en:
*Currently, this dataset is intended solely for article review purposes and may not be used for any other purposes.*
This dataset consists of a series of full snow profiles collected at the field site of Weissfluhjoch, Davos, Switzerland over the two snow seasons of 2022-2023 and 2023-2024. This series of snow profiles was collected with a high vertical and temporal resolution, with a specific focus on the melting seasons, during which up to 3 snow profiles per week were sampled. This dataset is part of the measurement campaign carried out within the SnowTinel project, whose aim is to explore the Sentinel-1 SAR backscattering response to melting Alpine snowpacks. Therefore, this dataset contains manual measurements of the main properties of snow which are responsible for scattering, i.e. temperature, density, specific surface area (SSA), liquid water content (LWC), surface roughness and snow water equivalent (SWE).
The dataset is composed of a total of 85 snow profiles, 38 carried out in 2022-2023 and 47 in 2023-2024.
  - Profiles of snow **temperature** were sampled at a vertical resolution of 10 cm (2022-2023) and 5 cm (2023-2024) using HI98501 Checktemp from Hanna Instruments;
   - Profiles of snow **density** were sampled at a vertical resolution of 3 cm using a box density cutter and a digital scale;
   - Profiles of snow **specific surface area** were sampled at a vertical resolution of 4 cm using the InfraSnow sensor from FPGA;
   - Profiles of snow **liquid water content** were sampled at a vertical resolution of 2 cm using the Denothmeter (2022-2023) and the New Capacitive Sensor from FPGA (2023-2024). In conditions of ripe snow, dielectric measurements were backed up by melting calorimetry measurements following a recently revised field protocol;
   - Snow **surface roughness** is expressed by the Root Mean Square of the Heights (RMSH) and the correlation length (CL), both computed from a digitized snow transect obtained by a digital photograph of a panel vertically inserted into the snow.
- **Snow water equivalent** was sampled in sections from the surface to the bottom of the snowpack with a cylinder cutter.

Additionally, **runoff** was automatically measured in the close proximity to the measurement field at a sub-hourly resolution by a lysimeter. The instrument was unfortunately discovered to be clogged at the start of the runoff in 2023 and only repaired in late May 2023. Therefore, the time series is not explanatory for the runoff start in 2023. The instrument was inspected timely and assessed as fully functional for the following snow season. The manually measured values of **SWE** are complemented by an automatically recorded time series at sub-hourly time intervals with a SSG1000 snow scale manufactured by Sommer Messtechnik, Austria.

 ---

- References:
  - HI98501 Checktemp: [https://www.hannainstruments.co.uk/modules/teapotknowledgehub/uploads/ist98501_06_18-60d496ca2cd31.pdf](https://www.hannainstruments.co.uk/modules/teapotknowledgehub/uploads/ist98501_06_18-60d496ca2cd31.pdf)
  - InfraSnow sensor: Wolfsperger, Fabian & Ziegler, Silvio & Schneebeli, Martin & Löwe, Henning. (2022). Evaluation of the InfraSnow: a handheld device to measure snow specific surface (SSA). 10.13140/RG.2.2.31566.95047; [https://snow-sen.com/infrasnow-ssa-sensor-2/](https://snow-sen.com/infrasnow-ssa-sensor-2/)
  - Denothmeter: Denoth, A. “An Electronic Device for Long-Term Snow Wetness Recording.” Annals of Glaciology 19 (1994): 104–6. [https://doi.org/10.3189/S0260305500011058](https://doi.org/10.3189/S0260305500011058)
  - New Capacitive Sensor: Wolfsperger, Fabian & Geisser, Michel & Ziegler, Silvio & Löwe, Henning. (2023). A NEW HANDHELD CAPACITIVE SENSOR TO MEASURE SNOW DENSITY AND LIQUID WATER CONTENT; [https://snow-sen.com/slf-snowpro-40/](https://snow-sen.com/slf-snowpro-40/)
  - Melting calorimetry: Barella, R., Bavay, M., Carletti, F., Ciapponi, N., Premier, V., and Marin, C.: Unlocking the potential of melting calorimetry: a field protocol for liquid water content measurement in snow, The Cryosphere, 18, 5323–5345, [https://doi.org/10.5194/tc-18-5323-2024](https://doi.org/10.5194/tc-18-5323-2024), 2024.
  - Surface roughness: B. Riccardo, C. Marin, M. Callegari, M. Gianinetto, T. Moranduzzo and C. Notarnicola, "A Low-Cost Portable Automatic System for Snow Surface Roughness Measurements Based on Digital Photography," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 5562-5565, doi: [https://doi.org/10.1109/IGARSS47720.2021.9553989](https://doi.org/10.1109/IGARSS47720.2021.9553989).
  - SSG: [https://www.sommer.at/de/produkte/schnee-eis/schneewaage-ssg-2](https://www.sommer.at/de/produkte/schnee-eis/schneewaage-ssg-2)

en: snow-ssa snow-lwc wet-snow snow-density surface-roughness snow-roughness snow-melt sentinel-1 snow-profiles radar snow-temperature satellite

en: SnowTinel high-temporal-resolution ground truth dataset for SAR remote sensing of snow

de:
Resource Title: snowtinel_profiles_2022_2023.csv
    Size: 937388 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/43461178-b0b2-4c9b-895e-8398b986f552/download/snowtinel_profiles_2022_2023.csv
    Description: Snow profiles collected on the snow season 2022-2023.

de:
Resource Title: snowtinel_surface_roughness_2022_2023.csv
    Size: 8331 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/73fa5ef7-87f6-4d3b-80c1-735434645659/download/snowtinel_surface_roughness_2022_2023.csv
    Description: Manually measured time series of surface roughness (2022-2023).

de:
Resource Title: ssg1000_data_2022_2024.smet
    Size: 3369857 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/aaf9b5e2-1621-4d29-a37f-8df71f673e35/download/ssg1000_data_2022_2024.smet
    Description: Snow water equivalent time series automatically recorded from the snow scale (2022-2024).

de:
Resource Title: lysimeter_data_2022_2024.smet
    Size: 3369865 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/84b800f0-f5df-48ca-9474-005e952118de/download/lysimeter_data_2022_2024.smet
    Description: Runoff time series automatically recorded from the lysimeter (2022-2024).

de:
Resource Title: snowtinel_surface_roughness_2023_2024.csv
    Size: 10407 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/95e1f75e-f4a1-448b-b8a1-418b3872e2a4/download/snowtinel_surface_roughness_2023_2024.csv
    Description: Manually measured time series of surface roughness (2023-2024).

de:
Resource Title: snowtinel_swe_2023_2024.csv
    Size: 624 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/40dcf569-8e0b-4496-a556-79259095bf34/download/snowtinel_swe_2023_2024.csv
    Description: Manually measured time series of snow water equivalent (2023-2024).

de:
Resource Title: README.txt
    Size: 9428 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/cf3ee004-243a-48de-92a3-14e46a8f25d4/download/readme.txt
    Description: General metadata and detailed data description.

format: TXT

media_type: text/plain

de:
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    Size: 1435676 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/6c8939cc-2947-43c4-b0d5-9750e3bd00ab/download/snowtinel_profiles_2023_2024.csv
    Description: Snow profiles collected on the snow season 2023-2024.

de:
Resource Title: snowtinel_swe_2022_2023.csv
    Size: 592 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/41520f26-a39e-40b9-b966-cc2ffec6f95d/download/snowtinel_swe_2022_2023.csv
    Description: Manually measured time series of snow water equivalent (2022-2023).

radar

display_name: radar

satellite

display_name: satellite

snow-density

display_name: snow-density

snow-lwc

display_name: snow-lwc

snow-melt

display_name: snow-melt

snow-profiles

display_name: snow-profiles

snow-roughness

display_name: snow-roughness

snow-ssa

display_name: snow-ssa

snow-temperature

display_name: snow-temperature

surface-roughness

display_name: surface-roughness

wet-snow

display_name: wet-snow


$oid: OBSOLETE

en:
Individual genotypes assessed at six nuclear microsatellite loci (two alleles per locus) for individuals of the butterfly Melanargia galathea (Marbled white) that were collected throughout Switzerland, along the regular grid of the Biodiversity Monitoring (BDM) of Switzerland. For each individual, the sampling site (number) and the genotype are given. Note that due to privacy restrictions, the original geographic coordinates remain disclosed. For original coordinates, data holders should be contacted (Swiss Federal Office for the Environment; Biodiversity Monitoring Switzerland).
The data refer to the following publication: Terzer et al., Distinct spatial patterns of genetic structure and diversity in the butterfly Marbled White (Melanargia galathea) inhabiting fragmented grasslands. Conservation Genetics, https://doi.org/0.1007/s10592-023-01593-4.

en: genetic-diversity genetic-structure monitoring melanargia-galathea conservation-genetics

envidat

approval_status: approved

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.

