en: sentinel-2 remote-sensing armenia vegetation-height foracca forest cnn

media_type: image/tiff

format: TIFF

en: Countrywide vegetation height models (VHM) were generated for Armenia based on Copernicus Sentinel-2 imagery within the framework of the FORACCA project. “The Forest Restoration and Climate Change in Armenia” (FORACCA) project is funded by the Swiss Agency for Development and Cooperation (SDC). The project is implemented by the Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL) in collaboration with Zoï Environment Network as well as the Forest Alliance of Armenia. A Convolutional Neural Network (CNN) model was trained in Switzerland to estimate the maximum vegetation height at the spatial resolution of the Sentinel-2 pixel of 10 m. Vegetation heights from the spatially higher-resolved VHM Lidar NFI were used as reference data for the CNN training. Then, the model was spatially transferred and VHMs for Armenia were generated annually based on available Sentinel-2 imagery from May – September of the respective year. Further details on the dataset and model to create the Sentinel-2 VHMs can be found in the paper Jiang et al. (2023, https://doi.org/10.1016/j.srs.2023.100099). Contains modified Copernicus Sentinel data.

armenia

display_name: armenia

cnn

display_name: cnn

foracca

display_name: foracca

forest

display_name: forest

remote-sensing

display_name: remote-sensing

vegetation-height

display_name: vegetation-height

en: Sentinel-2 Vegetation Height Model Armenia


maintainer: Frank Hagedorn

en: plant-traits biomass-dynamics soil-nutrients plant-community treeline-ecosystems climate-change

state: active

type: dataset

en: Data on soils, tree biomass, plant nutrients, rock mineralogy, climate of treeline ecosystems in Khibiny mountains, Kola Penninsula, Russia

format: XLS

en: Data on soil nutrients and charcteristics, tree and understory biomass, foliage nutrients, mineralogy, soil temperature and moisture along an elevation gradient from boreal forest to mountain tundra in teh Khibiny mountains (Kola Penninsula, Russia). Samples were taken along two gradients at 7 elevation level either under tree canopy (tree) and in open areas (open).

biomass-dynamics

display_name: biomass-dynamics

climate-change

display_name: climate-change

plant-community

display_name: plant-community

plant-traits

display_name: plant-traits

soil-nutrients

display_name: soil-nutrients

treeline-ecosystems

display_name: treeline-ecosystems

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester


maintainer: Loïc Pellissier Pellissier

en: community-indices elevation environmental-gradients alps functional-traits plant-herbivore-interactions herbivores-communities

en: Vegetion surveys

media_type: text/plain

en: Species inventories of plants

format: TXT

mimetype: text/plain

en: Nematode surveys

en: Surveys of soil nematode along elevation

en: Orthoptera traits

en: Orthoptera functional traits (incisive mandibular strength averaged by species)

en: Orthoptera surveys

en: Species inventories of Orthoptera

en: Plant traits

en: Plant functional traits (SLA, LDMC, carbon and nitrogen content, punch strenght; averaged by species)

en: Study sites

en: Coordinates and elevation of the study sites

en: Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017.

alps

display_name: alps

community-indices

display_name: community-indices

elevation

display_name: elevation

environmental-gradients

display_name: environmental-gradients

functional-traits

display_name: functional-traits

herbivores-communities

display_name: herbivores-communities

plant-herbivore-interactions

display_name: plant-herbivore-interactions

en: Above- and below-ground herbivore communities along elevation


maintainer: EnviDat Support

en: nematodes restoration topsoil-removal soil-biodiversity biodiversity soil-chemistry soil-biology bacteria grassland plants fungi soil

en: Ecosystem coupling_interactions

en:
Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic
Correlation	non-parametric Spearman rank correlations between biotic and abiotic parameters (ecological interactions)

en: Networks

en:
Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Correlation	non-parametric Spearman rank correlations
Vertex.1+2	Comparison between network components; N.pool = total soil nitrogen pool [kg ha-1 12 cm-1]; Corg.pool = soil organic carbon pool [kg ha-1 12 cm-1], pH = soil pH [CaCl2]; BD = bulk density [g cm-3]; FC = field capacity [%]
	PCoA 1 + 2 biotic communities [Veg = vegetation; Nem = nematoda; Fun = fungi; Prok = prokaryota]
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic

en: Distance to target results

en:
Community	biotic community (vegetation, nematoda, fungi, prokaryota)
Treatment	Treatment groups; Y.4-30= restored grasslands (time since restoration); Initial=intensively managed grasslands (I)
Comparison	within- or between-community Bray-Curtis dissimilarity distance comparison
Label	within-community distances of initial/restored grasslands: treatment abbreviation; between-communities distances of initial/restored-to-target grasslands: treatment abbreviation plus Target (T; species-rich grasslands)
mean	Mean distance to Target (6 plots per treatment group)
sd	Standard deviation of distances to Target (6 plots per treatment group)

en: Soil properties

en:
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
N.pool	Soil nitrogen pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
Corg.pool	Soil organic carbon pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
pH	soil pH [CaCl2]
BD	Bulk density [g cm-3]
FC	Field capacity [%]
Veg.1	principle coordinate axis 1 (vegetation)
Veg.1	principle coordinate axis 2 (vegetation)
Nem.1	principle coordinate axis 1 (nematoda)
Nem.2	principle coordinate axis 2 (nematoda)
Fun.1	principle coordinate axis 1 (fungi)
Fun.2	principle coordinate axis 2 (fungi)
Prok.1	principle coordinate axis 1 (prokaryota)
Prok.2	principle coordinate axis 2 (prokaryota)
Veg.H	Shannon diversity (vegetation)
Nem.H	Shannon diversity (nematoda)
Fun.H	Shannon diversity (fungi)
Prok.H	Shannon diversity (prokaryota)

en: Nematode data

en:
Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
ASV.ID	unit number.letter code per amplicon sequence variant (in total 904)
Taxonomic rank	Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	ASV.ID (see Taxonomy)

en: Fungi data

en:
Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
OTU.ID	consecutive numbers for operational taxonomic units (OTUe 1-7794)
Taxonomic rank	Domain, Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	OTU.ID (see Taxonomy)

en: Vegetation data

en:
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
column I-LW	Plant abundance per species (in total 327 plant species) expressed as cover percentage estimation according to van der Maarel (vdM 1979; 7 classes: 1-7); nomenclature according to Lauber & Wagner (1996)

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

Paper Citation:

_Resch, M.C., Schütz, M., Ochoa-Hueso, R., Buchmann, N., Frey, B., Graf, U., van der Putten, W.H., Zimmermann, S., Risch, A.C. (in review). Long-term recovery of above- and belowground interactions in restored grassland after topsoil removal and seed addition. Journal of Applied Ecology_

