en:
This dataset contains all data on which the following publication below is based.
Paper Citation:
Risch Anita C., Zimmermann, Stefan, Moser, Barbara, Schütz, Martin, Hagedorn, Frank, Firn, Jennifer, Fay, Philip A., Adler, Peter B., Biederman, Lori A., Blair, John M., Borer, Elizabeth T., Broadbent, Arthur A.D., Brown, Cynthia S., Cadotte, Marc W., Caldeira, Maria C., Davies, Kendi F., di Virgilio, Augustina, Eisenhauer, Nico, Eskelinen, Anu, Knops, Johannes M.H., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Prober, Suzanne M., Seabloom, Eric W., Siebert, Julia, Silveira, Maria L. , Speziale, Karina L., Stevens, Carly J., Tognetti, Pedro M., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties. Global Change Biology
Please cite this paper together with the citation for the datafile.
We assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth’s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).

en: Anthropogenic change and soil net N mineralization

en: nitrification mineralization fertilization climate nutrient-network grassland global-change nitrogen soil ammonification herbivore

ammonification

display_name: ammonification

climate

display_name: climate

fertilization

display_name: fertilization

global-change

display_name: global-change

grassland

display_name: grassland

herbivore

display_name: herbivore

mineralization

display_name: mineralization

nitrification

display_name: nitrification

nitrogen

display_name: nitrogen

nutrient-network

display_name: nutrient-network

soil

display_name: soil


$oid: OBSOLETE

en:
Monitoring terrestrial mammals through eDNA in rivers of northern Namibia
In 2022, we collected 20 water samples from 10 stations two rivers of Namibia. Samples were taken from the side of the river. At each station, we performed two filtration replicates using a peristaltic pump to conduct environmental DNA (eDNA) sampling. Each filtration targeted a maximum duration of 30 minutes, during which approximately 30 liters of water were filtered through each capsule. After filtration, the water inside the capsules was removed, and the capsules were filled with 50 ml of conservation buffer for preservation at room temperature. We followed strict contamination control protocols throughout both the fieldwork and laboratory processes, adhering to the guidelines of Valentini et al. (2016). We used eDNA primers targeting bacteria, bivalves, fish, vertebrates (V05) and all Eukaryotes (Euka03). To prevent contamination, each sample was processed using disposable gloves and single-use filtration equipment. The MiSeq Reagent Kit v3 (2x75 bp) (Illumina, San Diego, CA, USA) was used for paired-end sequencing at a theoretical sequencing depth of 200,000 reads per sample.
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

en: bivalves eukaryotes namibia edna metabarcoding

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: Environmental DNA Freshwater Namibia Namibia 2022

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

bivalves

display_name: bivalves

edna

display_name: edna

eukaryotes

display_name: eukaryotes

metabarcoding

display_name: metabarcoding

namibia

display_name: namibia


en: This dataset provides data on the occurrence of 672 European bryophyte species in European countries or territories. All species which are considered threatened or near threatened according to the European Red List (Hodgetts et al. 2019) are included (553 species). Further, all species evaluated in the European Red List assessed as LC, DD, NE and EX, which at the same time are either endemic, near endemic or globally rare, are included (119 species). Therefore, the list contains not only all European bryophyte species that are threatened or near threatened, but also all species endemic or near endemic to Europe as well as all globally rare species occurring in Europe. For each of the 553 threatened or near threatened species, we provide a conservation a priority score which was calculated based the Red List status and the proportion of the European population of the global population or the global rarity of the species. For details on the prioritisation approach see Bisang et al. (2025).

en: red-list conservation-priority europe range-size rarity bryophytes endemic

en: Priority ranking for the conservation of all European bryophyte species - dataset

format: XLS

bryophytes

display_name: bryophytes

conservation-priority

display_name: conservation-priority

endemic

display_name: endemic

europe

display_name: europe

range-size

display_name: range-size

rarity

display_name: rarity

red-list

display_name: red-list


en:
CHELSACanaryClim is a high-resolution, terrain-informed downscaling model developed specifically for the Canary Islands. It builds on the CHELSA v1.2 framework and integrates extensive local meteorological station data to refine temperature and precipitation estimates. The model incorporates high-resolution elevation data (down to 5 m), planetary boundary layer height, and ERA-Interim reanalysis inputs to simulate fine-scale climatic variation in mountainous island terrain.
CHELSACanaryClim-climatologies is a very-high resolution climate dataset generated with the CHELSACanaryClim downscaling model. It consists of monthly climatologies of air-temperatures and precipitation.

en: air-temperature climate precipitation canaries climatologies

en: CHELSACanaryClim-climatologies

de:
Resource Title: CHELSACanaryClim-climatologies: Daily Minimum Near-Surface Air Temperature
    Size: 104857600 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fchelsa01%2F&prefix=chelsa_canaryclim%2Fcanaries%2Fclimatologies%2Ftasmin%2F
    Description: Daily Minimum Near-Surface Air Temperature

Files are provided as Cloud-Optimized GeoTIFFs (COGs) with standardized metadata.

Each COG encodes scale and offset parameters in its metadata. Standards-compliant software will apply these fields automatically and return values in the correct units; however, legacy or non-compliant applications may not. If there is any uncertainty regarding units, please consult the file metadata to verify the applied scale/offset and units.

de:
Resource Title: CHELSACanaryClim-climatologies: Precipitation
    Size: 83886080 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fchelsa01%2F&prefix=chelsa_canaryclim%2Fcanaries%2Fclimatologies%2Fpr%2F
    Description: Precipitation

Files are provided as Cloud-Optimized GeoTIFFs (COGs) with standardized metadata.

Each COG encodes scale and offset parameters in its metadata. Standards-compliant software will apply these fields automatically and return values in the correct units; however, legacy or non-compliant applications may not. If there is any uncertainty regarding units, please consult the file metadata to verify the applied scale/offset and units.

de:
Resource Title: CHELSACanaryClim-climatologies: Daily Maximum Near-Surface Air Temperature
    Size: 104857600 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fchelsa01%2F&prefix=chelsa_canaryclim%2Fcanaries%2Fclimatologies%2Ftasmax%2F
    Description: Daily Maximum Near-Surface Air Temperature

Files are provided as Cloud-Optimized GeoTIFFs (COGs) with standardized metadata.

Each COG encodes scale and offset parameters in its metadata. Standards-compliant software will apply these fields automatically and return values in the correct units; however, legacy or non-compliant applications may not. If there is any uncertainty regarding units, please consult the file metadata to verify the applied scale/offset and units.

de:
Resource Title: CHELSACanaryClim-climatologies: Daily Mean Near-Surface Air Temperature
    Size: 83886080 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fchelsa01%2F&prefix=chelsa_canaryclim%2Fcanaries%2Fclimatologies%2Ftas%2F
    Description: Daily Mean Near-Surface Air Temperature

Files are provided as Cloud-Optimized GeoTIFFs (COGs) with standardized metadata.

Each COG encodes scale and offset parameters in its metadata. Standards-compliant software will apply these fields automatically and return values in the correct units; however, legacy or non-compliant applications may not. If there is any uncertainty regarding units, please consult the file metadata to verify the applied scale/offset and units.

air-temperature

display_name: air-temperature

canaries

display_name: canaries

climate

display_name: climate

climatologies

display_name: climatologies

precipitation

display_name: precipitation


en:
CHELSA is a mechanistic downscaling model that links large-scale atmospheric conditions with local topographic factors to produce very high-resolution climate data. It includes commonly used climate variables for impact modeling, such as air temperature, precipitation, humidity, solar radiation, wind speed, and derived variables.
CHELSAch-highres-climatologies is a high resolution climate dataset generated with the CHELSA downscaling model. It consists of long term - 30 year mean aggregated surface variables.
Climatological summaries are provided for: Near-Surface Wind Speed, Daily Mean Near-Surface Air Temperature, Daily Maximum Near-Surface Air Temperature, Daily Minimum Near-Surface Air Temperature. Air Temperature Lapse Rate, Total Cloud Cover Percentage, Precipitation, Surface Downwelling Shortwave Flux in Air
Files are provided in NetCDF format with standardized metadata.

en: precipitation humidity wind-speed air-temperature solar-radiation air-pressure climate

en: CHELSAch-highres-climatologies at high resolution

de:
Resource Title: CHELSAch-highres-climatologies: Daily Maximum Near-Surface Air Temperature
    Size: 129184563 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fch%2Fclimatologies%2Ftasmax%2F
    Description: Climatologies of Daily Maximum Near-Surface Air Temperature

Files are provided in NetCDF format with standardized metadata.

Each file encodes scale and offset parameters in its metadata. Standards-compliant software will apply these fields automatically and return values in the correct units; however, legacy or non-compliant applications may not. If there is any uncertainty regarding units, please consult the file metadata to verify the applied scale/offset and units.

de:
Resource Title: CHELSAch-highres-climatologies: Daily Mean Near-Surface Air Temperature
    Size: 129184563 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fch%2Fclimatologies%2Ftas%2F
    Description: Climatologies of Daily Mean Near-Surface Air Temperature

Files are provided in NetCDF format with standardized metadata.

Each file encodes scale and offset parameters in its metadata. Standards-compliant software will apply these fields automatically and return values in the correct units; however, legacy or non-compliant applications may not. If there is any uncertainty regarding units, please consult the file metadata to verify the applied scale/offset and units.

de:
Resource Title: CHELSAch-highres-climatologies: Precipitation
    Size: 129184563 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fch%2Fclimatologies%2Fpr%2F
    Description: Climatologies of Precipitation

Files are provided in NetCDF format with standardized metadata.