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

en: Nuclear microsatellite genotypes of the butterfly Melanargia galathea

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

format: CSV

conservation-genetics

display_name: conservation-genetics

genetic-diversity

display_name: genetic-diversity

genetic-structure

display_name: genetic-structure

melanargia-galathea

display_name: melanargia-galathea

monitoring

display_name: monitoring


en: The dataset contains individual genotypes at 11 nuclear microsatellite markers of samples of Swiss stone pine (*Pinus cembra*). Samples were collected in 12 natural and presumably planted stands in the canton of Fribourg (Switzerland). The dataset is complemented by individual genotypes (same markers) from samples of a subset of 40 populations across the European Alps (taken from Gugerli et al., Journal of Biogeography 2023; https://doi.org/10.1111/jbi.14586; dataset archived at Dryad, https://doi.org/10.5061/dryad.866t1g1v6).

en: molecular-markers genetic-structure pinus-cembra afforestation reproductive-matieral

en: Nuclear microsatellite genotypes of Pinus cembra

de:
Resource Title: PopInfoFR.csv
    Size: 887 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/71fd7622-d0a6-421d-b6ad-436c2cafebd6/resource/5fd90c6f-ee7d-4b34-bfb3-01a457760aa2/download/popinfofr.csv
    Description:

media_type: text/csv

de:
Resource Title: GenIndFR.csv
    Size: 29239 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/71fd7622-d0a6-421d-b6ad-436c2cafebd6/resource/8a9e5104-43b4-4e09-a3b8-ceae90131f0d/download/genindfr.csv
    Description:

de:
Resource Title: PopInfoAlps.csv
    Size: 2271 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/71fd7622-d0a6-421d-b6ad-436c2cafebd6/resource/557bdce7-f96a-474f-8302-d1ff00cae2b7/download/popinfoalps.csv
    Description:

de:
Resource Title: PopInfoAlps.csv
    Size: 2271 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/71fd7622-d0a6-421d-b6ad-436c2cafebd6/resource/80c99523-79be-4f9d-b9e6-f42932bfe941/download/popinfoalps.csv
    Description:

de:
Resource Title: Sonnenwyl_etal_ReadMe.txt
    Size: 1978 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/71fd7622-d0a6-421d-b6ad-436c2cafebd6/resource/1d053730-6b8c-42a6-9339-a4fafbaebd5e/download/sonnenwyl_etal_readme.txt
    Description: metadata and description of dataset

afforestation

display_name: afforestation

molecular-markers

display_name: molecular-markers

pinus-cembra

display_name: pinus-cembra

reproductive-matieral

display_name: reproductive-matieral


en:
The Jorat is one of the largest continously forested areas in the Swiss Plateau. On a forested area of 778 ha, the Parc Naturel du Jorat (PNJ), a periurban parc, has been established in 2022. To document the initial state of the forest within the perimeter of the PNJ, a sample plot inventory (SPI) was carried out on 132 sample plots (SP) in winter 2021/22.
This dataset contains results from this sample plot inventory. It consists of the following files:
- results_trees.csv: Results for or living and dead trees.
- results_regeneration.csv: Results for trees with DBH - 7.0 cm, assessed in three height classes.
- results_lying_deadwood.csv: Results for lying deadwood, assessed no three line transects
- results_trems.csv: Results for occurence of tree related microhabitats (TreMs)
- results_habitat_trees.csv: Results for occurence / densities of trees carrying at least one TreM or with a DBH -= 80 for living trees or -= 36 cm for dead trees respectively.
- lookup.csv: Contains lookup tables which describe the respective results in-depth.
- data_description.pdf: Briefly describes the datasets mentioned above.

en: beech swiss-plateau temperate-forests forest-in forest-reserves

en: Sample plot inventory data form the Parc Naturel du Jorat

de:
Resource Title: Results TreMs
    Size: 9840 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/93a5dba0-ae68-482d-bb87-5f5c62538cf3/download/results_trems.csv
    Description: The file results_trems.csv contains results for the density of TreMs per ha.

format: XLS

de:
Resource Title: Data description
    Size: 102082 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/f3201ed0-992c-4d3d-a460-1fa1ce4478a0/download/description_envidat.pdf
    Description: This document briefly describes the datasets.

format: PDF

media_type: application/pdf

de:
Resource Title: Results habitat trees
    Size: 1098 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/5f308438-a867-4aa0-8e2d-1e8d1f65c423/download/results_habitat_trees.csv
    Description: The file results_habitat_trees.csv contains results for the number of habitat trees.

de:
Resource Title: Results lying deadwood
    Size: 2436 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/c8f653da-6b07-4949-a7c7-19f9ae1b51e0/download/results_lying_deadwood.csv
    Description: The file results_lying_deadwood.csv contains results of the lying deadwood survey.

de:
Resource Title: Lookup
    Size: 9034 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/6e728238-c52f-4b72-ae3f-a5109aefbd72/download/lookup.csv
    Description: The file lookup.csv contains description for the values shown in the 5 result tables.

de:
Resource Title: Results trees
    Size: 494778 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/008f44a9-3aa7-4cb6-a69c-f11bbd63c8e7/download/results_trees.csv
    Description: The file results_trees.csv contains results of living, standing and lying and dead standing
trees as well as for stumps (trees found on the plot but broken / cut below 1.3 m).

de:
Resource Title: Results regeneration
    Size: 6112 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/db868a5f-c640-4346-ac88-10e1b7052dba/download/results_regeneration.csv
    Description: The file results_regeneration.csv contains results of the regeneration survey.

beech

display_name: beech

forest-in

display_name: forest-in

forest-reserves

display_name: forest-reserves

swiss-plateau

display_name: swiss-plateau

temperate-forests

display_name: temperate-forests


en:
The list gives the clearly identifiable plant species from Schinz (1775). Die Reise auf den Uetliberg. Verlag des Waysenhauses, Zürich). Several species given in Schinz (1775) are not identifiable to the species level and are not included in the list. Similarly, *Vicia pisiformis*, Erbsenartige Wicke, given by Schinz (1775) is probably a misidentification and also not included in the list.
Latin plant species name Schinz 1775: Latin plant name (if) given by Schinz (1775); na: not available
German plant species name Schinz 1775: German plant name given by Schinz (1775)
Latin plant species name Info Flora 2024: Latin plant name according to www.infoflora.ch in 2024
German plant species name Info Flora 2024: German plant name according to www.infoflora.ch in 2024
Plant species still occurring in 2020: Plant species given in Schinz (1775) still occurring at the Uetliberg in 2020 (Zürcherische Botanische Gesellschaft. 2020. Flora des Kanton Zürich. Haupt, Bern); occurrence of *Thesium alpinum*, Alpen-Bergflachs, according to floristic knowledge of Rolf Holderegger and Michèle Büttner; 1: still occurring; 0: no longer occurring / extinct.

en: zurich uetliberg plant-species flora-change occurrence

en: Plant species list from 1775 and 2020 for Uetliberg Zürich

flora-change

display_name: flora-change

occurrence

display_name: occurrence

plant-species

display_name: plant-species

uetliberg

display_name: uetliberg

zurich

display_name: zurich


en:
This dataset contains volumetric water content measurements, water level, and pressure for a location along the Illgraben debris-flow channel (46°18'34.92"N, 7°37'59.26"E). The measurements (165780 data points) were conducted during the 2022 debris-flow season (May to October) at a sampling rate of five minutes. The dataset specifically contains:
- At 0.25 m depth with an ECH2O EC-5 sensor: volumetric water content (VWC)
- At 0.45 m depth with an ECH2O EC-5 sensor: VWC
- At 0.65 m depth with a TEROS 12: VWC
- At 0.85 m depth with a TEROS 12: VWC
- At 0.80 m depth with a CTD10: water level (WL), temperature (T), electric conductivity (EC) and air reference pressure (Pref)
Please note that due to malfunctioning the CTD10 data was only available until 27 June 2022.
More specific sensor information can be found in the manuals under the following links:
[EC-5](https://publications.metergroup.com/Manuals/20431_EC-5_Manual_Web.pdf)
[TEROS 12](https://publications.metergroup.com/Manuals/20587_TEROS11-12_Manual_Web.pdf)
[CTD10](https://www.agrolan.co.il/UploadProductFiles/CTD-10-manual.pdf)
For a detailed description of the dataset and its use, please refer to the related publication.

en: debris-flow soil-moisture measurement illgraben water-content hydrology

en: Volumetric Water Content Measurements at Illgraben 2022

de:
Resource Title: readme.md
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    Format: text/markdown
    URL: https://www.envidat.ch/dataset/38eb1f60-9d3b-46bf-ab58-ba2f89ba0ed2/resource/dd78b123-0fa5-447a-af72-08bd58ab57fe/download/readme.md
    Description:

format: markdown

de:
Resource Title: SM_Illgraben_2022.csv
    Size: 1619689 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/38eb1f60-9d3b-46bf-ab58-ba2f89ba0ed2/resource/521b8f13-6af9-4575-824c-969ea8914adc/download/sm_illgraben_2022.csv
    Description:

debris-flow

display_name: debris-flow

hydrology

display_name: hydrology

illgraben

display_name: illgraben

measurement

display_name: measurement

soil-moisture

display_name: soil-moisture

water-content

display_name: water-content


en: With this study, our aim was to estimate the nutrient fluxes relevant for assessing nutrient sustainability as accurately as possible and to calculate nutrient balances for alternative forest management scenarios. Furthermore, we tested whether mapping units from existing geologic maps can serve as a basis for forest practitioners to estimate nutrient sustainability or whether more detailed data is needed. Positive fluxes include deposition and weathering, while negative fluxes include losses due to leaching and nutrient removal through timber harvesting in the balance. Weathering and leachate losses were modeled with a geochemical model. The SwissStandSim model was used to simulate the biomass growth under different harvesting and silvicultural strategies, allowing sustainability to be assessed for each nutrient at a given intensity of use. This assessment was made per rotation period of 100 years based on two criteria: i) nutrient supply; and ii) total stocking volume.

