maintainer: Luca Bütikofer

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

en:
Code used to produce the Land-use/land-cover forecasts for Switzerland across the 21st century at 100-m resolution.
The script is based on input data which are not included as they are not freely available, therefore it will not run as it is. These input data are described in the accompanying publication on Data Science access to them can be requested to their owners. Alternatively the code can be modified to run on other data.

en: Compressed (.zip) file containing six georeferenced raster files in the GeoTIFF format with embedded coordinate reference system, and a table (comma separated values, .csv) encoding a colour palette for plotting (HEX colour codes for each class). GeoTIFF file names include the time-step and the RCP (e.g. “LULC_2020_2049_RCP45.tiff”). Each raster file has 3484x2207 square pixels (i.e. 7.7 megapixels) and is projected in the Swiss coordinate system CH1903+/LV95 (EPSG:2056).

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

land-cover

display_name: land-cover

land-use

display_name: land-use

landscape

display_name: landscape

landscape-change

display_name: landscape-change

modelling

display_name: modelling

envidat

en: EnviDat: the Environmental Data Portal

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

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


urban-birds-point-counts-data

title_for_slug: urban-birds-point-counts-data

en: environmental-gradients biodiversity spatially-distributed-sampling periurban birds urban

state: active

type: dataset

format: XLS

en:
The study area included the cities of Basel, Lugano and Zurich. The sampling design reflected four main city districts related to four categories of tree cover (Figure 1): 1) industrial/commercial areas, 0-20% tree cover; 2) residential areas, 20-40% tree cover; 3) urban parks and cemeteries, 40-60% tree cover; 4) peri-urban forests (natural/semi-natural forests), 60-100% tree cover. The tree cover gradient was evaluated across a square grid with a square side length of 100 m. For each square, we evaluated the area covered by urban trees using the Urban Atlas Street Tree Layer 2018 (European Union’s Copernicus Land Monitoring Service information, 2021).
In each city, we established eight transects of 1.2 km, two for each tree cover category. Transects crossed contiguous grid squares belonging to the same tree cover categories as much as possible. We performed aural-visual point counts of birds every 200 m along each transect (six points per transect) during the breeding season (April-June 2024) (Bibby et al., 2000). The transects were walked at the time of highest activity of birds, which is from thirty minutes after sunrise until 4 hours later. Each transect was walked twice, 20-60 days apart, to account for imperfect detection of birds and sample completeness (Balestrieri et al., 2017; Kéry et al., 2005).

biodiversity

display_name: biodiversity

birds

display_name: birds

environmental-gradients

display_name: environmental-gradients

periurban

display_name: periurban

spatially-distributed-sampling

display_name: spatially-distributed-sampling

urban

display_name: urban

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

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.

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Urban birds point counts data


maintainer: Ariel Bergamini

en: pandiv moss bryophytes biomass nitrogen species-richness biodiversity-experiment functional-traits fungicide grassland

en: main data as used in Lin et al. 2025

en:
This dataset was used to analyse effects of different biodiversity and functional group treatments, fertilizer and fungicide applications as well as weeding of vascular plants on the species richness and species composition of bryophytes for the following publication: Lin M., Bergamini A., Pichon N.A., Allan E, Boch S. 2025. Nitrogen enrichment and vascular plant richness loss reduce bryophyte richness. Scientific Reports.
In this study we surveyed 96 of 216 2 m x 2 m experimental plots of the PaNDiv experiment, a large grassland field experiment in the Swiss lowlands (Pichon et al. 2020). In the PaNDiv experiment four experimental treatments were applied in a full-factorial design manipulating: (1) the nitrogen availability (through fertilization), (2) the presence of fungal pathogens (through fungicide application), (3) the functional composition of the vascular plant community (represented here by community-weighted mean (CWM) SLA), and (4) the vascular plant richness (sowing of either 1, 4, 8 or 20 species). To maintain the original sown vascular plant species richness and composition, one part of each plot (1.5 m × 2 m) was weeded, while the other part has been left unweeded since the beginning of the experiment (0.5 m × 2 m).
On each plot we surveyed two subplots, the unweeded subplot and a weeded subplot of the same area (0.5 m × 2 m). On each subplot we established a complete list of all bryophyte species together with an estimate of their cover.
The provided excel file contains three sheets:
1.	A sheet with raw species lists of all plots surveyed together with information on all treatments and an estimation of the percentage cover of vascular plants in a summer campaign in August 2022. Cover of bryophytes was estimated for each species in percent.
2.	A sheet with a list of all bryophyte species with its growth form (acroarpous/pleurocarpous)
3.	A sheet with some explantations
By means of the provided R code and the data in the excel file, all analyses can be repeated.
More details to the data sampled can be found in Lin et al (2025).