Each file encodes scale and offset parameters in its metadata. Standards-compliant software will apply these fields automatically and return values in the correct units; however, legacy or non-compliant applications may not. If there is any uncertainty regarding units, please consult the file metadata to verify the applied scale/offset and units.

de:
Resource Title: CHELSAch-highres-climatologies: Daily Minimum Near-Surface Air Temperature
    Size: 129184563 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fch%2Fclimatologies%2Ftasmin%2F
    Description: Climatologies of Daily Minimum Near-Surface Air Temperature

Files are provided in NetCDF format with standardized metadata.

Each file encodes scale and offset parameters in its metadata. Standards-compliant software will apply these fields automatically and return values in the correct units; however, legacy or non-compliant applications may not. If there is any uncertainty regarding units, please consult the file metadata to verify the applied scale/offset and units.

air-pressure

display_name: air-pressure

humidity

display_name: humidity

solar-radiation

display_name: solar-radiation

wind-speed

display_name: wind-speed


en:
This dataset contains all data on which the following publication below is based.
Paper Citation:
Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Schütz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2
Please cite this paper together with the citation for the datafile.
We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/

en: soil-biology worldwide soil-chemistry soil nutrient-network grassland mineralization global

global

display_name: global

grassland

display_name: grassland

mineralization

display_name: mineralization

nutrient-network

display_name: nutrient-network

soil

display_name: soil

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide


en: This work presents the numerical study of the thermal stabilisation methods to slow this permafrost thawing, protecting from landscapes and infrastructure damaging at an alpine site. We investigate different methods based on passive and active cooling system. The best approach mixes both methods and manages heat flow, potentially allowing excess energy to be used locally. This dataset allows one to test and check the different thermal stabilisation methods.

en: thermal-insulation climate-change permafrost thermal-stabilisation barrier-layer permafrost-degradation heat-pump solar-energy swiss-alps cooling-pipes

en: Assessment of thermal stabilisation measures based on numerical simulations at a Swiss Alpine permafrost site

format: ZIP

barrier-layer

display_name: barrier-layer

climate-change

display_name: climate-change

cooling-pipes

display_name: cooling-pipes

heat-pump

display_name: heat-pump

permafrost

display_name: permafrost

permafrost-degradation

display_name: permafrost-degradation

solar-energy

display_name: solar-energy

swiss-alps

display_name: swiss-alps

thermal-insulation

display_name: thermal-insulation

thermal-stabilisation

display_name: thermal-stabilisation


en:
Species range limits are expected to be dramatically altered under future climate change and many species are predicted to shift their distribution upslope to track their suitable conditions (i.e. based on their niche). However, there might be large discrepancies between the speed of the upward shift of the climatic niche and the actual migration velocity of the species, especially in long-lived organisms such as trees. Here, we compared the simulations of the upslope displacement of the bioclimatic envelope of 16 tree species inhabiting temperate mountain forests under ongoing and future climate change obtained by correlative species distribution models (SDMs) to those from a dynamic forest model accounting for dispersal, competition and demography. We then partitioned the discrepancy in upslope migration velocity between the SDMs and the dynamic forest model into different components by manipulating dispersal limitation, interspecific competition and demography.
This dataset contains the calibration and evaluation data used to create the bioclimatic envelope models, the predictors for the future scenarios (raster layers) and the bioclimatic input data used in the dynamic forest models used in the following publication (Scherrer et al. 2020).
Paper Citation:
Scherrer, D., Vitasse, Y., Guisan , A., Wohlgemuth, T., & Lischke, H. (2020). Competition and demography rather than dispersal limitation slow down upward shifts of trees’ upper elevation limits in the Alps. Journal of Ecology, in press.

en: Factors slowing down upward shifts of trees’ upper elevation limits

en: climate-change-impact treemig sdm mountain-forest treeline

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:
Resource Title: Bioclimatic data for TreeMig
    Size: 577449363 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/50bcbe58-bdbb-4937-aa83-0b983a7ff64d/resource/e2a4c17d-2b94-4d5e-8b3d-85c670b94285/download/treemig_bioclim_1500-2500.zip
    Description: Bioclimatic input data used for the TreeMig models in Scherrer et al. 2020. Data spans the whole simulation period of 1500 until 2500 and covers three different climate change scenarios.

media_type: application/zip

de:
Resource Title: Data for bioclimatic envelope model
    Size: 2745109 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/50bcbe58-bdbb-4937-aa83-0b983a7ff64d/resource/cbd5e811-0e38-45ed-b00e-fe7a86ed88db/download/species_environmental_calibrationevaluation.csv
    Description: This spreadsheet provides the data used to calibrate and evaluate the bioclimatic envelope models (SDMs) for our 16 target tree species. For copyright reasons the geographic coordinates of the points have been removed and only relevant information in the environmental space is provided.

format: CSV

media_type: text/csv

de:
Resource Title: Bioclimatic predictors used for future projections
    Size: 4002741 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/50bcbe58-bdbb-4937-aa83-0b983a7ff64d/resource/dcd9614c-30b0-4d36-ab09-2196b3b0ef7c/download/predictors.zip
    Description: The seven bioclimatic predictors (mean annual temperature, minimum temperature of the coldest month, temperature of the driest quarter, annual sum of precipitation, sum of precipitation of the driest quarter, slope and topographic position (concave-convex) used for correlative SDMs in Scherrer et al. 2020. Data for climate is available for both current (1981-2010) and future (2020-49, 2045-74,2070-99).

climate-change-impact

display_name: climate-change-impact

mountain-forest

display_name: mountain-forest

sdm

display_name: sdm

treeline

display_name: treeline

treemig

display_name: treemig


en:
Background information
The availability of nitrogen (N) and phosphorus (P) is considered to be a major factor limiting growth and productivity in terrestrial ecosystems globally. This project aimed to determine whether the growth stimulation documented in previous short‐term fertilisation trials persisted in a longer‐term study (12 years) in the treeline ecotone, and whether possible negative effects of nutrient addition offset the benefits of any growth stimulation.
Over the course of the 12 study years, NPK fertiliser corresponding to 15 or 30 kg N ha−1 a−1 was added annually to plots containing 30‐year‐old *Larix decidua* or 32‐year-old *Pinus uncinata* individuals with an understorey of mainly ericaceous dwarf shrubs. To quantify growth, annual shoot increments of trees and dwarf shrubs as well as radial growth increments of trees were measured. Nutrient concentrations in the soil were also measured and the foliar nutritional status of trees and dwarf shrubs was assessed.
 Experimental design
Over an elevation gradient of 140 m across the treeline afforestation site Stillberg, 22 locations were chosen that covered the whole range of microenvironmental conditions (*see* Nutrient addition experimental design.png). Half of the blocks included European larch (*L. decidua*) and the other half included mountain pine (*P. uncinata*). Within each block, three plantation quadrats were randomly selected as experimental plots and each plot was assigned to a control (no fertilisation) or to one of two fertiliser dose treatments (15 kg and 30 kg N ha−1 a−1). Treatments were assigned randomly but confined so that the location of fertilised plots within a block was not directly above control plots to avoid nutrient input from drainage. For details about the experiment, *see* Möhl et al (2019).
 Data description
The available datasets contain climate variables (2004-2016), nutrient isotope measurements (2010 & 2016), shrub growth measurements (2004-2016), soil parameter measurements and annual ring and shoot measurements (2004-2016). All data can be found here:
-https://figshare.com/articles/dataset/Twelve_years_of_low_nutrient_input_stimulates_growth_of_trees_and_dwarf_shrubs_in_the_treeline_ecotone/7025858-

en: tree-rings nitrogen-isotope dwarf-shrubs stillberg fertilization treeline

en: Nutrient addition experiment at the Alpine treeline site Stillberg, Switzerland

envi

en: Environment

de:
Resource Title: Metadata Nutrient Addition Stillberg Experimental Design
    Size: 1327266 bytes
    Format: image/png
    URL: https://www.envidat.ch/dataset/3bc7cad9-38af-453a-8f1e-3afb23000c62/resource/3874f6f4-be83-403a-b2f8-ab1a0dd465d3/download/nutrient_addition_experimental_design.png
    Description: Nutrient addition map of the study area with the investigated plots. Each circle (study block) contains one control plot and two plots that received NPK fertilizer corresponding to 15 (NPK15) or 30 (NPK30) kg N ha-1 a-1. Contour lines specify the elevation (m a.s.l.) in steps of 10 m.

format: PNG

media_type: image/png

de:
Resource Title: Data Nutrient Addition Stillberg
    Size: 339539 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/3bc7cad9-38af-453a-8f1e-3afb23000c62/resource/1eb39a79-2953-4aee-b7f0-b63744fef1e0/download/7025858.zip
    Description: This link leads to the figshare repository, which contains all nutrient addition data.

dwarf-shrubs

display_name: dwarf-shrubs

fertilization

display_name: fertilization

nitrogen-isotope

display_name: nitrogen-isotope

stillberg

display_name: stillberg

tree-rings

display_name: tree-rings


experimental-rockfall-trilogy-of-surava

en: We performed an experimental trilogy of induced rockfall experiments in a spruce stand in Surava (CH) within (i) the original forest, (ii) after a logging job, resulting in lying deadwood and (iii) the cleared, deadwwod-free state. The three experimental set-ups allow quantifying the deadwood effect on overall rockfall risk for the same forest (slope, species) in three different conditions.