en: beech mass-balance soil nutrient sustainability

envidat

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.

de: EnviDat: das Umwelt-Datenportal

en: Nutrient sustainability in beech forests

de:
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    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/b938dd05-54ab-4dc0-b7d8-a106567322f4/resource/ba211033-8761-4d53-a1fd-6e741926f3d6/download/soil_properties_taba2.xlsx
    Description:

de:
Resource Title: Nutrient_export_harvesting.xlsx
    Size: 15618 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/b938dd05-54ab-4dc0-b7d8-a106567322f4/resource/3f5aa362-7738-4a1e-8cce-9c0b60f0ef71/download/nutrient_export_harvesting.xlsx
    Description:

de:
Resource Title: Nutrient_mass_balances.xlsx
    Size: 27714 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/b938dd05-54ab-4dc0-b7d8-a106567322f4/resource/23bb8222-159c-42b8-8e06-dd14b8469600/download/nutrient_mass_balances.xlsx
    Description:

de:
Resource Title: Nutrient_tree_compartments.xlsx
    Size: 39892 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/b938dd05-54ab-4dc0-b7d8-a106567322f4/resource/59464da8-a4aa-4adf-86d1-df763223e9c4/download/nutrient_tree_compartments.xlsx
    Description:

mass-balance

display_name: mass-balance

nutrient

display_name: nutrient

soil

display_name: soil

sustainability

display_name: sustainability


en:
This dataset contains annual data on carbon, oxygen and hydrogen stable isotope ratios from tree-ring cellulose in 24 silver fir and 24 Douglas-fir trees in Switzerland for the period 2000-2020. It also contains the corresponding tree-ring widths and information on the trees (e.g., tree crown volume).
In addition, the dataset contains variables characterizing the local neighborhood of the sampled trees (competition and diversity indices) used in Charlet de Sauvage, J., Treydte, K., Saurer, M., & Lévesque, M. (2024). Triple-isotope analysis in tree-ring cellulose suggests only moderate effects of tree species mixture on the climate sensitivity of silver fir and Douglas-fir. Tree Physiology, tpae067. https://doi.org/10.1093/treephys/tpae067.
Oxygen and hydrogen stable isotope ratios were also used in Charlet de Sauvage, J., Saurer, M., Treydte, K.,  & Lévesque, M. (in revision) Decoupling of tree-ring cellulose δ18O and δ2H highlighted by their contrasting relationships to climate and to tree intrinsic variables. Plant, Cell & Environment.
A readme file accompanying the dataset explains each variable.

en: crown-volume tree-ring-cellulose stable-isotopes tree-ring-width abies-alba hydrogen species-mixture oxygen pseudotsuga-menziesii carbon

envidat

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

en: EnviDat: the Environmental Data Portal

de: EnviDat

en: Carbon, oxygen and hydrogen isotopes in tree-ring cellulose of silver fir and Douglas-fir in Switzerland

envi

de: Umwelt

de:
Resource Title: Readme dataset (.txt)
    Size: 3621 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4ab59ee2-10b0-4f82-9646-249acac8ce34/resource/82c6815d-812f-4a04-b544-4d7106dd7f86/download/readme_dataset_triple_isotope_a_alba_p_menziesii.txt
    Description: This file contains the description of each variable in the dataset and references to the corresponding scientific publications.

de:
Resource Title: Dataset triple isotope Abies alba Pseudotsuga menziesii (.csv)
    Size: 195358 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/4ab59ee2-10b0-4f82-9646-249acac8ce34/resource/f4cf47cb-e9b8-49ce-8edc-39cba67566a0/download/dataset_triple_isotope_a_alba_p_menziesii.csv
    Description: This file contains all the data on the trees, the tree-ring width and the stable isotope ratios.

abies-alba

display_name: abies-alba

carbon

display_name: carbon

crown-volume

display_name: crown-volume

hydrogen

display_name: hydrogen

oxygen

display_name: oxygen

pseudotsuga-menziesii

display_name: pseudotsuga-menziesii

species-mixture

display_name: species-mixture

stable-isotopes

display_name: stable-isotopes

tree-ring-cellulose

display_name: tree-ring-cellulose

tree-ring-width

display_name: tree-ring-width


en: Data (Quantitative Wood Anatomy, Maximum Latewood Density, Volumetric Density, holo-cellulose-to-wood-ratio) used in the manuscript "New Prospects for Temperature Reconstructions from Relict Wood Using Tree-Ring Anatomy". Wood samples stem from Larix sibirica collected in the Yamal region, northeastern Siberia, Russia. We investigated wood properties of living trees and subfossil wood buried in riverbank sediments, dated to approximately -2760 to -2600 BCE. Our goal was to evaluate the integrity of climate proxies in sub-fossil wood and develop practical methods to detect wood degradation when reconstructing Late Holocene and Common Era climate conditions using tree-ring data.

en: temperature-reconstruction wood-anatomy mxd paleoclimate tree-ring

en: Quantitative Wood Anatomy and Maximum Latewood Density from Yamal peninsula

envi

en: Environment

de:
Resource Title: Figure3_dataset.txt
    Size: 8581 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/743a897f-26e7-4871-a014-b0f95238564d/resource/2435516e-8dfc-4347-a35c-893eadcdf569/download/figure3_dataset.txt
    Description: This dataset contains the mean chronologies of MXD and QWA of the relict and living wood material presented in Figure 3. It also contains the JJA temperature record of the Salekhard Meteo station.

de:
Resource Title: Figure2_dataset.csv
    Size: 1668 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/743a897f-26e7-4871-a014-b0f95238564d/resource/b0e00f50-78a2-4006-ab8d-7d4d796efc05/download/bulkdensity.csv
    Description: This dataset contains data presented in Figure 2 in the paper "New Prospects for Temperature Reconstructions from Relict Wood Using Tree-Ring Anatomy".

Sample ID = Sample ID
group = living or relict wood material
Bulk wood density = volumetric measured density (kg / m^3)
Mean Tree Ring Density	= Density derived via X-ray densitometry (kg / m^3)
Cellulose_to_wood ratio = Holo-cellulose-to-wood ratio
Anatomical density = Area of cell wall / (area of cell wall + lumen area)

mxd

display_name: mxd

paleoclimate

display_name: paleoclimate

temperature-reconstruction

display_name: temperature-reconstruction

tree-ring

display_name: tree-ring

wood-anatomy

display_name: wood-anatomy


en:
Data on the diet of urban and rural bumblebees individuals, Bombus lapidarius and Bombus pascuorum, collected in Switzerland (cantons Bern, Basel and Zürich) obtained using metabarconding and chemical analyses from two pollen transportation structures, that is, the body and the leg-baskets (i.e., corbicula). The bumblebees were collected by Egenberger et al., 2019 (https://doi.org/10.5061/dryad.qt8mr4v)
The data contains:
- The composition and relartive abundance of plant species in the body and the leg-baskets, separately.
- The content and ratios of two groups of macronutrients, that is, amino acids and fatty acids.
The dataset contains the data used in the paper "Urban bumblebees diversify their foraging strategy to maintain nutrient intake" as well as unpublished data.

en: basel foraging-behaviour nutrient-intake pollen-metabarcoding bees lad-use-chages urban bern macronutrients dietary-patterns bumblebees rural bombus-pasquorum zurich bombus-lapidarius

de:
Resource Title: Dataset6_Functional_Diversity.csv
    Size: 86806 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ae82037f-c201-4f1d-9a57-162a25264ddb/resource/06afcb49-cc9f-45da-b64f-5860c277d498/download/dataset6_functional_diversity.csv
    Description: Processed data. The dataset contains the functional diversity metrics based on the plant composition from the leg-baskets per bumblebee individual. It contains:

- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels:  A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Fric.w: Weighted functional richness
-Fric: functional richness
-FEve.w: Weighted functional evenness
- FEve: functional evenness
- FDiv.w: weighted functional divergence
- FDiv: functional divergence

de:
Resource Title: Dataset4_Fattyacids.csv
    Size: 58574 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ae82037f-c201-4f1d-9a57-162a25264ddb/resource/0b2c5c45-629f-4b01-8c58-535c06c74fb2/download/dataset4_fattyacids.csv
    Description: The dataset contains the fatty acid content and ratios for the two bumblebees. Measures are taken only from pollen from the leg-baskets. SampleID is not the same as in the metabarcoding, several samples from different bumblebees were merged within a Location ,Landscape and Replica. Not for all the samples there is information on both amino acids and fatty acids. The columns are:

