provided at daily resolution. A detailed description of the modeling approach can be found in Brunner et al. 2019 submitted to NHESS.

discharge

display_name: discharge

hydrological-simulations

display_name: hydrological-simulations

low-flow

display_name: low-flow

precipitation

display_name: precipitation

prevah

display_name: prevah

soil-moisture

display_name: soil-moisture

en: Hydro-meteorological simulations for the period 1981-2018 for Switzerland


maintainer: Anita Risch

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

en: Datafile

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

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

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

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

Soil.moist	: Soil moisture (%)

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

pH: Soil pH

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

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

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

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

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

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

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

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

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

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

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

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

Plant.richness: Plant species richness

Plant.N: Plant tissue nitrogen content (%)

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

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

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

Paper Citation:
 > Risch AC, Ochoa-Hueso R, van der Putten WH, Bump JK, Busse MD, Frey B, Gwiazdowicz DJ, Page-Dumroese DS, Vandegehuchte ML, Zimmermann S, Schütz M. Size-dependent loss of aboveground animals differentially affects grassland ecosystem coupling and functions. 2018. Nature Communications 9: 3684. [doi: 10.1038/s41467-018-06105-4](https://doi.org/10.1038/s41467-018-06105-4).

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

#Methods
Study sites
The experimental exclosure setups were installed within the SNP (IUCN category Ia preserve; Dudley 2008), in south-eastern Switzerland. The park covers 172 km2 of forests and subalpine and alpine grasslands along with scattered rock outcrops and scree slopes. The entire area has been protected from human impact (no hunting, fishing, camping or off-trail hiking) since 1914. Large, fairly homogenous patches of short- and tall-grass vegetation, which originate from different historical management and grazing regimes, cover the park’s subalpine grasslands entirely. Short-grass vegetation developed in areas where cattle used to rest (nutrient input) prior to the park’s foundation (14th century to 1914) (Schütz and others 2003, 2006) and is dominated by lawn grass species such as Festuca rubra L., Briza media L. and Agrostis capillaris L. (Schütz and others 2003, 2006). Today, this vegetation type is intensively grazed by diverse vertebrate and invertebrate communities that inhabit the park and consume up to 60% of the available biomass (Risch and others 2013). Tall-grass vegetation developed where cattle formerly grazed, but did not rest, and is dominated by rather nutrient-poor tussocks of Carex sempervirens Vill. and Nardus stricta L. (Schütz and others 2003, 2006). This vegetation type receives considerably less grazing, with only roughly 20% of the biomass consumed (Risch and others 2013). Consequently, the two vegetation types together represent a long-term trajectory of changes in grazing regimes. Underlying bedrock of all grasslands is dolomite, which renders these grasslands rather poor in nutrients regardless of former and current land-use regimes.
Experimental design
To progressively exclude aboveground vertebrate and invertebrate animals, we established 18 size-selective exclosure setups (nine in short-grass, nine in tall-grass vegetation) distributed over six subalpine grasslands across the SNP (Risch and others 2013, 2015). Elevation differences of exclosure locations did not exceed 350 m (between 1975 and 2300 m a.s.l.). The exclosures were established immediately after snowmelt in spring 2009 and were left in place for five consecutive growing seasons (until end of 2013). They were, however, temporarily dismantled every fall (late October after first snowfall) to protect them from avalanches. They were re-established in the same location every spring immediately after snowmelt. Each size-selective exclosure setup consisted of five plots (2 x 3 m) that progressively excluded aboveground vertebrates and invertebrates from large to small. The plots are labelled according to the guilds that had access to them “L/M/S/I”, “M/S/I”, “S/I”, “I”, “None”; L = large mammals, M = medium mammals, S = small mammals, I = invertebrates, None = no animals had access. As we only had permission to have the experimental setup in place for five consecutive growing seasons, the experiment had to be completely dismantled in the late fall of 2013 and all material removed from the SNP.
Our exclosure design was aimed at excluding mammalian herbivores, but naturally also excluded the few medium and small mammalian predators, as well as the entire aboveground invertebrate food web. A total of 26 large to small mammal species can be found in the SNP, but large apex predators are missing (wolf, bear, lynx) . Reptiles, amphibians and birds are scarce to absent in the subalpine grasslands under study. Only two reptile species occur in the park and they are confined to rocky areas that warm up enough for them to survive. One frog species spawns in an isolated pond far from our grasslands. Only three bird species occasionally feed on the subalpine grasslands. Using game cameras (Moultrie 6MP Game Spy I-60 Infrared Digital Game Camera, Moultrie Feeders, Alabaster, AL, USA), we did observe that the medium- and small-sized mammals (marmot/hares and mice) were not afraid to enter the fences and feed on their designated plots. We never spotted reptiles, amphibians or birds on camera. We distinguished between 59 higher aboveground-dwelling invertebrate taxa that our size-selective exclosures excluded (see also methods for aboveground-dwelling invertebrates below).