en: forest gyroscope induced-rockfall rockfall gnss deposition-points lying-deadwood in-situ acceleration

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.

en: Experimental rockfall trilogy of Surava

title_for_slug: experimental-rockfall-trilogy-of-surava

de:
Resource Title: StoneNode Data Analysis Surava
    Size: 37282 bytes
    Format: application/vnd.wolfram.mathematica.package
    URL: https://www.envidat.ch/dataset/4da88d3f-5627-40d2-843d-c884a31238b1/resource/a1df211b-e2f9-40b7-86fe-b39624fe195a/download/surava_gyr_analysis_20210928_vfinal.m
    Description: Matlab script to detect frontal impacts (FI) on opposing objects within the in situ gathered StoneNode gyroscopic data of the Surava rockfall trilogy.

de:
Resource Title: In situ sensor data cleared area
    Size: 50806879 bytes
    Format: application/x-matlab
    URL: https://www.envidat.ch/dataset/4da88d3f-5627-40d2-843d-c884a31238b1/resource/dbad6e48-4c45-4e81-9345-fdb98c37c584/download/clr_data.mat
    Description: Matlab structure of all used StoneNode sensors which were several times released into the cleared forest state.

de:
Resource Title: ReleasePoint
    Size: 2360 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4da88d3f-5627-40d2-843d-c884a31238b1/resource/e57401a7-b80d-4055-838c-5582710f870b/download/release_lv95.zip
    Description: Zipped shape file of releasepoint used in RAMMS::ROCKFALL Simulations. Slightly shiftet from originally measured in DNSS Position, as this position was on the flat forest road.

de:
Resource Title: Trees_Surava_DW_CLR
    Size: 29181 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4da88d3f-5627-40d2-843d-c884a31238b1/resource/f1fea988-8bb2-40b4-a085-d2f2b3bea9e4/download/surava_trees_dw_clr.zip
    Description: Standing trees after forest management intervention

de:
Resource Title: In situ sensor data original forest
    Size: 49732594 bytes
    Format: application/x-matlab
    URL: https://www.envidat.ch/dataset/4da88d3f-5627-40d2-843d-c884a31238b1/resource/dde076b7-f5a8-4943-953d-8a66559d1b4d/download/org_data.mat
    Description: Matlab structure of all used StoneNode sensors which were several times released into the original forest state.

de:
Resource Title: DEM_Surava_5cm_Resolution
    Size: 116938116 bytes
    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/4da88d3f-5627-40d2-843d-c884a31238b1/resource/f75eab92-8c62-45e0-b2b9-4729e244b241/download/dhm.asc
    Description: Used DEM of the site, based on the LiDAR Pointcloud

format: pgp-keys

de:
Resource Title: In situ sensor data deadwood
    Size: 19834192 bytes
    Format: application/x-matlab
    URL: https://www.envidat.ch/dataset/4da88d3f-5627-40d2-843d-c884a31238b1/resource/9ab17365-ef04-4fce-b9bc-8fa50a57123c/download/dw_data.mat
    Description: Matlab structure of each In situ StoneNode sensor installed in different rocks and several released through the Deadwood forest state.

de:
Resource Title: Trees_Surava_ORG
    Size: 25380 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4da88d3f-5627-40d2-843d-c884a31238b1/resource/9dd5d8be-6e7e-429d-a6c2-3237cfc6dc84/download/trees_surava.zip
    Description: Zipped shapefile of the measured and in RAMMS::ROCKFALL used trees for the original (ORG) forest state.

acceleration

display_name: acceleration

deposition-points

display_name: deposition-points

forest

display_name: forest

gnss

display_name: gnss

gyroscope

display_name: gyroscope

in-situ

display_name: in-situ

induced-rockfall

display_name: induced-rockfall

lying-deadwood

display_name: lying-deadwood

rockfall

display_name: rockfall


ensemble-hydrograph-separation-scripts

en:
Calculation scripts that perform ensemble hydrograph separation.  Identical scripts in R and MATLAB are provided, along with demonstration input time series and the corresponding outputs.  These scripts were tested on R version 3.6.2 (2019-12-12) and on MATLAB versions 2018b and 2109b.
These scripts are made publicly available under GNU General Public License 3; for details see https://www.gnu.org/licenses/.
ETH Zurich, WSL, James Kirchner, and Julia Knapp make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose.  Users are solely responsible for determining the suitability and reliability of this software for their own purposes.

en: catchment runoff tracers isotopes hydrograph-separation streamflow

envidat

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: Ensemble hydrograph separation scripts

title_for_slug: ensemble-hydrograph-separation-scripts

envi

de: Umwelt

de:
Resource Title: R scripts and demonstration files
    Size: 235119 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/ddc39d77-c0a3-4a8c-858a-efda8b6dffd9/resource/212f3a19-0530-4cd9-b10d-cd387ac98018/download/ehs-v1.4-r-scripts-and-demo-files.zip
    Description: The ensemble hydrograph separation functions themselves are in EHS_v1.4.R.

EHS_demo_v1.4.R is a demonstration script that calls EHS_v1.4.R to analyze the demo input files.  The resulting output files are in the "demo output files" folder.

de:
Resource Title: *deprecated* MATLAB scripts and demonstration files (*deprecated version* - build 20200718 - do not use!)
    Size: 227403 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/ddc39d77-c0a3-4a8c-858a-efda8b6dffd9/resource/bd43b9fb-7fda-4c26-a38c-31016f035429/download/ehs-v1.4-matlab-scripts.zip
    Description: The ensemble hydrograph separation functions themselves are in EHS_Fnew.m, EHS_profile.m, and EHS_TTD.m.

EHS_demo_RUN_THIS.m is a demonstration script that calls the EHS functions to analyze the demo input files. The resulting output files are in the "demo output files" folder.  These were generated by the R versions of the scripts but have been verified to be numerically equal to the MATLAB outputs, within machine precision.

de:
Resource Title: MATLAB scripts and demonstration files (current version - build 20201119)
    Size: 178465 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/ddc39d77-c0a3-4a8c-858a-efda8b6dffd9/resource/27da91e3-24b1-4d76-883e-37619652d785/download/ehs-v1.4-matlab-scripts-build-20201119.zip
    Description: The ensemble hydrograph separation functions themselves are in EHS_Fnew.m, EHS_profile.m, and EHS_TTD.m.

Build 20201119 has improved commenting, naming, and structuring, and also minor bug fixes in EHS_Fnew, EHS_TTD, and EHS_profile.  Build 20200718 has been deprecated.  Use build 20201119 instead.

EHS_demo_RUN_THIS.m is a demonstration script that calls the EHS functions to analyze the demo input files. The resulting output files are in the "demo output files" folder.  These were generated by the R versions of the scripts but have been verified to be numerically equal to the MATLAB outputs, within machine precision.

catchment

display_name: catchment

hydrograph-separation

display_name: hydrograph-separation

isotopes

display_name: isotopes

runoff

display_name: runoff

streamflow

display_name: streamflow

tracers

display_name: tracers


en:
Alpine3D (A3D) runs between 1950 and 2022 in hourly resolution. Simulations are run for every year separately (year n = Sept. n-1 to Aug. n), starting again with no snow every 1st of September (i.e. there is no snow accumulation from year to year).
Meteorological conditions over the past 70 years were obtained from several datasets: ERA5 providing the hourly data over the full 1950-2022 period, IMIS and MeteoSuisse allowing to validate and correct the downscaled ERA5 data. ERA5 data were downscaled to a 50m grid using Toposcale (Fiddes et al., 2015) (see also https://github.com/joelfiddes/toposcale). This downscaling tool allowed us to obtain a 50 m grid from the ERA5 data including the effect of the local topography. The meteorological input data was further sued to run the 3-dimensional snow model Alpine 3D.
Temperature correction is based on an additive delta change correction following the procedure using harmonic functions filtering described by Michel et al. (2021). Given the hourly timestep provided by ERA5 and Toposcale, we defined an hourly delta change. In practice, this was done by repeating 24 times the delta-change derivation from Michel et al.  (2021) to obtain a day-of-the-year delta for each of the 24 hours of the day.
The simulation covers the slope at Spitze Stei region with  precipitation scaled using snow height measured by a drone flight. One of the 70 years runs was computed with a precipitation redistribution with the idea to mimic snow transport and preferential deposition. The precipitation redistribution grid was based on a single snow height measurement: one snow-free drone flight on 01.10.2021 and one peak snow season flight on 27.03.2022. The precipitation on each pixel is computed as i) multiplying the disaggregated KAS precipitation by the average slope elevation. ii) multiplying that value on each pixel with the weight obtain from the snow height as HS pixel/HS mean.