- SampleID_chemical: The ID for chemical analyses
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels:  A-E.
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- LocationLandscape: combination between location and landscape
- Caprylic, Pelargonic, ..., Lacceroic: Content of individual fatty acids acids.
- sum: total sum of fatty acid content (mg)
- unsaturatedsum: total sum unsaturated fatty acids (mg)
- saturatedsum: total sum saturated fatty acids (mg)
- omega3sum: total sum Omega 3 fatty acids (mg)
- omega6sum: total sum Omega 6 fatty acids (mg)
- omega9sum: total sum Omega 9 fatty acids (mg)
- omega6to3ratio: ratio Omega 6 to Omega3 fatty acids
-saturatedtounsaturatedration: ratio between saturated and unsaturated fatty acids

de:
Resource Title: Dataset3_Aminoacids.csv
    Size: 23314 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ae82037f-c201-4f1d-9a57-162a25264ddb/resource/ce8bb95f-caaf-4959-a7c9-07bf93806939/download/dataset3_aminoacids.csv
    Description: The dataset contains the amino acid content and ratios for the two bumblebees. Measures are taken only from pollen from the leg-baskets. SampleID is not the same as in the metabarcoding, several samples from different bumblebees were merged within a Location ,Landscape and Replica. Not for all the samples there is information on both amino acids and fatty acids. The columns are:

- SampleID_chemical: The ID for chemical analyses
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels:  A-E.
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- LocationLandscape: Combining location and landscape
- Asp,  Thr, ..., Arg: Content of individual amino acids. Amino acids are written as abbreviated names. FUll names be checked here: https://en.wikipedia.org/wiki/Amino_acid
- sum: total sum of amino acid content (mg)
- sumessential: total sum essential amino acids  (mg)
-sumnon-essential: total sum non-essential amino acids (mg)
- proportionnonessentialessential: proportion between essential and non-essential amino acids.

de:
Resource Title: Dataset1_Composotion_pollen.csv
    Size: 709634 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ae82037f-c201-4f1d-9a57-162a25264ddb/resource/0fd97e8a-015e-4f9b-8c28-14ce1ae4dc6a/download/dataset1_metabarcoding_pollen.csv
    Description: The dataset contains the filtered plant composition1  (removing species with too low relative abundances) and the relative abundance obtained from DNA metabarcoding on pollen samples for both the leg-baskets and the body. The dataset contains the following columns:

- SampleID: Combining bumblebee ID, location, landscape and replica
- Abundance: numeric, relative abundance
- BumblebeeID: numerical ID for each bumblebee individual within a location, landscape and replicate sampling place.
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels:  A-E.
- LocationLandscape:
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Transportation_structure: bumblebee pollen transportation structure, levels: B = Body; L = leg-baskets (i.e. corbicula)
- Kingddom: taxonomic
- Phylum: taxonomic
- Order: taxonomic
- Family: taxonomic
- Genus: taxonomic
- Species: plant sicentific name
-Binomial_abundance: Logic, indicating if the plant species is included (1).

The raw metabarcoding data is archived in NCBI project PRJNA1137320

de:
Resource Title: Dataset5_Species_richness.csv
    Size: 14377 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ae82037f-c201-4f1d-9a57-162a25264ddb/resource/79d981d0-0c62-4e78-810f-750644a08176/download/dataset5_species_richness.csv
    Description: Processed data. The dataset contains the plant species richness in the leg-baskets per bumblebee individual. It contains:

- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels:  A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Plant species richness: the number of plant species found in the pollen of the leg-baskets.

de:
Resource Title: Dataset8_PlantTraits.csv
    Size: 33177 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ae82037f-c201-4f1d-9a57-162a25264ddb/resource/79128caa-e0a4-43d6-8b60-ff111075a984/download/dataset8_planttraits.csv
    Description: The dataset contains the plant trait values obtined from published sources. It contains:
- Kingddom: taxonomic
- Phylum: taxonomic
- Order: taxonomic
- Family: taxonomic
- Genus: taxonomic
- Species: species name
- plant.species: full scientific name
- native_exotic: origin status. As presented in https://www.doi.org/10.16904/envidat.210
- pollination mode: vector of pollination. As presented in https://www.doi.org/10.16904/envidat.210
- Flowering_months_duration: Number of flowering months. As presented in https://www.doi.org/10.16904/envidat.210
- start_flowering: month of the starting of the flowering. As presented in https://www.doi.org/10.16904/envidat.210
- growth_form_category: plant growth form. As presented in https://www.doi.org/10.16904/envidat.210
- plant-height: plant heght in meters. As presented in https://www.doi.org/10.16904/envidat.210
- inflorescence: Logic, plant with single flowers (0) or inflorescences (1). As presented in https://www.doi.org/10.16904/envidat.210
- structural_blossom_class: depicting the accessibility of the flowers. As presented in https://www.doi.org/10.16904/envidat.210
- symmetry: floral symmetry. As presented in https://www.doi.org/10.16904/envidat.210
- nectar: logic, nectar present. As presented in https://www.doi.org/10.16904/envidat.210
- pollen: logic, pollen rewards present. As presented in https://www.doi.org/10.16904/envidat.210
- oil: logic. oil present. As presented in https://www.doi.org/10.16904/envidat.210
- sugar.concentration: the amount of sugar present in the nectar, in mg.

Additional information can be found in https://doi.org/10.1016/j.dib.2021.107243

de:
Resource Title: Dataset7_Phylogenetic_Diversity.csv
    Size: 74413 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ae82037f-c201-4f1d-9a57-162a25264ddb/resource/f34a3f6e-91f3-4619-a258-8e4408050032/download/dataset7_phylogenetic_diversity.csv
    Description: Processed data. The dataset contains the phylogeneitc diversity metrics based on the plant composition from the leg-baskets per bumblebee individual. It contains:

- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels:  A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- PVar: phylogenetic variance
- PRic: Phylogenetic richness
- PEve: Phylogenetic evenness
- PClu: Phylogenetic clustering

de:
Resource Title: Dataset2_sites.csv
    Size: 1248 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ae82037f-c201-4f1d-9a57-162a25264ddb/resource/75bace68-f772-4103-9fdf-8395ce1c3d51/download/dataset2_sites.csv
    Description: The dataset contains the information on the 16 study sites:

- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels:  A-E.
- Site_ID: combination of location, landscape and replicate
- X: X coordinate, WGS84
- Y: Y coordinate WGS84
- Plant S in landscape: Plant species richness within the 800 m calculated using both information from the Global Biodiversity Information. Facility (GBIF) and InfoFlora (www.infoflora.ch).
- % Buildings: the proportion of buildings and other impervious surfaces within 800 m radius.
- % Agricultural: the proportion of agricultural land and other impervious surfaces within 800 m radius.
- % Water: the proportion of water and other impervious surfaces within 800 m radius.
- % Meadows & grasslands: the proportion of meadows and grasslands and other impervious surfaces within 800 m radius.

The raw metabarcoding data is archived in NCBI project PRJNA1137320.

basel

display_name: basel

bees

display_name: bees

bern

display_name: bern

bombus-lapidarius

display_name: bombus-lapidarius

bombus-pasquorum

display_name: bombus-pasquorum

bumblebees

display_name: bumblebees

dietary-patterns

display_name: dietary-patterns

foraging-behaviour

display_name: foraging-behaviour

lad-use-chages

display_name: lad-use-chages

macronutrients

display_name: macronutrients

nutrient-intake

display_name: nutrient-intake

pollen-metabarcoding

display_name: pollen-metabarcoding

rural

display_name: rural

urban

display_name: urban

zurich

display_name: zurich


en:
Soil chemical and biological properties of soils affected by 10 different perturbations related with global change, applied individually or in combination. Greenhouse experiment. Perturbations applied to intact soil cores (15 cm diameter, 15 cm deep) collected in an extensively managed grassland on the WSL grounds in June 2023.
This dataset contains all data on which the publication below was based:
"Fioratti Junod M, Gombeer S, Holmes J, Zimmerman S, Rillig M, Risch AC and Cordero I. Soil functionality declines under multiple superimposed global change perturbations. XXXX "
Please, cite this publication together with the citation of the datafile.
Database includes:
Pot_ID: pot or soil core identifier.
Batch: numeric, 1 or 2.
Enzyme_plate: numeric, from 1 to 6.
Number_of_perturbations: numeric, from 0 to 10.
N_addition: binary. Yes = 1, No = 0.
P_addition: binary. Yes = 1, No = 0.
Defoliation: binary. Yes = 1, No = 0.
Trampling: binary. Yes = 1, No = 0.
Insecticide: binary. Yes = 1, No = 0.
Fungicide: binary. Yes = 1, No = 0.
Herbicide: binary. Yes = 1, No = 0.
Antibiotic: binary. Yes = 1, No = 0.
Drought: binary. Yes = 1, No = 0.
Heat_wave: binary. Yes = 1, No = 0.
Treatment: character. Description of the treatment, 15 levels.
SWC: soil water content. Numeric, unitless.
Green_biomass: green plant biomass. Numeric, g.
Brown_biomass: brown or dead plant biomass. Numeric, g.
Biomass_cut: plant biomass cut during defoliation treatments. Numeric, g.
CO2_flux_light: net CO2 flux under ambient light. Numeric, mg CO2 m-2 h-1.
CO2_flux_dark: ecosystem respiration. Numeric, mg CO2 m-2 h-1.
S: organic matter stabilisation factor (tea bag index). Numeric, unitless.
k: organic matter decomposition rate (tea bag index). Numeric, unitless.
DOC: dissolved organic carbon, mg Kg-1 dry soil.
IC: inorganic carbon, mg Kg-1 dry soil.
Ammonium_KCl:  plant available ammonium, mg Kg-1 dry soil.
Nitrate_KCl:  plant available nitrate, mg Kg-1 dry soil.
Phosphate: phosphate, mg Kg-1 dry soil.
MBC: microbial biomass carbon,  mg Kg-1 dry soil.
MBN: microbial biomass nitrogen,  mg Kg-1 dry soil.
PHO: phosphatase activity, nmol h-1 g-1 dry soil.
BG: β-glucosidase, nmol h-1 g-1 dry soil.
XYL: xylosidase, nmol h-1 g-1 dry soil.
CBH: cellobiohydrolase, nmol h-1 g-1 dry soil.
NAG: N-acetylglucosaminidase, nmol h-1 g-1 dry soil.
LAP: leucine aminopeptidase, nmol h-1 g-1 dry soil.
POX: phenoloxidase, nmol h-1 g-1 dry soil.
PER: peroxidase, nmol h-1 g-1 dry soil.
pH: soil pH, unitless
Water_stable_aggregates: water stable aggregates, %
Surface_infiltration_time: water surface infiltration time, s.

