en:
Starter kit for the TreeMig framework that allows for an easy application of the forest landscape model TreeMig for simulating forest dynamics in space under changing environmental and land use conditions.
The starter kit consists of a set of climate, land-use, topography and soil property input data for Switzerland, a script for installing the framework and running the GUI of the framework with the link to the input data, a tutorial covering a range of simulation examples for Switzerland and a transect through Europe, and an associated example simulation environment containing the model executables and other model inputs.

en: forest-dynamics forest forest-model spatial-modelling gui species-interaction r-package treemig bioclimatic-variables species-migration land-use

envidat

en: EnviDat: the Environmental Data Portal

en: Starter Kit for the TreeMig framework v1 for easy simulation of spatio-temporal forest dynamics

en:
Resource Title: TreeMig_Tutorial_2025-05-02.pdf
    Size: 2107648 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/a1dec65a-30ba-4688-90ac-50df5db73d4c/download/treemig_tutorial_2025-05-02.pdf
    Description: Tutorial for using the TreeMig framework; updated

en:
Resource Title: TreeMig_Tutorial_v2024_12.pdf
    Size: 3399819 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/5188a2e1-470c-4ed7-83c6-26dc72bd18ab/download/treemig_tutorial_v2024_12.pdf
    Description: Tutorial for using the TreeMig framework; updated

en:
Resource Title: TreeMig Simulation Environment and Input Data
    Size: 1602386904 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/c2ae78fd-418a-4cdb-a511-0914eb46224f/download/tmenvandinputdata.zip
    Description: The zip file consists of a R-script to provide the framework, an example simulation environment and a set of input data.
- The R-script installAndRunTreeMigWrapperAndGUI.R installs the R-package, links to the input data and starts the graphical user interface.
- The gridded input datasets in TMInputData are specifically tailored to Switzerland and have the same coordinate reference system and a congruent 200m spatial resolution. They consist of climate, topographic, land use and soil data.  For details see TMInputDataDetails.pdf.
- TMEnvironment is an example simulation environment, i.e. a set of hierarchically structured folders.  For the first example simulation of the tutorial it contains the
   - the TreeMig executables for MacOS, Linux and Windows
   - simulation control files that define the setup and input files of the simulation (in folder C)
   - species parameters (in folder P)
   - bioclimatic data  (in folder E)
   - folder R for results
   - folder Plots for plots of the simulation results.
   - This environment can be also used for the subsequent simulations of the tutorial.
- Some additional R-scripts demonstrate more complex applications described in the tutorial, e.g. coupling to another model.

en:
Resource Title: TreeMig_Tutorial_v2024_12_04.pdf
    Size: 6400462 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/d4c15251-917c-4102-b172-256fecd39161/download/treemig_tutorial_v2024_12_04.pdf
    Description:

en:
Resource Title: TreeMig Tutorial
    Size: 2046576 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/8e6ac324-ab2b-40bc-a12f-70960c8f4bd1/download/treemig_tutorial.pdf
    Description: Tutorial for using the TreeMig framework

en:
Resource Title: TreeMig Simulation Environment and Input Data
    Size: 1612103870 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/5eef16d1-80af-442b-9da2-f64b4fa94671/download/tmenvandinputdata.zip
    Description: The zip file consists of a R-script to provide the framework, an example simulation environment and a set of input data.
- The R-script installAndRunTreeMigWrapperAndGUI.R installs the R-package, links to the input data and starts the graphical user interface.
- The gridded input datasets in TMInputData are specifically tailored to Switzerland and have the same coordinate reference system and a congruent 200m spatial resolution. They consist of climate, topographic, land use and soil data.  For details see TMInputDataDetails.pdf.
- TMEnvironment is an example simulation environment, i.e. a set of hierarchically structured folders.  For the first example simulation of the tutorial it contains the
   - the TreeMig executables for MacOS, Linux and Windows
   - simulation control files that define the setup and input files of the simulation (in folder C)
   - species parameters (in folder P)
   - bioclimatic data  (in folder E)
   - folder R for results
   - folder Plots for plots of the simulation results.
   - This environment can be also used for the subsequent simulations of the tutorial.
- Some additional R-scripts demonstrate more complex applications described in the tutorial, e.g. coupling to another model.

en:
Resource Title: Description input data
    Size: 263110 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/3db8ca57-2838-4b26-82a9-9770b75b7096/download/readme_descriptioninputdata.pdf
    Description: Details where the input data come from and how they have been processed

bioclimatic-variables

display_name: bioclimatic-variables

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-model

display_name: forest-model

gui

display_name: gui

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-interaction

display_name: species-interaction

species-migration

display_name: species-migration

treemig

display_name: treemig


$oid: OBSOLETE

en:
The data correspond to the experiments presented and discussed in a paper regarding the interaction between turbulent wind fluctuations and snow saltation mass-fluxes (Paterna, 2016). Each of the nine data files corresponds to a different experiment presented in the paper and conducted in the winter 2014/2015 in the WSL/SLF cold wind tunnel in Davos. For each file the five columns indicate the time from the beginning of the experiment, the streamwise (u’) and the vertical (w’) wind velocity fluctuations, the streamwise (qx) and the vertical (qz) snow mass-flux components. From these time-series the scales of the snow saltation and of the turbulent flow are obtained with respect to the eddy-cycles and snow saltation cycles. From spectral analysis of the time-series a decoupling of the snow saltation from the turbulence forcing reveals two regimes of interaction: a turbulence-dependent regime occurring with weak saltation, and a turbulence-independent regime with strong saltation. Further details can be found at the link below.
Publication http://onlinelibrary.wiley.com/doi/10.1002/2016GL068171/abstract

en: saltation vertical-wind-motion snow-cover boundary-layer-winds snow-depth

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

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: Wind tunnel measurement data of drifting snow and turbulent wind fluctuations

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

format: ZIP

boundary-layer-winds

display_name: boundary-layer-winds

saltation

display_name: saltation

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth

vertical-wind-motion

display_name: vertical-wind-motion


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

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

en: Planning intentions in strategic plans of European urban regions

development-priorities

display_name: development-priorities

europe

display_name: europe

spatial-information

display_name: spatial-information

strategic-plans

display_name: strategic-plans

urban-region

display_name: urban-region


en:
Assessment of damges, i.e. symptoms, extent and causes on trees.
 Purpose:
Occurrence and extent of diseases
 Manual Citation:
 * Matthias Dobbertin, Christian Hug, Andreas Schwyzer, Serge Borer, Hanna Schmalz, 2016: Aufnahmeanleitung Kronenansprachen auf den Sanasilva- und den LWF-Flächen (Version 10). Sanasilva Inventur und Langfristige Waldökosystem-Forschung, Birmensdorf, 86 S. [---](https://www.wsl.ch/fileadmin/user_upload/WSL/Wald/Waldentwicklung_Monitoring/LWF/Sanasilva/ssi_anleitung_v10_extern.pdf)
 *  Eichhorn  J,  Roskams  P,  Potocic  N,  Timmermann  V,  Ferretti  M,  Mues  V,  Szepesi  A,  Durrant  D,  Seletkovic   I,   Schröck   H-W,   Nevalainen   S,   Bussotti   F,   Garcia   P,   Wulff   S,   2016:   Part   IV:   Visual   Assessment  of  Crown  Condition  and  Damaging Agents.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized  sampling,  assessment,  monitoring  and  analysis  of  the  effects  of  air  pollution  on  forests.  Thünen  Institute  of  Forest  Ecosystems, Eberswalde, Germany, 49 p. + Annex [---](https://www.icp-forests.org/pdf/manual/2016/ICP_Manual_2017_02_part04.pdf )
 Paper Citation:
 * Michel A, Seidling W, Prescher A K (2018) Forest Condition in Europe. 2018 Technical Report of ICP Forests. Report under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP). BFW-Dokumentation 25/2018, BFW Austrian Research Centre for Forests, Vienna, 92 p. [---](http://icp-forests.net/page/icp-forests-technical-report)
 *  Köhl M, San-Miguel-Ayanz J, Cools N, de Vos B, Fischer R, Camia A, Granke O, Hiederer R, Lorenz M, Montanarella L, Mues V, Nagel H-D, Poker J, Scheuschner T, Schlutow A (2011) Maintenance of Forest Ecosystem Health and Vitality. State of Europe s forests: status and trends in sustainable forest management in Europe 29-49. [---](http://www.foresteurope.org/documentos/State_of_Europes_Forests_2011_Report_Revised_November_2011.pdf)
 *  Brang P., 1998: Sanasilva-Bericht 1997. Zustand und Gefährdung des Schweizer Waldes – eine Zwischenbilanz nach 15 Jahren Waldschadenforschung. Berichte der Eidg. Forschungsanstalt für Wald, Schnee und Landschaft, Vol. 345. Eidg. Forschungsanstalt WSL, Birmensdorf, 102 S. [---](https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14555)

en: damage-cause-assessment icp-forests forest-vitality forest-condition swiss-forest-lab long-term-forest-ecosystem-research

envidat

en: EnviDat: the Environmental Data Portal

en: Crown Condition Assessment and Damage Cause Assessment LWF

format: HTML

damage-cause-assessment

display_name: damage-cause-assessment

forest-condition

display_name: forest-condition

forest-vitality

display_name: forest-vitality

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

swiss-forest-lab

display_name: swiss-forest-lab


en:
The colonization of cut pine stems by wood-inhabiting insects was investigated at various elevations. The study sites were located in the regions of Aosta Valley (Italy), Valais (Switzerland), and Grisons (Switzerland). In each region, there were two gradients in pine (Pinus sylvestris) forests, with three study sites at 900 m, 1200 m, and 1600 m a.s.l. each. Vital trees were felled in late autumn and the stems were colonized by pioneering xylophagous insects and their natural enemies next spring. Pieces of these stems were cut and exposed in emergence traps in a greenhouse. In each region the survey was done in two consecutive years.
Please contact author for terms of use.