en: Alpine3D Climate Data

snow-climatology

display_name: snow-climatology

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth


en:
The data consists of a collection of environmental data, tree-ring width and intra-annual δ13C measurements of tree rings of three conifer species (Pinus sylvestris, Picea abies, Abies alba), and the R scripts that were used to analyse the data. Data was collected at two pairs of north and south exposed sites in Switzerland, in the Eastern Grisons (Surava North and Surava South) and Central Valais (Buthan and Lens).
Intra-annual δ13C measurements were obtained using the laser ablation technique, which involved 10 sequential shots per ring. Shot 1 represents the δ13C value at the start of the ring, while Shot 10 corresponds to the δ13C value at the end of the ring's formation. Overall, the datasets consist of 400 profiles and 4000 isotope measurements.
Environmental data was collected at each site over decade, from 2012 to 2023. Key environmental variables include temperature, vapour pressure deficit (VPD), and soil water potential (SWP). Precipitation data was obtained from the EOBS v29.0e dataset (https://www.ecad.eu/download/ensembles/download.php) from the closest grid point. All data was aggregated on an hourly to daily scale.
In addition to environmental data, dendrometer data was used to derive the onset days, beginning of growth for each site (Onset_days.Rds). The data captures the dynamic relationship between environmental factors, such as soil and air moisture, and tree physiological responses, as reflected in δ13C variation. This dataset provides insights into the intra-seasonal interactions between environmental conditions and tree physiology. We provide the scripts for the main analyses performed in the Vitali, V. et al. 2025 article listed below, including the correlation of the isotope intra-annual time series with environmental variables.
This dataset contains all data on which the publication listed below is based.
Please cite this paper together with the citation for the datafile.

en: tree-ring stable-isotopes pinus-sylvestris vpd soil-water abies-alba picea-abies drought dendrochronology

en: 10 years of intra-annual carbon isotopes in tree rings from 3 conifer species

abies-alba

display_name: abies-alba

dendrochronology

display_name: dendrochronology

drought

display_name: drought

picea-abies

display_name: picea-abies

pinus-sylvestris

display_name: pinus-sylvestris

soil-water

display_name: soil-water

stable-isotopes

display_name: stable-isotopes

tree-ring

display_name: tree-ring

vpd

display_name: vpd


en:
The WFJ_RHOSSA dataset contains multi-instrument, multi-resolution snow stratigraphy measurements  for the seasonal evolution of the snowpack from the Weissfluhjoch research site, Davos, Switzerland.
The measurements were initiated during the RHOSSA field campaign conducted in the winter season 2015–2016 with a focus on density (RHO) and specific surface area (SSA) measurements.
The Instruments and methods used in the campaign at different spatial and temporal resolution are: SnowMicroPen, Density Cutter, IceCube, Traditional profiles, Stability tests and X-ray tomography. The measurements are complemented by simulation data from the model SNOWPACK.

en: specific-surface-area density microstructure snow-stratigraphy weissfluhjoch snowmicropen

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: WFJ_RHOSSA: Multi-instrument stratigraphy data for the seasonal evolution of an alpine snowpack

de:
Resource Title: README
    Size: 827 bytes
    Format: No Info
    URL: https://www.envidat.ch/dataset/eca24fe5-ed00-4ff6-9482-414ff98161d1/resource/312e0c4c-e5c6-447b-8c39-5fd9c7ba4adc/download/readme-2
    Description: Top level README file explaining the organization of the data

de:
Resource Title: wfj_rhossa_data
    Size: 427819008 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?prefix=doi/envidat.151/2020_rhossa_paper_calonne_tc
    Description: Link to data

density

display_name: density

microstructure

display_name: microstructure

snow-stratigraphy

display_name: snow-stratigraphy

snowmicropen

display_name: snowmicropen

specific-surface-area

display_name: specific-surface-area

weissfluhjoch

display_name: weissfluhjoch


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.

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

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.

en: EnviDat: the Environmental Data Portal

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

de:
Resource Title:
    Size: 1534677078 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.305/ros_data/ros_data.zip
    Description: The file contains all data needed to run simulations with the snow cover model SNOWPACK.

de:
Resource Title:
    Size: 768 bytes
    Format: text/plain
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.305/ros_data/readme.txt
    Description: Readme.txt

format: TXT

media_type: text/plain

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: paleo-climate air-temperature orography precipitation last-glacial-maximum glaciers

en: CHELSA-TraCE21k: Downscaled transient temperature and precipitation data since the last glacial maximum

envi

en: Environment

de:
Resource Title: Data Access
    Size: 3848290697216 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fchelsav1%2F&prefix=chelsa_trace%2F
    Description: High resolution, downscaled climate model data are used in a wide variety of applications in environmental sciences. Here we present the CHELSA-TraCE21k downscaling algorithm to create global monthly climatologies for temperature and precipitation at 30 arcsec spatial resolution in 100 year time steps for the last 21,000 years. Paleo orography at high spatial resolution and at each timestep is created by combining high resolution information on glacial cover from current and Last Glacial Maximum (LGM) glacier databases with the interpolation of a dynamic ice sheet model (ICE6G) and a coupling to mean annual temperatures from CCSM3-TraCE21k. Based on the reconstructed paleo orography, mean annual temperature and precipitation was downscaled using the CHELSA V1.2 algorithm.

The data is published under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

de:
Resource Title: Technical documentation
    Size: 478853 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/c88baa58-dbae-452c-8c2c-343e4c9fb885/resource/613fcb27-3649-4b5f-8880-984403cccd62/download/chelsa-trace21k_technical_documentation.pdf
    Description: Technical documentation containing file metadata and variable descriptions.

format: PDF

media_type: application/pdf

air-temperature

display_name: air-temperature

glaciers

display_name: glaciers

last-glacial-maximum

display_name: last-glacial-maximum

orography

display_name: orography

paleo-climate

display_name: paleo-climate

precipitation

display_name: precipitation


en:
Outlines of 18'737 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 24 January 2018. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping.pdf)
The generation of the data is described in:
Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.
Abstract.
Accurate and timely information on avalanche occurrence are key for avalanche warning, crisis management and avalanche documentation. Today such information is mainly available at isolated locations provided by observers in the field. The achieved reliability considering accuracy, completeness and reliability of the reported avalanche events is limited. In this study we present the spatial continuous mapping of a large avalanche period in January 2018 covering the majority of the Swiss Alps (12’500 km2).
We tested different satellite sensors available for rapid mapping during a first avalanche period. Based on these experiences, we tasked SPOT6/7 data for data acquisition to cover the second, much larger avalanche period. We manually mapped the outlines of 18’737 individual avalanche events, applying image enhancement techniques to analyze regions in cast shadow as well as brightly illuminated ones. The resulting dataset of mapped avalanche outlines, having a unique completeness and reliability, is evaluated to produce maps of avalanche occurrence and avalanche size. We validated the mapping of the avalanche outlines using photographs acquired from helicopters just after the avalanche period.
This study demonstrates the applicability of optical, very high spatial resolution satellite data to map an exceptional avalanche period with very high completeness, accuracy and reliability over a large region. The generated avalanche data is of great value to validate avalanche bulletins, complete existing avalanche databases and for research applications by enabling meaningful statistics on important avalanche parameters.
Koordinate System: CH1903+ LV95 LN02

en: hazard-mapping satellite snow avalanche natural-hazard

en: SPOT6 Avalanche outlines 24 January 2018

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

snow

display_name: snow


en:
**for English see below**
*******************************************************************************
-br/- -/br-
 Datenbeschrieb
-br/- -/br-
Dieser Datensatz enthält die **Umrisse der 11'120 Lawinen** die aus schwarzweiss Luftbildern, welche zwischen dem 25.2.1999 und dem 1.3.1999 aufgenommen wurden, kartiert wurden. Die Lawinenumrisse haben verschiedene Attribute welche im **beigelegten Beispielschlüssel** beschrieben sind (Beispielschluessel_1999_d.pdf).
Es gibt drei Shapefiles:
* **avalanches1999_endversion1.shp**: die kartierten Lawinen (für Attribute siehe Beispielschlüssel!)
* **area_images_1999.shp**: Fläche die durch die Luftbilder abgedeckt wurde
* **clouds_1999.shp**: grobe Umrisse der Wolken in den Bildern
 Bildverfügbarkeit:
Die entzerrten Luftbilder können von der [Swisstopo](https://s.geo.admin.ch/rpq3hufnb588) einzeln über den Downloadlink heruntergeladen werden (herzlichen Dank an Holger Heisig für die Prozessierung und ans BAFU für die Finanzierung der Prozessierung!!). Über "Erweiterte Werkzeuge"/ "Datei importieren" können die Bilder nach hereinziehen des Orthofotolinks direkt im map.geo.admin.ch angezeigt werden.
 Weiterführende Litertaur:
Details zu den Luftbildern und zur Kartierung, sowie eine kleine Analyse und Empfehlungen zur Verwendung der Daten finden sich unter
- Hafner, E., Margreth, S., and Bühler, Y.: Die grossflächigen Lawinenkartierungen 1999, 2018 und 2019: ein Überblick für die Praxis, FAN Agenda, 1/2025, 15 - 21, 2025.
- Dal, F. J., Hafner, E. D., Peters, T., Narnhofer, D., Caye Daudt, R., Heisig, H., & Bühler, Y. (2024). Automated snow avalanche mapping with deep learning in aerial imagery from the extreme avalanche winter of 1999. In K. Gisnås, P. Gauer, H. Dahle, M. Eckerstorfer, A. Mannberg, & K. Müller (Eds.), Proceedings of the international Snow Science Workshop 2024 (pp. 1264-1271). Norwegian Geotechnical Institute.
- Hafner, E. D., Techel, F., Heisig, H., Dal, J. F., & Bühler, Y. (2024). Remotely sensed avalanche activity during three extreme avalanche periods in Switzerland. In K. Gisnås, P. Gauer, H. Dahle, M. Eckerstorfer, A. Mannberg, & K. Müller (Eds.), Proceedings of the international Snow Science Workshop 2024 (pp. 1222-1229). Norwegian Geotechnical Institute.
*******************************************************************************
-br/- -/br-
 Data description
-br/- -/br-
This dataset contains the **outlines of 11'120 avalanches** mapped from panchromatic aerial imagery taken between February 25, 1999 and March 1, 1999. The avalanche outlines have various attributes which are described in the **attached example key** (ExampleKey_AvalMapping_1999_e.pdf).
There are three shapefiles:
* **avalanches1999_endversion1.shp**: the mapped avalanches (for attributes see example key!)
**area_images_1999.shp**: Area covered by the aerial images
**clouds_1999.shp**: rough outlines of the clouds in the images
 Image availability:
The rectified aerial images can be downloaded individually from [Swisstopo](https://s.geo.admin.ch/bg6beetinfdc) via the download link (many thanks to Holger Heisig for processing and to BAFU for financing the processing!!). The images can be displayed directly in map.geo.admin.ch via “Advanced tools”/“Import file” after dragging in the orthophoto link.
 Further Reading:
Details on the aerial images and mapping, as well as a brief analysis and tips on using the data can be found at
- Hafner, E., Margreth, S., and Bühler, Y.: Die grossflächigen Lawinenkartierungen 1999, 2018 und 2019: ein Überblick für die Praxis, FAN Agenda, 1/2025, 15 - 21, 2025.
- Dal, F. J., Hafner, E. D., Peters, T., Narnhofer, D., Caye Daudt, R., Heisig, H., & Bühler, Y. (2024). Automated snow avalanche mapping with deep learning in aerial imagery from the extreme avalanche winter of 1999. In K. Gisnås, P. Gauer, H. Dahle, M. Eckerstorfer, A. Mannberg, & K. Müller (Eds.), Proceedings of the international Snow Science Workshop 2024 (pp. 1264-1271). Norwegian Geotechnical Institute.
- Hafner, E. D., Techel, F., Heisig, H., Dal, J. F., & Bühler, Y. (2024). Remotely sensed avalanche activity during three extreme avalanche periods in Switzerland. In K. Gisnås, P. Gauer, H. Dahle, M. Eckerstorfer, A. Mannberg, & K. Müller (Eds.), Proceedings of the international Snow Science Workshop 2024 (pp. 1222-1229). Norwegian Geotechnical Institute.