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

Study area and experimental design
The study was conducted in and around two nature reserves, Eigental and Altläufe der Glatt, which were located approximately 5 km apart (47°27´ to 47°29´ N, 8°37´ to 8°32´ E, 417 to 572 m a.s.l., Canton of Zurich, Switzerland; Figure S1 and S2, Table S1). Mean annual temperature and precipitation are 9.8 ± 0.6 °C and 990 ± 168 mm (Kloten climate station 1988-2018; MeteoSchweiz, 2019). TFor this study, we used a space-for-time approach based on eight restoration sites that were between 3 and 32 years old. We measured recovery and restoration success by comparing the restored grasslands with intensively managed and semi-natural grasslands. Using a space-for-time approach requires high similarities in historical properties of the site, such as soil conditions and management regimes, to assure that temporal processes are appropriately represented by spatial patterns (Walker et al., 2010). This was the case in our study. The restored sites had similar soil conditions (i.e., soil type, structure, water availability) as the targeted semi-natural grasslands, while they shared the same agricultural legacy with intensively managed grasslands, i.e., biomass harvest and fertilization (manure and/or slurry) three to five times a year as well as tillage. We randomly established three 5 m x 5 m (25-m2) plots for plant identification and three 2 m x 2 m (4-m2) subplots for soil biotic and abiotic data collection at least 2 m away from the 25-m2 plots in each restoration site. Sites of similar age were grouped into four age classes: Y.4 (3 & 4 years after restoration), Y.18 (17 & 19 years), Y.24 (23 & 25 years), and Y.30 (27 & 32 years). Six intensively managed (Initial) and six semi-natural grassland (Target) sites complemented the experimental set-up, for a total of 36 plots. All plots were sampled under similar conditions, i.e., day of the year, air temperature, soil moisture, and time since last rain event, in June/July 2017 (intensively managed and semi-natural plots) and 2018 (restored plots).

Collection of plants and selected soil biota data
Plant species cover (in %) was visually estimated in each 25-m2 plot in mid-June (Braun-Blanquet, 1964; nomenclature: Lauber & Wagner, 1996). We calculated Shannon diversity and assessed plant community structure. We included soil microbial (fungi, procaryotes) and nematodes in our study as they represent the majority of soil biotic diversity and abundance (Bardgett & van der Putten, 2014), cover various trophic levels of the soil food web (Bongers & Ferris, 1999), and play key roles in soil functioning and ecosystem processes (Bardgett & van der Putten, 2014). In particular, soil nematodes were found to be well suited belowground indicators to evaluate recovery/development after restoration (e.g. Frouz, et al. 2008; Kardol et al., 2009; Resch et al., 2019). We randomly collected ten soil cores (2.2 cm diameter x 12 cm depths; sampler from Giddings Machine Company, Windsor, USA) in the 4-m2 subplots to assess soil nematode and microbial (fungal, prokaryotic) diversities and community structures. For soil nematodes, eight of the soil cores were combined and gently homogenized, placed in coolers and stored at 4 °C and transported to the laboratory (Netherlands Institute of Ecology, NIOO, Wageningen, Netherlands) within three days after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriators (Oostenbrink, 1960). After extraction, each sample was divided into three subsamples, two for molecular identification and one to determine nematode abundance (see Resch et al., 2019). For the molecular work, two subsamples were stored in 70% ethanol (final volume 10 mL each) and transported to the laboratory at the Swiss Federal Research Institute WSL (Birmensdorf, Switzerland). Each subsample was reduced to roughly 200 μL by centrifugation and removal of the supernatant. The remaining ethanol was vaporized (65 °C for 3 h). Thereafter, 180 μL ATL buffer solution (Qiagen, Hilden, Germany) was immediately added and samples were stored at 4 °C until further processing. From these samples, nematode metagenomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer`s protocol, except for the incubation step which was run at 56 °C for 4 h. PCR amplification of the V6-V8 region of the eukaryotic small-subunit (18S) was performed with 7.5 μL of genomic DNA template (ca. 1 ng/μL) in 25 μL reactions containing 5 μL PCR reaction buffer, 2.5 mM MgCL2, 0.2 mM dNTPs, 0.8 μM of each primer (NemF: Sapkota & Nicolaisen, 2015; 18Sr2b: Porazinska et al., 2009), 0.5 μL BSA, and 0.25 μL GoTaq G2 Hot Start Polymerase (Promega Corporation, Madison, USA). Amplification was using an initial DNA denaturation step of 95 °C for 2 min, followed by 35 cycles at 94 °C for 40 sec, 58 °C for 40 sec, 72 °C for 1 min, and a final elongation step at 72 °C for 10 min. Filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads was implemented using the DADA2 pipeline (v.1.12; Callahan et al., 2016) to finally assign amplicon sequence variants (ASVs) as taxonomic units.
We combined and homogenized the remaining two soil cores to assess soil microbes, placed them in coolers (4 °C) and transported them to the laboratory at WSL. Metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNAeasy PowerMax Soil Kit (Qiagen, Hilden, Germany) according to the manufacturer´s protocol. PCR amplification of the V3-V4 region of the small-subunit (16S) of prokaryotes (i.e., bacteria and archaea) and the ribosomal internal transcribed spacer region (ITS2) of fungi was performed with 1 ng of template DNA using PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates, pooled and sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, USA). Quality filtering, clustering into operational taxonomic units (OTUs, 97% similarity cutoffs) and taxonomic assignment were performed as previously described (Resch et al., 2021).Taxonomic classification of nematode, prokaryotic and fungal sequences was conducted querying against the most recent versions of PR2 (v.4.11.1; Guillou et al., 2013), SILVA (v.132; Quast et al., 2013), and UNITE (v.8; Nilsson et al., 2019) reference sequence databases. Taxonomic assignment cutoffs were set to confidence rankings ≥ 0.8 (below ranked as unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as OTUs or ASVs assigned to other than Fungi or Nematoda were manually removed prior to data analysis. The three datasets were filtered to discard singletons and doubletons. Taxonomic abundance matrices were rarefied to the lowest number of sequences per community to achieve parity of the total number of reads between samples (Prokaryotes: 10,929 reads; Fungi: 18,337 reads; Nematodes: 6,662 reads). We calculated Shannon diversity and assessed community structures for soil nematodes, prokaryotes and fungi based on their relative abundances of ASV or OTU at the taxon level.

Collection of soil physical and chemical properties
We randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) per 4-m2 subplot using a steel cylinder that fit into the soil corer. The cylinders were capped to avoid disturbance during transport and used to measure field capacity, rock content and fine earth density as previously described (Resch et al., 2021). We randomly collected another three soil cores (5 cm diameter, 12 cm depths) in each 4-m2 subplot to determine soil chemical properties. The cores were pooled, dried at 60 °C for 48 h and passed through a 2 mm sieve. We measured soil pH (CaCl2) on dried samples, total nitrogen (N) and organic carbon (C) concentration on dried and fine-ground samples (≤ 0.5 mm; for details see Resch et al., 2021). We calculated total N and organic C pools after correcting its concentration for soil depth, rock content and fine earth density.