The “L/M/S/I” plot (not fenced) was located at least 5 m from the 2.1 m tall and 7 x 9 m large main electrical fence that enclosed the other four plots. The bottom wire of this fence was mounted at 0.5 m height and was not electrified to enable safe access for medium and small mammals, while fencing out the large ones. Within each main fence, we randomly established four 2 x 3 m plots separated by 1-m wide walkways from one another and from the main fence line: 1) the “M/S/I” plots were unfenced, allowing access to all but the large mammals; 2) the “S/I” plots (10 x 10 cm electrical mesh fence) excluded all medium-sized mammals. Note that the bottom 10 cm of this fence remained non-electrified to enable safe access for small mammals; 3) the “I” plots (2 x 2 cm metal mesh fence) excluded all mammals. We double-folded the mesh at the bottom 50 cm to reduce the mesh size to smaller than 1 x 1 cm openings; and 4) the “None” plots were surrounded by a 1 m tall mosquito net (1.5 x 2 mm) to exclude all animals. The top of the plot was covered with a mosquito-meshed wooden frame mounted to the corner posts (roof). We treated these plots a few times with biocompatible insecticide (Clean kill original, Eco Belle GmbH, Waldshut-Tiengen, Germany) to remove insects that might have entered during data collection or that hatched from the soil, but amounts were negligible and did not impact soil moisture conditions within these plots.
To assess whether the design of the “None” exclosure (mesh and roof) affected the response variables within the plots and, therefore, influenced the results, we established an additional six “micro-climate control” exclosures (one in each of the six grasslands) (Risch and others 2013, 2015). These exclosures were built as the “None” exclosures but were open at the bottom (20 cm) of the 3-m side of the fence facing away from the prevailing wind direction to allow invertebrates to enter. A 20-cm high and 3-m long strip of metal mesh was used to block access to small mammals. Thus, this construction allowed a comparable micro-climate to the “None” plots, but also a comparable feeding pressure by invertebrates to the “I” plots. We compared various properties within these exclosures against one another to assess if our construction altered the conditions in the “None” plots. We showed that differences in plant (e.g., vegetation height, aboveground biomass) and soil properties (e.g., soil temperature, moisture) found between the “I” and the “None” treatments were not due to the construction of the “None” exclosure, but a function of animal exclusions, although the amount of UV light reaching the plant canopy was significantly reduced (Risch and others 2013).
Aboveground invertebrate sampling
Aboveground invertebrates were sampled with two different methods to capture both ground- and plant-dwelling organisms: 1) we randomly placed two pitfall traps (67 mm in diameter, covered with a roof) filled with 20% propylene glycol in one 1 x 1 m subplot of the 2 x 3 m treatment plots in spring 2013 (May) and emptied them every two weeks until late September 2013 (Vandegehuchte and others 2017b, 2018). A pitfall trap consisted of a plastic cylinder (13 cm depth, 6.75 cm diameter). Within each cylinder we placed a 100 ml plastic vial with outer diameter 6.70 cm and on top of the cylinder we placed a plastic funnel to guide the invertebrates into the vials. Each trap was cover with a cone-shaped and transparent plastic roof to protect the trap from rain (Vandegehuchte and others 2017b, 2018). Note that in the “None” plots only one trap was placed as control to check for effectiveness of the exclosure. 2) We vacuumed all invertebrates from a 60 x 60 cm area on another 1 x 1 m subplot with a suction sampler (Vortis, Burkhard manufacturing CO, Ltd., Rickmansworth, Hertfordshire, UK) every month from June to September 2013 (Vandegehuchte and others 2017b, 2018). For this purpose, we quickly placed a square plastic frame (60 x 60 x 40 cm) with a closable mosquito mesh sleeve attached to the top edge into the plot from the outside. The suction sample was then inserted into through the sleeve and operated for 45 s to collect the invertebrates (Vandegehuchte and others 2017b, 2018).
We sorted the ≈100 000 individuals collected with both methods by hand and identified each individual morphologically to the lowest taxonomic level feasible (59 taxa, including orders, suborders, subfamilies, families; phylum for Mollusca). These taxa belonged to the following feeding types: 19 herbivores, 16 detritivores, 9 predators, 8 mixed feeders, 5 omnivores and 2 non-classified feeders (or not feeding as adults) (Vandegehuchte and others 2017b). We summed the numbers from the two pitfall traps and the suction sampling over the course of the 2013 season to represent the aboveground invertebrate abundance and community composition of a plot. Note: we did not specifically attempt to catch flying invertebrates with e.g., sticky traps, thus a few flying insects may have been missed with our vacuum sampling approach.
Sampling of plant properties
The vascular plant species composition was assessed at peak biomass every summer (July) by estimating the frequency of occurrence of each species with the pin count method in each plot (Frank and McNaughton 1990). A total of 172 taxa occurred within our 90 plots and we calculated plant species richness for each plot separately. We used the 2013 data in this study. Plant quality was assessed every year in July and September; here we use plant quality at the end of the experiment (September 2013). Two 10 x 100 cm wide strips of vegetation per plot were clipped, combined, dried at 65°C, and ground (Pulverisette 16, Fritsch, Idar-Oberstein, Germany) to pass through a 0.5 mm sieve. Twenty randomly selected samples across all treatments were analysed for N (Leco TruSpec Analyser, Leco, St. Joseph, Michigan, USA) (Vandegehuchte and others 2015). Nitrogen concentrations of the other samples were then estimated from models established for the experiment and the entire SNP relating Fourier transform-near infrared reflectance (FT-NIR) spectra to the measured values of N using a multi-purpose FT-NIR spectrometer (Bruker Optics, Fällanden, Switzerland) (Vandegehuchte and others 2015). Root biomass was sampled every fall by collecting five 2.2 cm diameter x 10 cm deep soil samples (Giddings Machine Company, Windsor, CO, USA) per plot (450 samples year-1). The samples were dried at 30 °C and roots were sorted from the sample by hand. We sorted each sample for 1 h which allowed to retrieve over 90% of all roots present in the samples (Risch and others 2013). The roots were then dried at 65 °C for 48 and weighed to the nearest mg. We averaged the values per plot and used the 2013 data only in this study.