en: bark-beetles pinus elevation insects parasitoids predators pests gradient

en: Pine insects along elevational gradients

format: CSV

bark-beetles

display_name: bark-beetles

elevation

display_name: elevation

gradient

display_name: gradient

insects

display_name: insects

parasitoids

display_name: parasitoids

pests

display_name: pests

pinus

display_name: pinus

predators

display_name: predators


alan-spongy-moth-laboratory-experiment

en:
Data and Code for ALAN Spongy Moth lab experiment
In this repository you will find all the raw datasets used for the data analysis performed in our paper about the impacts of artificial light at night (ALAN) on the development of Spongy Moth caterpillars. The files are attached as a csv file.
Also attached is all the R-code later used for the statistical analysis and for generating the graphics displayed in the paper. This files are attached as an R.file.

en: insect-development spongy-moth lab-experiment light-pollution alan

en: ALAN Spongy Moth Laboratory Experiment

title_for_slug: alan-spongy-moth-laboratory-experiment

en:
Resource Title: SM_R_NvK.csv
    Size: 56547 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/7f7beb40-642a-4a45-a99f-e631c90e1042/resource/c93124fd-448c-403b-bc42-70f4b6722636/download/sm_r_nvk.csv
    Description: Raw data used for the Pupation and Cox Hazard/Kaplan Meier Analysis.

media_type: text/csv

en:
Resource Title: CoxHazard_KaplanMeier_Mort_Pup.R
    Size: 3933 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/7f7beb40-642a-4a45-a99f-e631c90e1042/resource/0a205353-8812-4f39-af3f-8d67d2d1495c/download/coxhazard_kaplanmeier_mort_pup.r
    Description: R code for the Cox hazard analysis and producing the Kaplan Meier survival curves for both Mortality and Pupation.

format: OCTET

en:
Resource Title: BodyMass_analysis.R
    Size: 4254 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/7f7beb40-642a-4a45-a99f-e631c90e1042/resource/0ff15832-2eb3-4420-9afd-869e36ce92ef/download/bodymass_analysis.r
    Description: R code for the bodymass analysis and producing the graphics.

en:
Resource Title: SM_R_long_NvK.csv
    Size: 567035 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/7f7beb40-642a-4a45-a99f-e631c90e1042/resource/401242a6-4430-4725-8c41-263a4f769041/download/sm_r_long_nvk.csv
    Description: Raw data modified and used for the Bodymass Analysis

en:
Resource Title: Pupation_analysis.R
    Size: 7606 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/7f7beb40-642a-4a45-a99f-e631c90e1042/resource/f5ad6fb7-b540-4742-8b77-e5fa86855b1d/download/pupation_analysis.r
    Description: R code for the pupation analysis and producing the graphics.

alan

display_name: alan

insect-development

display_name: insect-development

lab-experiment

display_name: lab-experiment

light-pollution

display_name: light-pollution

spongy-moth

display_name: spongy-moth


en:
Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence-only SDMs that often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence-only models.
Innovation: Recent methods correct for observer bias by using covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1’900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps.
Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as bias covariate (Target-BCC) displayed most realistic modelled species distributions, with a clear positive correlation (r≃0.5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.

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

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

envi

en: Environment

background-data

display_name: background-data

cluster

display_name: cluster

covariate-correction

display_name: covariate-correction

environmental-stratification

display_name: environmental-stratification

independent-dataset

display_name: independent-dataset

plant-species

display_name: plant-species

point-process-model

display_name: point-process-model

random-stratified-sampling

display_name: random-stratified-sampling

survey-effort

display_name: survey-effort

target-group

display_name: target-group


en:
Wind-Topo is a statistical downscaling model for near surface wind fields especially suited for highly complex terrain.
It is based on deep learning and was trained (calibrated) using the hourly wind speed and direction from 261 automatic measurement stations (IMIS and SwissMetNet) located in Switzerland. The periods 1st October 2015 to 1st October 2016 and 1st October 2017 to 1st October 2018 were used for training. The model was validated using 60 other stations on the period 1st October 2016 to 1st October 2017. Wind-Topo was trained using COSMO-1 data and a 53-meter Digital Elevation Model as input.
This dataset provides all the necessary code to understand, use and incorporate Wind-Topo in a new downscaling scheme. Specifically, the dataset contains the architecture of Wind-Topo and its optimized parameters, as well as a python script to downscale uniform wind fields with a prescribed vertical profile for any given 53-meter DEM.
Accompanies the publication "Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning" Dujardin and Lehning, Quarterly Journal of the Royal Meteorological Society, 2022. https://doi.org/10.1002/qj.4265
Please cite this publication if you use Wind-Topo or derive new models from it.
The code can be used under the GNU Affero General Public License (AGPL).

en: high-resolution convolutional-neural-networks complex-terrain meteorology machine-learning topography surface-winds downscaling

envidat

en: EnviDat: the Environmental Data Portal

complex-terrain

display_name: complex-terrain

convolutional-neural-networks

display_name: convolutional-neural-networks

downscaling

display_name: downscaling

high-resolution

display_name: high-resolution

machine-learning

display_name: machine-learning

meteorology

display_name: meteorology

surface-winds

display_name: surface-winds

topography

display_name: topography


en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Lens in Switzerland which is located within a natural coniferous forest with Scots pine (_Pinus sylvestris_; 150-170 yrs)) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lens is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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

en: Lens, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards

en:
Resource Title: Site photo of the forest station (LEB)
    Size: 382534 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/fea7c28b-0a5b-4f38-b3df-c1ce66c2cf43/resource/15eb26e6-46a4-43e6-8cdb-fd7072968eb3/download/leb.jpg
    Description: Site photo of the forest station from below (viewing upwards the slope)

format: jpeg

en:
Resource Title: Data plot of the forest station (LEB)
    Size: 234314 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/fea7c28b-0a5b-4f38-b3df-c1ce66c2cf43/resource/569d951e-97b2-44e0-9ad5-d861994a78eb/download/lebplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

format: PDF

media_type: application/pdf

en:
Resource Title: Forest station data (LEB)
    Size: 9240564 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/fea7c28b-0a5b-4f38-b3df-c1ce66c2cf43/resource/21e6ffa5-4cc5-4310-be18-91c2d579163c/download/lebforest.csv
    Description: Data file.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed


en: To study the performance of mature Scots pine (_Pinus sylvestris_ L.) under chronic drought conditions in comparison to their immediate physiological response to drought release, a controlled long-term and large-scale irrigation experiment has been set up in 2003. The experiment is located in a xeric mature Scots pine forest in the Pfynwald (46° 18' N, 7° 36' E, 615 m a.s.l.) in one of the driest inner-Alpine valleys of the European Alps, the Valais (mean annual temperature: 9.2°C, annual precipitation sum: 657 mm, both 1961-1990). Tree age is on average 100 years, the top height is 10.8 m and the stand density is 730 stems ha-1 with a basal area of 27.3 m2 ha-1. The forest is described as _Erico Pinetum sylvestris_ and the soil is a shallow pararendzina characterized by low water retention. The experimental site (1.2 ha; 800 trees) is split up into eight plots of 1'000 m2 each. During April-October, irrigation is applied on four randomly selected plots with sprinklers of 1 m height at night using water from an adjacent water channel. The amount of irrigation corresponds to a supplementary rainfall of 700 mm year-1. Trees in the other four plots grow under naturally dry conditions. Soil moisture has been monitored since the beginning of the project at 3 soil depths (10, 20 and 60 cm). The crown condition of each tree is being assessed each year since 2003. Tree measurement data such as diameter at breast height, tree height, and social status were assessed in 2002, 2009 and 2014. The duration of the irrigation experiment is planned for 20 years.

en: swiss-forest-lab drought dbh crown-condition forest tree-species diameter-at-breast-height irrigation tree lwf scots-pine

en: Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland

en:
Resource Title: Site photo 3
    Size: 8186031 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/0e18a914-f2e4-47df-a86c-a44a1b0055e4/resource/e3c46133-0b7c-4d9c-a3d0-ba0e91e663b5/download/20150709pfyn218.jpg
    Description:

en:
Resource Title: Site photo 1
    Size: 9330316 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/0e18a914-f2e4-47df-a86c-a44a1b0055e4/resource/e0eed15d-de04-44f0-8601-cb531f9fa916/download/20150709pfyn117.jpg
    Description:

en:
Resource Title: Swiss Forest Health Inventory
    Size: 1 bytes
    Format: text/html
    URL: https://www.wsl.ch/en/forest/forest-development-and-monitoring/sanasilva-forest-health-inventory.html
    Description:

media_type: text/html

en:
Resource Title: Shapefile	of measurement location
    Size: 0
    Format: No Info
    URL: https://map.geo.admin.ch/?topic=swisstopo&X=127951&Y=613350&zoom=11&lang=de&bgLay er=ch.swisstopo.pixelkarte-farbe&crosshair=marker
    Description:

en:
Resource Title: Long-term Irrigation	Experiment Pfynwald homepage
    Size: 1 bytes
    Format: text/html
    URL: https://www.wsl.ch/en/about-wsl/instrumented-field-sites-and-laboratories/experimental-sites-in-forests/pfynwald.html
    Description:

en:
Resource Title: Site photo 2
    Size: 9330316 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/0e18a914-f2e4-47df-a86c-a44a1b0055e4/resource/fd9b4ee3-e87e-4c0b-b7a8-832fb3f019e7/download/20150709pfyn117.jpg
    Description:

en:
Resource Title: Dataset
    Size: 1898851 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/0e18a914-f2e4-47df-a86c-a44a1b0055e4/resource/81280262-3628-458c-8dbf-33ed80eb222a/download/envidatpfyncrowncond2016.xlsx
    Description:

format: XLS

crown-condition

display_name: crown-condition

dbh

display_name: dbh

diameter-at-breast-height

display_name: diameter-at-breast-height

drought

display_name: drought

forest

display_name: forest

irrigation

display_name: irrigation

lwf

display_name: lwf

scots-pine

display_name: scots-pine

tree

display_name: tree

tree-species

display_name: tree-species


disdrometer-data-davos-wolfgang

en: The dataset contains information on precipitation amount and type for Davos Wolfgang (LON: 9.853594, LAT: 46.835577) from February 8 to March 19 2019. It includes: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

en: davos disdrometer precipitation fall-velocity raclets

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

en: Disdrometer Data Davos Wolfgang

title_for_slug: disdrometer-data-davos-wolfgang

envi

en: Environment

davos

display_name: davos

disdrometer

display_name: disdrometer

fall-velocity

display_name: fall-velocity

raclets

display_name: raclets


en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Nationalpark in Switzerland where one station is located within a natural coniferous forest stand (NAB) with mountain pine (_Pinus mugo_; 210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Nationalpark is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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

en: Nationalpark, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards

en:
Resource Title: Site photo of the field station (NAF)
    Size: 1645503 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/8d87a8ef-9db6-4803-a157-4b987ac831ea/resource/725b7b34-1c1e-439a-953a-a0268b7ffa54/download/naf.jpg
    Description: Site photo of the field station form the side.

en:
Resource Title: Site photo of the forest station (NAB)
    Size: 9753271 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/8d87a8ef-9db6-4803-a157-4b987ac831ea/resource/57d54635-9375-49f9-a122-33bc7eed5d61/download/nab.jpg
    Description: Site photo of the forest station from the side.

en:
Resource Title: Forest station data (NAB)
    Size: 10985874 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8d87a8ef-9db6-4803-a157-4b987ac831ea/resource/fae88674-0ef3-4ca0-b65e-4f07a56954ea/download/nabforest.csv
    Description: Data file.

en:
Resource Title: Data plot of the field station (NAF)
    Size: 318496 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/8d87a8ef-9db6-4803-a157-4b987ac831ea/resource/ab615db0-0b36-4e4f-8f7e-56709fce6d6f/download/nafplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Site aerial photo (Nationalpark)
    Size: 9198968 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/8d87a8ef-9db6-4803-a157-4b987ac831ea/resource/d9c7373d-b9e8-4e04-a640-0e677c4d24c6/download/nafront.jpg
    Description: Site aerial photo from both Nationalpark stations. Source: Google. 2016. Site aerial photo from both Nationalpark stations. Retrieved November 11, 2016, from https://www.google.ch/maps

en:
Resource Title: Data plot of the forest station (NAB)
    Size: 284343 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/8d87a8ef-9db6-4803-a157-4b987ac831ea/resource/7b08bed2-e64a-47a3-94e0-5d2748d0bea0/download/nabplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Field station data (NAF)
    Size: 11050183 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8d87a8ef-9db6-4803-a157-4b987ac831ea/resource/db066e17-9fbb-4925-b084-9be1ada86b42/download/naffield.csv
    Description: Data file.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab


en: We present forecasts of land-use/land-cover (LULC) change for Switzerland for three time-steps in the 21st century under the representative concentration pathways 4.5 and 8.5, and at 100-m spatial and 14-class thematic resolution. We modelled the spatial suitability for each LULC class with a neural network (NN) using -200 predictors and accounting for climate and policy changes. We used data augmentation to increase performance for underrepresented classes, resulting in an overall quantity disagreement of 0.053 and allocation disagreement of 0.15, which indicate good model performance. These class-specific spatial suitability maps outputted by the NN were then merged in a single LULC map per time-step using the CLUE-S algorithm, accounting for LULC demand for the future and a set of LULC transition rules. As the first LULC forecast for Switzerland at a thematic resolution comparable to available LULC  maps for the past, this product lends itself to applications in land-use planning, resource management, ecological and hydraulic modelling, habitat restoration and conservation.

en: land-use land-cover landscape machine-learning landscape-change modelling

en: High resolution land use forecasts for Switzerland in the 21st century

en:
Resource Title: Code.zip
    Size: 407530 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/69945cd4-3dfc-4b1d-bdd4-e88cc577c3c9/resource/593cc9c4-deb1-4637-a328-9d68f0e15cd6/download/code.zip
    Description: Code used to produce the Land-use/land-cover forecasts for Switzerland across the 21st century at 100-m resolution.

The script is based on input data which are not included as they are not freely available, therefore it will not run as it is. These input data are described in the accompanying publication on Data Science access to them can be requested to their owners. Alternatively the code can be modified to run on other data.

media_type: application/zip

en:
Resource Title: swiss_lulc_forecast_21th_century.zip
    Size: 16438707 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/69945cd4-3dfc-4b1d-bdd4-e88cc577c3c9/resource/347fffe5-f3cb-4309-914b-9057a30a32db/download/swiss_lulc_forecast_21th_century.zip
    Description: Compressed (.zip) file containing six georeferenced raster files in the GeoTIFF format with embedded coordinate reference system, and a table (comma separated values, .csv) encoding a colour palette for plotting (HEX colour codes for each class). GeoTIFF file names include the time-step and the RCP (e.g. “LULC_2020_2049_RCP45.tiff”). Each raster file has 3484x2207 square pixels (i.e. 7.7 megapixels) and is projected in the Swiss coordinate system CH1903+/LV95 (EPSG:2056).

land-cover

display_name: land-cover

land-use

display_name: land-use

landscape

display_name: landscape

landscape-change

display_name: landscape-change

machine-learning

display_name: machine-learning

modelling

display_name: modelling


en:
This dataset contains the spatial layers showing future land change pathways in Europe between 2015 and 2050, as simulated by the CLUMondo model and according to the Agricultural Futures Framework (AFF) scenarios.
Three scenarios are included:
- Land for Food and Land for Nature (LFLN)
- Land as Culture (LaC)
- Land for Society (LfS)

en: scenarios agriculture land-change land-use-modelling pathways

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

en: AFF agricultural land change scenarios in Europe 2015-2050

en:
Resource Title: ReadMe.pdf
    Size: 87640 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/15ae8f00-310e-4cee-95ed-d00f0aa25dbe/resource/573e45b3-27b1-4139-b61b-06c3adc8871e/download/readme.pdf
    Description:

en:
Resource Title: AFF_agricultural land_change scenarios_Europe_2015_2050.zip
    Size: 3851250 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/15ae8f00-310e-4cee-95ed-d00f0aa25dbe/resource/a8d3393e-178f-43a7-9538-1437b0afd8b3/download/aff_agricultural-land_change-scenarios_europe-2015_2050.zip
    Description: This dataset contains the spatial layers showing future land change pathways in Europe between 2015 and 2050, as simulated by the CLUMondo model according to the Agricultural Futures Framework (AFF) scenarios.

agriculture

display_name: agriculture

land-change

display_name: land-change

land-use-modelling

display_name: land-use-modelling

pathways

display_name: pathways

scenarios

display_name: scenarios


en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Neunkirch in Switzerland where one station is located within a natural deciduous forest stand (NEB) with European beech (_Fagus sylvatica_; 160 yrs), sycamore maple (_Acer pseudoplatanus_; 160 yrs) and lime trees (_Tilia sp._; 110 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NEF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Neunkirch is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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

en: Neunkirch, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards

envi

en: Environment

en:
Resource Title: Forest station data (NEB)
    Size: 10603940 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/20934806-9510-4de8-b2a6-acb1f9831de3/resource/0dc8b0c2-02f7-4052-bdfc-ac967a916888/download/nebforest.csv
    Description: Data file.

en:
Resource Title: Field station data (NEF)
    Size: 10713187 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/20934806-9510-4de8-b2a6-acb1f9831de3/resource/f7dc0cd8-8dd9-49f6-bfee-47d2945a23df/download/neffield.csv
    Description: Data file.

en:
Resource Title: Data plot of the forest station (NEB)
    Size: 229180 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/20934806-9510-4de8-b2a6-acb1f9831de3/resource/64f63815-a1df-4f25-92d3-75e2c19b2abd/download/nebplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Data plot of the field station (NEF)
    Size: 313178 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/20934806-9510-4de8-b2a6-acb1f9831de3/resource/084cafd7-038f-4922-a36c-af54c00399cb/download/nefplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Site photo of the forest station (NEB)
    Size: 5116944 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/20934806-9510-4de8-b2a6-acb1f9831de3/resource/49887857-0fad-44d6-a56c-58e8851a65a8/download/neb.jpg
    Description: Site photo of the forest station from below (viewing upwards the slope).

en:
Resource Title: Site photo of the field station (NEF)
    Size: 1877748 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/20934806-9510-4de8-b2a6-acb1f9831de3/resource/f8bb24e8-a6a2-4cf5-bf88-f688b7a4e488/download/nef.jpg
    Description: Site photo of the field station from the side.

