en: drosophila-suzukii simple-sequence-repeats nuclear-microsatellite-markers switzerland biological-control trichopria-drosophilae multilocus-genotypes population-genetics

en: Multilocus genotypes (biallelic coding for 21 nuclear microsatellites) of Trichopria drosophilae described in Gugerli et al. Agrarforschung Schweiz 2019. Missing data denoted as '999'. Population codes (PopNo) are the following: 1, Vezia; 3, Contone ; 5, Davesco; 6, Corteglia; 7, Mezzana; 8, Stabio; 9, Gordola; 10, Sementina; 12, Brusata di Novazzano; 14, Monteggio; 16, Giubiasco; 17, ComoBP; 18, VD; 19, Weil_D; 20, Dossenheim; 21, BioPlanet_Como. Coordinates (CoordCH_E, CoordCH_N) refer to the Swiss coordinates system CH1903.

mimetype: text/csv

en: Table comprising all pertinent information for multiplex PCRs of species-specific nuclear microsatellite makers for Trichopria drosophilae, comprising all 21 newly developed markers, details on full sequences with GenBank accession numbers, primer sequences (including labeling and pig-tails), and repeat motives. PCRs with respective annealing temperatures (AnnealingTemp) refer to Type-It Multiplex PCR Kit (Qiagen, Hilden, Germany).

en:
 Nuclear microsatellite markers and genotype data for _Trichopria ddrosophilae_

This data set comprises (i) the characteristics of a set of 21 species-specific nuclear microsatellites for PCR amplification in _Trichopria drosophilae_ (ii) and genotype data for samples collected in southern Switzerland (Canton of Ticino), with few reference samples from Canton of Vaud, southern Germany, and northern Italy (lab-reared population).
Markers were developed by Ecogenics GmbH, Balgach (Switzerland), using MiSeq Nano 2x250 v2 format (on a mix of 10 individuals). Multiplex PCR assays for multilocus genotyping were established by Ecological Genetics (WSL Birmensdorf), and population genetic analyses are found in Gugerli et al., Agrarforschung Schweiz 2019.

biological-control

display_name: biological-control

drosophila-suzukii

display_name: drosophila-suzukii

multilocus-genotypes

display_name: multilocus-genotypes

nuclear-microsatellite-markers

display_name: nuclear-microsatellite-markers

population-genetics

display_name: population-genetics

simple-sequence-repeats

display_name: simple-sequence-repeats

switzerland

display_name: switzerland

trichopria-drosophilae

display_name: trichopria-drosophilae

en: Nuclear microsatellite markers for Trichopria drosophilae, parasitoid wasp on Drosophila suzukii


historical-vegetation-height-model-nfi

title_for_slug: historical-vegetation-height-model-nfi

en: nfi dsm historical vhm forest photogrammetry

state: active

type: dataset

en: 1985-1991 Vegetation Height Model

format: GeoTIFF

en: 1979-1985 Vegetation Height Model

en: 1990-1998 Vegetation Height Model

media_type: text/plain

en: Description of available data

format: TXT

en: 1998-2006 Vegetation Height Model

en:
The main datasets available are 4 nationwide **Digital Surface Models (DSMs)** and corresponding **Vegetation Height Models (VHMs)** with a spatial resolution of 1 m.
The Vegetation Height Models are calculated by subtraction of a Digital Terrain Model (swissAlti3d version 2017, swisstopo) from the generated DSMs. DSMs were derived by image matching of **scanned historical images** (panchromatic 8-bit) acquired with a RC10 frame camera, in the context of the swisswide image acquisition campaigns of swisstopo.
Nationwide products could be generated for the **four epochs**
 1979-1985 / 1985-1991 / 1990-1998 / 1998-2006.
Additional information about successful image matching vs. interpolation of pixels and about the year of image acquisition are available in the corresponding files (**matched_area_xxxx_xxxx_binay.tif** and **meta_acquisition_year_xxxx_xxxx.json**, respectively).
To separate vegetation from non-vegetation areas a mask derived from a combination of information of the Topographic landscape model - TLM (swisstopo) and a NDVI layer was used and is supplied as **"TLM_mask.tif"**
Due to the occurrence of unrealistic vegetation height values caused by the failure of the image matching process (mainly overexposed images and clouds or very steep rocky terrain), pixel values > 60 m were set to NoData and < 0 m to 0.
All datasets are available in the Reference System **LV95 (EPSG: 2056) LN02 (EPSG: 5728)** and share the common **extent definition** (xmin ymin xmax ymax): 2480000 1070000 2840000 1300000

dsm

display_name: dsm

forest

display_name: forest

historical

display_name: historical

nfi

display_name: nfi

photogrammetry

display_name: photogrammetry

vhm

display_name: vhm

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Historical Vegetation Height Model NFI


maintainer: EnviDat Support

en: snow-water-equivalent snow-depth snow-density model-calibration-data model-validation-data

en: swe2hs calibration and validation data

en:
 Contents of this archive

 README.md

 A readme file containing a description of the other files.

 automatic_stations_validation_data.csv

 Daily data from the automatic weather stations used for validation.

 Data sources and providers are listed in the SWE2HS paper, Table 1.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * site_id: code for the measurement site
  - HS_interpolated: boolean flag for interpolated values
  - SWE_interpolated: : boolean flag for interpolated values

 coordinates_swe2hs_paper_stations.csv

 Coordinates of all measurement sites used in the SWE2HS paper.

 Columns:

  * site_id: code for the measurement site
  * location_name: location name of the measurement site
  * lon_[wgs84]: longitude in WGS84
  * lat_[wgs84]: latitude in WGS84
  * elevation_[m]: atitude of the measurement site in [m]
  * dataset: dataset in which the station is included

 manual_station_calibration_data.csv

 Calibration data from the Swiss manual observer stations. Long station records
 trmmed to 15 random water-years.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * SWE_from_profile_[m]: biweekly measurement from the SWE in the measurement
   pit. Corrected with the bulk density in order to match the snow depth from
   the measurement stake. Please refer to the paper for further details.
  * site_id: code for the measurement site

 manual_stations_validation_data.csv

 Validation data from the Swiss manual observer stations. Remaining water-years
 from the long sttaion record not used for the calibration data.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * SWE_from_profile_[m]: biweekly measurement from the SWE in the measurement
   pit. Corrected with the bulk density in order to match the snow depth from
   the measurement stake. Please refer to the paper for further details.
  * site_id: code for the measurement site

 manual_stations_used_deltasnow_parameters.csv

 Site specific DeltaSNOW model parameters used for the calculation of the daily
 SWE series in 'manual_stations_calibration_data.csv' and
 'manual_stations_validation_data.csv'.

 Parameters were optimized by reducing RMSE to the biweekly snow pit SWE measurements.
 Please refer to the paper for further details.

 A python implementation of the DeltaSNOW model was used for the calculation:
 pydeltasnow (https://pypi.org/project/pydeltasnow/)

 For a detailed description of the DeltaSNOW model parameters please see the
 respective publication:

 Winkler, M., Schellander, H., & Gruber, S. (2021). Snow water equivalents
 exclusively from snow depths and their temporal changes: the Δ snow model.
 Hydrology and Earth System Sciences, 25(3), 1165-1187.

 Columns:

  * site_id: code for the measurement site
  * rho_max: Maximum density of an individual snow layer produced by the DeltaSNOW model in [kg/m3]
  * rho_null: Fresh snow density for a newly created layer [kg/m3
  * c_ov: Overburden factor due to fresh snow [-]
  * k_ov: Defines the impact of the individual layer density on the compaction due to overburden [-]
  * k: Exponent of the exponential-law compaction [m3/kg]
  * tau: Uncertainty bound [m]
  * eta_null: Effective compactive viscosity of snow for “zero-density” [Pa s]

format: SERVICE

en:
The data in this repository was used for the calibration and validation of the
SWE2HS model in the following publication:
Aschauer, J., Michel, A., Jonas, T., & Marty, C. (2023). An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0. Geoscientific Model Development Discussions, 1-19. https://doi.org/10.5194/gmd-2022-258
Contains daily snow water equivalent and snow depth timeseries from stations in the European Alps.

model-calibration-data

display_name: model-calibration-data

model-validation-data

display_name: model-validation-data

snow-density

display_name: snow-density

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

en: SWE2HS model calibration and validation data


maintainer: Yohann Chauvier

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

media_type: application/zip

en: Non-copyright data, scripts, diversity outputs

format: ZIP

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

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

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

background-data

display_name: background-data

cluster

display_name: cluster

covariate-correction

display_name: covariate-correction

environmental-stratification

display_name: environmental-stratification

independent-dataset

display_name: independent-dataset

plant-species

display_name: plant-species

point-process-model

display_name: point-process-model

random-stratified-sampling

display_name: random-stratified-sampling

survey-effort

display_name: survey-effort

target-group

display_name: target-group

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


root-traits

maintainer: Ivano Brunner

title_for_slug: root-traits

en: fine-root-traits ingrowth-cores belowground-biomass drought soil-coring

en: Root-traits

mimetype: application/zip

en:
Fine-root traits of Scots pine in response to enhanced soil water availability deriving from long-term irrigation in the Pfynwald

Data_Fig.1.xlsx
Fine-root biomass of the topsoil (0-10 cm) in the dry and irrigated treatment of the Scots pine forest of the years 2003 to 2016 recorded by soil coring