en: avalanche-activity avalanche-mapping remote-sensing aerial-imagery avalanche

en: Avalanche outlines February and March 1999 from aerial imagery

aerial-imagery

display_name: aerial-imagery

avalanche-activity

display_name: avalanche-activity

avalanche-mapping

display_name: avalanche-mapping

remote-sensing

display_name: remote-sensing


en: This dataset contains bryophyte species count data and information about the observers bryophyte expertise for 2332 relevés conducted from 2011 to 2021 on 10-m2 plots in a long-term monitoring program in Switzerland. Plots were situated in raised bogs and fens of national importance, which were distributed across the whole country. The majority of the plots is represented by two relevés as sites are revisited every six years. **The dataset was prepared for usage in an unpublished article with the title of this dataset. Due to improvements in the analysis, results turned out to be more nuanced and it was found that the title of the data set is no longer valid.** A new dataset was created and is available here: [https://www.doi.org/10.16904/envidat.683](https://www.doi.org/10.16904/envidat.683)

en: fen observer-bias raised-bog liverworts mosses cover biodiversity-monitoring sphagnum

en: Greater observer expertise leads to higher estimates of bryophyte species richness

biodiversity-monitoring

display_name: biodiversity-monitoring

cover

display_name: cover

fen

display_name: fen

liverworts

display_name: liverworts

mosses

display_name: mosses

observer-bias

display_name: observer-bias

raised-bog

display_name: raised-bog

sphagnum

display_name: sphagnum


en:
Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland.
This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection ("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"). The excel file (xlsx) provides a short description of the raster layers.
Paper Citation:
Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)

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

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.

de:
Resource Title: Description
    Size: 15032 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/4ab13d14-6f96-41fd-96b0-b3ea45278b3d/resource/81c046c3-8d1d-45bc-a833-7d8240cebd12/download/predictors_description.xlsx
    Description: Description of the predictors provided in this dataset

de:
Resource Title: Predictors
    Size: 1022643006 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4ab13d14-6f96-41fd-96b0-b3ea45278b3d/resource/e0faab13-0d1b-492a-8539-5370d48b9e35/download/predictors.zip
    Description: Predictors generated and used in Descombes et al. (2020)

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph

switzerland

display_name: switzerland


en:
This dataset comprises of a post-processed set of terrestrial laser scans (TLS’s) of Antarctic sea ice obtained during the Sea Ice Physics and Ecosystem Experiment-2 (SIPEX-2, http://seaice.acecrc.org.au/sipex2012/) in September-November 2012. The post-processing steps include the registration of the individual scans into a single 3-dimensional point cloud, the removal of unwanted noise caused by particles in the air (i.e., snow crystals), and the final generation of surface grids based on the cleaned individual point returns. The final product includes the ‘xyz’ coordinates of the individual point measurements, and gridded surfaces covering study areas of 100m x 100 m, and at resolutions of 0.01 m, 0.1 m, 0.25 m, 0.5 m and 1 m for each of the survey dates. Additionally, subgrid statistics that include the mean elevation, standard deviation, minimum and maximum elevations, range, and number of point returns in each gridcell are generated. The final product is provided in space-delimited text files, with the surface grids provided in Digital Terrain Model (DTM) format ready for visualization in any GIS software.
How to cite:
Please also cite the original publication when using this data set.: Trujillo, E., K. Leonard, T. Maksym, and M. Lehning (2016), Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice, J. Geophys. Res. Earth Surf., 121, doi:[10.1002/2016JF003893](https://dx.doi.org/10.1002/2016JF003893).

en: tls terrestrial-laser-scans sea-ice sea-ice-elevation polar

envidat

en: EnviDat: the Environmental Data Portal

en: High resolution sea ice surface topography from the SIPEX-2 expedition, East Antarctica, 2012

de:
Resource Title: Data processing documentation
    Size: 848896 bytes
    Format: application/msword
    URL: https://www.envidat.ch/dataset/c88b82ed-d54e-43e6-b4e4-7ae1ee06a014/resource/bb51210c-6738-41d4-b4a4-31dd08f4725e/download/tlssipex-2metadata.doc
    Description:

format: msword

de:
Resource Title: Post Processed TLS's
    Size: 932144467 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c88b82ed-d54e-43e6-b4e4-7ae1ee06a014/resource/2557ecb5-e65d-4d60-97ce-fd6696ee2d00/download/10-16904-9sipex2dat.zip
    Description: Compressed dat files

polar

display_name: polar

sea-ice

display_name: sea-ice

sea-ice-elevation

display_name: sea-ice-elevation

terrestrial-laser-scans

display_name: terrestrial-laser-scans

tls

display_name: tls


en:
The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat.
Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

envidat

de: EnviDat: das Umwelt-Datenportal

en: Large GIS raster data derived from Natural Earth Data (Cross Blended Hypso with Shaded Relief and Water)

envi

en: Environment

de:
Resource Title: map preview 0.5MB
    Size: 482223 bytes
    Format: image/jpeg
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.68/gisdata/tiny_map_preview.jpg
    Description:

format: jpeg

de:
Resource Title: Bulk Data Access
    Size: 111669149696 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.68%2F
    Description: Link to the S3 bucket where all the data resources can be downloaded in bulk