bacteria

display_name: bacteria

biodiversity

display_name: biodiversity

fungi

display_name: fungi

grassland

display_name: grassland

nematodes

display_name: nematodes

plants

display_name: plants

restoration

display_name: restoration

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

topsoil-removal

display_name: topsoil-removal

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Long-term recovery of above-and belowground interactions in restored grasslands


en: stable-water-isotopes stream-water geochemical-solutes precipitation spatially-distributed-sampling

en: The dataset contains daily measurements of volumes, isotopes and solutes of stream water as well as precipitation measured several locations; meteorological variables are added. Date: 18.07.2019

en:
This dataset contain measurements of solute and stable water isotopes in stream water and precipitation in the Alp catchment and two of its tributaries (between 2015 -2018) . The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l. The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (precipitation rates, discharge) at daily intervals from several sampling locations.
An updated version of the isotope dataset is available here: https://www.doi.org/10.16904/envidat.242

geochemical-solutes

display_name: geochemical-solutes

precipitation

display_name: precipitation

spatially-distributed-sampling

display_name: spatially-distributed-sampling

stable-water-isotopes

display_name: stable-water-isotopes

stream-water

display_name: stream-water

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: Daily data of solute and stable water isotopes in stream water and precipitation in the Alp catchment, Central Switzerland


en: Alpine3D climate data

format: SERVICE

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

snow-climatology

display_name: snow-climatology

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth

en: Alpine3D Climate Data


maintainer: Leo Gallus Bont

en: geoprocessing forest openstreetmap accessible-forest seilaplan qgis

en: Juypter Notebook for Data Preparation (online)

en: Power lines vector data (close to the area of the sample forest range)

media_type: application/zip

format: ZIP

mimetype: application/zip

en: Railways vector data (close to the sample forest range)

en: Preview

media_type: image/png

format: PNG

mimetype: image/png

en: DEM (for the area of the sample forest range)

mimetype: image/tiff

en: Juypter Notebook for Data Preparation

en: Digitised sample perimeter

en: Forest vector data (for sample forest range)

en:
This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps).
The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin [Seilaplan]( https://doi.org/10.16904/envidat.software.1) for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting.
The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.

Additional notes for vector data:
Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar.

The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:

- Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
- Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif

This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.

Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.

This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

accessible-forest

display_name: accessible-forest

forest

display_name: forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan

en: Sample Geodata and Software for Demonstrating Geospatial Preprocessing for Forest Accessibility and Wood Harvesting at FOSS4G2019


en: ecohydrology nonlinear-deconvolution time-series nonstationarity rainfall-runoff-modelling hydrology precipitation streamflow

en: Demonstration scripts.zip

en: Zip-compressed folder containing demonstration scripts, source data, and output files for the ERRA results shown in "Introduction to ERRA" and in J.W. Kirchner, "Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using Ensemble Rainfall-Runoff Analysis (ERRA): proof of concept", Hydrology and Earth System Sciences, 2024.

en: An introduction to ERRA v1.06.pdf

media_type: application/pdf

en: "An introduction to ERRA" presents a series of vignettes that introduce some of the capabilities of the Ensemble Rainfall-Runoff Analysis (ERRA) script, and that show how to invoke them. This introduction also includes documentation for ERRA function calls, largely reproducing the documentation embedded in the ERRA script itself.

format: PDF

en: {{deprecated version}} An introduction to ERRA.pdf

en: {{deprecated version}} ERRA_scripts.zip

en: Zip-compressed folder containing two versions of the ERRA script. ERRA_v1.0.R uses Rcpp to speed up several key calculations, but requires a C++ compiler. ERRA_v1.0_no_Rcpp.R is a bit slower but does not require a C++ compiler or Rcpp. Public use of these scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

en: Zip-compressed folder containing two versions of the ERRA script. ERRA_v1.06.R uses Rcpp to speed up several key calculations, but requires a C++ compiler. ERRA_v1.06_no_Rcpp.R is a bit slower but does not require a C++ compiler or Rcpp. Public use of these scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

en:
ERRA is a data-driven, nonparametric, model-independent method for quantifying rainfall-runoff relationships across a spectrum of time lags, in systems that may be nonlinear, nonstationary, and spatially heterogeneous.
Researchers using ERRA in published work should cite J.W. Kirchner, "Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using Ensemble Rainfall-Runoff Analysis (ERRA): proof of concept", Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-28-4427-2024, 2024 (for ERRA itself) and J.W. Kirchner, "Impulse response functions for nonlinear, nonstationary, and heterogeneous systems, estimated by deconvolution and de-mixing of noisy time series", Sensors, 22(9), 3291, https://doi.org/10.3390/s22093291, 2022 (for the underlying mathematics).
This data set includes two versions of the ERRA script written in the open-source programming language R, a detailed user's guide, and sample scripts and source data for all of the results in Kirchner (2024).
These scripts are made publicly available under GNU General Public License 3; for details see https://www.gnu.org/licenses/. The data and documentation are made available under Creative Commons Attribution Share-Alike CC-BY-SA.
ETH Zurich, WSL, and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose. Users are solely responsible for determining the suitability and reliability of this software for their own purposes.

ecohydrology

display_name: ecohydrology

hydrology

display_name: hydrology

nonlinear-deconvolution

display_name: nonlinear-deconvolution

nonstationarity

display_name: nonstationarity

rainfall-runoff-modelling

display_name: rainfall-runoff-modelling

streamflow

display_name: streamflow

time-series

display_name: time-series

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: ERRA -- an R script for Ensemble Rainfall-Runoff Analysis


en: snow snow-water-equivalent snow-profile snow-height gcos

en: DATA ACCESS

media_type: text/csv

format: CSV

en: STATION METADATA

en:
This dataset contains long-term snow water equivalent and corresponding snow depth data 11 observer sites in Switzerland between 1200 and 2500 m a.s.l. compiled for the Global Climate Observing System (GCOS) and supported by MeteoSwiss. Snow depth (cm) and snow water equivalent (mm) are manually recorded every 2 weeks since the 1947 (depending on station). The attached metadata file gives details for each station.
The measurement series agree with GCOS objectives according to the GCOS Implementation Plan: This inlcudes:
• Raw data are archived in the snow and avalanche database at SLF.
• Measuring techniques are traceable and documented as snow depth and snow water equivalent have in general remained the same since beginning up to now. When planning new systems or changes of existing systems in the future, their impact will be assessed prior to implementation.
• Historical data of these 11 stations have been digitized and all data have been quality controlled.
• Detailed metadata (location of measurements) are available.
• Public availability of the data has been ensured by publishing the data on the Envidat portal (https://www.envidat.ch/dataset/gcos-swe-data).

gcos

display_name: gcos

snow

display_name: snow

snow-height

display_name: snow-height

snow-profile

display_name: snow-profile

snow-water-equivalent

display_name: snow-water-equivalent

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: GCOS SWE data from 11 stations in Switzerland


en: raster-processing force sentinel-2 remote-sensing switzerland

en: An example of the index EVI for one of the generated 30 by 30 km tiles located around the city of Zürich. The values are multiplied by 10.000. The date of the image is 24.07.2018.

en: Parameter file that defined all the parameters for processing the Sentinel-2 Level 2 imagery

format: OCTET

en:
We processed Sentinel-2 images from 2017 to 2023 for Switzerland with the Software FORCE (Frantz 2019). The respective parameter files can be found here: [Github](https://github.com/TLKoch/Sentinel-2_CH). All the images consist of several TB and therefore, access will be granted upon request.
The available bands (in spatial reference system EPSG 3035) are the following:
*Red, Green, Blue, NIR, Red-Edge-1, Red-Edge-2, Red-Edge-3, SWIR-1, SWIR-2*
The available indices (in spatial reference system EPSG 3035) are the following:
*CCI, CIRE, NDWI/NDMI, NDVI, EVI*
Further indices might be provided upon request.