Data_Tab1+2_2005.xlsx
Fine-root traits from roots of ingrowth cores from 2005 after two years of growth in the dry and irrigated treatment of the Scots pine forest

Data_Tab1+2_2016.xlsx
Fine-root traits from roots of ingrowth cores from 2016 after two years of growth, and from roots of the soil-coring sampling from 2016 in the dry and irrigated treatment of the Scots pine forest

belowground-biomass

display_name: belowground-biomass

drought

display_name: drought

fine-root-traits

display_name: fine-root-traits

ingrowth-cores

display_name: ingrowth-cores

soil-coring

display_name: soil-coring


maintainer: Vanessa Burg

en: bioenergy switzerland biogas manure review

en: Biogas from animal manure in Switzerland: energy potential, technology development and resource mobilization

media_type: application/pdf

en: Aim of this white paper is to provide decision-makers, administrations and stakeholders with the most current research findings in order to promote the optimal use of bioenergy from manure in the Swiss energy transition. For this purpose, the results of the Swiss competence center for bioenergy research - SCCER BIOSWEET - are summarized and presented in a broader context. If nothing else is mentioned, the results refer to Switzerland and in case of the feedstock to the domestic biomass potentials.

format: PDF

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

switzerland

display_name: switzerland


maintainer: Thorsten Bartels-Rausch

en: snow sea-salt-aerosol antartic laboratory-study polar cryosphere arctic

en: Experimental results of uptake coefficient vs. temperature and ozone concentration

media_type: text/csv

en:
Data to Figure 3: Measured uptake coefficients as a function of temperature at 5E12 molecules per cm3 ozone concentration.

Data to Figure 6: Measured uptake coefficients at different temperatures as a function of gas- phase ozone concentration.

format: CSV

en: Experimental Results: Uptake coefficients vs. temperature and ozone concentration

en:
* Final Data to Figure 3: Uptake coefficients as a function of temperature at 5E12 molecules per cm3 ozone concentration.
* Final Data to Figure 6: Uptake coefficients at different temperatures as a function of gas- phase ozone concentration.

The average ozone uptake coefficients of the three cycles between the bypass and the flow tube per repeat is taken as the mean uptake coefficient for each experiment. Gamma was corrected for gas phase diffusion limitation following the Cooney–Kim– Davis (CKD) method52 (see supporting material of publication for details).

en: The reaction of ozone with bromide in polar regions results in the formation of reactive bromide species with impacts on ozone budget and the oxidative capacity of the lower atmosphere. Here, we present a data investigating the temperature dependence of bromide oxidation by ozone using a coated wall flow tube reactor coated with an aqueous mixture of citric acid and sodium bromide, a proxy for sea salt aerosol in snow or the free troposphere. Thus study shows the effect of of organic species at relatively mild temperatures between the freezing point and eutectic temperature as typical for Earth's cryosphere.

antartic

display_name: antartic

arctic

display_name: arctic

cryosphere

display_name: cryosphere

laboratory-study

display_name: laboratory-study

polar

display_name: polar

sea-salt-aerosol

display_name: sea-salt-aerosol

snow

display_name: snow

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Kinetic experiments on the oxidation of bromide by ozone from 289-245 K.


en: ecology statistical-interpolation biodiversity forest microclimate climate-change radiation-transfer

en: Predicted microclimate topsoil temperatures at 5 cm below ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

en: Predicted microclimate air temperatures at 5 cm above ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

en: Predicted microclimate air temperatures at 100 cm above ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

en: Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate-change impacts on biodiversity and ecosystem functioning. Here we combine a national network of microclimate temperature measurements with a novel radiative transfer model to map monthly minimum, mean and maximum temperatures during the vegetation period at a 10 meter spatial resolution across Switzerland. The temperature measurements took place in 107 sampling plots distributed across different habitat types, with 62 plots in forests, 22 below trees outside forests, and 23 in open grasslands. In each plot we measured temperature in the topsoil (-5cm), as well as in the air at 5cm and 100cm height above ground. Spatial interpolation was achieved by using a hybrid approach based on linear mixed effects models with input from detailed radiation estimates that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography and vegetation height. Our data reveals strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 °C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 °C. We also found that in open grasslands, maximum temperatures at 5 cm above ground are on average 3.4 °C warmer than that of macroclimate, suggesting that in such habitats heat exposure close to the ground is often underestimated when using macroclimatic data. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by RSME’s ranging from 1.18 to 3.43 °C, with minimum temperatures generally being predicted more accurately than maximum temperatures. The microclimate maps presented here enable a more biologically relevant perspective when analysing climate-species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change.

biodiversity

display_name: biodiversity

climate-change

display_name: climate-change

ecology

display_name: ecology

forest

display_name: forest

microclimate

display_name: microclimate

radiation-transfer

display_name: radiation-transfer

statistical-interpolation

display_name: statistical-interpolation

en: Monthly topsoil and near surface microclimate temperature data for Switzerland


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

mimetype: text/plain

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

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

catchment-data

display_name: catchment-data

meteorological-data

display_name: meteorological-data

rain-on-snow

display_name: rain-on-snow

snow-hydrology

display_name: snow-hydrology

snowpack

display_name: snowpack

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


maintainer: Marco Moretti

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

en: Deprecated-Predictors

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

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

en: Protected areas

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

en: Elevation and waterbodies

en: Zip file containing two rasters: the digital elevation model of Switzerland and the waterbodies of Switzerland.

en: Taxonomic and functional metrics in the community plots

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

en: Variable importance, model evaluation, partial dependence plots

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

en: Spatial environmental predictors and responses

en:
The .zip file contains the rasters of the environmental predictors and the predicted responses:
-  **Environmental predictors.**:
  -  The raster on the climate contains the PCA axes on the CHELSA variables.
  -  The raster on resource availability contains the PCA axes on plant communities
  -  The raster on land-use contains the land-use composition at 2500 m.
  -  The raster on beekeeping intensity contains thedensity of hives within 2500 m
-  **Responses**:
- The predicted responses (alpha and beta taxonomic and functional metrics and the traits) without masking the waterbodies, used in the analyses (Diversity_stack_revised_Selected_descriptors.tif). It contains a stack of 20 responses in this order: "belowgound", "cleptoparasite","FDis", "feeding_specialization", "FEve", "FRic", "InvSimpson", "ITD","LCBD_fun" ,"LCBD_taxo","phenoduration", "phenostart", "Rao", "Richness", "Shannon", "Simpson","solitary", "TED", "tong_length", "TOP". "FEve", "FRic", "InvSimpson","Rao", "Simpson" were not included in the linked paper.
- The predicted responses (alpha and beta taxonomic and functional metrics and the traits) masking the waterbodies (masked.stack.all.diversity.tiff). It includes a stack of 15 responses (excludes "FEve", "FRic", "InvSimpson","Rao", "Simpson").

en: Wild bee trait data

en: Text file containing the scores of the 8 used traits. The data originates from the European trait database (compiler: Stuart Roberts; pollinator loss module of the EU- FP6 ALARM project).

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

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

anthophila

display_name: anthophila

beta-diversity

display_name: beta-diversity

climate

display_name: climate

community-indices

display_name: community-indices

elevation-gradient

display_name: elevation-gradient

functional-diversity

display_name: functional-diversity

hymenoptera

display_name: hymenoptera

predictive-modelling

display_name: predictive-modelling

protected-areas

display_name: protected-areas

resource-availability

display_name: resource-availability

switzerland

display_name: switzerland

urbanization

display_name: urbanization

wild-bee

display_name: wild-bee

en: Data on wild bee taxonomic and functional diversity in Switzerland


maintainer: Markus Schlegel

en: monitoring metabarcoding environmental-dna spore-dispersal air-sampling

en: Data files and analysis scripts (Version 1)

en: This ZIP archive contains the data and source code necessary to reproduce the analyses for the manuscript "A case study on the application of spore sampling for the monitoring of fungi". Please refer to README.pdf inside the archive for more information.

en: Data files and analysis scripts (Version 2)

en: This ZIP archive contains the data and source code necessary to reproduce the analyses for the manuscript "A case study on the application of spore sampling for the monitoring of macrofungi". Please refer to README.pdf inside the archive for more information.

en:
Resources associated with the manuscript *A case study on the application of spore sampling for the monitoring of macrofungi* ([Schlegel et al. 2024](https://doi.org/10.1111/1755-0998.13941)).
Airborne environmental DNA (eDNA) monitoring of fungi was evaluated on a species-rich grassland site. Extensive fruiting body surveys yielded 29 waxcap and clavarioid species, 19 of which were also detectable in air eDNA. The analysis indicates that spores of locally occurring fruiting bodies were detected, in addition to a large diversity of common fungal species, while rare and threatened species were under-represented in the air.
*Version 1* of the dataset was submitted during the review phase. *Version 2* contains minor changes and clarifications, and is up to date with the [published supplement](https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2F1755-0998.13941&file=men13941-sup-0001-AppendixS1.pdf) of the study.
The contents of the ZIP archive:
* analysis scripts in [R-Markdown](https://rmarkdown.rstudio.com) format, including the rendered PDF output with figures and tables (*analysis* directory)
* eDNA sample metadata (*amplicon_meta*) and results of the amplicon clustering (*amplicon_data*) and the source code of the pipeline (*amplicon_pipeline*)
* positions of all fruiting body groups found in the surveys (3838 GPS points) (*surveys*)
* positions of the spore traps, taxonomy of studied grassland fungi and red-list species, and many more resources
Please refer to `README.pdf` inside the archive for a complete description of the file structure and information on how to reproduce the analyses.