en: soil-nutrients drought pesticide land-use heatwave soil-biodiversity soil

envidat

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.

de: EnviDat: das Umwelt-Datenportal

en: Escalating effects of multiple perturbations on soil functionality

drought

display_name: drought

heatwave

display_name: heatwave

land-use

display_name: land-use

pesticide

display_name: pesticide

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-nutrients

display_name: soil-nutrients


en:
Each set includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the danger ratings for dry-snow conditions assigned to the location of the station. The data set of RF 1 contains the danger ratings published in the official Swiss avalanche bulletin, and the data set of RF 2 is a quality-controlled subset of danger ratings.
These data are the basis of the following publication:  Pérez-Guillén, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., Pérez-Cruz, F., and Schweizer, J.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, 2022.

en: avalanche avalanche-forcasting avalanche-danger-level numerical-avalanche-forecasting machine-learning

en: Weather, snowpack and danger ratings data for automated avalanche danger level predictions

de:
Resource Title: Data_weather_snowpack_danger_forecast
    Size: 185690485 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/54d7dc19-e70c-4998-bfb6-caffe41c83e6/resource/11c8a22e-3e76-4f1f-8880-17ebf6525e9a/download/data_rf1_forecast.csv
    Description: This data set includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the danger ratings for dry-snow conditions assigned in the Swiss avalanche bulletin to the location of the weather station.

de:
Resource Title: Data_RF2_tidy
    Size: 17378680 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/54d7dc19-e70c-4998-bfb6-caffe41c83e6/resource/7f6d8de7-194c-4ae6-9e0d-473a45342918/download/data_rf2_tidy.csv
    Description: This subset of data includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the quality-controlled danger ratings for dry-snow avalanche conditions forecast in Switzerland.

avalanche

display_name: avalanche

avalanche-danger-level

display_name: avalanche-danger-level

avalanche-forcasting

display_name: avalanche-forcasting

machine-learning

display_name: machine-learning

numerical-avalanche-forecasting

display_name: numerical-avalanche-forecasting


en:
The European Snow Booklet (ESB) is a book of reference for snow measurements that has been produced through collaboration with many European snow practitioners and snow scientists in the framework of the European Cooperation in Science and Technology (COST) Action ES1404 “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction (HarmoSnow)”. The ESB provides a unique collection of information about operational snow observations in the European countries and the methods used to perform basic measurements of snow on the ground: snow depth (HS), depth of snowfall (HN), water equivalent of the snow cover (SWE) and presence of snow on the ground (PSG). Information and station metadata (for example location, elevation) for these basic snow variables were collected through a comprehensive survey, the ESB questionnaire between August 2017 and March 2018. Numerous institutions of 38 European countries provided detailed information describing the status of the operational snow observations and the methods used at the time of the survey. Based on the information provided, a country report was written for each European country. Similarities and differences among the countries, that is, the choice of snow variables to be measured, the measurement principles applied, the number of stations, or the spatial and elevational station distribution are pointed out. Thus the collection of country reports demonstrates the relevance of snow measurements for each country. Thus, the intention of the ESB is to foster better knowledge transfer regarding snow measurements between the snow science and operational communities and to improve the communication of information to the general public.
For detailed information on the European countries, we refer to the ESB, which can be downloaded here (envidat.ch). Please note that the ESB is not a living document and information and station metadata are from August 2017 till March 2018, except for Latvia (metadata updated in December 2018).

de: EnviDat

en: European Snow Booklet – an Inventory of Snow Measurements in Europe

envi

de: Umwelt

de:
Resource Title: COST European Snow Booklet (ESB) - DORA Link
    Size: 1 bytes
    Format: No Info
    URL: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:20198
    Description: DORA Link of the COST European Snow Booklet (ESB).

de:
Resource Title: List of authors and list of the editorial board of the European Snow Booklet
    Size: 14675 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/492dfd3d-99d4-4cfb-b129-77e98344a237/resource/1e7628d8-50b7-45cc-8974-b50907b365fc/download/list_of_authors_and_editorial_board.xlsx
    Description: Here you can find the list of all lead authors of the different chapters of the European Snow Booklet (ESB).
In addition a list of the editorial board that reviewed the ESB is given.

de:
Resource Title: European Snow Booklet public metadata
    Size: 1536715 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/492dfd3d-99d4-4cfb-b129-77e98344a237/resource/d05304f6-6862-4f6c-b4b9-3601201cc824/download/metadata.xlsx
    Description: The core of the European Snow Booklet (ESB), the country reports of 38 European countries, are based on information and metadata on snow measurements collected through a comprehensive survey, the ESB Questionnaire, between August 2017 and March 2018. The station metadata (for example location, elevation, measurement start date) from each country was provided by one or several institutions responsible for snow observations. By answering the questionnaire the contacts agreed that the information and metadata provided could be presented in the ESB. For cases where institutions indicated that information on station locations could be made publicly available, the metadata for the variables snow depth (HS), depth of snowfall (HN) and water equivalent of the snow cover (SWE), is additionally provided here for download (METADATA.xlsx).
Please note that not all institutions gave permission to publish their station metadata. This metadata has to be requested from the non-personal institution contacts provided in the METADATA.xlsx file. In some cases, however, no institutional contact is available and thus the contact of the country representative who filled in the questionnaire is used.
For current, updated station metadata we refer to the (non-personal) institutional contacts provided in METADATA.xlsx and/or the institution webpages. Note, however, that we cannot guarantee for completeness of the dataset for all countries because operational networks are prone to changes.

de:
Resource Title: COST European Snow Booklet (ESB)
    Size: 136492575 bytes
    Format: application/pdf
    URL: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl%3A20198/datastream/PDF/Haberkorn-2019-European_Snow_Booklet-%28published_version%29.pdf
    Description: Please find here the web version of the COST European Snow Booklet (ESB).

depth-of-snowfall

display_name: depth-of-snowfall

harmonisation-of-snow-measurements

display_name: harmonisation-of-snow-measurements

snow-depth

display_name: snow-depth

snow-measurement-methods

display_name: snow-measurement-methods

water-equivalent-of-the-snow-cover

display_name: water-equivalent-of-the-snow-cover


en:
This file contains isotopic and environmental data from seven individuals of Larix decidua, sampled at two sites located at an elevation of 1300 meters above sea level in the Lötschental valley, Switzerland, during the period of 2012-2013.
Within this file, you will find two datasets. The first dataset, labeled 'd18Ocellulose,' comprises individual intra-annual d18O cellulose measurements, along with corresponding temperature, vapor pressure deficit, and relative humidity data. These measurements are aggregated for the period during which each tree-ring section was in the cell wall formation phase.
The second dataset, identified as 'd18ONW-XYL,' includes individual d18O measurements for both xylem and needle water, in conjunction with temperature, vapor pressure deficit, and relative humidity data.

en: water-isotopes oxygen-isotopes loetschental larix-decidua tree-rings

envidat

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

en: Intraannual d18O measurements of Larix decidua from the Loetschental 2012/2013

de:
Resource Title: Readme.csv
    Size: 2380 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/117ba920-b302-42b5-99fb-7ed70109964b/resource/8219db12-f157-42b7-bc7d-c31127eeae27/download/readme.csv
    Description:

de:
Resource Title: d18Ocellulose.csv
    Size: 18388 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/117ba920-b302-42b5-99fb-7ed70109964b/resource/52643369-b585-4ae5-af08-aefbbabfe447/download/d18ocellulose.csv
    Description: d18O cellulose provides intra-annual measurements of d18O cellulose from seven individuals of Larix decidua growing at an elevation of 1300 m above sea level in the Lötschental valley, Switzerland, for the years 2012-2013.