 Processing
After downloading Level 1 data for the 14 Sentinel-2 tiles covering Switzerland (T31TGN, T32TLT, T32TMT, T32UMU, T32TNT, T32TPT, T31TGM, T32TLS, T32TMS, T32TNS, T32TPS, T32TLR, T32TMR, T32TNR) with an estimated cloud cover of less than 80 \%, we processed the data further with FORCE v. 3.7.8-12.
FORCE was used for radiometric correction, including atmospheric and topographic corrections using the digital elevation model from Copernicus Land Monitoring Service (EU-DEM v. 1.1), with a 25 m spatial resolution, a bidirectional reflectance distribution function (BRDF) correction, and an adjacency effect correction. For cloud masking, the improved Fmask algorithm with buffers of 300 m for clouds, 90 m for shadows, and 30 m for snow were used (Frantz et al. 2018; Zhu et al. 2015). All bands were resampled using cubic convolution to 10 m spatial resolution. Shadows, low and poor illumination as well as saturation were masked out. The co-registration of all images was made with monthly composites of near-infrared Landsat Collection 2 images from 2014 to 2021 (Rengarajan et al. 2020;Rufin et al. 2021). With this the geometric consistency improved. The processed images are available in tiles of 30 by 30 km.

 Example images
Uploaded is an example of the index EVI for one of the generated 30 by 30 km tiles located around the city of Zürich. The values are multiplied by 10.000. The date of the image is 24.07.2018.

force

display_name: force

raster-processing

display_name: raster-processing

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

en: Sentinel-2 imagery of Switzerland


maintainer: Sofia Pagliarin

en: barcelona imperviousness milan urban-atlas copernicus soil-sealing

en: Information on data sources

en: Shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h), see Dataset description

en:
Dataset description

This dataset is a recalculation of the Copernicus 2015 high resolution layer (HRL) of imperviousness density data (IMD) at different spatial/territorial scales for the case studies of Barcelona and Milan. The selected spatial/territorial scales are the following:

 * a)	Barcelona city boundaries
 * b)	Barcelona metropolitan area, Àrea Metropolitana de Barcelona (AMB)
 * c)	Barcelona greater city (Urban Atlas)
 * d)	Barcelona functional urban area (Urban Atlas)
 * e)	Milan city boundaries
 * f)	Milan metropolitan area, Piano Intercomunale Milanese (PIM)
 * g)	Milan greater city (Urban Atlas)
 * h)	Milan functional urban area (Urban Atlas)

In each of the spatial/territorial scales listed above, the number of 20x20mt cells corresponding to each of the 101 values of imperviousness (0-100% soil sealing: 0% means fully non-sealed area; 100% means fully sealed area) is provided, as well as the converted measure into squared kilometres (km2).

 Dataset composition

The dataset is provided in .csv format and is composed of:

_IMD15_BCN_MI_Sources.csv_: Information on data sources

 _IMD15_BCN.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona:
 * a)	Barcelona city boundaries (label: bcn_city)
 * b)	Barcelona metropolitan area, Àrea metropolitana de Barcelona (AMB) (label: bcn_amb)
 * c)	Barcelona greater city (Urban Atlas) (label: bcn_grc)
 * d)	Barcelona functional urban area (Urban Atlas) (label: bcn_fua)

 _IMD15_MI.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan:
 * e)	Milan city boundaries (label: mi_city)
 * f)	Milan metropolitan area, Piano intercomunale milanese (PIM) (label: mi_pim)
 * g)	Milan greater city (Urban Atlas) (label: mi_grc)
 * h)	Milan functional urban area (Urban Atlas) (label: mi_fua)

_IMD15_BCN_MI.mpk_: the shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h).

Regarding the territorial scale as per letter f), the list of municipalities included in the Milan metropolitan area in 2016 was provided to me in 2016 from a person working at the PIM.

In the IMD15_BCN.csv and IMD15_MI.csv, the following columns are included:

 * Level: the territorial level as defined above (a)-d) for Barcelona and e)-h) for Milan);
 * Value: the 101 values of imperviousness density expressed as a percentage of soil sealing (0-100%: 0% means fully non-sealed area; 100% means fully sealed area);
 * Count: the number of 20x20mt cells corresponding to a certain percentage of soil sealing or imperviousness;
 * Km2: the conversion of the 20x20mt cells into squared kilometres (km2) to facilitate the use of the dataset.

 Further information on the Dataset

This dataset is the result of a combination between different databases of different types and that have been downloaded from different sources. Below, I describe the main steps in data management that resulted in the production of the dataset in an Esri ArcGIS (ArcMap, Version 10.7) project.

 1. The high resolution layer (HRL) of the imperviousness density data (IMD) for 2015 has been downloaded from the official website of Copernicus. At the time of producing the dataset (April/May 2021), the 2018 version of the IMD HRL database was not yet validated, so the 2015 version was chosen instead. The type of this dataset is raster.

 2. For both Barcelona and Milan, shapefiles of their administrative boundaries have been downloaded from official sources, i.e. the ISTAT (Italian National Statistical Institute) and the ICGC (Catalan Institute for Cartography and Geology). These files have been reprojected to match the IMD HRL projection, i.e. ETRS 1989 LAEA.

 3. Urban Atlas (UA) boundaries for the Greater Cities (GRC) and Functional Urban Areas (FUA) of Barcelona and Milan have been checked and reconstructed in Esri ArcGIS from the administrative boundaries files by using a Eurostat correspondence table. This is because at the time of the dataset creation (April/May 2021), the 2018 Urban Atlas shapefiles for these two cities were not fully updated or validated on the Copernicus Urban Atlas website. Therefore, I had to re-create the GRC and FUA boundaries by using the Eurostat correspondence table as an alternative (but still official) data source. The use of the Eurostat correspondence table with the codes and names of municipalities was also useful to detect discrepancies, basically stemming from changes in municipality names and codes and that created inconsistent spatial features. When detected, these discrepancies have been checked with the ISTAT and ICGC offices in charge of producing Urban Atlas data before the final GRC and FUA boundaries were defined.

Steps 2) and 3) were the most time consuming, because they required other tools to be used in Esri ArcGIS, like spatial joins and geoprocessing tools for shapefiles (in particular dissolve and area re-calculator in editing sessions) for each of the spatial/territorial scales as indicated in letters a)-h).

Once the databases for both Barcelona and Milan as described in points 2) and 3) were ready (uploaded in Esri ArcGIS, reprojected and their correctness checked), they have been ‘crossed’ (i.e. clipped) with the IMD HRL as described in point 1) and a specific raster for each territorial level has been calculated. The procedure in Esri ArcGIS was the following:

 * Clipping: Arctoolbox > Data management tools > Raster > Raster Processing > Clip. The ‘input’ file is the HRL IMD raster file as described in point 1) and the ‘output’ file is each of the spatial/territorial files. The option "Use Input Features for Clipping Geometry (optional)” was selected for each of the clipping.
 * Delete and create raster attribute table: Once the clipping has been done, the raster has to be recalculated first through Arctoolbox > Data management tools > Raster > Raster properties > Delete Raster Attribute Table and then through Arctoolbox > Data management tools > Raster > Raster properties > Build Raster Attribute Table; the "overwrite" option has been selected.