air-sampling

display_name: air-sampling

environmental-dna

display_name: environmental-dna

metabarcoding

display_name: metabarcoding

monitoring

display_name: monitoring

spore-dispersal

display_name: spore-dispersal

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Supporting information for case study on the application of spore sampling for the monitoring of macrofungi


maintainer: Anita C Risch

en: herbivore-exclusion nutrient-addition microbial-respiration microbial-biomass-carbon anthropogenic-management climate soil-properties

en: Risch et al. GEB_data.xlsx

en:
Study sites and experimental design
We collected data from 23 sites that are part of the Nutrient Network Global Research Cooperative (NutNet, https://nutnet.umn.edu/). The mean annual air temperature (MAT) across these sites ranged from -4 to 22°C, mean annual precipitation (MAP) from 252 to 1592 mm, and elevations from 6 to 4261 m above sea level (Fig 1a, Supplementary Table S1), hence cover a wide range of climatic conditions under which grasslands occur (Fig 1b). Soil organic C concentrations ranged between 0.8 to 7.8%, soil total N concentrations between 0.1 and 0.6%, and the soil C:N ratio between 9.1 and 21.5. Soil clay content spanned from 3.0 to 35%, and soil pH from 3.4 to 7.6 (Supplementary Table S2).
At each site, the effects of nutrient addition and herbivore exclusion were tested via a randomized-block design (Borer et al., 2014). Three blocks with 10 treatment plots each were established at each site, except for the site at bldr.us (only two blocks). Each of these 10 plots was randomly assigned to a nutrient or fencing treatment. An individual plot was 5 x 5 m, divided into four 2.5 x 2.5 m subplots. Each subplot was further divided into four 1 x 1 m square sampling plots, one of which was set aside for soil sampling (Borer et al., 2014). Plots were separated by at least 1 m wide walkways. We collected soil samples from four different treatments for this study: (i) untreated control plots (Control), (ii) herbivore exclusion plots (Fence), (iii) plots fertilized with N, P, K, plus nine essential macro and micronutrients (NPK), and (iv) plots with simultaneous fertilizer addition and herbivore exclusion (NPK+Fence). The experiments were established at different times in the past, with years of treatment different among sites (2 – 9 years since start of treatment; Supplementary Table S1). For the nutrient additions, all sites applied 10 g N m-2 each year as time-release urea; 10 g P m-2 yr-1 as triple-super phosphate; 10 g K m-2 yr-1 as potassium sulfate. A micro-nutrient mix (Fe, S, Mg, Mn, Cu, Zn, B, Mo, Ca) was applied at 100 g m-2 together with K in the first year of treatments but not thereafter.
We excluded large vertebrate herbivores (Fence) by fencing two plots, one with and one without NPK additions, within each block. The fences excluded all aboveground mammalian herbivores with a body mass of over 50 g (Borer et al., 2014). At most sites, the fences were 180 cm high, and the fence contained a wire mesh (1 cm holes) for the bottom 90 cm with a 30 cm outward-facing flange stapled to the ground to exclude burrowing animals. Climbing and subterranean animals may potentially still access these plots (Borer et al., 2014). For slight modifications in fence design at a few sites see Supplementary Table S3. Most sites only had wild herbivores, although four sites were also grazed by domestic animals (Supplementary Table S1).

Collection of soil samples, soil microbial respiration, microbial biomass, and other soil properties
Each of the 23 sites received a package containing identical material from the Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Switzerland to be used for sampling (Risch et al., 2015; Risch et al., 2019). We collected two soil cores of 5 cm diameter and 12 cm depth in each sampling plot and composited them to measure MR, MBC, and soil chemical properties (see below). An additional sample (5 x 12 cm) was collected to assess soil physical properties. This sample remained within a steel sampling core after collection and both ends were tightly closed with plastic caps to avoid disturbance. All soils were shipped cooled to the laboratory at (Location will be disclosed after manuscript acceptance) within a few days after collection. Soils were sampled roughly 6 weeks prior to peak biomass at each site during 2015 and 2016.
To assess MR (CO2 production) in a laboratory incubation experiment we weighed duplicate soil samples (8 g dry soil equivalent) into 50-ml Falcon tubes. No additional substrate (glucose, sugar) was added to these samples. We adjusted the soil moisture of each sample to 60% field capacity. We then placed a 15 ml plastic test tube (Semadeni 1701A) containing 7.25 ml 0.05 M NaOH over each soil sample. The test tube was fixed with a plastic rod so that it was not in contact with the soil sample. The Falcon tubes were then sealed with a screw cap and placed in an incubator under completely dark conditions at 20°C. The CO2 produced by microbial respiration was absorbed by the 0.05 M NaOH. For five weeks we measured the decrease in conductivity within the 0.05 M NaOH solution on a weekly basis with a Multimeter WTW Multi 3410 (WTW GmbH, Germany) and replaced the 0.05 M NaOH with fresh solution. We included Falcon tubes without soil samples in each incubation run as blanks to test if tubes were tight and no CO2 could enter or escape. We calibrated the relationship between conductivity reduction and NaOH absorbed as follows: 400 ml 0.05 M NaOH was placed in a beaker and its conductivity was measured with the multimeter. While stirring, air containing CO2 was blown into the solution for approximately one minute, which reacts with NaOH to form Na2CO3. After this process, conductivity was measured again. We then transferred 7.25 ml of the solution into a smaller beaker and added 1 ml of 0.1 M BaCl2 to precipitate Na2CO3 and then titrated the solution with 0.05 M HCl to determine the remaining NaOH. We then repeated these steps with the remaining solution a total of nine times and plotted the conductivities (y-axis) against the NaOH consumed (x-axis, Supplementary Fig S1). This regression line was used to infer the consumption of NaOH from the conductivity reduction in the incubation experiments and to calculate CO2 evolution during incubation. In addition, we determined the optimum concentration for the NaOH solution in series of preliminary experiments, so that the concentration was not too high to become insensitive, but also not too low so that not all NaOH reacts during incubation. We then calculated MR (mg CO2-C kg dry soil-1 h-1) as total amount of CO2 released over the 5 weeks divided by the duration of the entire incubation in hrs.
Soil microbial biomass carbon (MBC; mg C kg soil-1 ) was measured at the beginning of the experiment by measuring the maximal respiratory response to the addition of glucose solution (4 mg glucose per g soil dry weight dissolved in distilled water; substrate-induced respiration method) on approximately 5.5 g of soil (J. P. E. Anderson & Domsch, 1978; Nico Eisenhauer et al., 2018; Scheu, 1992). For this purpose we used an O2-micro-compensation apparatus (Scheu, 1992). More specifically, substrate-induced respiration was calculated from the respiratory response to D-glucose for 10 hr at 20°C. Glucose was added according to preliminary studies to saturate the catabolic enzymes of microorganisms (4 mg g soil-1 dissolved in 400 ml deionized water). The mean of the lowest three readings within the first 10 hrs (between the initial peak caused by disturbing the soil and the peak caused by microbial growth) was taken as maximum initial respiratory response (MIRR; ml O2 kg soil-1 h-1) and microbial biomass (mg C kg soil-1) was calculated as 38 x MIRR (Beck et al., 1997; Cesarz et al., 2022; Thakur et al., 2015).
The rest of the composited sample was dried at 65°C for 48 h, ground and sieved (2 mm mesh) to assess the soil pH, mineral soil total C and N and C:N ratio, and mineral soil organic C (Risch et al., 2019). The undisturbed sample was used to assess water holding capacity (WHC), bulk density (BD), and soil texture [sand, silt, clay; methods in (Risch et al., 2019)]. We used the percentage of sand and clay as an indicator of soil texture in this study. MAT (°C), MAP (mm) and temperature of the wettest quarter (°C) were obtained from www.worldclim.com (Fick & Hijmans, 2017; Hijmans, Cameron, Parra, Jones, & Jarvis, 2005). These variables were selected as they were found to be drivers of soil nutrient processes across these sites in earlier studies (Risch et al., 2020; Risch et al., 2019). Mean annual soil temperatures (MAST; °C) for the 0 to 5 cm soil layer were obtained for each site from the SoilTemp maps (J. Lembrechts et al., 2021; J. J. Lembrechts et al., 2022), global gridded modelled products of soil bioclimatic variables for the 1979-2013 period at a 1-km² resolution, based on CHELSA, ERA5 and in-situ soil temperature measurements.
Numerical calculations and statistical analyses
We calculated MMQ as MR/MBC. We corrected this measure using the average soil temperature of each site (MMQsoil). This temperature correction is necessary as incubation temperatures are usually much higher than site mean annual soil temperatures (see Xu et al. 2017). MMQsoil = MMQ x Q10(MAST – 20)/10, where Q10 was assumed to be 2 (Xu et al. 2017). See Supplementary Fig S2 for comparison of air and soil temperatures across the 23 sites as well as the incubation temperature.
Some of the explanatory variables (clay, soil organic C, C:N ratio) were skewed and were thus log-transformed prior to analyses. All continuous explanatory variables were centred and scaled to have a mean of zero and variance of one. To avoid collinearity between them we filtered them using correlation analysis (Supplementary Fig S3). From the variables that were strongly correlated (Pearson’s |r| > 0.70) (Dormann et al., 2013), we selected the ones that allowed us to minimize the number of variables (Supplementary Fig S3). Specifically, soil total N concentration, soil total C concentration, soil sand content and soil bulk density were dropped from the dataset. We then assessed how these edaphoclimatic variables are related to MMQ across our global grasslands.
For this, we used linear mixed effects models (LMMs) fitted by maximum likelihood with the lme function in the nlme package (version 3.1-153) (Pinheiro, Bates, DebRoy, & Sarkar, 2021) in R version 3.6.3. (R Core Team, 2019). We used treatment as a fixed effect and plot nested in site as random effects to assess treatment differences in MMQsoil, as well as MR, and MBC. The number of years since the treatment started was included as a fixed effect in all the initial models but was not significant and therefore not retained in the models. To assess how differences in MMQsoil were affected by environmental factors (soil, climatic properties) we again used LMMs. Soil and climatic properties were included as fixed effects and plot nested in site as random effects. We did not include interactions between environmental variables. We then used the MuMin package (Barton, 2018) (version 1.42.1) to select the best models that explained the most variation based on Akaike’s information criterion (AIC; model.avg function). We used the corrected AIC (AICc) to account for our small sample size and selected the top models that fell within 4 AICc units (delta AICc < 4) (Burnham & Anderson, 2002; Johnson & Omland, 2004). We present all our top models rather than model averages. Conditional averages are provided in the Supplementary material.
Based on findings from analyses described above and the literature, we developed a conceptual model of direct and indirect relationships between both edaphoclimatic variables and experimental treatments (Supplement Figure S4) to obtain a more holistic approach in understanding how these properties affect MMQsoil. We had data from 23 sites with 272 observations. We tested this model using structural equation modelling based on a d-sep approach (Lefcheck, 2016; Shipley, 2009). We considered those environmental drivers that were included in our top LMMs, namely temperature of the wettest quarter (T.q.wet), soil pH, water holding capacity (WHC) and soil organic C (organic C; Supplementary Figure S4). These factors were allowed to directly affect MMQsoil, and via their interactions with treatments. In addition, treatments were allowed to directly affect MMQsoil. Treatments were included as dummy variables in the model. We tested our conceptual model (Supplementary Fig S4) using the piecewiseSEM package (version 2.0.2; Lefcheck, 2016) in R 3.4.0, in which a structured set of linear models are fitted individually. This approach allowed us to account for the nested experimental design, and overcome some of the limitations of standard structural equation models, such as small sample sizes (Lefcheck, 2016; Shipley, 2009). We used the lme function of the nlme package to model response variables, including site as a random factor. Good fit of the SEM was assumed when Fisher’s C values were non-significant (p > 0.05). For all significant interactions between soil or climate variables and treatments detected in the SEMs, we calculated treatment effect sizes, i.e., the differences in MMQsoil between Control and treatments as log response ratios (LRR) and plotted these values against the climate or soil factors. The LRR were defined as log(Control/Treatment), where treatment was either Fence, NPK or NPK+Fence. To assess which of the LRR-climate or soil property relationships were significant we again used LMMs, in which soil and climatic properties were included as fixed effects and plot nested in site as random effects.