Sample.ID: ID composed by Site and tree ID/year/position of the cut within the tree ring
Year: Calendar year of the measured radial increment
Site: Site identifier
Tree: Tree identifier
perce_i indicates the percetatge of the ring that represent the beginning of the section
perce indicates the percetatge of the ring that represent the end of the section
tW: DOY when the section entered the cell-wall thickening phase
tM: DOY when the section entered in the mature phase
timeW: amount of days that the section spent in the cell-wall thickening phase
d18O_cel: d18O measurement of the section
d18O_xyl: individual average d18O of xylem water for the period when each tree-ring section was in the cell wall formation phase
d18O_NW: individual average d18O of needle water for the period when each tree-ring section was in the cell wall formation phase
d18O_5cm: individual average d18O of soil water at 5 cm for the period when each tree-ring section was in the cell wall formation phase
d18O_60cm: individual average d18O of soil water at 60 cm for the period when each tree-ring section was in the cell wall formation phase
RH: average relative humidity (%) for the period when each tree-ring section was in the cell wall formation phase
temp: average temperature (°C) for the period when each tree-ring section was in the cell wall formation phase
VPD: average vapour pressure deficit (kPa) for the period when each tree-ring section was in the cell wall formation phase

de:
Resource Title: d18ONW-XYL.csv
    Size: 15926 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/117ba920-b302-42b5-99fb-7ed70109964b/resource/66e07cce-5866-4333-aec1-663862dca75f/download/d18onw-xyl.csv
    Description: d18ONW-XYL provides d18O measurements of xylem and needle water from seven individuals of Larix decidua growing at an elevation of 1300 m above sea level in the Lötschental valley, Switzerland, for the years 2012-2013.

Site: Site identifier
Year: Calendar year of the measured radial increment
Date: date of sample collection
Tree: Tree identifier
RH:  relative humidity (%)
temp: temperature (°C)
VPD: vapour pressure deficit (kPa)
NW: d18O measurement of needle water
XYL: d18O measurement of xylem water

de:
Resource Title: MartinezSancho_NewPhyto2023_dataset.xlsx
    Size: 50387 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/117ba920-b302-42b5-99fb-7ed70109964b/resource/85a56705-afb1-4ca3-90bb-cb72e7907f23/download/martinezsancho_newphyto2023_dataset.xlsx
    Description: MartinezSancho_NewPhyto2023_dataset.xlsx provides isotopic and environmental data from seven individuals of Larix decidua growing at an elevation of 1300 m above sea level in the Lötschental valley, Switzerland, for the years 2012-2013

larix-decidua

display_name: larix-decidua

loetschental

display_name: loetschental

oxygen-isotopes

display_name: oxygen-isotopes

tree-rings

display_name: tree-rings

water-isotopes

display_name: water-isotopes


en:
Meteorological measurements recorded in the Dischma valley from 2014-2016. In 2014 and 2015 we used 11 mobile weather stations from sensorscope to record meteorological parameter in the upper Dischma valley in the closer surroundings of the Gletschboden area. The meteorological stations are eqiupped with at least air temperature/humidity, wind velocity and wind direction sensors. Some stations are additionally equipped with precipitation, shortwave radiation and snow surface temperature sensors.
Three transects were installed at different aspects and were equipped with air temperature/humidity and wind sensors. Transect 1 (stations 2-4) provides meteorological Information on an east-north-east facing slope at elevations ranging between 2100 m and 2500 m. Transect 2 (stations 5-7) provides meteorological Information on a south-west slope and transect 3 (stations 8-10) on a north-west slope. Station 1 is fully equipped with meteorological sensors (temperature/humidity, wind, IR, up and downwand shortwave radiation and precipitation).
In 2016, mobile stations from sensorscope were replaced with six permanent meteorological stations. Meteorological stations 1-3 are equipped with an air temperature/humidity sensor, two wind speed sensors, a wind direction sensor and an incoming and outgoing shortwave radiation sensor. Stations 4 and 6 are equipped with an air temperature/humidity sensor and a wind speed/direction sensor. Station 5 is a equipped with an air temperature/humidity sensor, a wind speed/direction sensor, a snow surface temperature sensor, an incoming and outgoing shortwave radiation sensor and an incoming longwave radiation sensor.

en: meteo-station dischma meteorological-data meteorological-measurements dischmex

envidat

en: EnviDat: the Environmental Data Portal

en: DISCHMEX - Meteorological measurements

de:
Resource Title: Meteorological measurements 2016
    Size: 279067 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/c4a42aa7-8b55-4f5e-8bf1-98c7b9ac2dc7/resource/89050c91-6a0e-4dd0-becd-272cfa7c5997/download/experimental_setup_2016.pdf
    Description: Location of the meteorological stations

de:
Resource Title: Meteorological measurements 2014
    Size: 1159577 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/c4a42aa7-8b55-4f5e-8bf1-98c7b9ac2dc7/resource/f61e3ca2-c536-4639-bc74-9ec68267173e/download/experimental_setup.pdf
    Description: Location of the meteorological stations

de:
Resource Title: Meteorological measurements 2016
    Size: 7715938 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c4a42aa7-8b55-4f5e-8bf1-98c7b9ac2dc7/resource/47c944c9-8f73-40ab-ac41-7273ee9a6ee4/download/data.zip
    Description: data

format: ZIP

media_type: application/zip

de:
Resource Title: Meteorological measurement 2014
    Size: 5503565 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c4a42aa7-8b55-4f5e-8bf1-98c7b9ac2dc7/resource/6da110c0-3ace-4958-911b-d89366660993/download/climaps-data.zip
    Description: data

de:
Resource Title: Meteorological measurement 2015
    Size: 13943 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c4a42aa7-8b55-4f5e-8bf1-98c7b9ac2dc7/resource/3bb31127-bc4f-47a3-860d-f0429b9907d6/download/sensorscope.xlsx
    Description: Overview sensors

de:
Resource Title: Meteorological measurements 2014
    Size: 14489 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c4a42aa7-8b55-4f5e-8bf1-98c7b9ac2dc7/resource/5193f4a9-be97-447f-8499-55616c2711e7/download/sensorscope.xlsx
    Description: Overview sensors

de:
Resource Title: Meteorological measurement 2015
    Size: 34228880 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c4a42aa7-8b55-4f5e-8bf1-98c7b9ac2dc7/resource/019aae08-8c90-4fb1-bd3b-f5e77a50b512/download/climaps-data.zip
    Description: data

de:
Resource Title: Meteorological measurement 2015
    Size: 1159577 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/c4a42aa7-8b55-4f5e-8bf1-98c7b9ac2dc7/resource/96d21f93-9ec1-4bea-a7ee-1fb9079d92e0/download/experimental_setup.pdf
    Description: Location of the meteorological stations