gis

display_name: gis

large-files

display_name: large-files

natural-earth

display_name: natural-earth

qgis

display_name: qgis


de: EnviDat

en:
This dataset contains all data on which the following publication below is based.
Paper Citation:
Risch, A.C., Frossard, A., Schütz, M., Frey, B., Morris, A.W., Bump, J.K. (accepted) Effects of elk and bison carcasses on soil microbial communities and ecosystem functions in Yellowstone, USA. (accepted). Functional Ecology
doi: ...
Methods
Study area and study sites
This study was conducted in YNP’s Northern Range (NR), located in north-western Wyoming and south-western Montana, USA (~44.9163° N, 110.4169° W). The NR expands over ~1000 km2 and features long cold winters and short dry summers. Grasslands and shrublands dominate the NR that is the home of large migratory herds of bison (winter counts 2017: ~3919 individuals; Geremia, Wallen, & White, 2017) and elk (~5349 individuals) as well as their main predators, approximately five packs of wolves with a total of 33 individuals (Smith et al., 2017). As part of a long-term research program within YNP, wolf predation has been studied since their reintroduction in 1995.
For our study, we received ground-truthed coordinates of bison and elk carcasses from winter 2016/17 (November 2016 through April 2017) from the YNP Wolf Project. Between June 20 and July 1, 2017, we visited 24 carcasses in total. At five sites, we could not sample as the carcasses were no longer found. In total we located remains (hairmats, rumen content, bones, teeth) of 19 adult male and female carcasses (7 bison, 12 elk; Supplementary Table 1). Live body weights of adult bison and elk are approximately 730 kg (male bison), 450 kg (female bison), 330 kg (male elk), and 235 kg (female elk, Meagher, 1973; Quimby & Johnson, 1951).
The kills and subsequent consumption happened between 34 and 173 days prior to our sampling (hereafter “days since kill”, DSK), for which we accounted in our statistics. Note that wolves and other scavengers consumed the soft tissue of the carcasses quickly, hence, there is close to no soft tissue left for decomposition as compared to an intact body left on the soil surface. The 19 carcass sites covered the extent of YNP’s NR, with both bison and elk carcasses showing similar distributions; elevation ranged from 1703 to 2884 m a.s.l. (Supplementary Fig 1 & Supplementary Table 1). The carcasses were all located in grassland or sage-brush shrubland, with or without sparsely scattered trees, and both bison and elk carcasses showed the same distribution of DSK. At each study site, we selected a reference plot (hereafter “control”) that was of comparable size, slope aspect and vegetation to the carcass location (hereafter “carcass”). The control was at least 10 m away (Danell, Berteaux, & Brathen, 2002; Melis et al., 2007) from the carcass itself to ensure the absence of potential direct and indirect carcass effects (paired design; (Bump, Webster, et al., 2009; Bump, Peterson, et al., 2009).
Ecosystem functions and soil properties
We randomly collected 50 g of mineral soil from three locations on both control and carcass plots to a depth of 5 cm with sterile techniques and gently mixed the material to obtain a composite sample. Half the soil sample was immediately bagged in plastic bags (whirl packs), stored in a cooler with ice packs (~5 ºC), sieved (2-mm) and frozen within 4-6 hours of collection to assess soil microbial communities. For this purpose, we extracted total genomic DNA from 0.5 g soil using the PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany). DNA concentrations were measured using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplifications of partial bacterial small-subunit ribosomal RNA genes (region V3–V4 of 16S rRNA) and fungal ribosomal internal transcribed spacers (region ITS2) were performed as described previously (Frey et al., 2016). Each sample consisting of 40 ng DNA was amplified in triplicate and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantified with the Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were sent to the Genome Quebec Innovation Center (Montreal, Canada) for barcoding using the Fluidigm Access Array technology and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA).
Quality control of bacterial and fungal reads was performed using a customized pipeline (Supplementary Table 2; Frey et al., 2016). Paired-ends reads were matched with USEARCH (Edgar & Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, & Alekseyev, 2013) and PCR primers were trimmed (allowing for 1 mismatch, read length -300 bp for 16S and -200 bp for ITS primers) using Cutadapt (M. Martin, 2011). Sequences were dereplicated and singleton reads removed prior to clustering into operational taxonomic units (OTUs) at 97% identity using USEARCH (Edgar, 2013). The remaining centroid sequences were tested for the presence of ribosomal signatures using Metaxa2 (Bengtsson-Palme et al., 2015) or ITSx (Bengtsson-Palme et al., 2013). Taxonomic assignments of the OTUs were obtained using Bayesian classifier (Wang, Garrity, Tiedje, & Cole, 2007) with a minimum bootstrap support of 60% implemented in mothur (Schloss et al., 2009) by querying the bacterial and fungal reads against the SILVA Release 128 (Quast et al., 2013) and UNITE 8.0 (Abarenkov et al., 2010) reference databases for 16S and ITS OTUs, respectively.
Abundances of the bacterial 16S rRNA gene and fungal ITS amplicon were determined by quantitative real-time PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) as described previously (Frossard et al., 2018). The same primers (without barcodes) and cycling conditions as for the sequencing approach were used for the 16S and ITS qPCR. Three standard curves per target region were obtained using tenfold serial dilutions of plasmids generated from cloned targets (Frey, Niklaus, Kremer, Lüscher, & Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight).
The other half of the soil sample was bagged in paper, dried to constant weight at 60°C, passed through a 2 mm sieve and analyzed for total C and N concentration with a CE Instruments NC 2100 soil analyzer (CE Elantech Inc., Lakewood NJ, USA). We also collected 20 mature and undamaged leaves of the dominant grass species growing on control and carcass sites, but taxa were not recorded. The plant material was dried at 60°C, finely ground till homogenized and also analyzed to obtain total C and N concentrations. Soil temperature (10 cm depth) was measured with a waterproof digital thermometer (Barnstead International, Dubuque IA, USA) at three locations each at the control and carcass site. Soil moisture (0 – 10 cm depth) was measured with time domain reflectometry (Field-Scout TDR-100; Spectrum Technologies, Plainfield IL, USA) at five randomly chosen points on control and carcass sites. We measured soil respiration at five randomly chosen points at both control and carcass sites with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA). For each measurement the soil chamber (15 cm high; 10 cm diameter) was tightly placed on the soil surface, after clipping plants to avoid measuring plant respiration or photosynthesis. Measurements were conducted over 120 s.
In addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter & Howson, 1977; Latter & Walton, 1988). Cotton cloth tensile strength loss (CTSL) is a measure of decomposition, and an index to express the combined effect of soil microclimatic, physical, chemical and biological properties on decomposition while accounting for OM quality (Latter & Walton, 1988; Risch, Jurgensen, & Frank, 2007; Withington & Sanford Jr., 2007). We placed five 20 cm wide x 13 cm long sheets of 100% unbleached cotton cloth (American Type SM 1/18’’, Warp: 34/1, Weft: 20/1, Weave plain, 29.5 picks/cm warp, 22 picks/cm weft, 237 g/m2; Daniel Jenny & Co., Switzerland;) at each carcass and control site vertically into the soil by making slits with a flat spade to a depth of 12 cm. We inserted each cloth with the spade, and then pushed the slit closed to assure tight contact with the soil. The cloths were retrieved after 18 to 27 days. After retrieval, the cloths were air-dried, remaining soil gently removed by hand, and 1.5 cm wide strips were cut at the 3.5-5.0 cm (top) and the 9-10.5 cm (bottom) soil depth. The strips were equilibrated at 50 % relative humidity and 20°C for 48 hours (climate chamber) prior to strength testing (Scanpro Awetron TH-1 tensile strength tester; AB Lorentzen and Wettre, Kista, Sweden). Cotton rotting rate (CRR) = (CTScontrol - CTSfinal/CTSfinal)1/3 * (365/t), where CTScontrol is the cotton tensile strength of a control cloth and CTSfinal the cotton tensile strength of the incubated sample, t is the incubation period in days. Control cloths were inserted into the ground and immediately retrieved to account for tensile strength loss associated with cloth insertion. We averaged the CRR of top and bottom strips for further analyses as no difference was found between the two. All sampling and cloth insertion took place between June 20 and July 1, 2017, cloths were retrieved between July 17 and 20, 2017. Soil respiration, average CRR, vegetation N concentration and vegetation C:N ratio are defined as ecosystem functions, soil C and N concentration, soil temperature and moisture as soil abiotic properties, and bacterial and fungal richness (number of taxa), diversity (Shannon) and abundance as soil biotic properties.
Statistical analyses
Univariate analyses for ecosystem functions, soil biotic and abiotic properties
We tested whether individual ecosystem functions, soil biotic and abiotic properties differed between carcass and control (“Location”), bison and elk (“Species”) and days since kill (“DSK”). For this purpose, we used linear mixed effect models (LMM, “nlme” package v 3.1 – 131.1 in R v 3.4.4; Pinheiro, Bates, DebRoy, & Sarkar, 2018; R Core Team, 2019) with Location, Species, Location x Species and DSK as fixed effects. Site was included as random effect to account for the paired design. We developed a separate model for all dependent variables. All but bacterial richness, fungal richness, fungal diversity and vegetation N concentration were natural-log transformed to meet model assumptions. For each LMM, we calculated contrasts to assess the specific comparisons we were interested in with the “lsmeans” package v 2.27-62 (Lenth & Love, 2018): 1) carcass vs control, 2) carcass bison vs control bison, and 3) carcass elk vs control elk. We also tested whether we had differences between bison and elk carcasses or the sites where bison and elk were killed and included contrasts 4) carcass bison vs carcass elk and 5) control bison vs control elk.
We calculated the log response ratio (LRR = ln[carcass/control]) to obtain carcass effects for all variables for both species separately. LRR - 0 indicates higher value at control compared to carcass, LRR - 0 indicates higher values at carcass compared control. We used LRRs for visualization and to assess spatial patterns in carcass effects across YNP. For this purpose we calculated the Moran’s I statistic for each ecosystem function, soil biotic and abiotic property based on a latitude-longitude matrix with the “moran.test” function in the “spdep” package version 1.1-3 (Bivand et al., 2019).
Multivariate analyses
Rare OTUs, defined as OTUs with a low abundance of reads, were retained in multivariate methods because they only marginally influence these analyses (Gobet, Quince, & Ramette, 2010). Bray–Curtis dissimilarity matrices were generated based on square-root-transformed matrices. We used Principal Coordinate Analyses (PCoA) to assess how soil bacterial and fungal communities differed between control and carcass of bison and elk (“vegan” package v 2.5-4, Oksanen et al., 2019). We then extracted PCoA axes scores 1 and 2 and used LMM (“nlme” package) with Location, Species, Location x Species and DSK as fixed effects. Site was, again, included as random effect. We again calculated the contrasts as described above using the “lsmeans” package. We also assessed how ecosystem functions, and soil abiotic and biotic properties were related to the soil bacteria and fungi community structure associated with bison and elk control and carcasses using the “envfit” function in the “vegan” package (Oksanen et al., 2019).
Indicator species analyses were performed using the multipatt function implemented in the “indicspecies” package version 1.7.6 with 100000 permutations (De Caceres & Jansen, 2016). This step allowed to identify OTUs that led to changes in multivariate patterns between control and carcass of both bison and elk separately (De Cáceres, Legendre, & Moretti, 2010). The multipatt function uses a point biserial correlation coefficient statistical test. Indicator OTUs were defined as bacterial and fungal OTUs with more than 50 sequences, i.e., removing rare taxa and taxa with low abundances containing little indicator information (Rime et al., 2015) and that were significantly correlated with Location (p - 0.05, correlation coefficient - 0.3). A heatmap of these OTUs were generated with the vegan and ggplot2 packages. The indicator analyses were performed in R version 3.3.3 (R Core Team, 2017).
References
Abarenkov, K., Henrik Nilsson, R., Larsson, K.-H., Alexander, I. J., Eberhardt, U., Erland, S., … Kõljalg, U. (2010). The UNITE database for molecular identification of fungi – recent updates and future perspectives. New Phytologist, 186(2), 281–285. doi:10.1111/j.1469-8137.2009.03160.x
Bengtsson-Palme, J., Hartmann, M., Eriksson, K. M., Pal, C., Thorell, K., Larsson, D. G. J., & Nilsson, R. H. (2015). metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15(6), 1403–1414. doi:10.1111/1755-0998.12399
Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., … Nilsson, R. H. (2013). Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods in Ecology and Evolution, 4(10), 914–919. doi:10.1111/2041-210X.12073
Bivand, R., Altman, M., Anselin, L., Assuncao, R., Berke, O., Blanchet, G., … Yu, D. (2019). spdep: Spatial dependence, weighthing schemes, statistics. R package version 1.1-3.
Bump, J. K., Peterson, R. O., & Vucetich, J. A. (2009). Wolves modulate soil nutrient heterogeneity and foliar nitrogen by configuring the distribution of ungulate carcasses. Ecology, 90(11), 3159–3167.
Bump, J. K., Webster, C. R., Vucetich, J. A., Peterson, R. O., Shields, J. M., & Powers, M. D. (2009). Ungulate carcasses perforate ecological filters and create biogeochemical hotspots in forest herbaceous layers allowing trees a competitive advantage. Ecosystems, 12(6), 996–1007. doi:10.1007/s10021-009-9274-0
Danell, K., Berteaux, D., & Brathen, K. A. (2002). Effect of muskox carcasses on nitrogen concentration in tundra vegetation. Arctic, 55(4), 389392.
De Caceres, M., & Jansen, F. (2016). indicspecies: relationship between species and groups of species. R package version 1.7.6.
De Cáceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by combining groups of sites. Oikos, 119(10), 1674–1684. doi:10.1111/j.1600-0706.2010.18334.x
Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10, 996.
Edgar, R. C., & Flyvbjerg, H. (2015). Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics, 31(21), 3476–3482. doi:10.1093/bioinformatics/btv401
Frey, B., Niklaus, P. A., Kremer, J., Lüscher, P., & Zimmermann, S. (2011). Heavy-machinery traffic impacts methane emissions as well as methanogen abundance and community structure in oxic forest soils. Applied and Environmental Microbiology, 77(17), 6060–6068. doi:10.1128/AEM.05206-11
Frey, B., Rime, T., Phillips, M., Stierli, B., Hajdas, I., Widmer, F., & Hartmann, M. (2016). Microbial diversity in European alpine permafrost and active layers. FEMS Microbial Ecology, 92(3), fiw018.
Frossard, A., Donhauser, J., Mestrot, A., Gygax, S., Bååth, E., & Frey, B. (2018). Long- and short-term effects of mercury pollution on the soil microbiome. Soil Biology and Biochemistry, 120, 191–199. doi:https://doi.org/10.1016/j.soilbio.2018.01.028
Geremia, C., Wallen, R., & White, P. J. (2017). Status report of the Yellowstone bison population, September 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center for Resources.
Gobet, A., Quince, C., & Ramette, A. (2010). Multivariate cutoff level analysis (MultiCoLA) of large community data sets. Nucleic Acids Research, 38(15), e155–e155. doi:10.1093/nar/gkq545
Latter, P., & Howson, G. (1977). The use of cotton strips to indicate cellulose decomposition in the field. Pedobiologia, (17), 145–155.
Latter, P., & Walton, D. (1988). The cotton strip assay for cellulose decomposition studies in soil: history of the assay and development. In Cotton strip assay: an index for decomposition in soils (pp. 7–9). ITE Symposium, Institute of Terrestrial Ecology, Natural Environment Research Council, UK.
Lenth, R., & Love, J. (2018). lsmeans: least-squares means. R package version 2.27-62.
Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal, 17(1), 10–12.
Meagher, M. M. (1973). The bison of Yellowstone National Park. NPS Scientific Monograph (Vol. 1). National Park Service, Yellowstone Center for Resources.
Melis, C., Selva, N., Teurlings, I., Skarpe, C., Linnell, J. D. C., & Andersen, R. (2007). Soil and vegetation nutrient response to bison carcasses in Białowieża Primeval Forest, Poland. Ecological Research, 22(5), 807–813. doi:10.1007/s11284-006-0321-4
Nikolenko, S. I., Korobeynikov, A. I., & Alekseyev, M. A. (2013). BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics, 14(1), S7. doi:10.1186/1471-2164-14-S1-S7
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., … Wagner, H. H. (2019). vegan: community ecology package. R package version 2.5-4.
Pinheiro, J., Bates, D., DebRoy, S., & Sarkar, D. (2018). nlme: Linear and nonlinear mixed effect models. R package version 3.1-131.1.
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., … Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research, 41(Database issue), D590–D596. doi:10.1093/nar/gks1219
Quimby, D. C., & Johnson, D. E. (1951). Weights and measurements of Rocky Mountain elk. Journal of Wildlife Management, 15, 57–62.
R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Zurich, Switzerland.
R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
Rime, T., Hartmann, M., Brunner, I., Widmer, F., Zeyer, J., & Frey, B. (2015). Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield. Molecular Ecology, 24(5), 1091–1108. doi:10.1111/mec.13051
Risch, A. C., Jurgensen, M. F., & Frank, D. A. (2007). Effects of grazing and soil micro-climate on decomposition rates in a spatio-temporally heterogeneous grassland. Plant and Soil, 298(1–2), 191–201. doi:10.1007/s11104-007-9354-x
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., … Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537–7541. doi:10.1128/AEM.01541-09
Smith, D., Stahler, D., Cassidy, K., Stahler, E., Metz, M., Cassidy, B., … Cato, E. (2018). Yellowstone National Park wolf project annual report 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center of Resources.
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267. doi:10.1128/AEM.00062-07
Withington, C., & Sanford Jr., R. (2007). Decomposition rates of buried substances increase with altitude in a forest-alpine tundra ecotone. Soil Biology and Biochemistry, (39), 68–75.
Please cite this paper together with the citation for the datafile.