en:
Resource Title: Site aerial photo (Neunkirch)
    Size: 8951072 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/20934806-9510-4de8-b2a6-acb1f9831de3/resource/68f3a7a3-21b6-45cd-8578-6232edf6d1ef/download/nefront.jpg
    Description: Site aerial photo from both Neunkirch stations. Source: Google. 2016. Site aerial photo from both Neunkirch stations. Retrieved November 11, 2016, from https://www.google.ch/maps

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

temperature

display_name: temperature

wind-speed

display_name: wind-speed


greenland-shrubs-and-microclimate

en:
Study Aim
We collected these data to alternatively train and validate high resolution (~ 90 m) Species Distribution Models (SDMs) and Species Abundance Models (SAMs) for _Betula nana_ L. (dwarf birch, Betulaceae) and _Salix glauca_ L. (grey willow, Salicaceae) in Southwest Greenland to assess how well such models can predict local-scale patterns.
 Data Description
Individual (presence-absence, abundance, maximum vegetative height) and community (species composition, maximum canopy height) shrub data for two fjords near Nuuk, Southwest Greenland. Also provided are corresponding downscaled climate data as well as calculated topographic and terrain wetness indicator variables.
 Nuup Kangerlua (Godthåbsfjord)
_Betula nana_ and _Salix glauca_ presence-absence, abundance, community species richness
 Kangerluarsunnguaq (Kobbefjord)
Shrub presence-absence, abundance, maximum vegetative height, community composition, maximum shrub canopy height
 Methods
 Field survey in Nuup Kangerlua
We conducted a stratified systematic plant survey along the length of Nuup Kangerlua (NK) fjord in Soutwesth Greenland (Fig. 1 in Chardon et al. 2022; following Nabe-Nielsen et al., 2017). At five distinct sites, we sampled along elevational gradients to collect data on presences, absences, abundance, and species composition of all woody species using a 0.7 x 0.7 m pin-point frame (Fig. 1e in Chardon et al. 2022). For model training, we converted these pin-point data to percent cover estimates based on the number of pins dropped (n = 25 per plot) and averaged them across the 119 spatio-climatic grids (see next section) corresponding to the plot locations (for details see Appendix S2 in Chardon et al. 2022).
 Field survey in Kangerluarsunnguaq
We conducted a random stratified plant survey in Kangerluarsunnguaq (K) fjord in Southwest Greenland. We used a preliminary Species Abundance Model trained with summed pin counts of _Betula nana_ in NK fjord (see Fig. S1.3 in Chardon et al. 2022) to stratify the ~ 27 x 17 km fjord landscape into low, medium, and high abundances classes. We randomly selected 90 x 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2 in Chardon et al. 2022). Within each grid, we sampled within three 1 m2 quadrats arranged in a randomly rotated equilateral triangle centered on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Fig. 1h in Chardon et al. 2022) to record woody species presences, absences, and composition, estimated percent cover, and measured maximum shrub species vegetatitve height. At every plot, we also visually scanned the area in a 20 m radius from the plot and recorded the presence of any additional shrub species to estimate grid-level species richness. As in NK fjord, we averaged these data at the grid level (for details see Appendix S2 in Chardon et al. 2022).
 Biotic variables
We calculated biotic microscale variables from the plant survey data collected in NK and K fjords. We calculated shrub species richness, diversity, and competition (i.e. sum of non-B. nana or non-S. glauca pin hits or percent cover). In K fjord, we also calculated canopy height as the community weighted mean (by abundance) of maximum vegetative shrub height.
 Climate variables
We computed high resolution temperature, precipitation, and insolation for local scale data for the study area by statistically downscaling climate time series (1982 - 2013) from the monthly CHELSA data (Karger et al. 2017). We downscaled these data from 30 arc sec (~ 400 m at the latitude of our study) to our target grid size of ~ 90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1 (Howat et al., 2014, 2015). We then calculated 30-year averages of the climate parameters: average summer (June – August) maximum temperature, yearly maximum temperature, yearly minimum temperature, temperature continentality (yearly max. - min. temperatures), cumulative Spring (March – May) precipitation, cumulative summer precipitation, and average summer incident solar radiation (henceforth, insolation) (for calculation details see Appendices S2, S3 in Chardon et al. 2022 and Appendix S2 in von Oppen et al. 2021).
 Topography and terrain wetness indicator variables
We calculated several topographic and terrain wetness indices at a local scale. We derived slope, aspect, and the SAGA wetness index (hereafter TWI; Boehner et al., 2002; Boehner and Selige, 2006) from the GIMP DEM. TWI is a measure of how ‘wet’ an area is, based on water drainage from the surrounding landscape. We also calculated the tasseled cap wetness component (hereafter TCW, Crist and Cicone 1984) from satellite images (for details see Appendices S2, S3 in Chardon et al. 2022) as an alternative measure of wetness.
 Computer code
Attached as zip file and available on GitLab (https://gitlab.com/nathaliechardon/gl_microclim)
 Third-party data
Data used to calculate climate, topography, and terrain wetness indicator variables are publicly available (see Appendix S2 in Chardon et al. 2022 for all data references).

en: climate sdm field-data validation accessible-shrub-forest arctic

en: Greenland shrubs and microclimate

title_for_slug: greenland-shrubs-and-microclimate

en:
Resource Title: Kangerluarsunnguaq
    Size: 93883 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/040d25dd-71c0-40e6-99a8-849427250f5d/resource/dc1bf94c-e651-4193-9a42-de3c9f3b11e9/download/kangerluarsunnguaq.csv
    Description: ## Collected August 2020 (PI: Nathalie Isabelle Chardon)

These data collected August 2020 (PI: Nathalie Isabelle Chardon). Please contact N. I. Chardon (nathalie.chardon_at_gmail.com) for individual quadrat (i.e. 1 m x 1 m) data, which are averaged at the 90 m spatial grid here.

## COLUMN ABBREVIATIONS

### Metadata for 90 m spatial grid

id: sampling zone and unique 90 m grid identifier

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

date: sampling day.month.year

### Species presence [1] or absence [0] at grid level

alcr: _Alnus crispa_

bena: _Betula nana_

dila: _Diapensia lapponica_

emni: _Empetrum nigrum_

hahy: _Harimanella hypoides_

lopr: _Loiseleuria procumbens_

oxpa: _Oxycoccus palustre_

phca: _Phyllodoce caerulea_

rhod: _Rhododendron_ sp.

saar: _Salix arctica_

sagl: _Salix glauca_

sahe: _Salix herbacea_

vaul: _Vaccinium uliginosum_

drin: _Dryas integrifolia_

thpr: _Thymus praecox_

### Habitat characteristics [% cover]

shrub: all shrub species

gram: graminoids

forb: forbs

bryo: bryophytes

lich: lichens

litt: litter

bare: bare ground

stone: stones (> 2 cm)

org: organic crust

### Shrub species height (maximum vegetative height) [mm] and cover (estimated in 1x1 m quadrat) [%]

alcr_c: _Alnus crispa_ cover

alcr_h: _Alnus crispa_ height

bena_c: _Betula nana_ cover

bena_h: _Betula nana_ height

dila_c: _Diapensia lapponica_ cover

dila_h: _Diapensia lapponica_ height

emni_c: _Empetrum nigrum_ cover

emni_h: _Empetrum nigrum_ height

hahy_c: _Harimanella hypoides_ cover

hahy_h: _Harimanella hypoides_ height

lopr_c: _Loiseleuria procumbens_ cover

lopr_h: _Loiseleuria procumbens_ height

oxpa_c: _Oxycoccus palustre_ cover

oxpa_h: _Oxycoccus palustre_ height

phca_c: _Phyllodoce caerulea_ cover

phca_height: _Phyllodoce caerulea_ height

rhod_c: _Rhododendron_ sp. cover

rhod_h: _Rhododendron_ sp. height

saar_c: _Salix arctica_ cover

saar_h: _Salix arctica_ height

sagl_c: _Salix glauca_ cover

sagl_h: _Salix glauca_ height

sahe_c: _Salix herbacea_ cover

sahe_h: _Salix herbacea_ height

vaul_c: _Vaccinium uliginosum_ cover

vaul_h: _Vaccinium uliginosum_ height

drin_c: _Dryas integrifolia_ cover

drin_h: _Dryas integrifolia_ height

### Community characteristics

rich_pa: shrub species richness at grid level

rich: shrub species richness averaged across 3 1x1 m sampling quadrats per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on _Betula nana_ (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on _Salix glauca_ (i.e. summed cover of non-_S. glauca_ species) [% cover]

ht_cwm: maximum shrub canopy height (i.e. community weighted mean by abundance of maximum shrub height) [mm]

### Topography and terrain wetness (calculated from)

tcws: Tasseled Cap Wetness component (Sentinal images)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

### Downscaled climate (time series 1984 – 2013)

insoljja: average June-August insolation [MJ cm-2 yr-1]

precipmam: cumulative March-May precipitation [mm]

tempcont: yearly maximum – minimum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

precipjja: cumulative June-August precipitation [mm]

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

en:
Resource Title: Nuup Kangerlua
    Size: 27166 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/040d25dd-71c0-40e6-99a8-849427250f5d/resource/49bbe7fa-5694-4aff-9754-1d66bdc0978e/download/nuupkangerlua.csv
    Description: ## Collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen)

These data collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen). Please contact J. Nabe-Nielsen (jnn_at_ecos.au.dk) to request access to data on all surveyed shrub species. Available here are only data used in Chardon et al. 2022.