dischma

display_name: dischma

dischmex

display_name: dischmex

meteo-station

display_name: meteo-station

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements


en:
This dataset contains all data on which the following publication below is based.
Paper Citation:
 - Risch AC, Ochoa-Hueso R, van der Putten WH, Bump JK, Busse MD, Frey B, Gwiazdowicz DJ, Page-Dumroese DS, Vandegehuchte ML, Zimmermann S, Schütz M. Size-dependent loss of aboveground animals differentially affects grassland ecosystem coupling and functions. 2018. Nature Communications 9: 3684.  [doi: 10.1038/s41467-018-06105-4](https://doi.org/10.1038/s41467-018-06105-4).
Please cite this paper together with the citation for the datafile.
Methods
Study sites
The experimental exclosure setups were installed within the SNP (IUCN category Ia preserve; Dudley 2008), in south-eastern Switzerland. The park covers 172 km2 of forests and subalpine and alpine grasslands along with scattered rock outcrops and scree slopes. The entire area has been protected from human impact (no hunting, fishing, camping or off-trail hiking) since 1914. Large, fairly homogenous patches of short- and tall-grass vegetation, which originate from different historical management and grazing regimes, cover the park’s subalpine grasslands entirely. Short-grass vegetation developed in areas where cattle used to rest (nutrient input) prior to the park’s foundation (14th century to 1914) (Schütz and others 2003, 2006) and is dominated by lawn grass species such as Festuca rubra L., Briza media L. and Agrostis capillaris L. (Schütz and others 2003, 2006). Today, this vegetation type is intensively grazed by diverse vertebrate and invertebrate communities that inhabit the park and consume up to 60% of the available biomass (Risch and others 2013). Tall-grass vegetation developed where cattle formerly grazed, but did not rest, and is dominated by rather nutrient-poor tussocks of Carex sempervirens Vill. and Nardus stricta L. (Schütz and others 2003, 2006). This vegetation type receives considerably less grazing, with only roughly 20% of the biomass consumed (Risch and others 2013). Consequently, the two vegetation types together represent a long-term trajectory of changes in grazing regimes. Underlying bedrock of all grasslands is dolomite, which renders these grasslands rather poor in nutrients regardless of former and current land-use regimes.
Experimental design
To progressively exclude aboveground vertebrate and invertebrate animals, we established 18 size-selective exclosure setups (nine in short-grass, nine in tall-grass vegetation) distributed over six subalpine grasslands across the SNP (Risch and others 2013, 2015). Elevation differences of exclosure locations did not exceed 350 m (between 1975 and 2300 m a.s.l.). The exclosures were established immediately after snowmelt in spring 2009 and were left in place for five consecutive growing seasons (until end of 2013). They were, however, temporarily dismantled every fall (late October after first snowfall) to protect them from avalanches. They were re-established in the same location every spring immediately after snowmelt. Each size-selective exclosure setup consisted of five plots (2 x 3 m) that progressively excluded aboveground vertebrates and invertebrates from large to small. The plots are labelled according to the guilds that had access to them “L/M/S/I”, “M/S/I”, “S/I”, “I”, “None”; L = large mammals, M = medium mammals, S = small mammals, I = invertebrates, None = no animals had access. As we only had permission to have the experimental setup in place for five consecutive growing seasons, the experiment had to be completely dismantled in the late fall of 2013 and all material removed from the SNP.
Our exclosure design was aimed at excluding mammalian herbivores, but naturally also excluded the few medium and small mammalian predators, as well as the entire aboveground invertebrate food web. A total of 26 large to small mammal species can be found in the SNP, but large apex predators are missing (wolf, bear, lynx) . Reptiles, amphibians and birds are scarce to absent in the subalpine grasslands under study. Only two reptile species occur in the park and they are confined to rocky areas that warm up enough for them to survive. One frog species spawns in an isolated pond far from our grasslands. Only three bird species occasionally feed on the subalpine grasslands. Using game cameras (Moultrie 6MP Game Spy I-60 Infrared Digital Game Camera, Moultrie Feeders, Alabaster, AL, USA), we did observe that the medium- and small-sized mammals (marmot/hares and mice) were not afraid to enter the fences and feed on their designated plots. We never spotted reptiles, amphibians or birds on camera. We distinguished between 59 higher aboveground-dwelling invertebrate taxa that our size-selective exclosures excluded (see also methods for aboveground-dwelling invertebrates below).
The “L/M/S/I” plot (not fenced) was located at least 5 m from the 2.1 m tall and 7 x 9 m large main electrical fence that enclosed the other four plots. The bottom wire of this fence was mounted at 0.5 m height and was not electrified to enable safe access for medium and small mammals, while fencing out the large ones. Within each main fence, we randomly established four 2 x 3 m plots separated by 1-m wide walkways from one another and from the main fence line: 1) the “M/S/I” plots were unfenced, allowing access to all but the large mammals; 2) the “S/I” plots (10 x 10 cm electrical mesh fence) excluded all medium-sized mammals. Note that the bottom 10 cm of this fence remained non-electrified to enable safe access for small mammals; 3) the “I” plots (2 x 2 cm metal mesh fence) excluded all mammals. We double-folded the mesh at the bottom 50 cm to reduce the mesh size to smaller than 1 x 1 cm openings; and 4) the “None” plots were surrounded by a 1 m tall mosquito net (1.5 x 2 mm) to exclude all animals. The top of the plot was covered with a mosquito-meshed wooden frame mounted to the corner posts (roof). We treated these plots a few times with biocompatible insecticide (Clean kill original, Eco Belle GmbH, Waldshut-Tiengen, Germany) to remove insects that might have entered during data collection or that hatched from the soil, but amounts were negligible and did not impact soil moisture conditions within these plots.
To assess whether the design of the “None” exclosure (mesh and roof) affected the response variables within the plots and, therefore, influenced the results, we established an additional six “micro-climate control” exclosures (one in each of the six grasslands) (Risch and others 2013, 2015). These exclosures were built as the “None” exclosures but were open at the bottom (20 cm) of the 3-m side of the fence facing away from the prevailing wind direction to allow invertebrates to enter. A 20-cm high and 3-m long strip of metal mesh was used to block access to small mammals. Thus, this construction allowed a comparable micro-climate to the “None” plots, but also a comparable feeding pressure by invertebrates to the “I” plots. We compared various properties within these exclosures against one another to assess if our construction altered the conditions in the “None” plots. We showed that differences in plant (e.g., vegetation height, aboveground biomass) and soil properties (e.g., soil temperature, moisture) found between the “I” and the “None” treatments were not due to the construction of the “None” exclosure, but a function of animal exclusions, although the amount of UV light reaching the plant canopy was significantly reduced (Risch and others 2013).
Aboveground invertebrate sampling
Aboveground invertebrates were sampled with two different methods to capture both ground- and plant-dwelling organisms: 1) we randomly placed two pitfall traps (67 mm in diameter, covered with a roof) filled with 20% propylene glycol in one 1 x 1 m subplot of the 2 x 3 m treatment plots in spring 2013 (May) and emptied them every two weeks until late September 2013 (Vandegehuchte and others 2017b, 2018). A pitfall trap consisted of a plastic cylinder (13 cm depth, 6.75 cm diameter). Within each cylinder we placed a 100 ml plastic vial with outer diameter 6.70 cm and on top of the cylinder we placed a plastic funnel to guide the invertebrates into the vials. Each trap was cover with a cone-shaped and transparent plastic roof to protect the trap from rain (Vandegehuchte and others 2017b, 2018). Note that in the “None” plots only one trap was placed as control to check for effectiveness of the exclosure. 2) We vacuumed all invertebrates from a 60 x 60 cm area on another 1 x 1 m subplot with a suction sampler (Vortis, Burkhard manufacturing CO, Ltd., Rickmansworth, Hertfordshire, UK) every month from June to September 2013 (Vandegehuchte and others 2017b, 2018). For this purpose, we quickly placed a square plastic frame (60 x 60 x 40 cm) with a closable mosquito mesh sleeve attached to the top edge into the plot from the outside. The suction sample was then inserted into through the sleeve and operated for 45 s to collect the invertebrates (Vandegehuchte and others 2017b, 2018).
We sorted the ≈100 000 individuals collected with both methods by hand and identified each individual morphologically to the lowest taxonomic level feasible (59 taxa, including orders, suborders, subfamilies, families; phylum for Mollusca). These taxa belonged to the following feeding types: 19 herbivores, 16 detritivores, 9 predators, 8 mixed feeders, 5 omnivores and 2 non-classified feeders (or not feeding as adults) (Vandegehuchte and others 2017b). We summed the numbers from the two pitfall traps and the suction sampling over the course of the 2013 season to represent the aboveground invertebrate abundance and community composition of a plot. Note: we did not specifically attempt to catch flying invertebrates with e.g., sticky traps, thus a few flying insects may have been missed with our vacuum sampling approach.
Sampling of plant properties
The vascular plant species composition was assessed at peak biomass every summer (July) by estimating the frequency of occurrence of each species with the pin count method in each plot (Frank and McNaughton 1990). A total of 172 taxa occurred within our 90 plots and we calculated plant species richness for each plot separately. We used the 2013 data in this study. Plant quality was assessed every year in July and September; here we use plant quality at the end of the experiment (September 2013). Two 10 x 100 cm wide strips of vegetation per plot were clipped, combined, dried at 65°C, and ground (Pulverisette 16, Fritsch, Idar-Oberstein, Germany) to pass through a 0.5 mm sieve. Twenty randomly selected samples across all treatments were analysed for N (Leco TruSpec Analyser, Leco, St. Joseph, Michigan, USA) (Vandegehuchte and others 2015). Nitrogen concentrations of the other samples were then estimated from models established for the experiment and the entire SNP relating Fourier transform-near infrared reflectance (FT-NIR) spectra to the measured values of N using a multi-purpose FT-NIR spectrometer (Bruker Optics, Fällanden, Switzerland) (Vandegehuchte and others 2015). Root biomass was sampled every fall by collecting five 2.2 cm diameter x 10 cm deep soil samples (Giddings Machine Company, Windsor, CO, USA) per plot (450 samples year-1). The samples were dried at 30 °C and roots were sorted from the sample by hand. We sorted each sample for 1 h which allowed to retrieve over 90% of all roots present in the samples (Risch and others 2013). The roots were then dried at 65 °C for 48 and weighed to the nearest mg. We averaged the values per plot and used the 2013 data only in this study.
Sampling of edaphic communities
In 2009, 2010, and 2011 we collected three composited soil samples (5 cm diameter x 10 cm depth; AMS Samplers, American Falls, ID, USA) and assessed bacterial community structure using T-RFLP profiling (Liu and others 1997; Blackwood and others 2003; Hodel and others 2014). We detected a total of 89 operational taxonomic units (OTUs). These values are in accordance with other studies reporting OTU richness (Wirthner and others 2011; Zumsteg and others 2012; Meola and others 2014) using T-RFLP profiling, a method that detects the most abundant, and thus likely, the most relevant, taxa. We averaged the data over the three years of collections for our calculations. Microbial biomass carbon (MBC) was determined with the substrate-induced method (Anderson and Domsch 1978) every fall (September) between 2009 and 2013 by collecting three mineral soil samples (5 cm diameter × 10 cm mineral soil core, AMS Samplers, American Falls, ID, USA). The three samples were combined (90 samples for each sampling year), immediately put on ice, taken to the laboratory, passed through a 2-mm sieve and stored at 4°C. Again, we only used the 2013 data in this study.