en: Elk and bison carcasses in Yellowstone, USA

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

envi

de: Umwelt

bacteria

display_name: bacteria

biogeochemical-cycling

display_name: biogeochemical-cycling

carcass

display_name: carcass

carrion

display_name: carrion

decomposition

display_name: decomposition

detritus

display_name: detritus

fungi

display_name: fungi

soil-biodiversity

display_name: soil-biodiversity

wolf

display_name: wolf

yellowstone

display_name: yellowstone


en:
This dataset contains the data used  and collected to investigate the reliability of avalanche size estimates and avalanche outlines (see related publications). Namely this is
 Avalanche size estimates
* the 10 pictures depicting avalanches
* the survey results from the 170 participants
 Avalanche outlines mapped from oblique photographs
* the pictures of the six avalanches
* the avalanche outlines mapped by the 10 participants
* the reference mapped from the georeferenced photographs
 Avalanche outlines from remotely-sensed imagery
* the orthophotos in the four different resolutions (2m, 1m, 50cm, 25cm)
* the area of interest (AoI) delimiting the research area
* the avalanche mappings in each resolution for all five participants
* the shade masks used to separate illuminated and shaded areas
The acquisition of the orthophotos has been partially supported by the Swiss National Science Foundation (SNF; Grant N°200021_172800).

en: avalanche-size-estimation remote-sensing avalanche reliability avalanche-mapping

en: Data reliability study: avalanche size estimates and outlines

avalanche

display_name: avalanche

avalanche-mapping

display_name: avalanche-mapping

avalanche-size-estimation

display_name: avalanche-size-estimation

reliability

display_name: reliability

remote-sensing

display_name: remote-sensing


en:
We developed a workflow to generate Large Scale Hazard Simulations for avalanches based on digital elevation models and information on the protective function of the forest. This datasets contains the potential avalanche release areas (PRA) as polygons, the simulation outputs (maximum pressure, maximum flow velocity and maximum flow height) as .tif rasters and the outlines of the simulated avalanches (polygon) for the entire area of the canton of Grisons (7105 km2). The simulations are performed for the scenarios wit return periods of 10, 30, 100 and 300 years, once with (FOR) and once without (NoFor) taking the effect of the forest into account.
The details can be found in this publication:
Bühler, Y., Bebi, P., Christen, M., Margreth, S., Stoffel, L., Stoffel, A., Marty, C., Schmucki, G., Caviezel, A., Kühne, R., Wohlwend, S., and Bartelt, P.: Automated avalanche hazard indication mapping on state wide scale, Nat. Hazards Earth Syst. Sci. Discuss., 2022, 1-22, 10.5194/nhess-2022-11, 2022.