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

Paper Citation:

Risch Anita C., Zimmermann, Stefan, Schütz, Martin, Borer, Elizabeth T., Broadbent, Arthur A.D., Caldeira, Maria C., Davies, Kendi F., Eisenhauer, Nico, Eskelinen, Anu, Fay, Philip A., Hagedorn, Frank, Knops, Johannes M.H., Lembrechts, Jonas, J., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Seabloom, Eric W., Silveira, Maria L., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Drivers of the microbial metabolic quotient across global grasslands. Global Ecology and Biogeography

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

The microbial metabolic quotient (MMQ; mg CO2-C mg MBC-1 h-1), defined as the amount of microbial CO2 respired (MR; mg CO2-C kg soil-1 h-1) per unit of microbial biomass C (MBC; mg C kg soil-1), is a key parameter for understanding the microbial regulation of the carbon (C) cycle, including soil C sequestration. Here, we experimentally tested hypotheses about the individual and interactive effects of multiple nutrient addition (NPK+micronutrients) and herbivore exclusion on MR, MBC, and MMQ across 23 sites (5 continents). Our sites encompassed a wide range of edaphoclimatic conditions, thus we assessed which edaphoclimatic variables affected MMQ the most and how they interacted with our treatments. Soils were collected in plots with established experimental treatments. MR was assessed in a five-week laboratory incubation without glucose addition, MBC via substrate-induced respiration. MMQ was calculated as MR/MBC and corrected for soil temperatures (MMQsoil). Using LMMs and SEMs, we analysed how edaphoclimatic characteristics and treatments interactively affected MMQsoil. MMQsoil was higher in locations with higher mean annual temperature, lower water holding capacity, and soil organic C concentration, but did not respond to our treatments across sites as neither MR nor MBC changed. We attributed this relative homeostasis to our treatments to the modulating influence of edaphoclimatic variables. For example, herbivore exclusion, regardless of fertilization, led to greater MMQsoil only at sites with lower soil organic C (<1.7%). Our results pinpoint the main variables related to MMQsoil across grasslands and emphasize the importance of the local edaphoclimatic conditions in controlling the response of the C cycle to anthropogenic stressors. By testing hypotheses about MMQsoil across global edaphoclimatic gradients, this work also helps to align the conflicting results of prior studies.

anthropogenic-management

display_name: anthropogenic-management

herbivore-exclusion

display_name: herbivore-exclusion

microbial-biomass-carbon

display_name: microbial-biomass-carbon

microbial-respiration

display_name: microbial-respiration

nutrient-addition

display_name: nutrient-addition

soil-properties

display_name: soil-properties


maintainer: Benjamin Reuter

en: avalanche-danger avalanche snowpack snow-instability avalanche-activity snow-cover-modeling weissfluhjoch avalanche-problem-type snow crocus

en: 1999-2017 Avalanche problem types from SNOWPACK

en:
Avalanche problem types were derived from snow cover simulations with the model SNOWPACK at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are shown in Figure 8 in Reuter et al. (2021).

en: 2015-2016 Seismic avalanche acitivity catalogue

en:
Avalanches were indentified in seismic data recorded by two seismic arrays, one deployed at Wannengrat (2,500 m) and another one deployed in the Dischma valley (2,000 m).
The data cover the period from 01.01.2016 - 30.04.2016 and are presented in Figure 7 in Reuter et al. (2021).

en: 2015-2016 Avalanche problem types from Crocus

en:
Avalanche problem types were derived from a snow cover simulation with the model Crocus for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

en: 1999-2017 Avalanche problem types from Crocus

en:
Avalanche problem types were derived from snow cover simulations with the model Crocus at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are presented in Figure 8 in Reuter et al. (2021).

en: 2015-2016 Avalanche problem types from SNOWPACK

en:
Avalanche problem types were derived from a snow cover simulation with the model SNOWAPCK for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

en:
Avalanche problem types were derived from snow cover simulations with the models Crocus and SNOWPACK at the Weissfluhjoch study plot, Davos, CH. The data include annual frequencies of avalanche problem types for the seasons 1999-2017 and daily presence of avalanche problem types for the period 01.01.2016 - 30.04.2016.
Avalanche activity was derived from two seismic sensor arrays deployed no further than 15 km from Weissfluhjoch, Davos, CH. The data cover the period 01.01.2016 - 30.04.2016.

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

avalanche-danger

display_name: avalanche-danger

avalanche-problem-type

display_name: avalanche-problem-type

crocus

display_name: crocus

snow

display_name: snow

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability

snowpack

display_name: snowpack

weissfluhjoch

display_name: weissfluhjoch

en: Simulated avalanche problem types and seismic avalanche activity around Weissfluhjoch


maintainer: Marcia Phillips

en: permos meteo-station borehole permafrost ritigraben

en: DATA ACCESS

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

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: RIT2: Meteorological station at Ritigraben borehole site


en: moss functional-taxonomic-group biodiversity lichen-growth-form disturbance bryophyte-life-strategy madeira-island photobiont lichen-growthform climate-change-impact elevational-gradient bryophyte endemic climate-change lichen functional-taxonomic-groups liverwort endemic-species

en: MadCrypto – Bryophyte and macrolichen diversity in laurel forests of Madeira

en:
This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species).

The dataset was used for the paper
Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science.

format: XLS

en:
This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species).

The dataset was used for the paper
Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science 30: 1122–1133.