Soil samples (5 cm diameter x 10 cm depth) to extract soil arthropods were collected in June, July, and August 2011 with a soil corer lined with a plastic sleeve to ensure an undisturbed sample (total of 270 samples). The plastic line core was immediately sealed on both ends using cling film and put into a cooler. All plots were sampled within three days and the extraction of arthropods started the evening of the sampling day using a high-gradient Tullgren funnel apparatus (Crossley and Blair 1991; Vandegehuchte and others 2015). Samples were kept in the extractor for four days and the soil arthropods were collected in 95% ethanol. All individuals were counted and each individual was identified morphologically to the lowest level feasible [76 taxa, including orders, suborders, subfamilies, families (Protura, Thysanoptera, Aphidina, Psylina, Coleoptera, Brachycera, Nematocera, Auchenorryncha, Heteroptera, Formicidae); sub-phylum for Myriapoda, for Acari and Collembola also including morpho-species). Note that we also included larval stages (nine of the 76 taxa) (Vandegehuchte and others 2015). All data were summed over the season. A detailed species list for mites and collembolans is published (Vandegehuchte and others 2017a) [https://doi.org/10.1371/journal.pone.0118679.s001]. Earthworms are rare in the SNP and therefore were not included. We collected eight random 2.2 cm diameter x 10 cm deep soil cores from each plot in September 2013 to determine the soil nematode community composition. The samples were mixed and the nematodes were extracted from 100 ml of fresh soil using Oostenbrink elutriators (Oostenbrink 1960). All nematodes in a 1 ml of the 10 ml extract were counted, a minimum of 150 individuals sample-1 were identified to genus or family level using (Bongers 1988), the numbers of all nematodes were extrapolated to the entire sample and expressed for a 100 g dry sample. In total we identified 63 genus or family levels (Vandegehuchte and others 2015). The list of all the nematodes found is published (Vandegehuchte and others 2015) [http://www.oikosjournal.org/appendix/oik-03341] or DOI: [doi: 10.1111/oik.03341].
We are aware that sampling soil microbes from 2009 to 2011 and soil arthropods in 2011 was not ideal, but we are positive that this does not bias the results. Most of the parameters measured in our experiment either already showed a treatment response after the first growing season (e.g., plant biomass) or did not respond over the entire time experiment (e.g., microbial biomass C). The microbial community composition (2009 – 2011) was highly influenced by inter-annual differences in temperature and precipitation, but did not differ between treatments or vegetation types (Hodel and others 2014). We therefore felt comfortable using the 2009 through 2011 data for describing the soil microbial community in our experimental treatments. Similarly, we are positive that our soil arthropod data are representative. We did assess soil arthropods in August 2012 and found no differences to the August 2011 data. However, we did not feel comfortable combining the 2011 June, July, August data with only August data for 2012 for our analyses.
Sampling of soil properties
We collected three soil samples (5 cm diameter x 10 cm depth) in each plot in September 2013 after removing the vegetation. First, we collected the top layer of mineral soil rich in organic matter, the surface organic layer or rhizosphere, typically 1 to 3 cm in depth with a soil corer (AMS Samples, American Falls, Idaho, USA). Second, we collected a 10 cm mineral soil core beneath this surface layer. The cores for each layer were composited, dried at 65 °C for 48 h and fine-ground to pass a 0.5 mm screen. We then analysed all samples for total C using a Leco TruSpec Analyser (Leco, St. Joseph, Michigan, USA). Mineral soil pH was measured potentiometrically in 1:2 soil:CaCl2 solution with an equilibration time of 30 min.
Soil net N mineralisation was assessed during the 2013 growing season (Risch and others 2015). For this purpose, we randomly collected a 5 cm diameter x10 cm deep soil sample with a soil corer (AMS Samples, American Falls, Idaho, USA) after clipping the vegetation in June 2013. After weighing and sieving (4 mm mesh) the soil, we extracted a 20 g subsample in 1 mol l-1 KCl for 1.5 h on an end-over-end shaker and thereafter filtered it through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnenmühle FineArt GmbH, Dassel, Germany). From these filtrates NO3- concentrations were measured colorimetrically (Norman and Stucki 1981) and NH4+with flow injection analysis (FIAS 300, Perkin Elmer, Waltham Massachusetts, USA) (Risch and others 2015). We dried the rest of the sample 105 °C to constant mass to determine fine,fraction bulk density. A second soil sample was collected within each plot in June 2013 with a corer lined with a 5 x 13 cm aluminium cylinder. The corer was driven 11.5 cm deep into the soil so that the top 1.5 cm of the cylinder remained empty. Into this space we placed a polyester bag (250 µm) filled an ion-exchanger resin to capture the incoming N. The bag was filled with a 1:1 mixture of acidic and alkaline exchanger resin (ion-exchanger I KA/ion exchanger IIIAA, Merck AG, Darmstadt, Germany). We then removed 1.5 cm soil at the bottom of the cylinder and placed a second resin exchanger bag into this space to capture the N leached from the soil column. To assure that the exchange resin was saturated with H+ and Cl- prior to filling the bags, the mixture was stirred with 1.2 ml l-1 HCl for 1 h and then rinsed with demineralized water until the electrical conductivity of the water reached 5 µm cm-1. The cylinder with the resin bags in place was reinserted into the soil with the top flush to the soil surface and incubated for three months. We recollected the cylinders in September 2013. Each resin bag and 20 g of sieved soil (4 mm mesh) from each cylinder were then separately extracted with KCl and NO3- and NH4+ concentrations were measured. Nitrate and NH4+ concentrations of all samples were then converted to a content basis by multiplying their values with fine fraction bulk density. Net N mineralisation was thereafter calculated as the difference between the N content of the samples collected at the end of the three-month incubation (including the N extracted from the bottom resin bag) and the N content at the beginning of the incubation (Risch and others 2015).
Soil CO2 emissions were measured every two weeks between 0900 and 1700 hrs from early May through late September 2013 with a PP-Systems SRC-1 soil respiration chamber (15 cm high, 10 cm diameter; closed circuit) attached to a PP-Systems EGM-4 infrared gas analyser (PP-Systems, Amesbury, MA, USA) on two locations per plot (Risch and others 2013). The chamber was placed on randomly placed, permanently installed PVC collars (10 cm diameter) driven 5 cm into the soil at the beginning of the study (Risch and others 2013). Freshly germinated plants growing within the collars were removed prior to each measurement to avoid measuring plant respiration or photosynthesis. The two measurements collected per plot and sampling date were averaged.
Soil moisture (with time domain reflectometry; Field-Scout TDR-100, Spectrum Technologies, Plainfield, Illionois, USA) and temperature (with a waterproof digital pocket thermometer; Barnstead International, Dubuque, Iowa, USA) were measured at five random locations per plot every two weeks during the growing seasons during the experiment for the 0 to 10 cm depth (Risch and others 2013, 2015). As soil moisture and soil temperature were highly negatively correlated (Risch and others 2013), we only used soil moisture for this study. We used plot-level averages of all values available to capture soil moisture variability during the five years of the experiment. The results remained unchanged when we only used soil moisture from the 2013 growing season.
Numeral calculations and statistical analyses
Ecosystem coupling. We conducted principal component analyses (PCAs; unscaled) at the complete dataset level using the abundances of each taxonomical entity to describe each of the five different communities used in this study: aboveground-dwelling invertebrates, vascular plants, soil microorganisms, soil arthropods and soil nematodes. We retained the first two components (PCA axis 1 and PCA axis 2) of each analysis as we found them to adequately represent the temporal and spatial variability of our 90 treatment plots in previous studies55,67. Together they explained a total of 71.70% of the variation for aboveground invertebrates, 44.36% for plants, 44.85% for soil microorganisms, 61.85% for soil arthropods and 77.19% for soil nematodes. In addition, we used soil pH and soil organic C content as a proxy for soil chemical properties, soil bulk density as a proxy for soil physical properties and soil moisture (negatively correlated with soil temperature) as a proxy for soil micro-climatic conditions for an overall total of fourteen constituents.
We calculated ecosystem coupling9 for each exclosure treatment within each vegetation type (i.e., 2  5 treatment combinations in total) as an integrated measure of pairwise ecological interactions between ecosystem constituents representing ecological communities and the soil abiotic environment. These ecological interactions are defined by non-parametric Spearman rank correlation analyses between two constituents, excluding interactions involving two abiotic constituents (e.g., soil pH vs. soil moisture) and interactions between the first (PC1) and second (PC2) component of each community type, as these are orthogonal by definition. Interactions between abiotic constituents were excluded from the analyses because the focus of our study was on communities and how they interact with one another and their surrounding environment; therefore, including abiotic-abiotic interactions was not of interest here. Given that the effectiveness of our experimental design resulted in that no community composition data of aboveground-dwelling invertebrates was available for the “None” plots (all animals excluded), only thirteen instead of fourteen constituents were included in the ecosystem coupling calculations for this treatment. The complete absence of aboveground invertebrates represents the most extreme case of disturbance between aboveground animal communities and the rest of the ecosystem constituents. This may have resulted in a slight overestimation of ecosystem coupling for these plots.
	Average ecosystem coupling was calculated as follows:
Ecosystem coupling=
where Xi is the absolute Coupling was calculated value of the Spearman’s rho coefficient of the ith correlation for each treatment within each vegetation type (i.e., based on nine replicates each), considering and n is the number of pairwise comparisons considered (n = a total of 80;  interactions (56 in the case of the “None” treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the “None” treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the “None” treatment).	Coupling was calculated for each treatment within each vegetation type (i.e., based on nine replicates each), considering a total of 80 interactions (56 in the case of the “None” treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the “None” treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the “None” treatment).
To establish whether constituents were significantly and positively coupled within treatments (i.e., the average of their correlation coefficients were greater than in a null model where correlation only happens by chance), we calculated one-tailed p-values based on permutation tests with 999 permutations.

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

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