## COLUMN ABBREVIATIONS

### Metadata for 90 m spatial grid

year: sampling year

alt: average altitude of grid as recorded during field surveys [m]

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

### Species presence [1] or absence [0] at grid level

pa_bena: _Betula nana_

pa_sagl: _Salix glauca_

### Shrub species cover (calculated from pin hits in 0.7 x 0.7 m frame, based on total of 25 hits) [%]

cov_bena: _Betula nana_ cover

cov_sagl: _Salix glauca_ cover

### Downscaled climate (time series 1984 – 2013)

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

tempcont: yearly maximum – minimum temperature [ºC]

precipmam: cumulative March-May precipitation [mm]

precipjja: cumulative June-August precipitation [mm]

insoljja: average June-August insolation [MJ cm-2 yr-1]

### Topography and terrain wetness (calculated from)

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

tcws: Tasseled Cap Wetness component (Sentinal images)

### Community characteristics

rich: shrub species richness averaged across pin-point frames per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on Betula nana (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on Salix glauca (i.e. summed cover of non-_S. glauca_ species) [% cover]

en:
Resource Title: GitLab Repository
    Size: 235019467 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/040d25dd-71c0-40e6-99a8-849427250f5d/resource/9a2b483b-baf7-40ff-98a2-faf92ef7b7a2/download/gl_microclim.zip
    Description: Zip drive of GitLab repository contents (https://gitlab.com/nathaliechardon/gl_microclim).

accessible-shrub-forest

display_name: accessible-shrub-forest

arctic

display_name: arctic

climate

display_name: climate

field-data

display_name: field-data

sdm

display_name: sdm

validation

display_name: validation


en:
This dataset contains all data, on which the following publication below is based.
**Paper Citation**:
Neff, F., Resch, M. C., Marty, A., Rolley, J. D., Schütz, M., Risch, A. C, Gossner, M. M. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands: a functional approach. Ecological Applications, 30, e02133. [10.1002/eap.2133](https://doi.org/10.1002/eap.2133)
Please cite this paper together with the citation for the datafile.
 Methods
 Study site
The study area is situated within and nearby to Eigental nature reserve (47°27’36” to 47°29’06” N, 8°37’12” to 8°37’44” E, 461 to 507 m a.s.l.) in the vicinity of Zurich airport (Canton Zurich, Switzerland). Mean annual precipitation and temperature is 903 ± 136 mm and 9.14°C ± 0.50°C (mean ± SD for 2007-2017 (*MeteoSchweiz 2018*)). In 1967, the Eigental nature reserve was established to protect small and isolated remnants of species-rich, semi-natural grasslands (roughly 12 ha), which were embedded in an otherwise intensively managed landscape. It is characterized by oligo- to mesotrophic Molinion (semi-wet, matrix species *Molinia caerulea*) and Mesobromion (semi-dry, matrix species *Bromus erectus*) meadows (*Delarze et al. 2015*), reflecting small-scale habitat heterogeneity, mainly due to site-specific groundwater levels and slope inclination. As in most Central European grasslands, management is necessary to prevent shrub and tree invasions as well as to secure low levels of available soil nutrients and thus to maintain these species-rich habitats ([*Poschlod and WallisDeVries 2002*](https://doi.org/10.1016/S0006-3207(01)00201-4)).
In 1990, the government of the Canton Zurich decided to enlarge the Eigental nature reserve as a counter measure against degradation and biodiversity loss in semi-natural grasslands due to overutilization and the excessive input of nutrients (mostly nitrogen). Eleven patches of adjacent intensively managed grassland (in total roughly 20 ha) were targeted to be transformed into semi-natural grasslands. As a first restoration measure, fertilization was ceased, and biomass harvested three times to remove excessive soil nutrients from the original system and thus benefit plant species with low competitive ability on the long run. In 1995, the restoration efforts were increased and a large-scale experiment comprising three restoration measures with increasing intervention intensities was implemented:
- **Harvest only**: Initial restoration measures were continued with mowing and removing of the aboveground biomass two times a year (early summer and autumn).
- **Topsoil**: Removal of topsoil, depending on the thickness of the A horizon the upper 10 to 20 cm, in four randomly selected areas within the eleven patches in late autumn 1995. The size of the restoration area depended on individual patch size (2700 to 7000 m2).
- **Topsoil + Propagules**: Plant propagules were added on half of the area where topsoil was removed via application of fresh, seed-containing hay and hand-collected propagules of target species originating from semi-dry and semi-wet species-rich grasslands with local and regional provenance (within radius of 7 to 30 km) (1995, 1996, 1997).
Management of *Topsoil* and *Topsoil + Propagules* started five years after treatment implementation and included yearly mowing and removing of aboveground biomass (late summer or early autumn). The experiment was complemented with intensively managed grassland sites that share the same agricultural history as the restored sites (**Initial**; swards dominated by *Lolium perenne*, *L. multiflorum* and *Trifolium repens*): mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes. Finally, sites were selected in target semi-dry and semi-wet grasslands (**Target**) located within the Eigental nature reserve and another nature reserve nearby (Altläufe der Glatt; 47°28’29” to 47°27’41” N, 8°31’56” to 8°32’26” E, 418 to 420 m a.s.l.). The selected target sites are mown and aboveground biomass removed once a year in late summer or early autumn. For each of the five treatments, we selected eleven plots (5 m × 5 m) spread across the sites. Altogether, the experiment included 55 plots.
 Arthropod sampling
Aboveground arthropods were sampled using suction sampling on four consecutive days in early July 2017 before the grasslands were mown. Arthropods were sampled in two locations on each 5 m × 5 m plot, once in the south-western and once in the north-eastern corner to account for possible spatial heterogeneity within the plots. Arthropods were sorted to order or lower taxonomic levels and individuals were identified to species level. We focused on three groups (Hemiptera: Auchenorrhyncha, Hemiptera: Heteroptera, Orthoptera),
 Functional traits
We used two sets of functional traits in this study.
**Morphometric traits**:
Body volume, body shape, hind femur shape, hind/front leg ratio, wing length, leg length, antenna length and eye width. We used trait measurements from [*Simons et al. (2016)*](http://dx.doi.org/10.1890/15-0616.1) and [*Neff et al. (2019)*](https://doi.org/10.1007/s10980-019-00872-1) and complemented them with measurements on study specimens. These measurements were conducted using a high-resolution measuring stereo microscope (Leica DVM6, Leica Microsystems) including automated high-resolution photo stacking with the software Leica Application Suite X (LAS X, © 2018 Leica Microsystems CMS GmbH) and Leica Map Premium (Leica Microsystems, © 1996-2017 Digital Surf) at WSL Birmensdorf. The eight morphometric traits were calculated from direct measurements of body parts on specimens of all sampled species. From each species, we measured at least one female and one male specimen. Additionally, for species that show wing dimorphism, we included the different wing morphs and weighted them by their prevalence reported in literature. For few species, of which not all wing morphs were available for measurements (10 cases), we estimated relative wing length from congeneric species or from the literature.
**Life-history traits**
Based on an existing data set collected by [*Gossner et al. (2015)*](http://dx.doi.org/10.1890/14-2159.1). We included traits describing different life-history characteristics of herbivore insect species, namely: feeding specialization, feeding tissue, hibernation stage and number of generations per year, which are related to insect species’ vulnerability to changes in plant community composition, microhabitat use and disturbance tolerance. To represent potential changes in habitat moisture with abandonment of intensive land use (e.g., change in ground-water level), we also included two traits related to preferred habitat moisture of the study species: moisture preference, describing species’ optimum habitat moisture, and moisture range, which describes the species’ range of preferable moisture conditions.
 References
Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensräume der Schweiz: Ökologie - Gefährdung - Kennarten. 3rd ed. Ott, Bern.
Gossner, M. M., N. K. Simons, R. Achtziger, T. Blick, W. H. O. Dorow, F. Dziock, F. Köhler, W. Rabitsch, and W. W. Weisser. 2015. A summary of eight traits of Coleoptera, Hemiptera, Orthoptera and Araneae, occurring in grasslands in Germany. Scientific Data 2:150013.
MeteoSchweiz. 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Zürich.
Neff, F., N. Blüthgen, M. N. Chisté, N. K. Simons, J. Steckel, W. W. Weisser, C. Westphal, L. Pellissier, and M. M. Gossner. 2019. Cross-scale effects of land use on the functional composition of herbivorous insect communities. Landscape Ecology 34:2001–2015.
Poschlod, P., and M. F. WallisDeVries. 2002. The historical and socioeconomic perspective of calcareous grasslands—lessons from the distant and recent past. Biological Conservation 104:361–376.
Simons, N. K., W. W. Weisser, and M. M. Gossner. 2016. Multi-taxa approach shows consistent shifts in arthropod functional traits along grassland land-use intensity gradient. Ecology 97:754–764.

en: long-term-experiment functional-traits grassland restoration functional-diversity insects diversity topsoil-removal

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

en: Grassland restoration: insects and insect traits

en:
Resource Title: Insect traits
    Size: 11167 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e4752eb3-24f1-48b5-a6c4-e0a4134af08d/resource/bddffb02-61f5-44b1-b96a-92bb043415b4/download/insect_traits.csv
    Description: Functional traits of all herbivorous insect species sampled in the study.