biodiversity-experiment

display_name: biodiversity-experiment

biomass

display_name: biomass

bryophytes

display_name: bryophytes

functional-traits

display_name: functional-traits

fungicide

display_name: fungicide

grassland

display_name: grassland

moss

display_name: moss

nitrogen

display_name: nitrogen

pandiv

display_name: pandiv

species-richness

display_name: species-richness

en: Data on bryophyte diversity and treatment factors in the PaNDiv experiment


maintainer: EnviDat Support

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

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: 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: 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: 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: 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: 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: 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:
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

fungi

display_name: fungi

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

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


maintainer: Stephan Zimmermann

en: nutrient beech soil sustainability mass-balance

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

beech

display_name: beech

mass-balance

display_name: mass-balance

nutrient

display_name: nutrient

sustainability

display_name: sustainability

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Nutrient sustainability in beech forests


maintainer: Henning Löwe

en: angle-of-repose grain-shape fast-sintering discrete-element-modelling granular-snow

media_type: text/plain

format: TXT

mimetype: text/plain

en: Angle of repose and 3D CT images

en:
5 Snow types were examined and glass beads as a cohesion-less reference, named as follows:

RG: rounded grains (sieved with 0.7 - 1.4 mm)

FC: faceted grains (1.4 - 2 mm)

FCS: small faceted grains (0.7 - 1.4 mm)

IB: ice beads (4 mm)

IBS: small ice beads (2 - 2.2 mm)

GB: glass beads(2 mm)

format: SERVICE

en:
Angle of repose experiments were performed with different snow types at temperatures between -2 and -40°C. They were used to examine granular snow dynamics on the grain-scale with focus on the role of grain shape and cohesion. The angle of repose was observed by sieving snow onto a round, freestanding base until a stationary heap was formed.

This dataset consists of 1) the images of the experimental heaps that were taken to determine the angle of repose, 2) one binary 3D micro computed tomography image of each snow type. The CT images were taken with the SLF micro-CT40 to characterize snow properties and grain shape.

The experiments with natural snow types (rounded and faceted grains) and spherical model snow allowed for an examination of the differences in granular properties between natural grain shapes and spherical particles in view of Discrete Element Modelling. With the chosen temperatures, the effect of sintering could be observed that increases the angle of repose with increasing temperature.

angle-of-repose

display_name: angle-of-repose

discrete-element-modelling

display_name: discrete-element-modelling

fast-sintering

display_name: fast-sintering

grain-shape

display_name: grain-shape

granular-snow

display_name: granular-snow

en: Angle of repose experiments with natural and spherical snow


maintainer: Lisa Hülsmann

en: primeval-forests forest-reserves fagus-sylvatica forest-inventory tree-mortality mortality-models

en: Ukrainian data

media_type: text/csv

en:
READ ME for

Hülsmann L, Bugmann H, Commarmot B, Meyer P, Zimmermann S, Brang P
Data from: Does one model fit all? Patterns of beech mortality in natural forests of three European regions.
Ecological Applications http://dx.doi.org/10.5061/dryad.h4s6t

Forest inventory data of beech for the development of individual-tree mortality models from strict forests reserves in Switzerland, Germany and Ukraine.

The data package provides the files
CH_reduced: inventory data of the reduced dataset from Switzerland that was used to assess the influence of additional environmental and stand characteristics on beech mortality
CH: inventory data from strict forest reserves in Switzerland
DE: inventory data from strict forest reserves in Germany
UA: inventory data from a primeval beech forest in Ukraine

A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory.
Diameter at breast height: dbh (mm)
Annual relative basal area increment: relBAI (), calculated based on the first and the second dbh measurement as the compound annual growth rate of the trees basal area (Bigler and Bugmann 2004)
Status of the tree: y, 0 = alive, 1 = dead
Length of the mortality period: dt (years)

Additional environmental and stand characteristics were derived for the reduced Swiss dataset.
Note the varying seasonal level of the covariates considering the entire year, the growing season from April to September (G) and the winter month January to March (W).
All climatic covariates were calculated for both the ‘growth’ (2) and the ‘mortality’ period (3). The stand covariates were derived for the second inventory of each record (2).