Sampling of edaphic communities
In 2009, 2010, and 2011 we collected three composited soil samples (5 cm diameter x 10 cm depth; AMS Samplers, American Falls, ID, USA) and assessed bacterial community structure using T-RFLP profiling (Liu and others 1997; Blackwood and others 2003; Hodel and others 2014). We detected a total of 89 operational taxonomic units (OTUs). These values are in accordance with other studies reporting OTU richness (Wirthner and others 2011; Zumsteg and others 2012; Meola and others 2014) using T-RFLP profiling, a method that detects the most abundant, and thus likely, the most relevant, taxa. We averaged the data over the three years of collections for our calculations. Microbial biomass carbon (MBC) was determined with the substrate-induced method (Anderson and Domsch 1978) every fall (September) between 2009 and 2013 by collecting three mineral soil samples (5 cm diameter × 10 cm mineral soil core, AMS Samplers, American Falls, ID, USA). The three samples were combined (90 samples for each sampling year), immediately put on ice, taken to the laboratory, passed through a 2-mm sieve and stored at 4°C. Again, we only used the 2013 data in this study.
Soil samples (5 cm diameter x 10 cm depth) to extract soil arthropods were collected in June, July, and August 2011 with a soil corer lined with a plastic sleeve to ensure an undisturbed sample (total of 270 samples). The plastic line core was immediately sealed on both ends using cling film and put into a cooler. All plots were sampled within three days and the extraction of arthropods started the evening of the sampling day using a high-gradient Tullgren funnel apparatus (Crossley and Blair 1991; Vandegehuchte and others 2015). Samples were kept in the extractor for four days and the soil arthropods were collected in 95% ethanol. All individuals were counted and each individual was identified morphologically to the lowest level feasible [76 taxa, including orders, suborders, subfamilies, families (Protura, Thysanoptera, Aphidina, Psylina, Coleoptera, Brachycera, Nematocera, Auchenorryncha, Heteroptera, Formicidae); sub-phylum for Myriapoda, for Acari and Collembola also including morpho-species). Note that we also included larval stages (nine of the 76 taxa) (Vandegehuchte and others 2015). All data were summed over the season. A detailed species list for mites and collembolans is published (Vandegehuchte and others 2017a) [https://doi.org/10.1371/journal.pone.0118679.s001]. Earthworms are rare in the SNP and therefore were not included. We collected eight random 2.2 cm diameter x 10 cm deep soil cores from each plot in September 2013 to determine the soil nematode community composition. The samples were mixed and the nematodes were extracted from 100 ml of fresh soil using Oostenbrink elutriators (Oostenbrink 1960). All nematodes in a 1 ml of the 10 ml extract were counted, a minimum of 150 individuals sample-1 were identified to genus or family level using (Bongers 1988), the numbers of all nematodes were extrapolated to the entire sample and expressed for a 100 g dry sample. In total we identified 63 genus or family levels (Vandegehuchte and others 2015). The list of all the nematodes found is published (Vandegehuchte and others 2015) [http://www.oikosjournal.org/appendix/oik-03341] or DOI: [doi: 10.1111/oik.03341].
We are aware that sampling soil microbes from 2009 to 2011 and soil arthropods in 2011 was not ideal, but we are positive that this does not bias the results. Most of the parameters measured in our experiment either already showed a treatment response after the first growing season (e.g., plant biomass) or did not respond over the entire time experiment (e.g., microbial biomass C). The microbial community composition (2009 – 2011) was highly influenced by inter-annual differences in temperature and precipitation, but did not differ between treatments or vegetation types (Hodel and others 2014). We therefore felt comfortable using the 2009 through 2011 data for describing the soil microbial community in our experimental treatments. Similarly, we are positive that our soil arthropod data are representative. We did assess soil arthropods in August 2012 and found no differences to the August 2011 data. However, we did not feel comfortable combining the 2011 June, July, August data with only August data for 2012 for our analyses.
Sampling of soil properties
We collected three soil samples (5 cm diameter x 10 cm depth) in each plot in September 2013 after removing the vegetation. First, we collected the top layer of mineral soil rich in organic matter, the surface organic layer or rhizosphere, typically 1 to 3 cm in depth with a soil corer (AMS Samples, American Falls, Idaho, USA). Second, we collected a 10 cm mineral soil core beneath this surface layer. The cores for each layer were composited, dried at 65 °C for 48 h and fine-ground to pass a 0.5 mm screen. We then analysed all samples for total C using a Leco TruSpec Analyser (Leco, St. Joseph, Michigan, USA). Mineral soil pH was measured potentiometrically in 1:2 soil:CaCl2 solution with an equilibration time of 30 min.
Soil net N mineralisation was assessed during the 2013 growing season (Risch and others 2015). For this purpose, we randomly collected a 5 cm diameter x10 cm deep soil sample with a soil corer (AMS Samples, American Falls, Idaho, USA) after clipping the vegetation in June 2013. After weighing and sieving (4 mm mesh) the soil, we extracted a 20 g subsample in 1 mol l-1 KCl for 1.5 h on an end-over-end shaker and thereafter filtered it through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnenmühle FineArt GmbH, Dassel, Germany). From these filtrates NO3- concentrations were measured colorimetrically (Norman and Stucki 1981) and NH4+with flow injection analysis (FIAS 300, Perkin Elmer, Waltham Massachusetts, USA) (Risch and others 2015). We dried the rest of the sample 105 °C to constant mass to determine fine,fraction bulk density. A second soil sample was collected within each plot in June 2013 with a corer lined with a 5 x 13 cm aluminium cylinder. The corer was driven 11.5 cm deep into the soil so that the top 1.5 cm of the cylinder remained empty. Into this space we placed a polyester bag (250 µm) filled an ion-exchanger resin to capture the incoming N. The bag was filled with a 1:1 mixture of acidic and alkaline exchanger