en: modelling hazard-mapping avalanche release ramms impact-pressure

en: Large Scale Hazard Indication Simulations for avalanches, canton of Grisons

de:
Resource Title: Scenario 10 years without forest
    Size: 924911068 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/e6f8ad1b-8e7e-442e-a2f9-7c8cff814110/resource/60fe952f-bdec-418b-9399-36bdb4ef2faf/download/10ynofor.7z
    Description: PRA_.shp = avalanche release areas shape file with the attributes in the attribute table
_maxHEIGHT.tif = maximum flow height [m]
_maxPRESSURE.tif = maximum impact pressure [kPa]
_maxVELOCITY.tif = maximum flow velocity [m/s]
_outlines = outline shape file of every avalanche simulation linked to the PRA shape file by ID

de:
Resource Title: Scenario 100 years with forest
    Size: 805376043 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/e6f8ad1b-8e7e-442e-a2f9-7c8cff814110/resource/01d6b5be-6c29-4e2e-99da-f964c91de64f/download/100yfor.7z
    Description: PRA_.shp = avalanche release areas shape file with the attributes in the attribute table
_maxHEIGHT.tif = maximum flow height [m]
_maxPRESSURE.tif = maximum impact pressure [kPa]
_maxVELOCITY.tif = maximum flow velocity [m/s]
_outlines = outline shape file of every avalanche simulation linked to the PRA shape file by ID

de:
Resource Title: Scenario 10years with Forest
    Size: 661720583 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/e6f8ad1b-8e7e-442e-a2f9-7c8cff814110/resource/d3574570-66a9-4c25-b196-727a7123dd7a/download/10yfor.7z
    Description: PRA_.shp = avalanche release areas shape file with the attributes in the attribute table
_maxHEIGHT.tif = maximum flow height [m]
_maxPRESSURE.tif = maximum impact pressure [kPa]
_maxVELOCITY.tif = maximum flow velocity [m/s]
_outlines = outline shape file of every avalanche simulation linked to the PRA shape file by ID

de:
Resource Title: Scenario 300 years with forest
    Size: 846835441 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/e6f8ad1b-8e7e-442e-a2f9-7c8cff814110/resource/3d0bff73-c8e1-4f2e-9a38-f83b3a4b98ee/download/300yfor.7z
    Description: PRA_.shp = avalanche release areas shape file with the attributes in the attribute table
_maxHEIGHT.tif = maximum flow height [m]
_maxPRESSURE.tif = maximum impact pressure [kPa]
_maxVELOCITY.tif = maximum flow velocity [m/s]
_outlines = outline shape file of every avalanche simulation linked to the PRA shape file by ID

de:
Resource Title: Scenario 30 years without forest
    Size: 945391364 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/e6f8ad1b-8e7e-442e-a2f9-7c8cff814110/resource/d332e2fd-d8b0-4d4b-9c3e-fefed822908a/download/30ynofor.7z
    Description: PRA_.shp = avalanche release areas shape file with the attributes in the attribute table
_maxHEIGHT.tif = maximum flow height [m]
_maxPRESSURE.tif = maximum impact pressure [kPa]
_maxVELOCITY.tif = maximum flow velocity [m/s]
_outlines = outline shape file of every avalanche simulation linked to the PRA shape file by ID

de:
Resource Title: Scenario 30 years with forest
    Size: 692742534 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/e6f8ad1b-8e7e-442e-a2f9-7c8cff814110/resource/5986053e-b75f-484b-8adc-c3e19261adef/download/30yfor.7z
    Description: PRA_.shp = avalanche release areas shape file with the attributes in the attribute table
_maxHEIGHT.tif = maximum flow height [m]
_maxPRESSURE.tif = maximum impact pressure [kPa]
_maxVELOCITY.tif = maximum flow velocity [m/s]
_outlines = outline shape file of every avalanche simulation linked to the PRA shape file by ID

de:
Resource Title: Scenario 100 years without forest
    Size: 1020375084 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/e6f8ad1b-8e7e-442e-a2f9-7c8cff814110/resource/d5c001b9-1321-4f3d-9f93-89b0c9479dd0/download/100ynofor.7z
    Description: PRA_.shp = avalanche release areas shape file with the attributes in the attribute table
_maxHEIGHT.tif = maximum flow height [m]
_maxPRESSURE.tif = maximum impact pressure [kPa]
_maxVELOCITY.tif = maximum flow velocity [m/s]
_outlines = outline shape file of every avalanche simulation linked to the PRA shape file by ID

de:
Resource Title: Extent of the canton of Grisons
    Size: 685814 bytes
    Format: application/json
    URL: https://www.envidat.ch/dataset/e6f8ad1b-8e7e-442e-a2f9-7c8cff814110/resource/70e9127c-8510-497c-a3f5-9637aa6d2b8e/download/extent_kt_gr.json
    Description:

format: JSON

media_type: application/json

de:
Resource Title: Scenario 300 years without forest
    Size: 1042271198 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/e6f8ad1b-8e7e-442e-a2f9-7c8cff814110/resource/953acbf3-3a6a-4df1-acf8-ef4108f5f0a9/download/300ynofor.7z
    Description: PRA_.shp = avalanche release areas shape file with the attributes in the attribute table
_maxHEIGHT.tif = maximum flow height [m]
_maxPRESSURE.tif = maximum impact pressure [kPa]
_maxVELOCITY.tif = maximum flow velocity [m/s]
_outlines = outline shape file of every avalanche simulation linked to the PRA shape file by ID

hazard-mapping

display_name: hazard-mapping

impact-pressure

display_name: impact-pressure

modelling

display_name: modelling

ramms

display_name: ramms

release

display_name: release


en:
Outlines of 6'041 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 16 January 2019. The dataset was acquired following a period with very high avalanche danger. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping16012019.pdf)
The generation of the data is described in:
Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.
The data was comprehensivly validated in a subset area in
Hafner, E.D.; Techel, F.; Leinss, S.; Bühler, Y., 2021: Mapping avalanches with satellites - evaluation of performance and completeness. Cryosphere, 15, 2: 983-1004. doi: 10.5194/tc-15-983-2021

en: natural-hazard hazard-mapping satellite snow avalanche

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.

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

en: SPOT6 Avalanche outlines 16 January 2019

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

snow

display_name: snow


en: Snow and air data was monitored at Gotschnagrat (LON: 46.859 LAT: 9.849) by an infrarot radiometer (Campbell SI-111) for snow temperature (°C), a snow height sensor (Lufft SHM-31) for snow height change (cm) and a temperature and humidity sensor (Campbell CS-215) for air temperature (°C) and relative humidity (%). No filter was applied to the sensors and the smapling frequency was 1 Hz.

en: Snow Drift Station - Snow and Air Data

en: snow snow-temperature raclets temperature snow-depth snow-height

envidat

en: EnviDat: the Environmental Data Portal

de:
Resource Title: Air Temperature
    Size: 1049937 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c589c4cf-9f53-4d76-b1df-bae465ddda1f/resource/78d7e676-0f61-45a9-9dcf-67d0404e5382/download/air_temperature.xlsx
    Description: The air temperature (°C) was measured with a Campbell CS-215 which was mounted at a height of 2.2 m above ground. The time is given in UTC+1.

de:
Resource Title: Snow Surface Temperature
    Size: 1101244 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c589c4cf-9f53-4d76-b1df-bae465ddda1f/resource/7a1b830b-a1a2-42b8-8789-915904920f54/download/snow_surface_temperature.xlsx
    Description: The snow surface temperature (°C) was measured with a Campbell SI-111 at 0.7 m above ground. The time is given in UTC+1.

de:
Resource Title: Relative Humidity
    Size: 991506 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c589c4cf-9f53-4d76-b1df-bae465ddda1f/resource/64062bd7-0d09-4038-95cc-1028e196698e/download/air_relative_humidity.xlsx
    Description: The relative humidity (%) was measured with a Campbell CS-215 at a height of 2.2 m above ground. The time is given in UTC+1.

de:
Resource Title: Snow Depth
    Size: 830403 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c589c4cf-9f53-4d76-b1df-bae465ddda1f/resource/50a2778f-14cd-4871-a9cc-67c1a4d1b31d/download/snow_depth.xlsx
    Description: The snow depth (cm) was measured with a Lufft SHM-31 which was mounted at 1.7 m above ground. The time is given in UTC+1.

raclets

display_name: raclets

snow-depth

display_name: snow-depth

snow-height

display_name: snow-height

snow-temperature

display_name: snow-temperature

temperature

display_name: temperature


en:
Validation points, validation area, ground truth coverage, SPOT 6 avalanche outlines, Sentinel-1 avalanche outlines, Sentinel-2 avalanche outlines, Davos avalanche mapping (DAvalMap) avalanche outlines as shapefiles and a detailed attribute description (DataDescription_EvalSatMappingMethods.pdf).
Coordinate system: CH1903+_LV95
The generation of this dataset is described in detail in:
Hafner, E. D., Techel, F., Leinss, S., and Bühler, Y.: Mapping avalanches with satellites – evaluation of performance and completeness, The Cryosphere, https://doi.org/10.5194/tc-2020-272, 2021.

en: avalanche-hazard-mapping validation satellite snow avalanche

envidat

de: EnviDat: das Umwelt-Datenportal

en: Satellite avalanche mapping validation data

envi

en: Environment

avalanche

display_name: avalanche

avalanche-hazard-mapping

display_name: avalanche-hazard-mapping

validation

display_name: validation