- **Species**: Insect species name (following [Fauna Europaea](https://fauna-eu.org))
- **Author**: Author and year of species' description (following [Fauna Europaea](https://fauna-eu.org))
- **Group**: Insect group as used in the analyses
- **Body volume**: Body volume determined by body length, body width and body height (in mm$^3$)
- **Body_shape**: Ratio of body length to body width
- **Hind_femur_shape**: Ratio of hind-femur length to hind-femur width
- **Hind_front_leg_ratio**: Ratio of front-leg length to hind-leg length (leg length = femur length + tibia length)
- **Wing_length_rel**: Ratio of wing length to body length
- **Leg_length_rel**: Ratio of front-leg length to body length (leg length = femur length + tibia length)
- **Antenna_length_rel**: Ratio antenna length to body length
- **Eye_width_rel**: Ratio of eye width to head width (Auchenorrhyncha, Heteroptera) / to head height (Orthoptera)
- **Feeding_specialization**: Insect specialization on food source (*m*: monophagous, *o*: oligophagous, *p*: polyphagous)
- **Feeding_tissue**: Feeding tissue used for feeding (*c*: chewers, *x*: xylem, *p*: phloem, *m*: mesophyll, *m-p/r*: mesophyll as well as phloem and/or reproductive organs, *r*: reproductive organs, *se*: ripe seeds)
- **Hibernation**: Hibernation stage (*e*: egg, *e/n*: egg and nymph, *n*: nymph, *a*: adult)
- **Generations**: Number of generations per year (*1.5* stands for *1-2* generations per year)
- **Moisture_pref_mean**: mean of preferred habitat moistures (*1*: xerophilous, *2*: moderately xerophilous, *3*: moderately mesohygrophilous, *4*: mesohygrophilous, *5*: moderately hygrophilous, *6*: hygrophilous, *7*: moderately hydrophilous, *8*: hydrophilous)
- **Moisture_pref_range**: range of preferred habitat moistures (indicating degree of specialization)

en:
Resource Title: Insect abundance
    Size: 33818 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e4752eb3-24f1-48b5-a6c4-e0a4134af08d/resource/2d60fcd6-a2f1-45fd-a450-3d560eb43822/download/insect_abundance.csv
    Description: Abundance of adult insects recorded in the samplings on all experimental sites

- **Plot**: ID of Plot
- **Treatment**: Experimental treatment
- **Species**:  Insect species name (following [Fauna Europaea](https://fauna-eu.org))
- **Author**: Author and year of species' description (following [Fauna Europaea](https://fauna-eu.org))
- **Group**: Insect group as used in the analyses
- **Number_Adults**: Number of adult individuals recorded
- **Herbivore**: Logical indicating whether the species is feeding as a herbivore (*TRUE*) or not (*FALSE*)

en:
Resource Title: Plot information
    Size: 4938 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e4752eb3-24f1-48b5-a6c4-e0a4134af08d/resource/04c37490-b0c2-4681-aded-068fa4ee8290/download/plot_info.csv
    Description: Information on the experimental plots.

- **Plot**: ID of Plot
- **Treatment**: Experimental treatment
- **Landuse_history**: recent agricultural use for Initial; agricultural use before restoration for all restored sites
- **Last_tilled**: Last time the site was tilled
- **Intensity**: Frequency and intensity of mowing and manuring (*intensity1*: mowing once and no manure, i*ntensity2*: 2-3 times mowing and no manure, *intensity3*: 2-3 times mowing and manuring, *intensity4*: 4-5 times mowing and manuring)
- **Slope**: slope gradient according to classes defined by FAO (1990)
- **X**: X coordinate of the plot (CH1903)
- **Y**: Y coordinate of the plot (CH1903)
- **Cluster**: Spatial cluster of the plot

diversity

display_name: diversity

functional-diversity

display_name: functional-diversity

functional-traits

display_name: functional-traits

grassland

display_name: grassland

insects

display_name: insects

long-term-experiment

display_name: long-term-experiment

restoration

display_name: restoration

topsoil-removal

display_name: topsoil-removal


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

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

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

envi

en: Environment

convection-in-snow

display_name: convection-in-snow

numerical-simulation

display_name: numerical-simulation

openfoam

display_name: openfoam

snow-density-profile

display_name: snow-density-profile

vapor-transport

display_name: vapor-transport


en:
This dataset provides distribution data of lichens in Switzerland of the National Data and Information Centre, called Swiss Lichens. SwissLichens is a partner of InfoSpecies, the network of Swiss data and information centres for fauna, flora and fungi. One of its main objectives is to document the spatial and temporal distribution of species in Switzerland.
The SwissLichens database currently contains more than 120’000 georeferenced lichen observations, distributed throughout Swizerland. The oldest observations date back to 1790. A large portion of the records dtae from the last two decades of the last century to the present day. The database is continuously updated with new findings. The data have been validated and originate from national inventories, from research projects, from floristic observations by volunteers as well as from private and public herbaria and from the literature.
Each record consists of the species name, information of the location (Swiss Coordinates, precision of the coordinates, elevation above sea level, municipality and canton), the date of observation, the ecotype (epiphytic, terricol, lignicol, saxicol), as well as information on the conservation status of the species (red list status, conservation priority status, status in the Nature and Cultural Heritage Act). Information on the ecology (habitat, substrate) is partially available.
They are free of charge for research projects and can be requested from InfoSpecies using a form.
Licence under www.infospecies.ch. Data is free of charge for research projects and available on request.

en: national-data-centre lichens biodiversity lichen-conservation switzerland lichen-distribution swisslichens

en: SwissLichens - Distribution of lichens in Switzerland

biodiversity

display_name: biodiversity

lichen-conservation

display_name: lichen-conservation

lichen-distribution

display_name: lichen-distribution

lichens

display_name: lichens

national-data-centre

display_name: national-data-centre

swisslichens

display_name: swisslichens

switzerland

display_name: switzerland


en:
This data set contains roughly 5600 photographs of plants and plant parts from 153 sites recorded in all major biogeographic regions of Switzerland using smartphone cameras. Image names contain a letter referring to depicted plant parts: b = bark, i = inflorescence, f = infructescense, s = several parts, v = vegetative parts.
Photos are grouped in folders by observation (individual or population), i.e., photos in the same folder are of the same species, observed at the same time and sampling plot. At a higher organisational level, these folders are grouped by sampling plot (usually 3×3 m in open areas and 10×10 m in forests). The main folder contains metadata including taxon name, sampling time and location, as well as the TypoCH habitat type encoded as integers.
This data set accompanies the following publication:
Popp, M. R., Zimmermann, N. E. & Brun, P. Evaluating the use of automated plant identification tools in biodiversity monitoring—a case study in Switzerland. _Ecological Informatics_ **103316** (2025) doi:[10.1016/j.ecoinf.2025.103316](https://doi.org/10.1016/j.ecoinf.2025.103316).

en: species-observations testing photo plant-species vascular-plants

en: Photos of vegetative and reproductive parts of 560 vascular plant species taken in all biogeographic regions of Switzerland

photo

display_name: photo

plant-species

display_name: plant-species

species-observations

display_name: species-observations

testing

display_name: testing

vascular-plants

display_name: vascular-plants


en:
This dataset includes the FORTRAN code of the FSM2trans model version used in the paper: Quéno et al., Snow redistribution in an intermediate-complexity snow hydrology modelling framework, The Cryosphere, 2024.
The FSM2trans code is derived from the FSM2 model (Essery et al., 2015) and its variant FSM2oshd (Mott et al., 2023). In this model, we implemented and adapted the snowdrift model SnowTran-3D (Liston et al., 2007) and the avalanching model SnowSlide (Bernhardt and Schulz, 2010).
This dataset does not include the processing scripts to prepare meteorological input data for the model, provided as bin files.

en: avalanche source-code snowdrift snow-modelling snowpack

en: FSM2trans code

en:
Resource Title: code.zip
    Size: 90894 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/d2398461-752f-4984-a944-2e565ed0c1ce/resource/2f7b6697-4863-4ab8-8975-11603ba6dd5e/download/code.zip
    Description:

en:
Resource Title: namelist.zip
    Size: 1490 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/d2398461-752f-4984-a944-2e565ed0c1ce/resource/b59836a9-e725-4276-92d2-d3e17efc0966/download/namelist.zip
    Description:

en:
Resource Title: README.txt
    Size: 957 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/d2398461-752f-4984-a944-2e565ed0c1ce/resource/7446d1e9-634a-4da1-b7ef-7ffc28ea61c4/download/readme.txt
    Description:

format: TXT

media_type: text/plain

avalanche

display_name: avalanche

snow-modelling

display_name: snow-modelling

snowdrift

display_name: snowdrift

snowpack

display_name: snowpack

source-code

display_name: source-code


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

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

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

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


data-interactive-avalanche-segmentation

en:
Images and corresponding avalanche annotations used for interactive avalanche segmentation:
SLF train, vali and test
WebNew
GroundPic
UserPic
UIBK dataset: List of images used
Pictures are from SLF (mostly from the Dischma Cams) if not otherwise indicated. If used, the data needs to be properly cited!