resin (ion-exchanger I KA/ion exchanger IIIAA, Merck AG, Darmstadt, Germany). We then removed 1.5 cm soil at the bottom of the cylinder and placed a second resin exchanger bag into this space to capture the N leached from the soil column. To assure that the exchange resin was saturated with H+ and Cl- prior to filling the bags, the mixture was stirred with 1.2 ml l-1 HCl for 1 h and then rinsed with demineralized water until the electrical conductivity of the water reached 5 µm cm-1. The cylinder with the resin bags in place was reinserted into the soil with the top flush to the soil surface and incubated for three months. We recollected the cylinders in September 2013. Each resin bag and 20 g of sieved soil (4 mm mesh) from each cylinder were then separately extracted with KCl and NO3- and NH4+ concentrations were measured. Nitrate and NH4+ concentrations of all samples were then converted to a content basis by multiplying their values with fine fraction bulk density. Net N mineralisation was thereafter calculated as the difference between the N content of the samples collected at the end of the three-month incubation (including the N extracted from the bottom resin bag) and the N content at the beginning of the incubation (Risch and others 2015).
Soil CO2 emissions were measured every two weeks between 0900 and 1700 hrs from early May through late September 2013 with a PP-Systems SRC-1 soil respiration chamber (15 cm high, 10 cm diameter; closed circuit) attached to a PP-Systems EGM-4 infrared gas analyser (PP-Systems, Amesbury, MA, USA) on two locations per plot (Risch and others 2013). The chamber was placed on randomly placed, permanently installed PVC collars (10 cm diameter) driven 5 cm into the soil at the beginning of the study (Risch and others 2013). Freshly germinated plants growing within the collars were removed prior to each measurement to avoid measuring plant respiration or photosynthesis. The two measurements collected per plot and sampling date were averaged.
Soil moisture (with time domain reflectometry; Field-Scout TDR-100, Spectrum Technologies, Plainfield, Illionois, USA) and temperature (with a waterproof digital pocket thermometer; Barnstead International, Dubuque, Iowa, USA) were measured at five random locations per plot every two weeks during the growing seasons during the experiment for the 0 to 10 cm depth (Risch and others 2013, 2015). As soil moisture and soil temperature were highly negatively correlated (Risch and others 2013), we only used soil moisture for this study. We used plot-level averages of all values available to capture soil moisture variability during the five years of the experiment. The results remained unchanged when we only used soil moisture from the 2013 growing season.
Numeral calculations and statistical analyses
Ecosystem coupling. We conducted principal component analyses (PCAs; unscaled) at the complete dataset level using the abundances of each taxonomical entity to describe each of the five different communities used in this study: aboveground-dwelling invertebrates, vascular plants, soil microorganisms, soil arthropods and soil nematodes. We retained the first two components (PCA axis 1 and PCA axis 2) of each analysis as we found them to adequately represent the temporal and spatial variability of our 90 treatment plots in previous studies55,67. Together they explained a total of 71.70% of the variation for aboveground invertebrates, 44.36% for plants, 44.85% for soil microorganisms, 61.85% for soil arthropods and 77.19% for soil nematodes. In addition, we used soil pH and soil organic C content as a proxy for soil chemical properties, soil bulk density as a proxy for soil physical properties and soil moisture (negatively correlated with soil temperature) as a proxy for soil micro-climatic conditions for an overall total of fourteen constituents.
We calculated ecosystem coupling9 for each exclosure treatment within each vegetation type (i.e., 2  5 treatment combinations in total) as an integrated measure of pairwise ecological interactions between ecosystem constituents representing ecological communities and the soil abiotic environment. These ecological interactions are defined by non-parametric Spearman rank correlation analyses between two constituents, excluding interactions involving two abiotic constituents (e.g., soil pH vs. soil moisture) and interactions between the first (PC1) and second (PC2) component of each community type, as these are orthogonal by definition. Interactions between abiotic constituents were excluded from the analyses because the focus of our study was on communities and how they interact with one another and their surrounding environment; therefore, including abiotic-abiotic interactions was not of interest here. Given that the effectiveness of our experimental design resulted in that no community composition data of aboveground-dwelling invertebrates was available for the “None” plots (all animals excluded), only thirteen instead of fourteen constituents were included in the ecosystem coupling calculations for this treatment. The complete absence of aboveground invertebrates represents the most extreme case of disturbance between aboveground animal communities and the rest of the ecosystem constituents. This may have resulted in a slight overestimation of ecosystem coupling for these plots.
	Average ecosystem coupling was calculated as follows:
Ecosystem coupling=
where Xi is the absolute Coupling was calculated value of the Spearman’s rho coefficient of the ith correlation for each treatment within each vegetation type (i.e., based on nine replicates each), considering and n is the number of pairwise comparisons considered (n = a total of 80; interactions (56 in the case of the “None” treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the “None” treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the “None” treatment).	Coupling was calculated for each treatment within each vegetation type (i.e., based on nine replicates each), considering a total of 80 interactions (56 in the case of the “None” treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the “None” treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the “None” treatment).
To establish whether constituents were significantly and positively coupled within treatments (i.e., the average of their correlation coefficients were greater than in a null model where correlation only happens by chance), we calculated one-tailed p-values based on permutation tests with 999 permutations.