The excel file contains 5 sheets:
1) Plot information
2) Bryophyte data with species specific information, separated per substrate
3) Macrolichen data with species specific information, separated per substrate
4) Bryophyte data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 2 and 4 might therefore differ slightly.
5) Macrolichen data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 3 and 5 might therefore differ slightly.

biodiversity

display_name: biodiversity

bryophyte

display_name: bryophyte

bryophyte-life-strategy

display_name: bryophyte-life-strategy

climate-change

display_name: climate-change

climate-change-impact

display_name: climate-change-impact

disturbance

display_name: disturbance

elevational-gradient

display_name: elevational-gradient

endemic

display_name: endemic

endemic-species

display_name: endemic-species

functional-taxonomic-group

display_name: functional-taxonomic-group

functional-taxonomic-groups

display_name: functional-taxonomic-groups

lichen

display_name: lichen

lichen-growth-form

display_name: lichen-growth-form

lichen-growthform

display_name: lichen-growthform

liverwort

display_name: liverwort

madeira-island

display_name: madeira-island

moss

display_name: moss

photobiont

display_name: photobiont


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

en: Site photo of the field station (NOF)

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

mimetype: image/jpeg

en: Site aerial photo (Novaggio)

en: Site aerial photo from both Novaggio stations. Source: Google. 2016. Site aerial photo from both Novaggio stations. Retrieved November 15, 2016, from https://www.google.ch/maps

en: Data plot of the forest station (NOB)

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

mimetype: application/pdf

en: Field station data (NOF)

en: Data file.

en: Forest station data (NOB)

en: Data plot of the field station (NOF)

en: Site photo of the forest station (NOB)

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

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 Novaggio in Switzerland where one station is located within a natural deciduous forest stand (NOB) with Turkey oak (_Quercus cerris_; 70 yrs), sweet chestnut (_Castanea sativa_; 90 yrs) and silver birch (_Betula pendula_; 70 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Novaggio 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

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


maintainer: Joan Casanelles Abella

en: floral-inventories plants zurich urban antwerp floral-resources poznan angiospermae paris tartu

en: List of plant species sampled in the five cities (Antwerp, Paris, Poznan, Tartu and Zurich) during the different sampling perionds (four for Zurich, three for the other cities) with their frequency (oc1-oc16) within a 100 m radius

en: Floral units, mean number of flowers per floral unit and floral abundance of the different plants counted in quadrats in the study sites in Zurich during the four sampling periods

en: Trait values for the sampled plants

en: Data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland) sampled between April and July 2018.

angiospermae

display_name: angiospermae

antwerp

display_name: antwerp

floral-inventories

display_name: floral-inventories

floral-resources

display_name: floral-resources

paris

display_name: paris

plants

display_name: plants

poznan

display_name: poznan

tartu

display_name: tartu

urban

display_name: urban

zurich

display_name: zurich

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Flowering Plants (Angiospermae) in Urban Green Areas in five European Cities


intratrait

maintainer: Christian Rixen

title_for_slug: intratrait

en: climate-change plant-traits elevation-gradient cold-adapted-plants elevation-range neighbour-interactions

en: Plant trait data along elevation gradients on mountains

en:
This data set was used to test whether species specialized to high elevations or with narrow elevational ranges show more conservative (i.e. less variable) trait responses across their elevational distribution, or in response to neighbours, than species from lower elevations or with wider elevational ranges. We did so by studying intraspecific trait variation of 66 species along 40 elevational gradients in four countries (Switzerland, Australia, New Zealand, China) in both hemispheres. As an indication of potential neighbour interactions that could drive trait variation, we also analysed plant species’ height ratio, its height relative to its nearest neighbour.
The following traits and parameters were measured and are available in this data set:
As an indication of plant stature, we measured vegetative and generative height, where vegetative height was distance from soil to highest vegetative leaf and generative height was distance to the highest point on the reproductive shoot. As a measure of reproductive investment, we noted the presence of flowers on the randomly chosen individuals (see below). As a measure of individual and genet basal area, we measured individual plant and patch diameters, in two dimensions (along the largest diameter and perpendicular to it). In clonal plant species, plant diameter was equivalent to an individual rosette, whereas patch diameter referred to the whole genet and could represent the size of a tuft, tussock or cushion. For genera with more singular growth forms (e.g., some Gentiana species) plant and patch diameter were the same. The two diameter measurements were made at right angles, allowing estimates of patch and plant areas to be calculated as an ellipse (i.e., area = 0.5 a 0.5 b Π). All traits were measured on ten randomly selected individuals per site. Flower count data was considered in a binary fashion on a per individual basis (because for some species individuals only produce one flower when flowering) so that the presence or absence of flower(s) was a nominal value between 0 and 10 for each species at each site. We then collected at least three leaves (up to 30 for small and light leaves) from each of the first three individuals selected from each species for determination of leaf dry matter content (LDMC) and specific leaf area (SLA). For calculations of LDMC and SLA, fresh leaves were scanned on a flatbed scanner to determine leaf area. Leaves were then weighed on a balance to a precision of +/- 0.001g, prior to being air dried and reweighed with a balance to a precision of +/- 0.0001g. LDMC was calculated by dividing dry leaf mass by fresh leaf mass. SLA was calculated by dividing leaf area by dry leaf mass. Additionally, within an area of 10 cm diameter around the target individual, we determined the tallest neighbouring species and measured its vegetative and generative height, and estimated the percent cover of the target species, other vegetation, rock, and bare soil.
For more details see Rixen et al. 2022, Journal of Ecology.

climate-change

display_name: climate-change

cold-adapted-plants

display_name: cold-adapted-plants

elevation-gradient

display_name: elevation-gradient

elevation-range

display_name: elevation-range

neighbour-interactions

display_name: neighbour-interactions

plant-traits

display_name: plant-traits

en: intratrait


weissfluhjoch-dataset-for-esm-snowmip

maintainer: Nander Wever

title_for_slug: weissfluhjoch-dataset-for-esm-snowmip

en: snow-models snow-depth weissfluhjoch esm-snowmip snow

en: Dataset Weissfluhjoch for ESM-SnowMIP

en: Conversion script

en:
Use this script to convert WFJ_optimaldataset_v6.smet to wfj.smet, the file that is used for the ESM-SnowMIP project. It also requires the file: WFJ_AirPressure_Data.zip

Use: bash create_wfj_dataset.sh > input/wfj.smet

mimetype: text/x-sh

en: ini file

en: This ini file is required by data-converter from MeteoIO version 1887 (http://models.slf.ch/p/meteoio/)

en: This Weissfluhjoch dataset is a processed version of the Weissfluhjoch dataset version 6 from https://doi.org/10.16904/6. This dataset was specially created for the ESM-SnowMIP project. Here it is documented how this dataset has been created.

esm-snowmip

display_name: esm-snowmip

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-models

display_name: snow-models

weissfluhjoch

display_name: weissfluhjoch

en: Weissfluhjoch dataset for ESM-SnowMIP


maintainer: Dirk Nikolaus Karger

en: reptiles iucn niche-models sdm climate-variablity amphibians generalized-linear-models generalized-additive-models species-distribution-models interannual-climate-variability random-forests mammals range-maps chelsa

en: Amphibians

en: Species distribution models based on different climate predictor groups

en: File containing the record dimension, genus, and species of the respective netcdf files

en: File containing the climate predictors as SAGA grid files (.sgrd)

en: Reptiles

en: Readme file containing the file naming convention, file dimensions, and variable names

en: Mammals

en: We modelled the global distribution of 730 amphibian, 1276 reptile, and 1961 mammal species globally as a function of current climate at a 0.5° spatial resolution using four different predictor groups composed of different combinations of input variables: mean climatic conditions, spatial climatic variability, and temporal (interannual) climatic variability.

amphibians

display_name: amphibians

chelsa

display_name: chelsa

climate-variablity

display_name: climate-variablity

generalized-additive-models

display_name: generalized-additive-models

generalized-linear-models

display_name: generalized-linear-models

interannual-climate-variability

display_name: interannual-climate-variability

iucn

display_name: iucn

mammals

display_name: mammals

niche-models

display_name: niche-models

random-forests

display_name: random-forests

range-maps

display_name: range-maps

reptiles

display_name: reptiles

sdm

display_name: sdm

species-distribution-models

display_name: species-distribution-models

en: Global species distributions for mammals, reptiles, and amphibians


en: national-dishes food-taste-similarity agro-food-trade latent-semantic-analysis food-preferences

en: National dishes, migration data and data for bilateral food trade flows

en:
The data include information on national dishes, migration data and data for bilateral food
trade flows for 5 years as well as importer and exporter GDP, population, distance between
most populated cities, bilateral information on Preferential Trade Agreements, common
official primary langauge, common colonizer post-1945, colonial relationship for overall
171 countries. The final data set contains 145,350 observations (29,070 country pairs for
5 years)

en: dyadic food similarity measures, pairwise similarity and dissimilarity (chord diagram)

en:
The figures show the distribution of two dyadic food tastes similarity measures on the base of
the national dishes: (i) the Manhattan distance and the (ii) latent semantic analysis. The chord diagram
shows the results for country pairs with most and less similarity on basis of the LSA-approach.

en: information on ingredients of national dishes

en: shows information of ingredients of all national dishes of 171 countries and their names

en: The data described in this article were collected daily over the period 4 June 2018 to 23 August 2018 and contains information of several data sources. The database includes information on national recipes and their ingredients for 171 countries, measures for food taste similarities between all 171 countries as well as bilateral migration and agro-food trade data for 5 years. The database can be used for analyzing e.g., the relation between food preferences and international trade or food preferences and health outcomes (e.g., obesity) across countries.

agro-food-trade

display_name: agro-food-trade

food-preferences

display_name: food-preferences

food-taste-similarity

display_name: food-taste-similarity

latent-semantic-analysis

display_name: latent-semantic-analysis

national-dishes

display_name: national-dishes

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Data of national dishes, their similarity and trade flows


amtliche-vermessung-darstellung-kontur

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

title_for_slug: amtliche-vermessung-darstellung-kontur

de: av katasterplan

label: geocat.ch permalink

de: WMS Amtliche_Vermessung_Kontur

de: Web Map Services WMS

format: WMS

de: WMTS Amtliche_Vermessung_Kontur

de: Web Map Tile Services WMTS

format: WMTS

de: WFS Amtliche_Vermessung_Kontur

de: Web Feature Services WFS

format: WFS

de: Daten der amtlichen Vermessung in schwarz und ohne Flächenfüllungen dargestellt. Kann zur Überlagerung mit flächengefüllten Informationen nützlich sein.