 Other tools used for the raster files in Esri ArcGIS have been the spatial analyst tools (in particular, Zonal > Zonal Statistics). As an additional check, the colour scheme of each of the newly created raster for each of the spatial/territorial attributes as per letters a)-h) above has been changed to check the consistency of its overlay with the original HRL IMD file. However, a perfect match between the shapefiles as per letters a)-h) and the raster files could not be achieved since the raster files are composed of 20x20mt cells.

The newly created attribute tables of each of the raster files have been exported and saved as .txt files. These .txt files have then been copied in the excel corresponding to the final published dataset.

barcelona

display_name: barcelona

copernicus

display_name: copernicus

imperviousness

display_name: imperviousness

milan

display_name: milan

soil-sealing

display_name: soil-sealing

urban-atlas

display_name: urban-atlas

en: Soil sealing Barcelona and Milan different territorial levels


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

en: Releve data

en: The CSV file contains all releves considered in the mentioned study. The columns include plot identification, survey period, survey year, habitat category of the surveyed object, vegetation type of the plot, anonymized identification of the observer, observer expertise category, the observers survey experience, scaled mean indicator values (i.e., light, nutrients, reaction, temperature and wetness), counts for the four groups (all species, liverworts, mosses exkl. Sphagnum spp. and Sphagnum spp.), the size categories (small, medium, large) and the modified cover classes (NA, r, +, 1, 2, 3-5).

en:
This dataset contains bryophyte species count data and information about the observers bryophyte expertise for 2950 relevés conducted from 2011 to 2023 on 10-m2 plots in a long-term monitoring program in Switzerland. Plots were situated in raised bogs and fens of national importance, which were distributed across the whole country. The majority of the plots is represented by two relevés as sites are revisited every six years. The dataset was used in the paper mentioned below to test if species richness estimates differed among categories of observer expertise.

Moser T, Boch S, Bedolla A, Ecker KT, Graf UH, Holderegger R, Küchler H, Pichon
NA, Bergamini A (2025) Greater observer expertise leads to higher liverwort species richness in bryophyte surveys. _Journal of Vegetation Science_.

biodiversity-monitoring

display_name: biodiversity-monitoring

cover

display_name: cover

fen

display_name: fen

liverworts

display_name: liverworts

mosses

display_name: mosses

observer-bias

display_name: observer-bias

raised-bog

display_name: raised-bog

sphagnum

display_name: sphagnum

vegetation-survey

display_name: vegetation-survey

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal


en: snow-mechanics microstructure ice slow-snow-compression-experiments x-ray-tomography

en: README

en: README file gives an overview of the organization of the data

en: slow snow compression experiments

en: Compression experiments, X-ray images and with supplemented README file

en:
We conducted consecutive loading-relaxation experiments at low strain rates to study the viscoplastic behavior of the intact ice matrix in snow. The experiments were conducted using a micro-compression stage within the X-ray tomography scanner in the SLF cold laboratory.

Next, to evaluate the experiments, a novel, implicit solution of a transient scalar model was developed to estimate the stress exponent and time scales in the effective creep relation (Glen's law). The result reveals that, for the first time, a transition in the exponent in Glen's law depends on geometrical grain size. A cross-over of stress exponent $n=1.9$ for fine grains to $n=4.4$ for coarse grains is interpreted as a transition from grain boundary sliding to dislocation creep.

The dataset includes compression force data from 11 experiments and corresponding 3D image data from tomography scans.

ice

display_name: ice

microstructure

display_name: microstructure

slow-snow-compression-experiments

display_name: slow-snow-compression-experiments

snow-mechanics

display_name: snow-mechanics

x-ray-tomography

display_name: x-ray-tomography

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: A grain-size driven transition in the deformation mechanism in slow snow compression


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

en: Description

en: Description of the predictors provided in this dataset

en: Predictors

en: Predictors generated and used in Descombes et al. (2020)

en:
Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland.

This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection ("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"). The excel file (xlsx) provides a short description of the raster layers.

Paper Citation:

Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph

switzerland

display_name: switzerland


en: geoinformation-system open-source digital-elevation-model swiss-geodata seilaplan wms-data digital-planning cable-yarding qgis-plugin

en: WMS-Layer als Hintergrundkarten laden

en: View video on youtube!

media_type: text/html

format: HTML

en:
In order to digitally plan a cable line using the QGIS plugin ‘Seilaplan’, maps with various background information are helpful. In this tutorial we show you how to obtain maps that are helpful for cable line planning, for example a national map of Switzerland at different scales, the NFI vegetation height model or the NFI forest mix rate. For this we explain what WMS datasets are and how to integrate them into QGIS. No download of large data is needed for this, only a good internet connection. Please note that the tutorial language is German!

Link for the integration of WMS data: https://wms.geo.admin.ch/

Link to the description on the Swisstopo website:
https://www.geo.admin.ch/en/geo-services/geo-services/portrayal-services-web-mapping/web-map-services-wms.html

Link to the Seilaplan website: https://seilaplan.wsl.ch

**************************

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung sind verschiedene Hintergrundkarten hilfreich. In diesem Tutorialvideo zeigen wir, was WMS Daten sind und wie man diese in QGIS einbinden kann. Dafür müssen die Daten nicht heruntergeladen werden. Es braucht lediglich eine gute Internetverbindung. Für die Seillinienplanung hilfreiche Karten sind bspw. die Landeskarte der Schweiz in verschiedenen Massstäben, das Vegetationshöhenmodell LFI oder der Waldmischungsgrad LFI.

Link zur Einbindung der WMS Daten: https://wms.geo.admin.ch/

Link zur Beschreibung auf der Swisstopo Webseite: https://www.geo.admin.ch/de/geo-dienstleistungen/geodienste/darstellungsdienste-webmapping-webgis-anwendungen/web-map-services-wms.html

Link zur Seilaplan-Website: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

seilaplan

display_name: seilaplan

swiss-geodata

display_name: swiss-geodata

wms-data

display_name: wms-data

en: Seilaplan Tutorial: Load WMS layers as background maps


automatic-detection-of-avalanches

maintainer: Matthias Heck

title_for_slug: automatic-detection-of-avalanches

en: monitoring seismic avalanche-activity snow hidden-markov-model

en: Results and Scripts

en: This zip package contains the classification results, coherence results, reference data set, python scripts used for the classification and the start and end times for the pre-processed continuous seismic data. A further readme file is also include for more detailed information.

en: This dataset contains the results obtained by an automatic classification using hidden Markov models of a continuous seismic dataset. To avoid long computational times, we reduced the seismic data using pre-processing step. The start and end times of the windows used for the classification are also included in this dataset. Furthermore, an avalanche reference data set is included and the python scripts used to perform the processing steps and the classification.

avalanche-activity

display_name: avalanche-activity

hidden-markov-model

display_name: hidden-markov-model

monitoring

display_name: monitoring

seismic

display_name: seismic

snow

display_name: snow

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Automatic detection of avalanches


maintainer: Giulia Francesca Azzurra Donati

en: ecosystem-services biodiversity network-analysis ecological-corridors circuit-theory urban-planning

en: Amphibian movement resistance layers and scripts for connectivity modelling - part 2

en: The zipped folder includes the script to create and analyse the networks from Circuitscape outputs. The subfolders “Boundaries”, “Layers”, and “Reference_net” contain files either called by the code or created in the process. The subfolder “Outputs_from_Circuitscape” contains the outputs from Circuitscape including node and branch cumulative currents in a network format.