We considered six ecosystem functions and process rates commonly used to assess ecosystem functioning (Meyer and others 2015; Manning and others 2018). Plant N content represents a measure of forage quality, while plant richness has been shown to stabilise biomass production, thus allowing the system to respond to changes in herbivory. Soil net N mineralisation, soil respiration, root biomass, and microbial biomass represent fluxes or stocks of energy. For all functions and processes higher values represent higher functioning (Manning and others 2018). All these variables were measured in the last year of the experiment (2013). We then quantified ecosystem multifunctionality using the multiple threshold approach (Byrnes and others 2014; Manning and others 2018), which considers the number of functions that are above a certain threshold, over a series of threshold values (typically 10-99%) that are defined based on the maximum value of each function. We weighted all our functions equally for these calculations (Manning and others 2018). The number of functions in a plot with values higher than a given threshold value for the respective function is summed up. The sum represents ecosystem multifunctionality for that plot. Given that choosing any particular threshold as a measure of ecosystem multifunctionality is arbitrary, we calculated the average of thresholds from 10-90% (in 10% intervals) as a more integrated representation of ecosystem multifunctionality.
We used Pearson correlations to explore the relationships between ecosystem coupling (all interactions, biotic-biotic interactions, abiotic-biotic interactions involving above- and belowground constituents, and all interactions, biotic-biotic interactions, abiotic-biotic interactions involving belowground constituents only) and ecosystem multifunctionality by calculating the slopes of all relationships between ecosystem coupling and multifunctionality for all thresholds between 10 and 99%. We also related ecosystem coupling with the average of multifunctionality at thresholds between 30-80% as explained before and considered this correlation as a robust indication of the type of association between these two variables. In addition, we explored the relationships between ecosystem coupling (all interactions, biotic-biotic interactions, abiotic-biotic interactions involving above- and belowground constituents, and all interactions, biotic-biotic interactions, abiotic-biotic interactions involving belowground constituents only) and individual ecosystem functions. The effects of exclosures and vegetation type on individual functions and multifunctionality were evaluated using linear mixed effects models ('lme' function of the nlme package), with exclosure and vegetation type as fixed effects and fence as a random factor. All statistical analyses and numerical calculations were done in R version 3.4.0 (R Core Team 2016).