spatial: Stadt Bern

geoinformation-der-stadt-bern

fr: L’Office du cadastre de la ville de Berne

de: Geoinformation Stadt Bern

en: The Land Surveying Office of the City of Bern

it: L'ufficio del catasto nella Città di Berna

fr: L’Office du cadastre (Geoinformation Stadt Bern) est l’organisme compétent pour la collecte, l’accès et la diffusion d’informations géographiques dans la ville de Berne.

de: Geoinformation Stadt Bern (ehemals Vermessungsamt) ist die Kompetenzstelle für die Beschaffung, den Zugang und die Verbreitung von Geoinformationen in der Stadt Bern.

en: The Surveying Office (Geoinformation Stadt Bern) is the centre of competence in the City of Bern for the procurement of, access to and dissemination of geographic information.

it: L'ufficio del catasto (Geoinformation Stadt Bern) è il centro di competenza per il reperimento, l'accesso e la diffusione di geoinformazioni nella Città di Berna.

political_level: commune

value: Geoinformation Stadt Bern - Geocat Harvester

de: Amtliche Vermessung (Darstellung Kontur)


bahnhofe

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: bahnhofe

de: bahnhof bus poi tram ov offentlicher-verkehr

fr: Map (Preview)

de: Map (Preview) Stadtplan

en: Map (Preview)

it: Map (Preview)

de: Link zum Thema im Internet-Stadtplan der Stadt Bern

de: WMS Bahnhoefe

de: WFS Bahnhoefe

de: Standorte der Bahnhöfe in der Stadt Bern und Agglomeration.

spatial: Agglomeration Bern

de: Bahnhöfe


ov-haltestellen

regi

de: Regionen und Städte

title_for_slug: ov-haltestellen

de: bus poi tram ov offentlicher-verkehr

de: WMS Haltestellen

de: Web Map Services WMS

de: WFS Haltestellen

de: Standorte Tram- und Bushaltestellen sowie Drahtseilbahnstationen in der Stadt Bern und Agglomeration.

de: ÖV-Haltestellen


amtliche-vermessung-darstellung-farbig

envi

de: Umwelt

title_for_slug: amtliche-vermessung-darstellung-farbig

de: av katasterplan kreis

de: WMS Amtliche_Vermessung_farbig

de: WMTS Amtliche_Vermessung_farbig

de: Web Map Tile Services WMTS

de: WFS Amtliche_Vermessung_farbig

de: Web Feature Services WFS

de: Plan für das Grundbuch der Stadt Bern - farbig.

geoinformation-der-stadt-bern

de: Geoinformation Stadt Bern

de: Geoinformation Stadt Bern (ehemals Vermessungsamt) ist die Kompetenzstelle für die Beschaffung, den Zugang und die Verbreitung von Geoinformationen in der Stadt Bern.

de: Amtliche Vermessung (Darstellung farbig)


agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

regi

de: Regionen und Städte

de: baum bauminventar baumkataster stadtbaum

de: Map (Preview) Stadtplan

de: Link zum Thema im Internet-Stadtplan der Stadt Bern

de: WMS Baumkataster

de: WFS Baumkataster

de: Durch Stadtgrün bewirtschaftete Bäume, Symbole für Laubbaum, Nadelbaum und Obstbaum, mit den Informationen über Gattung, Art, deutscher Name, Pflanzjahr und Standort.

de: Baumkataster


envi

de: Umwelt

de: av katasterplan

de: WMS Amtliche_Vermessung_fuer_Orthofoto

de: Web Map Services WMS

de: WMTS Amtliche_Vermessung_fuer_Orthofoto

de: WFS Amtliche_Vermessung_fuer_Orthofoto

de: Daten der amtlichen Vermessung in weiss/gelb und ohne Flächenfüllungen dargestellt. Kann zur Überlagerung mit Orthofotos nützlich sein.

de: Amtliche Vermessung (Darstellung für Orthofoto)


de: av katasterplan kreis

de: WMS Amtliche_Vermessung_schwarzweiss

de: WMTS Amtliche_Vermessung_schwarzweiss

de: Web Map Tile Services WMTS

de: WFS Amtliche_Vermessung_schwarzweiss

de: Web Feature Services WFS

de: Plan für das Grundbuch der Stadt Bern in Graustufen mit blau eingefärbten Gewässern.

geoinformation-der-stadt-bern

de: Geoinformation Stadt Bern

de: Geoinformation Stadt Bern (ehemals Vermessungsamt) ist die Kompetenzstelle für die Beschaffung, den Zugang und die Verbreitung von Geoinformationen in der Stadt Bern.

de: Amtliche Vermessung (Darstellung schwarz/weiss)


statische-waldgrenzen

agri

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

regi

de: Regionen und Städte

envi

de: Umwelt

title_for_slug: statische-waldgrenzen

fr: limite-forestiere bois

de: waldgrenze wald

en: forest-line wood

it: limite-del-bosco bosco

label: Öffentliches Auskunftssystem des Kantons Basel-Stadt

de: Geodaten-Shop

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

de: WMS BS

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

de: WFS BS

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

de: Grenzt Wald an Bauzonen, so wird der dynamische Waldbegriff aus ortsplanerischen Gründen zugunsten des statischen Waldbegriffs aufgegeben. Dabei wird im Rahmen eines Waldfeststellungsverfahrens eine Waldgrenzenkarte erstellt, welche die Grenze zwischen Wald und Bauzonen auf unbestimmte Zeit rechtlich fixiert.

spatial: Basel-Stadt und Umgebung

kanton-basel-stadt

fr: Canton de Bâle-Ville

de: Kanton Basel-Stadt

en: Canton of Basel-City

it: Cantone di Basilea-Città

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

political_level: canton

value: BS - Geocat Harvester

de: Statische Waldgrenzen


pflanzenschutzmittelverzeichnis

title_for_slug: Pflanzenschutzmittelverzeichnis

fr: plantes produits-phytosanitaires

de: pflanzen pflanzenschutzmittel

it: prodotti-fitosanitari piante

fr: Produits phytosanitaires

de: Pflanzenschutzmittelverzeichnis

it: Prodotti fitosanitari

fr: Les produits phytosanitaires autorisés figurent dans un index des produits phytosanitaires. Cet index comprend des indications sur l’utilisation prévue, les restrictions d’application, le dosage, les indications relatives aux dangers et les charges liées à l’utilisation.

de: Die bewilligten Pflanzenschutzmittel werden im Pflanzenschutzmittelverzeichnis aufgelistet. Das Verzeichnis enthält Angaben zur vorgesehenen Anwendung, zu Anwendungseinschränkungen, Aufwandmengen, Gefahrenkennzeichnung und zu Anwendungsauflagen.

it: I prodotti fitosanitari autorizzati figurano in un elenco dei prodotti fitosanitari contenente indicazioni sull'applicazione prevista, restrizioni e dosi di applicazione, simboli di pericolo e condizioni d'uso.

format: XML

pflanzen

display_name: pflanzen

pflanzenschutzmittel

display_name: pflanzenschutzmittel

piante

display_name: piante

plantes

display_name: plantes

prodotti-fitosanitari

display_name: prodotti-fitosanitari

produits-phytosanitaires

display_name: produits-phytosanitaires

en: Index of phytosanitary products

fr: Office fédéral de la sécurité alimentaire et des affaires vétérinaires OSAV

de: Bundesamt für Lebensmittelsicherheit und Veterinärwesen BLV

en: Federal Food Safety and Veterinary Office FSVO

it: Ufficio federale della sicurezza alimentare e di veterinaria USAV

fr: L’OSAV a pour principale mission de promouvoir activement la santé et le bien-être de l’homme et de l’animal. Son action repose essentiellement sur la sécurité des aliments et une alimentation saine, pour l’homme, et sur la protection et la santé des animaux.

en: The main task of the FSVO is to actively promote the health and well-being of humans and animals. The central pillars of this approach are food safety and healthy eating for humans, and animal health and welfare for animals.

de: Hauptaufgabe des BLV ist es, die Gesundheit und das Wohlbefinden von Mensch und Tier aktiv zu fördern. Die Hauptpfeiler dafür sind beim Menschen Lebensmittelsicherheit und gesunde Ernährung und beim Tier Tierschutz und Tiergesundheit.

it: Il compito principale dell’USAV consiste nel promuovere attivamente la salute e il benessere dell’essere umano e degli animali. Per l’uomo il focus è posto soprattutto sulla sicurezza alimentare e su un’alimentazione sana, mentre per gli animali sulla loro protezione e salute.