en: Amphibian movement resistance layers and scripts for connectivity modeling - part 1

en: The zipped folder includes the script to aggregate the resistance layers into squared and hexagonal grids. The subfolders “Boundaries”, “Layers”, “Reference_grids”, and “Resistance_from_SDMs” contain files either called by the code or created in the process.The subfolder “Inputs_for_Circuitscape” contains the inputs to run Circuitscape, after doing some subsequent processing steps in QGIS (listed in the R script) with the files created with the script.

en: This data includes (1) Scripts to aggregate landscape resistance layers into squared and hexagonal grids (i.e., different representations and resolutions), (2) Input resistance layers and focal nodes in .txt format to run in Circuitscape (Python implementation v4.0.5). Circuitscape is a software tool for modeling and analyzing landscape connectivity, which simulates movement of organisms across landscapes by estimating resistance to movement across each point of the landscape. (3) Scripts for the ecological network analysis, and (4) environmental predictors for amphibian whole-life cycle habitats used to describe the local environment for BGI design (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related).

biodiversity

display_name: biodiversity

circuit-theory

display_name: circuit-theory

ecological-corridors

display_name: ecological-corridors

ecosystem-services

display_name: ecosystem-services

network-analysis

display_name: network-analysis

urban-planning

display_name: urban-planning

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: Multi-Scale Prioritization framework for Urban Blue-Green Infrastructure Planning to Support Biodiversity: Data & Codes


en: switzerland environmental---history historical-carthography ecology wetlands environmental--policy

en: Wetlands 1850 - 2010 (shapefiles)

en:
 History of wetlands - GIS-Layers
 Location of wetlands based on signatures in topographic maps
* Basis 1850: original measuring table sheets Dufour created between the years 1834 and 1863.
* Basis 1900/ Basis 1950: The Siegfried maps were updated and published at regular intervals between 1870 and 1949. This long period of time made it possible to reconstruct wetlands for two periods based on the Siegfried Maps: the first edition (hereinafter referred to as "1900") and the last edition ("1950").
* Basis 2010: Map sheets from the National Map from 2006 to 2011 were used for the most recent time period of the reconstruction.
 Location of wetlands due to reconstructions
The reconstruction **must not** be interpreted in the sense of a surface-accurate separation, but shows without claim to location accuracy the approximate extent of the areas that would have corresponded to the mapping criteria for wetlands used in today's topographic maps around 1850, 1900 and 1950. The basics can be found in Stuber & Bürgi 2019, chapter 5.4.

en:
Naturally, large parts of the Swiss Plateau are characterised by wetlands
and meandering rivers. That this is no longer the case today is the result
of centuries of efforts to obtain dry land. But how did this process take
place? What were the relevant actors and what were their motivations? And
what can be said about the ecological consequences of this development?

In a research project on the history of wetlands in Switzerland since 1700,
we conducted (a) a historical analysis of the development of land use in
wetlands and the actors involved, (b) a historical-cartographic
reconstruction of wetland extent since 1850 and (c) an evaluation of
ecological effects of changes in wetlands on various organisms groups.

The series of GIS layers on wetland history stem from the second part of
the project. The area reconstruction is based on digitized and homogenized
signatures from national map series, as they have been available since
about 1850. Details about the digitalization process and the homogenization
procedures applied ("Rekonstruktionen") are included in Stuber & Bürgi
2019.

Book Citation:
 > Stuber M, Bürgi M (2019) Vom «eroberten Land» zum Renaturierungsprojekt. Geschichte der Feuchtgebiete in der Schweiz seit 1700. "Bristol Schriftenreihe", Band 59. Haupt Verlag, Bern, Stuttgart, Wien. 262 Seiten.

ecology

display_name: ecology

environmental---history

display_name: environmental---history

environmental--policy

display_name: environmental--policy

historical-carthography

display_name: historical-carthography

switzerland

display_name: switzerland

wetlands

display_name: wetlands

en: History of wetlands in Switzerland since 1850


en: elasticity geometrical crystallographic microstructure fem anisotropy x-ray-tomography

en:
The study aims to determine the effective elastic properties of snow, firn, and bubbly ice based on microstructural quantities. Anisotropy, one of these quantities (the other being ice volume fraction) in snow and ice, has two types: geometrical and crystallographic, resulting in snow's macroscopic anisotropic elastic behavior. The research focuses on the impact of geometrical anisotropy on potential ice volume fractions in snow and ice. 391 micro-CT images from various locations, including laboratories, the Alps, the Arctic, and Antarctica, were analyzed to achieve this.
 The analysis involved microstructure-based finite element simulations, which inherently consider microstructure and calculate the elasticity tensor. Hashin-Shtrikman bounds were utilized to predict the elastic properties of the microstructure samples. These bounds effectively captured the nonlinear interplay between geometrical anisotropy, captured by the Eshelby tensor and density. HS bounds have the advantage of the correct limiting behavior for low to high-ice volume fractions.
 We derived parameterization for five transversely isotropic elasticity tensor components, requiring only two free parameters. This parameterization was valid for ice volume fractions ranging from 0.06 to 0.93. The analysis employing the Thomsen parameter highlighted the dominance of geometrical anisotropy until an ice volume fraction of 0.7. However, to fully comprehend the elasticity of bubbly ice, a comprehensive approach is necessary to integrate coupled elastic theories that account for both geometrical and crystallographic anisotropy.
This dataset includes a Jupyter notebook with all the necessary functions required to predict the elasticity tensor of snow for the given ice volume fraction and anisotropy. Also, the code contains the least squares optimization function to compute the elasticity tensor for the six components of stress and strain. For example, we consider our dataset to calculate the samples' elasticity tensor and reproduce Fig. 7 from the paper. We take the stress and strain values obtained from load states as input for this example.
Also, a .csv file contains all the microstructural information: ice volume fraction, anisotropy, correlation functions, voxels size, and no. of voxels of the samples and the elasticity tensor obtained from finite element simulations and from present work parameterization.

anisotropy

display_name: anisotropy

crystallographic

display_name: crystallographic

elasticity

display_name: elasticity

fem

display_name: fem

geometrical

display_name: geometrical

microstructure

display_name: microstructure

x-ray-tomography

display_name: x-ray-tomography

en: Effective, anisotropic elasticity tensor of snow, firn, and bubbly ice


en: diurnal-variability dust-particles biological-particles aerosol-cloud-interactions ice-nucleation fluorescent-particles

en: Figure 5.xlsx

en: This file contains data presented in Figure 5 in the main text.

en: Figure 7.xlsx

en: This file contains data presented in Figure 7 in the main text.

en: Figure 1.xlsx

en: This file contains data presented in Figure 1 in the main text.

en: Figure 4.xlsx

en: This file contains data presented in Figure 4 in the main text.

en: Data for figures in the Supplementary.xlsx

en: This file contains data presented in Figure S2 to S36 in the supplementary.

en: Figure 6.xlsx

en: This file contains data presented in Figure 6 in the main text.

en: Figure 2.xlsx

en: This file contains data presented in Figure 2 in the main text.

en: Figure 3.xlsx

en: This file contains data presented in Figure 3 in the main text.