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en: manure bioenergy biogas review switzerland

en: Biogas aus Hofdünger in der Schweiz

en: Ziel dieses Whitepapers ist es, Entscheidungsträgern, Verwaltungen und Stakeholdern die aktuellsten Forschungsergebnisse zur Verfügung zu stellen, um die optimale Nutzung von Bioenergie aus Hofdünger in der Schweizer Energiewende zu fördern. Zu diesem Zweck werden die Ergebnisse des Schweizer Kompetenzzentrums für Bioenergieforschung - SCCER BIOSWEET - zusammengefasst und in einem breiteren Kontext dargestellt. Wenn nichts anderes erwähnt wird, beziehen sich die Ergebnisse auf die Schweiz und im Falle der Ressourcen auf die heimischen Biomassepotenziale.

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

switzerland

display_name: switzerland

education

en: Education and science

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

en: Dataset

media_type: ZIP

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

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

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

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

lee-side-flow-field

display_name: lee-side-flow-field

lidar

display_name: lidar

preferential-deposition

display_name: preferential-deposition

tls

display_name: tls

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


maintainer: Christian G. Sommer

en: wind-tunnel snow wind-packing wind-crust kinect

en: Processing scripts

en: Contains the Matlab- and R-scripts used to process the Kinect data

en: Kinect data

media_type: Link

en: Links to the dataset

en: README

en: Describes the dataset in detail

en:
This data sets contains the Microsoft Kinect data from 15 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winter 2016/17. The Kinect measures distributed snow depth.

The Kinect data is organized per experiment. Each experiment subfolder contains the processed Kinect depth images and some additional files.