political_level: confederation


maintainer: Technischer Ansprechpartner (bitte im Vermerk angeben, dass es sich um die Schnittstelle mep401) handelt)

title_for_slug: Bei der Swissmedic registrierte Schweizer und Liechtensteiner Wirtschaftsakteure (Medizinprodukte)

name: Fachliche Ansprechpartner  (bitte im Vermerk angeben, dass es sich um die Schnittstelle mep401) handelt)

author: Schnittstellen-Team "Internet"

label: swissdamed: Suche nach Akteuren

fr: Swissdamed: Recherche d'acteurs

de: swissdamed: Suche nach Akteuren

en: swissdamed: Search for actors

it: swissdamed: Ricerca operatori

fr: Service swissdamed

en: Service swissdamed

de: Service swissdamed

it: Service swissdamed

fr: Base de données des opérateurs économiques enregistrés (ayant leur siège en Suisse ou dans la Principauté de Liechtenstein) conformément à l'art. 55, al. 1 ODim/art. 48, al. 1 OICM: liste de tous les fabricants, importateurs et mandataires suisses enregistrés, y compris les fabricants étrangers qui leur sont liés.

en: Database of registered economic operators (based in Switzerland or the Principality of Liechtenstein) in accordance with Art. 55 para. 1 MepV/Art. 48 para. 1 IvDV: Listing of all registered Swiss manufacturers, importers and authorised representatives, including their associated foreign manufacturers.

de: Datenbank der registrierten Wirtschaftsakteure (mit Sitz in der Schweiz oder im Fürstentum Liechtenstein) gemäss Art. 55 Abs. 1 MepV/ Art. 48 Abs. 1 IvDV: Auflistung aller registrierten Schweizer Hersteller, Importeure und Bevollmächtigten inkl. der mit ihnen verbundenen ausländischen Herstellern.

it: Banca dati degli operatori economici registrati (con sede in Svizzera o nel Principato del Liechtenstein) ai sensi dell'art. 55 cpv. 1 MedDO/art. 48 cpv. 1 IvDO: elenco di tutti i fabbricanti, importatori e mandatari svizzeri registrati, compresi i fabbricanti esteri associati.

fr: Opérateurs économiques enregistrés chez Swissmedic (Dispositifs médicaux)

en: Swiss and Liechtenstein economic operators registered at Swissmedic (Medical devices)

it: Operatori economici svizzeri e del liechtenstein registrati presso Swissmedic (Dispositivi medici)

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

it: Istruzione, cultura e sport

heal

fr: Santé

de: Gesundheit

en: Health

it: Salute

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

swissmedic

fr: Swissmedic Institut suisse des produits thérapeutiques

en: Swissmedic Swiss Agency for Therapeutic Products

it: Swissmedic Istituto svizzero per gli agenti terapeutici

fr:
Swissmedic est l’autorité d’autorisation et de contrôle des produits thérapeutiques en Suisse. Les tâches de Swissmedic reposent sur le droit des produits thérapeutiques.

Contrairement aux médicaments, les dispositifs médicaux ne sont soumis à aucune autorisation officielle. Dans le secteur des dispositifs médicaux, Swissmedic a donc pour principale mission d’assurer une surveillance efficace du marché.

en:
Swissmedic is the Swiss authority responsible for the authorisation and supervision of therapeutic products. Swissmedic's activities are based on the Law on Therapeutic Products.

Unlike medicinal products, medical devices do not undergo an official authorisation procedure. Swissmedic's focus in the area of medical devices is thus on effective market surveillance.

de:
Arzneimittel dürfen in der Schweiz nur vertrieben werden, wenn sie vom Schweizerischen Heilmittelinstitut zugelassen sind. Swissmedic ist durch ihre Aufgaben in den Bereichen Bewilligung, Zulassung und Überwachung von Arzneimitteln in den gesamten Lebenszyklus eines Arzneimittels eingebunden.

Anders als Arzneimittel durchlaufen Medizinprodukte keine behördliche Zulassung. Der Schwerpunkt von Swissmedic im Bereich Medizinprodukte liegt somit in einer effizienten Marktüberwachung.

it:
Swissmedic è l’autorità centrale svizzera di omologazione e controllo per gli agenti terapeutici. L’attività di Swissmedic si basa sul diritto degli agenti terapeutici.

Diversamente dai medicamenti, i dispositivi medici non sono sottoposti ad alcuna omologazione ufficiale. L’attività di Swissmedic nel campo dei dispositivi medici si concentra quindi sulla sorveglianza efficiente del mercato.


maintainer: Office fédéral de la santé publique - section gestion de données et statistique DMS

title_for_slug: Medizinische Qualitätsindikatoren im Bereich der Pflegeheime - Daten 2022

fr: qualite indicateurs indicateurs-de-qualite ems

de: indikatoren pflegeheime qualitatsindikatoren qualitat

en: nursing-home medical-quality-indicators indicators quality

it: indicatori casa-di-cura qualita case-di-cura medici

label: Loi fédérale sur l’assurance-maladie (LAMal)

label: Ordonnance sur l’assurance-maladie (OAMal)

label: Loi sur la statistique fédérale (LSF)

de: Publikation «Medizinische Qualitätsindikatoren 2022»

en: Publication «Medical Quality Indicators 2022»

it: Pubblicazione «Indicatori medici di qualità 2022»

fr: Publication « Indicateurs de qualité médicaux 2022»

fr: Données sur les indicateurs de qualité médicaux 2022

de: Daten zu den medizinischen Qualitätsindikatoren 2022

en: Data Medical quality indicators 2022

it: Dati degli indicatori medici di qualità 2022

de: Beschreibung der medizinischen Qualitätsindikatoren im Bereich der Pflegeheimen 2022

fr: Description des indicateurs de qualtié médicaux des EMS 2022

it: Descrizione degli indicatori medici di qualità 2022

de: Berechnung der risikoadjustierten medizinischen Qualitätsindikatoren

fr: Calcul des indicateurs de qualité médicaux ajustés en fonction des risques

it: Calcolo degli indicatori medici di qualità adeguati ai rischi

fr:
La troisième édition de la publication « Indicateurs de qualité médicaux » dans le domaine des établissements médico-sociaux (EMS), sortie au deuxième trimestre 2024, porte sur les données 2022 livrées par les EMS à l’Office fédéral de la santé publique (OFSP) par l’intermédiaire de l’Office fédéral de la statistique (OFS). 1279 établissements y sont représentés.
La publication « Indicateurs de qualité médicaux » est fondée sur l’article 59a de la loi fédérale sur l’assurance-maladie (LAMal SR 832.10) selon lequel les fournisseurs de prestations doivent communiquer aux autorités fédérales compétentes les données nécessaires à la surveillance de l’application des dispositions de la LAMal relatives au caractère économique et à la qualité des prestations. Les EMS, en Suisse, sont ainsi tenus de fournir les données permettant le calcul des indicateurs de qualité médicaux (art. 59a, al.1, let. f, LAMal). En application de l’art. 59a LAMal, dans le cadre d’un projet pilote de 2009 à 2018, sous la direction la fédération ARTISET (anciennement CURAVIVA Suisse) avec, comme membres du comité de projet, l’OFSP, la Conférence suisse des directrices et directeurs cantonaux de la santé (CDS) et l’OFS, des indicateurs de qualité adéquats ont été élaborés et évalués et six ont été sélectionnés. La publication présente les valeurs de ces six indicateurs pour chacun des établissements représentés.
La qualité des données 2022, fournies pour l’édition de septembre 2024, a été suffisamment élevée pour permettre le calcul d’indicateurs au niveau des EMS ajustés en fonction des risques. Dans ce contexte, les indicateurs de qualité médicaux sélectionnés représentent bien la qualité des prestations de soins fournies selon la LAMal dans les EMS et sont comparables d’un établissement à l’autre. Ces indicateurs ne peuvent toutefois pas appréhender entièrement la qualité de l’établissement et s’y réduire. Ils n’illustrent en effet ni la qualité globale de la structure, ni celle des processus mis en oeuvre, ni d’autres aspects de la qualité, comme la satisfaction des patients ou la qualité de vie dans les institutions. C’est pourquoi les indicateurs de qualité médicaux sélectionnés peuvent être utilisés comme outil de comparaison de la qualité des prestations de soins fournies selon la LAMal dans les EMS, dans les domaines mesurés, mais ne peuvent en aucun cas servir à l’établissement d’un classement ou d’une échelle de qualité globale des EMS (ranking). Ils devraient aussi servir à l’évaluation d’éventuelles lacunes de la qualité dans les EMS pour identifier et exploiter les potentiels d’amélioration de l’établissement. En ce sens, la présente publication doit aussi être vue comme une aide et une base de discussion au sein des milieux intéressés.

en:
The third edition of publication «Medical quality indicators» in the area of nursing homes, published in the second quarter of 2024, refers to the data from 2022, which was provided by the nursing homes to the Federal Office of Public Health (FOPH) via the Federal Statistical Office (FSO). It includes 1279 nursing homes.
The publication based on article 59a of the Health Insurance Act (RS 832.10). Based on this article, care providers must provide the competent federal authorities with the data required to monitor the application of the provisions of the Health Insurance Act concerning the economic nature and quality of care services. Nursing homes in Switzerland are thus obliged to provide the data required to calculate medical quality indicators (Art. 59a, para. 1, letter f Health Insurance Act). In application of Art. 59a of the Health Insurance Act, suitable quality indicators were developed, evaluated and six were selected in 2009 - 2018 as part of a pilot project led by the federation ARTISET (formerly CURAVIVA Switzerland) with the FOPH, the Swiss Conference of Cantonal Health Directors (GDK) and the FSO in the project committee. The publication shows the values of these six indicators for each of the nursing homes included.
In this respect, the selected medical quality indicators reflect well the quality of the care services based on the Health Insurance Act provided by the nursing well and are comparable from home to home. However, these indicators cannot fully capture the quality of the home, so they cannot be limited to this. They do not reflect the overall structural or process quality, nor do they reflect other quality aspects such as patient satisfaction or quality of life in the homes. For this reason, the selected medical quality indicators can be used as instruments for comparing the quality of the care services based on the Health Insurance Act provided by the nursing in the areas measured, but can in no way be used to draw up a ranking list or a scale of the overall quality of nursing homes (ranking). They should also make it possible to examine any quality gaps in the homes in order to identify and exploit the potential for improvement in the respective institution. In this sense, this publication should also be seen as an aid and basis for discussion for interested parties.