en: interactive avalanche snow deep-learning segmentation

en: Data interactive avalanche segmentation

title_for_slug: data-interactive-avalanche-segmentation

envi

en: Environment

deep-learning

display_name: deep-learning

interactive

display_name: interactive

segmentation

display_name: segmentation

snow

display_name: snow


en:
The data was generated during a landscape experiment performed on 69 plots in Sihlwald forest between 2016 and 2019. Branch bundles of different sizes consisting of beech branches were exposed in the forest for one year to be colonized by saproxylic beetles and later reared for one year in emergence traps. All beetles emerging from the branches were collected. Overall 244 branch bundles are included and we found 66 beetles species (23511 individuals) of which 43 were classified as saproxylic (20873 individuals).
 Species data (2 tables)
Two community matrices (including abundance): one for saproxylic beetles and one for all beetles. Beetles were classfied as saproxylic following an extended list of the list reported in Schmidl & Bussler (2004).
 Model data (1 table)
*Response variables*
Species richness and abundance of (saproxylic) beetles.
*Branch bundles*
Branch bundles consisted of 1, 3, 6 or 12 branches and each bundle size was installed at each of the 69 plots. The bundles consisted of standardized freshly cut beech branches (Fagus sylvatica) with a length of 80 cm and a diameter between 3 and 6 cm. Total surface and volume of each bundle is reported.
*Tree on which the bundle was installed*
Information on the tree to which the bundles were attached (tree species, dbh, distance and azimut from the plot center).
*Temperature and light availability*
Temperature (plot level) and light availability (bundle level) were calculated.
*Deadwood availability in the landscape*
Based on a map of lying dead wood, dead wood amount and isolation were calculated for concentric circles with a radius ranging from 20 to 200 m (in 10 m steps) around each branch bundle.
 Variable descriptions (1 table)

en: conservation forest-management habitat-availability sloss biodiversity habitat-heterogeneity colonisation saproxylic-beetles dead-wood

en: Beetle communities reared from beech branches and associated forest and environmental variables

en:
Resource Title: Forest and environmental variables
    Size: 158262 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/bdc548f5-00b1-4d6b-9b97-13d9016002e4/resource/66dbaff5-2b06-4e82-9f92-9e40d298f938/download/data_branch_bundles.csv
    Description: Data on the branch bundle, forest and environmental variables

en:
Resource Title: Community matrix (saproxylic beetles)
    Size: 23120 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/bdc548f5-00b1-4d6b-9b97-13d9016002e4/resource/1873211d-631c-493e-a998-103395d5de43/download/abundance_saproxylics.csv
    Description: Abundances of saproxylic beetle species for each branch bundle

en:
Resource Title: Community matrix (all beetles)
    Size: 34872 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/bdc548f5-00b1-4d6b-9b97-13d9016002e4/resource/30938085-43a4-43f0-bdde-2f57dd557064/download/abundance_all_beetles.csv
    Description: Abundances of all beetle species for each branch bundle

en:
Resource Title: Variable description
    Size: 3005 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/bdc548f5-00b1-4d6b-9b97-13d9016002e4/resource/8113973a-149d-4514-8b03-11201870881c/download/variable_description.csv
    Description: Detailed description of the forest and environmental variables

biodiversity

display_name: biodiversity

colonisation

display_name: colonisation

conservation

display_name: conservation

dead-wood

display_name: dead-wood

forest-management

display_name: forest-management

habitat-availability

display_name: habitat-availability

habitat-heterogeneity

display_name: habitat-heterogeneity

saproxylic-beetles

display_name: saproxylic-beetles

sloss

display_name: sloss


en:
Two stem core samples of 10 trees outside the plot of each main species in the plot were taken at breast height (1.3 m above ground) with a SUUNTO corer. Tree ring width and density were determined with the instruments CATRAS and TSAP, the Densitometer DENDRO 2003 and a stereo-microscope. The selected trees included at least 10 pre-dominant or dominant trees.
 Purpose:
N and C stable isotope signals
 Paper Citation:
 * Tomlinson, G., Siegwolf, R.T.W., Buchmann, N., Schleppi, P., Waldner, P., Weber, P., 2014. The mobility of nitrogen across tree-rings of Norway spruce (Picea abies L.) and the effect of extraction method on tree-ring d15N and d13C values. Rapid Commun. Mass Spectrom. 28, 1258-1264.

en: stable-isotopes icp-forests dendrochronology dominant-trees swiss-forest-lab long-term-forest-ecosystem-research tree-ring-width

en: Dendrochronological analyses of tree core samples (IsoN) adjacent to LWF sites

dendrochronology

display_name: dendrochronology

dominant-trees

display_name: dominant-trees

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

stable-isotopes

display_name: stable-isotopes

swiss-forest-lab

display_name: swiss-forest-lab

tree-ring-width

display_name: tree-ring-width


en:
This dataset contains the input data generated for the pseudo-global warming (PGW) experiments used in the study of the March 2022 Antarctic heatwave with the CRYOWRF model. It includes:
- PGW delta fields derived from CMIP6 simulations, used to perturb ERA5 reanalysis data for past and  future storyline experiments.
- Model configuration files required to reproduce the CRYOWRF simulations.
These resources allow researchers to reproduce the experimental setup and generate model outputs consistent with the results presented in the study.

en: climate-change heatwave antarctica era5 atmospheric-rivers cmip6 extreme-events event-attribution

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: Storylines of the March 2022 heatwave event at Concordia - East Antarctica

en:
Resource Title: PGW_deltas.tar
    Size: 3701853962 bytes
    Format: application/x-tar
    URL: https://www.envidat.ch/dataset/79b7c457-e689-49bd-8330-29b0508d2b2c/resource/9936c23c-dc86-4eaa-8a6f-5a49cffb338c/download/pgw_deltas.tar
    Description: PGW delta fields derived from CMIP6 simulations, used to perturb ERA5 reanalysis data for future storyline experiments.

en:
Resource Title: CRYOWRF_ANT_20220315_ConfigFiles.tar
    Size: 912259096 bytes
    Format: application/x-tar
    URL: https://www.envidat.ch/dataset/79b7c457-e689-49bd-8330-29b0508d2b2c/resource/7f77d116-cd3b-4eec-a6d6-061b0d7b43f6/download/cryowrf_ant_20220315_configfiles.tar
    Description: Model configuration files required to reproduce the CRYOWRF simulations over Antarctica.

antarctica

display_name: antarctica

atmospheric-rivers

display_name: atmospheric-rivers

climate-change

display_name: climate-change

event-attribution

display_name: event-attribution

extreme-events

display_name: extreme-events

heatwave

display_name: heatwave


en: This dataset includes gridded data on snow depth (m), snow water equivalent (mm), runoff from snow melt (mm) and snow cover fraction for Swtzerland. The data is spanning the water years 2016-2022 at a high spatial resolution of 250 m. Data are stored as daily results.

en: snow-cover-fraction seasonal-snow-data oshd snow-depth-distribution snow-melt

envidat

en: EnviDat: the Environmental Data Portal

en: Seasonal snow data for Switzerland OSHD - FSM2sohd

envi

en: Environment

oshd

display_name: oshd

seasonal-snow-data

display_name: seasonal-snow-data

snow-cover-fraction

display_name: snow-cover-fraction

snow-depth-distribution

display_name: snow-depth-distribution

snow-melt

display_name: snow-melt


de: stadt-st-gallen

de: Die Parkplatzverfügbarkeit wird aktuell für die Parkhäuser, welche der PLS Parkleitsystem St.Gallen AG angehören, sowie für das Parkhaus Central angegeben.Die Parkplatzverfügbarkeit der Parkhäuser der PLS Parkleitsystem St.Gallen AG sind hier publiziert: [https://www.pls-sg.ch/parkraeume/](https://www.pls-sg.ch/parkraeume/)[](https://www.pls-sg.ch/parkraeume/)Die Parkplatzverfügbarkeit des Parkhaus Central sind hier publiziert: [https://parkhaus-central.ch/](https://parkhaus-central.ch/)Zu statistischen Auswertungen der Parkhausbelegung können die CityParking St. Gallen AG ([https://cityparking.ch/](https://cityparking.ch/)), das Parkhaus Central St.Gallen ([https://parkhaus-central.ch/](https://parkhaus-central.ch/)) oder die PLS Parkleitsystem St.Gallen AG ([https://www.pls-sg.ch/parkraeume/](https://www.pls-sg.ch/parkraeume/)) Auskunft geben.

de: Freie Parkplätze in der Stadt St.Gallen

de: parkhaus parkplatz verkehr auto real-time

stadt-st-gallen

fr: Avec une population de 80 000 habitants, Saint-Gall, huitième ville de Suisse, est la métropole économique, politique, sociale et culturelle de la Suisse orientale. Ses 1400 ans d'histoire se mêlent à la créativité urbaine, à une économie innovante et à un paysage préservé.

en: With a population of 80,000, St.Gallen, Switzerland’s eighth largest city, is the economic, political, social and cultural metropolis of eastern Switzerland. Its 1400 years of history blend with urban creativity, an innovative economy and an unspoilt landscape.