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

This dataset is the basis of the following publication:

Sommer C.G., Lehning M. & Fierz C. (2018). Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow. Front. Earth Sci. 6:4. doi: 10.3389/feart.2018.00004

kinect

display_name: kinect

snow

display_name: snow

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing

wind-tunnel

display_name: wind-tunnel

en: Wind crust formation: Microsoft Kinect data


maintainer: Lisa Hülsmann

en: forest-reserves tree-mortality empirical-mortality-models tree-growth inventory-data

en: Calibration data

media_type: text/csv

en:
A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory.

 Meta data
Reserve: Name of the forest reserve

Plot: Permanent plot number

 Tree data
Species: Latin species name (or species group, cf. _Quercus_ spp.)

y: Status of the tree with 0 = alive and 1 = dead

dt: Length of the mortality period (years)

DBH: Diameter at breast height (mm) of the second inventory

relBAI: Annual relative basal area increment (-) calculated based on the first and the second DBH measurement as the compound annual growth rate of the trees basal area

 Stand data
_All stand variables were calculated for the second inventory._

mDBH: Mean diameter at breast height (mm)

iqrDBH: Interquartile range of diameter at breast height (mm)

BA: Basal area per hectare (m2 ha-1)

N: Number of trees per hectare (ha-1)

 Climate data
_Both climate variables were calculated for the period between the second and the third inventory._

P: Mean annual precipitation sum (mm)

mT: Mean annual air temperature (°C)

format: CSV

mimetype: text/csv

en:
The dataset comprises > 90 000 records from inventories in 54 strict forest reserves in [Switzerland](https://www.wsl.ch/de/wald/biodiversitaet-naturschutz-urwald/naturwaldreservate.html) and [Lower Saxony / Germany](http://naturwaelder.de/) along a considerable environmental gradient. It was used to develop parsimonious, species-specific mortality models for 18 European tree species based on tree size and growth as well as additional covariates on stand structure and climate.

 Inventory data
Measurements had been conducted repeatedly on up to 14 permanent plots per reserve for up to 60 years with re-measurement intervals of 4 - 27 years. The permanent plots vary in size between 0.03 and 3.47 ha. The inventories provide diameter measurements at breast height (DBH) and information on the species and status (alive or dead) of trees with DBH ≥ 4 cm for Switzerland and ≥ 7 cm for Germany.

 Data selection
We excluded three permanent plots where at least 80 % of the trees died during an interval of 10 years, and mortality could be clearly assigned to a disturbance agent. Mortality in the remaining stands was rather low, with a mean annual mortality rate of 1.5 % and strong variation between plots from 0 to 6.5 % (assessed for trees of all species with DBH ≥ 7 cm).
We only used data from permanent plots with at least 20 trees per species to obtain reliable plot-level mortality rates even for species with low mortality rates (about 5 % during 10 years), and selected tree species occurring on at least 10 plots to cover sufficient ecological gradients. This led to a dataset of 197 permanent plots and 18 tree or shrub species: _Abies alba_ Mill., _Acer campestre_ L., _Acer pseudoplatanus_ L., _Alnus incana_ Moench., _Betula pendula_ Roth, _Carpinus betulus_ L., _Cornus mas_ L., _Corylus avellana_ L., _Fagus sylvatica_ L., _Fraxinus excelsior_ L., _Picea abies_ (L.) Karst, _Pinus mugo_ Turra, _Pinus sylvestris_ L., _Quercus pubescens_ Willd., _Quercus_ spp. (_Q. petraea_ Liebl. and _Q. robur_ L.; not properly differentiated in the Swiss inventories), _Sorbus aria_ Crantz, _Tilia cordata_ Mill. and _Ulmus glabra_ Huds..

 Predictors of tree mortality
We considered tree size and growth as key indicators for mortality risk. Radial stem growth between the first and second inventory and DBH at the second inventory were used to predict tree status (alive or dead) at the third inventory. To this end, the annual relative basal area increment (relBAI) was calculated as the compound annual growth rate of the trees basal area. Additional covariates on stand structure and climate comprise mean annual precipitation sum (P), mean annual air temperature (mT), the mean and the interquartile range of DBH (mDBH, iqrDBH), basal area (BA) and the number of trees (N) per hectare.