de:
Die im zweiten Quartal 2024 erschienene dritte Ausgabe der Publikation «Medizinische Qualitätsindikatoren im Bereich der Pflegeheime» bezieht sich auf die Daten aus den Jahren 2022, die von den Pflegeheimen über das Bundesamt für Statistik (BFS) an das Bundesamt für Gesundheit (BAG) geliefert wurden. Darin sind 1279 Pflegeheime enthalten.
Die Publikation «Medizinische Qualitätsindikatoren» basiert auf Artikel 59a des Bundesgesetzes über die Krankenversicherung (KVG, SR 832.10). Dieser Artikel verpflichtet die Leistungserbringer, den zuständigen Bundesbehörden die Daten bekannt zu geben, die benötigt werden, um die Anwendung der KVG-Bestimmungen über die Wirtschaftlichkeit und Qualität der Leistungen zu überwachen. Die Pflegeheime in der Schweiz sind demnach angehalten, Daten zur Berechnung der medizinischen Qualitätsindikatoren zu liefern (Art. 59a Abs. 1 Bst. f KVG). In Anwendung von Artikel 59a KVG wurden 2009–2018 im Rahmen eines Pilotprojekts unter der Leitung der Föderation ARTISET (ehemalige CURAVIVA Schweiz) mit dem BAG, der Schweizerischen Konferenz der kantonalen Gesundheitsdirektorinnen und -direktoren (GDK) und dem BFS im Projektausschuss geeignete Qualitätsindikatoren erarbeitet und ausgewertet sowie sechs davon ausgewählt. In der Publikation werden die Werte dieser sechs Indikatoren für jedes der darin enthaltenen Pflegeheime ausgewiesen.
Die Qualität der Daten 2022, die für die Ausgabe vom September 2024 bereitgestellt wurden, war hoch genug, um die Berechnung von risikoadjustierten Indikatoren auf der Ebene der Pflegeheime zu ermöglichen. Insofern bilden die ausgewählten medizinischen Qualitätsindikatoren die Qualität der in den Pflegeheimen erbrachten KVG-Pflegeleistungen gut ab und sind von Heim zu Heim vergleichbar. Diese Indikatoren können jedoch die Qualität des Heimes nicht vollständig erfassen, so dass man sich nicht darauf beschränken kann. Sie bilden weder die gesamte Struktur- oder Prozessqualität noch andere Qualitätsaspekte wie die Patientenzufriedenheit oder die Lebensqualität in den Heimen ab. Deswegen können die ausgewählten medizinischen Qualitätsindikatoren als Instrumente zum Vergleich der Qualität der in den Pflegeheimen in den gemessenen Bereichen erbrachten KVG-Pflegeleistungen verwendet werden, aber keinesfalls zur Erstellung einer Rangliste oder einer Skala der Gesamtqualität der Pflegeheime (Ranking) dienen. Sie sollten ebenfalls die Prüfung allfälliger Qualitätslücken in den Heimen ermöglichen, um das Verbesserungspotenzial des jeweiligen Betriebs zu erkennen und zu nutzen. In diesem Sinne ist die vorliegende Publikation auch als Hilfestellung und Diskussionsgrundlage für die interessierten Kreise zu verstehen.

it:
La terza edizione della pubblicazione «Indicatori medici di qualità» nell’ambito delle case di cura, pubblicata nel secondo trimestre del 2024 tratta i dati del 2022 forniti dalle case di cura all’Ufficio federale della sanità pubblica (UFSP) tramite l’Ufficio federale di statistica (UST), e comprende 1279 case di cura.
La pubblicazione «Indicatori medici di qualità» si basa sull’articolo 59a della legge federale sull’assicurazione malattie (LAMal SR 832.10), secondo cui i fornitori di prestazioni sono tenuti a comunicare alle competenti autorità federali i dati di cui necessitano per vigilare sull’applicazione delle disposizioni della legge sull’assicurazione malattie relative all’economicità e alla qualità delle prestazioni. Le case di cura svizzere devono pertanto fornire i dati che consentono di calcolare gli indicatori medici di qualità (art. 59a cpv. 1 lett. f LAMal). In applicazione dell’art. 59a LAMal, nel quadro di un progetto pilota che si è svolto dal 2009 al 2018 sotto la direzione della federazione ARTISET (prima CURAVIVA Svizzera) con l’UFSP, la Conferenza svizzera delle direttrici e dei direttori cantonali della sanità (CDS) e l’UST in qualità di membri del comitato di progetto, sono stati sviluppati e valutati indicatori della qualità adeguati e ne sono stati selezionati sei. La pubblicazione presenta i valori di questi sei indicatori per ciascuna delle case di cura incluse.

La qualità dei dati del 2022, forniti per l'edizione di settembre 2024, è stata sufficientemente elevata da consentire il calcolo di indicatori al livello delle case di cura adeguati ai rischi. In questo contesto, gli indicatori medici di qualità selezionati rappresentano bene la qualità delle prestazioni di cura coperte dalla LAMal erogate dalle case di cura e sono comparabili da uno stabilimento all'altro. Questi indicatori non possono riflettere appieno la qualità dello stabilimento, pertanto non ci si può limitare ad essi. In effetti non illustrano né la qualità complessiva della struttura né quella dei processi applicati, né altri aspetti della qualità come la soddisfazione dei residenti o la qualità di vita negli istituti. Per questo motivo, gli indicatori medici di qualità selezionati possono essere utilizzati come strumenti di confronto della qualità delle prestazioni di cura coperte dalla LAMal erogate dalle case di cura nei temi di misurazione, ma in alcun modo servire per effettuare una classificazione o una scala della qualità complessiva che metta a confronto gli istituti (ranking). Dovrebbero anche consentire la valutazione di eventuali lacune in termini di qualità all’interno delle case di cura al fine di identificare e utilizzare i potenziali di miglioramento dell’istituto in questione. Pertanto la presente pubblicazione deve essere anche considerata un ausilio e una base di discussione all’interno delle cerchie interessate.

casa-di-cura

display_name: casa-di-cura

case-di-cura

display_name: case-di-cura

ems

display_name: ems

indicateurs

display_name: indicateurs

indicateurs-de-qualite

display_name: indicateurs-de-qualite

indicatori

display_name: indicatori

indicators

display_name: indicators

indikatoren

display_name: indikatoren

medical-quality-indicators

display_name: medical-quality-indicators

medici

display_name: medici

nursing-home

display_name: nursing-home

pflegeheime

display_name: pflegeheime

qualita

display_name: qualita

qualitat

display_name: qualitat

qualitatsindikatoren

display_name: qualitatsindikatoren

qualite

display_name: qualite

quality

display_name: quality

fr: Indicateurs de qualité médicaux dans le domaine des EMS - données 2022

en: Medical quality indicators in the area of nursing homes - data 2022

it: Indicatori medici di qualità nell'ambito delle case di cura nell'ambito delle case di cura - Dati 2022

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

it: Economia e finanze

name: Bundesamt für Gesundheit BAG - Office fédéral de la santé publique OFSP - Ufficio federale della sanità pubblica UFSP

bundesamt-fur-gesundheit-bag

de: Bundesamt für Gesundheit BAG

en: Federal Office of Public Health FOPH

fr: L'Office fédéral de la santé publique (OFSP) fait partie du Département fédéral de l'intérieur. De concert avec les cantons, il assume la responsabilité des domaines touchant à la santé publique ainsi que la mise en œuvre de la politique sanitaire. Autorité à vocation nationale, il représente les intérêts sanitaires de la Suisse dans les organisations internationales et auprès d'autres Etats.

de: Das Bundesamt für Gesundheit (BAG) ist Teil des Eidgenössischen Departements des Innern. Es ist - zusammen mit den Kantonen - verantwortlich für die Gesundheit der Schweizer Bevölkerung und für die Entwicklung der nationalen Gesundheitspolitik. Zudem vertritt das BAG als nationale Behörde die Schweiz in Gesundheitsbelangen in internationalen Organisationen und gegenüber anderen Staaten.

en: The Federal Office of Public Health (FOPH) is part of the Federal Department of Home Affairs. Along with the cantons it is responsible for public health in Switzerland and for developing national health policy. As the national health authority, the FOPH also represents Switzerland's interests in the field of health in international organisations and with respect to other countries.

it: L'Ufficio federale della sanità pubblica (UFSP) è incorporato nel Dipartimento federale dell'interno. Unitamente ai Cantoni, è responsabile della salute della popolazione svizzera e dello sviluppo della politica nazionale in materia di salute. In qualità di autorità nazionale rappresenta inoltre gli interessi della Svizzera in materia di sanità in seno a organizzazioni internazionali e nei rapporti con altri Stati.