en:
This repository contains all observational data sets during CALISHTO campaign used for the paper: Gao, K., Vogel, F., Foskinis, R., Vratolis, S., Gini, M. I., Granakis, K., Zografou, O., Fetfatzis, P., Berne, A., Papagiannis, A., Möhler, O., Eleftheridadis, K., and Nenes, A.: On the drivers of ice nucleating particle diurnal variability in Eastern Mediterranean clouds, npj Climate and Atmospheric Science, Preprint link: https://www.researchsquare.com/article/rs-4378562/v1.
CALISHTO campaign was conducted between 12 October and 27 November 2021 to observe ice nucleating particles and aerosol properties at the Helmos Hellenic Atmospheric Aerosol and Climate Change station in Eastern Mediterranean, to understand cloud-aerosol interactions. To evaluate the spatial-temporal variabilities and characteristics of ice nucleating particles, a high-time resolution ice nucleation spectrometer was employed and different online aerosol property measurements were conducted in-situ, including particle number concentration, particle size distribution, particle fluorescent properties, and particle chemical composition. In addition, planetary boundary layer conditions at the observation site were determined by remote sensing techniques employed at a lower site.

aerosol-cloud-interactions

display_name: aerosol-cloud-interactions

biological-particles

display_name: biological-particles

diurnal-variability

display_name: diurnal-variability

dust-particles

display_name: dust-particles

fluorescent-particles

display_name: fluorescent-particles

ice-nucleation

display_name: ice-nucleation

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: CALISHTO campaign dataset for the publication On the Drivers of Ice Nucleating Particle Diurnal Variability in Eastern Mediterranean Clouds


maintainer: Ionuț Iosifescu Enescu

en: Bulk Data Access

en: Link to the S3 bucket where all the data resources can be downloaded in bulk

format: GeoTIFF

en: map preview 0.5MB

mimetype: image/jpeg

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

Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

gis

display_name: gis

large-files

display_name: large-files

natural-earth

display_name: natural-earth

qgis

display_name: qgis

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


en: bivalves metabarcoding namibia eukaryotes edna

en: Data access

en: Please use cyberduck to download for now

en:
Monitoring terrestrial mammals through eDNA in rivers of northern Namibia

In 2022, we collected 20 water samples from 10 stations two rivers of Namibia. Samples were taken from the side of the river. At each station, we performed two filtration replicates using a peristaltic pump to conduct environmental DNA (eDNA) sampling. Each filtration targeted a maximum duration of 30 minutes, during which approximately 30 liters of water were filtered through each capsule. After filtration, the water inside the capsules was removed, and the capsules were filled with 50 ml of conservation buffer for preservation at room temperature. We followed strict contamination control protocols throughout both the fieldwork and laboratory processes, adhering to the guidelines of Valentini et al. (2016). We used eDNA primers targeting bacteria, bivalves, fish, vertebrates (V05) and all Eukaryotes (Euka03). To prevent contamination, each sample was processed using disposable gloves and single-use filtration equipment. The MiSeq Reagent Kit v3 (2x75 bp) (Illumina, San Diego, CA, USA) was used for paired-end sequencing at a theoretical sequencing depth of 200,000 reads per sample.

Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

bivalves

display_name: bivalves

edna

display_name: edna

eukaryotes

display_name: eukaryotes

metabarcoding

display_name: metabarcoding

namibia

display_name: namibia

en: Environmental DNA Freshwater Namibia Namibia 2022


maintainer: Felix Neff

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

en: Insect abundance

en:
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*)

mimetype: text/csv

en: Plot information

en:
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

en: Insect traits

en:
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:
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.

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: Grassland restoration: insects and insect traits


bats-in-beaver-systems

title_for_slug: bats-in-beaver-systems

en: nature-conservation chiroptera deadwood habitat-heterogeneity wetland ecosystem-engineer flying-arthropods beaver castor

en:
bats_biodiv.day.guilds with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"date" (sampling date),
"month" (sampling month),
"guild" (which feeding guild information),
"feeding" (number of feeding buzzes summed per 5 minutes and identification level), "abundance" (sequences per 5 minutes and identification level),
"richness" (species richness),
"richness_feeding" (species richness of feeding bats)

en:
dead_wood for deadwood with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"type" (deadwood type),
"height_length" (the height or lenghth of a piece of deadwood in m),
"DBH_diamlarge" (diameter at breast height or diameter of larger end if lying in m),
"diamsmall (diameter of smaller end if lying in m",
"volume" (calculated volume in m3)

en:
gewichtungsfaktor with columns
"Project" (sampling site),
"Treatment" (sampling location within site, with levels ctrl for Control and pool for Pool),
"date" (sampling date),
"gew" (weighting factor to standardize night length)

en: bats_raw_values_overview to produce raw value summaries and tables

en: sampling dates.xlsx

en:
For the time information about the research data, created (2022-09-23 to 2022-09-23) refers to the GIS data, collected (2022-06-13 to 2022-07-31) to the field sampling. Details can be found in the csv “sampling_dates”.

sampling_dates with columns
"site" (sampling site),
"bats_1" (first period of bat survey in June),
"bats_2" (second period of bat survey in June),
"bats_3" (first period of bat survey in July),
"bats_4" (second period of bat survey in July),
"flying_arthropods_1" (arthropod sampling in June),
"flying_arthropods_2" (arthropod sampling in July),
"deadwood survey" (date of deadwood survey),
"vegetation survey" (date of vegetation survey)

en:
bats.environment with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"canopy" (modelled tree canopy cover, averaged across radius of 100 m, unitless),
"structure_ruggedness" (mean ruggedness of the total vertical structure > 3 m, averaged across radius of 100 m, unitless), "vegetation_ruggedness" (mean ruggedness of the canopy > 3 m, later called canopy_heterogenneity, averaged across radius of 100 m, unitless),
"pool_size" (maximum width of beaver pool in m),
"area_deadwood_monitoring" (total area of deadwood moonitoring in m3),
"age" (beaver presence in years)

en:
bats_biodiv.day with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"date" (sampling date),
"month" (sampling month),
"feeding" (number of feeding buzzes summed per 5 minutes and identification level), "abundance" (sequences per 5 minutes and identification level),
"richness" (species richness),
"richness_feeding" (species richness of feeding bats)

en: R script bats_data_exploration_Figure_nighly to explore the data and display nightly species richness, activity and feeding activity

en:
site_info contains columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"location" (sampling location row within site),
"sample_nr" (sampling number within location)

en: R script bats_data_cleaning to clean the raw data

en: README text file providing context for the data. Please do not hesitate to contact us if you have any questions

en: R script bats_pool_size to test the beaver pool size on bat richness, activity and feeding activity

en: R script bats_SEM to test the influence of the beaver on bat richness, activity and feeding activity through feeding and habitat heterogeneity

en:
Raw data data_bats with columns:
“Categories” (empty or "feeding", indicating that call sequence contains feeding buzz)
“Sequence” (sequence number)
“Timestamp” (recording time)
“Temperature” (temperature recorded by batlogger in degree celcius)
“Serial” (serial number of bat logger)
“Duration” (duration of call sequence in seconds)
“Call.Count” (call count number, how many calls could be detected)
“Verification.1” (bat sequence verification)
“Notes” (remarks for identification)
“treatment” (treatment ctrl = Control and Pool)
“slot” (slot number, 4 slots, two in June, two in July)
“Laufnummer” (unique identifier per sampling point)