 Further information
For further information, refer to Hülsmann _et al_. (in press) How to kill a tree – Empirical mortality models for eighteen species and their performance in a dynamic forest model. _Ecological Applications_.

empirical-mortality-models

display_name: empirical-mortality-models

forest-reserves

display_name: forest-reserves

inventory-data

display_name: inventory-data

tree-growth

display_name: tree-growth

tree-mortality

display_name: tree-mortality

envidat

en: EnviDat: the Environmental Data Portal

en: Calibration data for empirical mortality models of 18 European tree species


maintainer: Mahdi Jafari

en: openfoam vapor-transport convection-in-snow snow-density-profile numerical-simulation

en: snowpackBuoyantPimpleFoam

en:
This zipped folder includes (1) the source code files, (2) an example case for simulation, and (3) an instruction how to compile and use the new solver.

Note that the most updated version of snowpackBuoyantPimpleFoam can be found in the WSL/SLF GitLab repository: https://gitlabext.wsl.ch/snow-models/snowpackbuoyantpimplefoam

en: snowpackBuoyantPimpleFoam is a two-phase solver implemented to model convection of water vapor with phase change in snowpacks. This new solver is based on the standard solver of buoyantPimpleFoam in the open-source fluid dynamics software, OpenFOAM 5.0 (www.openfoam.org).

convection-in-snow

display_name: convection-in-snow

numerical-simulation

display_name: numerical-simulation

openfoam

display_name: openfoam

snow-density-profile

display_name: snow-density-profile

vapor-transport

display_name: vapor-transport

education

en: Education and science

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: snowpackBuoyantPimpleFoam: an OpenFOAM Eulerian–Eulerian two-phase solver for modelling convection of water vapor in snowpacks


en: advection 18o metamorphism snow stable-water-isotope

en: Read-me

en: A short description of the data contained in the zipped-file. Recommended for reading before downloading the dataset.

en: Data files and read-me

en: raw data and micro-CT data available from contacts

en:
Stable water isotopes (δ18O) obtained from snow and ice samples of polar regions are used to reconstruct past climate variability, but heat and mass transport processes can affect the isotopic composition. Here we present an experimental study on the effect on the snow isotopic composition by airflow through a snow pack in controlled laboratory conditions. The influence of isothermal and controlled temperature gradient conditions on the δ18O content in the snow and interstitial water vapor is elucidated. The observed disequilibrium between snow and vapor isotopes led to exchange of isotopes between snow and vapor under non-equilibrium processes, significantly changing the δ18O content of the snow. The type of metamorphism of the snow had a significant influence on this process.
Ebner, P. P., Steen-Larsen, H. C., Stenni, B., Schneebeli, M., and Steinfeld, A.: Experimental observation of transient δ18O interaction between snow and advective airflow under various temperature gradient conditions, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-16, accepted, 2017.

advection

display_name: advection

metamorphism

display_name: metamorphism

stable-water-isotope

display_name: stable-water-isotope

en: Experiments on stable water isotopes,  snow metamorphism, and advection


maintainer: Frank Techel

en: avalanche-danger norway avalanche-observations switzerland rutschblock-test extended-column-test

en: avalanche occurrence data (Norway, Sitzerland) - avalanche sizes per day and warning region

en: ECT data (Norway, Switzerland)

en: avalanche occurrence data (Norway, Sitzerland) - largest avalanche size per day and warning region

en: Rutschblock data (Switzerland)

en:
Observational data used to quantitatively describe the key elements describing avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size.
The data set consists of
- Rutschblock test results (Switzerland)
- Extended Column Test results (Switzerland, Norway)
- Avalanche occurrence data (Switzerland, Norway).
The data were extracted from the respective operational databases of the national avalanche warning services in Switzerland (WSL Institute for Snow and Avalanche Research SLF Davos, Switzerland) and Norway (The Norwegian Water Resources and Energy Directorate NVE).
For further information regarding the data, please refer to the publication or contact the author.

avalanche-danger

display_name: avalanche-danger

avalanche-observations

display_name: avalanche-observations

extended-column-test

display_name: extended-column-test

norway

display_name: norway

rutschblock-test

display_name: rutschblock-test

switzerland

display_name: switzerland

en: Stability tests, avalanche observations, Switzerland, Norway


maintainer: M. Carol Resch

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

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: 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 feeding types

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

diversity

display_name: diversity

grassland

display_name: grassland

long-term-experiment

display_name: long-term-experiment

nematodes

display_name: nematodes

restoration

display_name: restoration

topsoil-removal

display_name: topsoil-removal

vegetation

display_name: vegetation

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

en: Grassland restoration: nematodes and plant communities