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.

it: Con una popolazione di 80.000 abitanti, San Gallo, l'ottava città più grande della Svizzera, è la metropoli economica, politica, sociale e culturale della Svizzera orientale. I suoi 1400 anni di storia si fondono con la creatività urbana, un'economia innovativa e un paesaggio incontaminato.

de: Stadt St.Gallen

en: City of St.Gallen

fr: Ville de Saint-Gall

it: Città di San Gallo

political_level: commune

value: DCAT Harvester Stadt St. Gallen

tran

de: Verkehr

en: Transport

fr: Transports

it: Trasporti

de: Freie Parkplätze in der Stadt St.Gallen (csv)

de: csv

de: Freie Parkplätze in der Stadt St.Gallen (json)

de: json

format: JSON

media_type: application/json

de: Freie Parkplätze in der Stadt St.Gallen (fgb)

de: fgb

de: Freie Parkplätze in der Stadt St.Gallen (geojson)

de: geojson

de: Freie Parkplätze in der Stadt St.Gallen (gpx)

de: gpx

format: GPX

de: Freie Parkplätze in der Stadt St.Gallen (jsonl)

de: jsonl

de: Freie Parkplätze in der Stadt St.Gallen (kml)

de: kml

format: KML

de: Freie Parkplätze in der Stadt St.Gallen (ov2)

de: Freie Parkplätze in der Stadt St.Gallen (parquet)

de: parquet

format: PARQUET

de: Freie Parkplätze in der Stadt St.Gallen (rdfxml)

de: rdfxml

format: RDF XML

media_type: application/rdf+xml

de: Freie Parkplätze in der Stadt St.Gallen (jsonld)

de: jsonld

format: JSON-LD

de: Freie Parkplätze in der Stadt St.Gallen (turtle)

de: turtle

format: RDF Turtle

media_type: text/turtle

de: Freie Parkplätze in der Stadt St.Gallen (n3)

de: Freie Parkplätze in der Stadt St.Gallen (shp)

de: shp

de: Freie Parkplätze in der Stadt St.Gallen (xls)


de: Der Datensatz zeigt Windmessungen am Bahnhofplatz St.Gallen. Die Daten werden stündlich aggregiert.

de: real-time sensor wind offene-sensordaten

stadt-st-gallen

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.

de: Stadt St.Gallen

de: stadt-st-gallen

envi

de: Umwelt

de: Windmessung Bahnhofplatz Stadt St.Gallen (csv)

de: csv

de: Windmessung Bahnhofplatz Stadt St.Gallen (json)

de: json

de: Windmessung Bahnhofplatz Stadt St.Gallen (fgb)

de: fgb

de: Windmessung Bahnhofplatz Stadt St.Gallen (geojson)

de: geojson

de: Windmessung Bahnhofplatz Stadt St.Gallen (gpx)

de: gpx

de: Windmessung Bahnhofplatz Stadt St.Gallen (jsonl)

de: Windmessung Bahnhofplatz Stadt St.Gallen (kml)

de: Windmessung Bahnhofplatz Stadt St.Gallen (ov2)

de: Windmessung Bahnhofplatz Stadt St.Gallen (parquet)

de: Windmessung Bahnhofplatz Stadt St.Gallen (rdfxml)

de: Windmessung Bahnhofplatz Stadt St.Gallen (jsonld)

de: Windmessung Bahnhofplatz Stadt St.Gallen (turtle)

de: Windmessung Bahnhofplatz Stadt St.Gallen (n3)

de: Windmessung Bahnhofplatz Stadt St.Gallen (shp)

de: Windmessung Bahnhofplatz Stadt St.Gallen (xls)

de: xls


de: Die Stadt St.Gallen verfügt an der Vadianstrasse (Höhe St.Galler Stadtwerke) über einen Sensor, der die Anzahl vorbeigehender Fussgänger misst. Dieser Datensatz zeigt die Anzahl Personen von rechts (Neumarkt) und links (Multergasse) kommend, sowie deren Summe stündlich aggregiert.Der Datensatz zeigt unbearbeitete Live-Sensordaten. Das heisst, Messfehler oder Messunterbrüche beim Sensor werden nicht erfasst oder korrigiert.Die Archivdaten der früheren Jahre 2019-2024 (Juni) können Sie unter folgendem Link als .csv-Datei herunterladen:[Passantenfrequenz Vadianstrasse 2019-2024](http://stada.sg.ch/publicdocs/ogd_archiv/fussganger-stgaller-innenstadt-vadianstrasse-archivdaten-2019-2024.csv)

de: fussganger real-time sensor passanten offene-sensordaten

stadt-st-gallen

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.

de: Stadt St.Gallen

de: stadt-st-gallen

tran

de: Verkehr

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (csv)

de: csv

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (json)

de: json

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (fgb)

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (geojson)

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (gpx)

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (jsonl)

de: jsonl

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (kml)

de: kml

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (ov2)

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (parquet)

de: parquet

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (rdfxml)

de: rdfxml

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (jsonld)

de: jsonld

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (turtle)

de: turtle

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (n3)

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (shp)

de: shp

de: Passantenfrequenz Vadianstrasse, Stadt St.Gallen (Höhe St.Galler Stadtwerke) (xls)


de: Dieser Datensatz bietet eine Übersicht über die von der Stadtverwaltung verwendeten Aforms und den entsprechenden Zugriffzahlen.

de: aforms formulare

stadt-st-gallen

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.

de: Stadt St.Gallen

de: stadt-st-gallen

gove

de: Regierung und öffentlicher Sektor

en: Government and public sector

fr: Gouvernement et secteur public

it: Governo e settore pubblico

de: Aufrufe und Erfolgsraten der digitalen Formulare der Stadt St.Gallen (Aforms) (csv)

de: csv

de: Aufrufe und Erfolgsraten der digitalen Formulare der Stadt St.Gallen (Aforms) (json)

de: json

de: Aufrufe und Erfolgsraten der digitalen Formulare der Stadt St.Gallen (Aforms) (jsonl)

de: Aufrufe und Erfolgsraten der digitalen Formulare der Stadt St.Gallen (Aforms) (parquet)

de: Aufrufe und Erfolgsraten der digitalen Formulare der Stadt St.Gallen (Aforms) (rdfxml)

de: Aufrufe und Erfolgsraten der digitalen Formulare der Stadt St.Gallen (Aforms) (jsonld)

de: Aufrufe und Erfolgsraten der digitalen Formulare der Stadt St.Gallen (Aforms) (turtle)

de: Aufrufe und Erfolgsraten der digitalen Formulare der Stadt St.Gallen (Aforms) (n3)

de: Aufrufe und Erfolgsraten der digitalen Formulare der Stadt St.Gallen (Aforms) (xls)

de: xls

aforms

display_name: aforms

formulare

display_name: formulare


de: Der Datensatz bildet eine Schnittstelle zum Newsfeed der Medienmitteilungen der Stadtverwaltung. Der Link zum Newsfeed befindet sich unter "Referenzen"

de: newsfeed medienmitteilung news real-time

educ

de: Bildung, Kultur und Sport

en: Education, culture and sport

fr: Éducation, culture et sport

it: Istruzione, cultura e sport

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (csv)

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (json)

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (jsonl)

de: jsonl

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (parquet)

de: parquet

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (rdfxml)

de: rdfxml

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (jsonld)

de: jsonld

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (turtle)

de: turtle

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (n3)

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (xls)

medienmitteilung

display_name: medienmitteilung

news

display_name: news

newsfeed

display_name: newsfeed

real-time

display_name: real-time


de: Die Stadt St.Gallen verfügt über CO2 Sensoren in einigen Innenräumen und Sitzungszimmern der Stadtverwaltung. Dieser Datensatz zeigt die Messwerte dieser Sensoren stündlich aggregiert. Abhängig vom Sensor werden auch noch weitere Werte wie Luftdruck und Anzahl flüchtige organische Stoffe in der Luft angezeigt.

de: real-time sensor co2 temperatur feuchtigkeit offene-sensordaten voc

stadt-st-gallen

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.

de: Stadt St.Gallen

de: stadt-st-gallen

envi

de: Umwelt

de: CO2 Sensoren in den Innenräumen der Stadtverwaltung St.Gallen (csv)

de: csv

de: CO2 Sensoren in den Innenräumen der Stadtverwaltung St.Gallen (json)

de: json

de: CO2 Sensoren in den Innenräumen der Stadtverwaltung St.Gallen (jsonl)

de: CO2 Sensoren in den Innenräumen der Stadtverwaltung St.Gallen (parquet)

de: CO2 Sensoren in den Innenräumen der Stadtverwaltung St.Gallen (rdfxml)

de: CO2 Sensoren in den Innenräumen der Stadtverwaltung St.Gallen (jsonld)

de: CO2 Sensoren in den Innenräumen der Stadtverwaltung St.Gallen (turtle)

de: CO2 Sensoren in den Innenräumen der Stadtverwaltung St.Gallen (n3)

de: CO2 Sensoren in den Innenräumen der Stadtverwaltung St.Gallen (xls)

de: xls

feuchtigkeit

display_name: feuchtigkeit

offene-sensordaten

display_name: offene-sensordaten

sensor

display_name: sensor

temperatur

display_name: temperatur

voc

display_name: voc


luftwerte-in-der-stadt-st-gallen

de: Die Stadt St.Gallen verfügt über Luftsensoren an verschiedenen Standorten der Stadt, welche die Luftfeuchtigkeit und Temperatur messen. Die Daten werden stündlich aggregiert.

de: real-time sensor feuchtigkeit temperatur offene-sensordaten

de: Luftwerte in der Stadt St.Gallen

title_for_slug: luftwerte-in-der-stadt-st-gallen

de: Luftwerte in der Stadt St.Gallen (csv)

de: Luftwerte in der Stadt St.Gallen (json)

de: Luftwerte in der Stadt St.Gallen (fgb)

de: fgb

de: Luftwerte in der Stadt St.Gallen (geojson)

de: geojson

de: Luftwerte in der Stadt St.Gallen (gpx)

de: gpx

de: Luftwerte in der Stadt St.Gallen (jsonl)

de: jsonl

de: Luftwerte in der Stadt St.Gallen (kml)

de: kml

de: Luftwerte in der Stadt St.Gallen (ov2)

de: Luftwerte in der Stadt St.Gallen (parquet)

de: parquet

de: Luftwerte in der Stadt St.Gallen (rdfxml)

de: rdfxml

de: Luftwerte in der Stadt St.Gallen (jsonld)

de: jsonld

de: Luftwerte in der Stadt St.Gallen (turtle)

de: turtle

de: Luftwerte in der Stadt St.Gallen (n3)