en:
arthropods_flying the arthropod abundance with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"month" (sampling month),
"abundance" (number of flying arthropods)

en: R script bats_glmm to test the influence of beavers on bat richness, activity and feeding activity

en:
vegetation_characteristics data on vegetation_cover and cumulative_cover with columns "laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"Vegetation_cover" (estimated vegetation cover over 5x1 m plot in %),
"Cumulative cover" (summed up cover over tree, shrub and herb layer in %),
"Cumulative cover trees" (tree cover level, higher than 3 metres in %),

en:
bat_guilds with columns
"SP" (identification level),
"EndGuilde" (information on feeding guild)

en: R script bats_age to test the beaver pool age effect on bat richness, activity and feeding activity

en:
This dataset contains processed bat acoustic data collected to evaluate the ecological impacts of beaver-engineered ponds on bat species richness, activity, and feeding activity. It includes nightly activity and feeding call counts identified to species or species group level, site and treatment information (beaver pond vs. control areas along the same stream), and associated environmental covariates such as deadwood volume, arthropod abundance, and vegetation structure.

Methods: Bats were recorded using passive acoustic detectors placed at paired beaver-influenced and control sites. The resulting data was processed in the software Batscope. Arthropod abundance was estimated using flight-interception traps. The amount of deadwood and vegetation cover was measured in the field. Canopy cover and canopy heterogeneity were modelled from GIS models.

Temporal and Spatial Coverage:
Data were collected from eight sites in Switzerland between June and July 2022, each site sampled for 16 nights.

beaver

display_name: beaver

castor

display_name: castor

chiroptera

display_name: chiroptera

deadwood

display_name: deadwood

ecosystem-engineer

display_name: ecosystem-engineer

flying-arthropods

display_name: flying-arthropods

habitat-heterogeneity

display_name: habitat-heterogeneity

nature-conservation

display_name: nature-conservation

wetland

display_name: wetland

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Bats in beaver systems


en: numerical-model vapor-transport snow pattern-formation python

en:
Data and code v1.0 downloaded from the git.

Running the code requires an installation of FEniCS via docker.

en:
This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format.

The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication:

_Schürholt, K., Kowalski, J., Löwe, H.; Elements of future snowpack modeling - Part 1: A physical
instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_

The code and potential updates can be accessed directly through git via:

https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics

numerical-model

display_name: numerical-model

pattern-formation

display_name: pattern-formation

python

display_name: python

snow

display_name: snow

vapor-transport

display_name: vapor-transport

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: A numerical solver for heat and mass transport in snow based on FEniCS


en: surge-waves debris-flow rainfall radar illgraben

en: Radar-based flow height measurement of debris flow at CD28 at Illgraben. Flow height is derived relative to pre-event channel-bed elevation. Sensor location: 46.309677N, 7.633304E

en: Cumulative rainfall measured at CD1 in the Illgraben catchment. Sensor location: 46.283287N / 7.619689E

en:
This dataset contains measurements from two debris flows recorded in 2024 at the Illgraben debris-flow observatory in Switzerland. The first debris flow was recorded on 15 June 2024 and the second debris flow was recorden on 21 June 2024.

This dataset contains the following reseach data for both:
a) Cummulative rainfall measured at CD1 in the catchment.
b) Radar-based flow height of debris flows measured at CD28.

Abstract
In recent years, the destructive impact of debris flows in alpine regions has become increasingly evident. Surge waves within debris flows increase peak discharge and magnify the hazard potential. Hence, understanding the dynamic complexity of debris flows is crucial to mitigate their risk. In order to capture the dynamic processes involved in the formation and interaction of surge waves, it is necessary to obtain distributed observations in the spatiotemporal domain. In this study, we present near-torrent distributed seismic measurements to monitor the Illgraben channel located in the Swiss Alps. With 33 nodal sensors, we detected and tracked surge waves along a 2-kilometer torrent section across the Illgraben fan, which allowed us to gain valuable new insights into the spatial scales relevant for surge wave formation and their long-distance propagation characteristics. We observed erosion-deposition waves that emerged out of the muddy flow tail and propagated with constant velocity along the torrent, reaching meter-scale flow heights within only 100s of meters of flow distance which significantly impact the hazard potential of debris flows. Our results provide valuable observations of surge waves from their formation to their annihilation. We can differentiate flow regimes based on their seismic signature and track them along the torrent, thus mapping the debris-flow evolution in time and space. The observations elucidate large-scale debris flow dynamics thus improving our understanding of hazard potential and the effectiveness of structural countermeasures.

debris-flow

display_name: debris-flow

illgraben

display_name: illgraben

radar

display_name: radar

rainfall

display_name: rainfall

surge-waves

display_name: surge-waves

en: Debris flow observation at Illgraben 2024: Event Data 15 June / 21 June


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

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

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

catchment-data

display_name: catchment-data

meteorological-data

display_name: meteorological-data

rain-on-snow

display_name: rain-on-snow

snow-hydrology

display_name: snow-hydrology

snowpack

display_name: snowpack

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


en: forest-structure snow-depth airborne-laser-scanning canopy-height snow-distribution

en: Canopy height maps

en: Canopy height maps of each of the three field areas Davos, Engadine and Grand Mesa

en: Terrain elevation maps

en: Digital elevation model computed from snow-off flights for each of the field areas Davos, Engadine and Grand Mesa

en: Snow depth maps

en: Snow depth maps from two snow-on flights at each of the three field areas Davos (March 20th and 31st 2017), Engadien (March 20th and 31st, 2017) and Grand Mesa (February 8th and 16th, 2017)

en: Readme

en:
This dataset includes snow depth, canopy height and terrain elevation maps of forest stands in the Grisons (CH) and at Grand Mesa (CO,USA) derived from airborne lidar. Data were acquired i) within a pilot mission of NASA's Airborne Snow Observatory in the Swiss Alps in March 2017 and ii) during NASA’s SnowEx campaign at Grand Mesa in February 2017. Snow depth maps are available for two dates separated by approx.1 week, and include an area of ca. 0.5km2 for each of the three sites Davos, Engadine and Grand Mesa.
All data were presented and analyzed in the publication 'Revisiting Snow Cover Variability and Canopy Structure within Forest Stands: Insights from Airborne Lidar Data' (Mazzotti et al., 2019, WRR, doi: 10.1029/2019WR024898). This publication must be cited when using this dataset.

Paper Citation:
 > _Giulia Mazzotti; William Ryan Currier; Jeffrey S. Deems; Justin M. Pflug; Jessica D. Lundquist; Tobias Jonas (2019). Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data. Water Resources Research, 55, 6198– 6216, [doi: 10.1029/2019WR024898](https://doi.org/10.1029/2019WR024898)._

airborne-laser-scanning

display_name: airborne-laser-scanning

canopy-height

display_name: canopy-height

forest-structure

display_name: forest-structure

snow-depth

display_name: snow-depth

snow-distribution

display_name: snow-distribution

educ

en: Education, culture and sport

en: ALS-based snow depth and canopy height maps from flights in 2017 (Grisons, CH and Grand Mesa, CO)