Mean diameter at breast height: dbh.mean.2 (mm)
Interquartile range of diameter at breast height: dbh.IQR.2 (mm)
Median diameter at breast height: dbh.median.2 (mm)
Basal area per hectare: BA.2 (m2 ha-1)
Number of trees per hectare: N.2 (ha-1)
pH-value: pH ()
Available water capacity in the soil: AWC (mm)
Mean annual precipitation sum: P.2, P.3 (mm)
Mean precipitation sum during growing season: PG.2, PG.3 (mm)
Mean annual air temperature: mT.2, mT.3 (°C)
Mean air temperature during growing season: mTG.2, mTG.3 (°C)
Mean air temperature during winter months: mTW.2, mTW.3 (°C)
Mean growing degree-days: GDD.2, GDD.3 ()
Mean number of months during growing season with water deficit: mDEFdurG.2, mDEFdurG.3 ()
Mean annual maximum water deficit: mDEFmax.2, mDEFmax.3 (mm)
Overall maximum of the annual maximum water deficit: maxDEFmax.2, maxDEFmax.3 (mm)
Mean of cumulative water deficit during growing season: mDEFcumG.2, mDEFcumG.3 (mm)
Overall maximum of cumulative water deficitduring growing season: maxDEFcumG.2, maxDEFcumG.3 (mm)
Percentage of years with at least one month water deficit: percDEF.2, percDEF.3 (%)

format: CSV

mimetype: text/csv

en: Swiss data full

en: Swiss data reduced

en: German data

en:
The datasets comprise nearly 19’000 trees of European beech (_Fagus sylvatica_ L.) from unmanaged forests in Switzerland, Germany / Lower Saxony and Ukraine. Tree death was modelled as a function of size and growth, i.e., stem diameter (DBH) and relative basal area increment (relBAI). To explain the spatial and temporal variability in mortality patterns, we considered a large set of environmental and stand characteristics.

 Inventory data
The strict forest reserves in Switzerland and Germany had been established in the period of 1961-1975 and 1971-1974, respectively. Every reserve included up to 10 permanent plots ranging from 0.09 to 1.8 ha in size, with slightly irregular re-measurement intervals. Permanent plots with pure or mixed beech stands were selected from the reserves of both networks. Reserves with considerable wind disturbance during the monitored intervals were excluded from the analysis. In addition to data from the Swiss and German reserves, data from a 10 ha plot in the primeval beech forest Uholka in Western Ukraine including three remeasurements were used. The inventory data provide diameter measurements at breast height (dbh) for revisited trees with a diameter of more than 4, 7 and 6 cm for Switzerland, Germany and Ukraine, respectively.

 Mortality predictors
A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory. As an explanatory variable, the annual relative basal area increment (relBAI) was calculated based on the first and the second dbh measurement as the compound annual growth rate of the trees basal area. Tree dbh in the second inventory was used in addition to relBAI to model tree status (alive or dead) of the third inventory.

To increase the generality of the mortality models, we selected environmental variables that are known to have a considerable influence on growth and mortality of beech. We emphasized the effects of water availability using a large set of drought characteristics that were calculated based on the local site water balance. We also related beech mortality to soil pH, temperature, precipitation and growing degree-days. Additionally, we considered stand characteristics that reflect the development stage, competition and structure of the forests.

 Further information
For further information, refer to Hülsmann _et al_. (2016) Does one model fit all? patterns of beech mortality in natural forests of three European regions. _Ecological Applications_.

fagus-sylvatica

display_name: fagus-sylvatica

forest-inventory

display_name: forest-inventory

forest-reserves

display_name: forest-reserves

mortality-models

display_name: mortality-models

primeval-forests

display_name: primeval-forests

tree-mortality

display_name: tree-mortality

en: Data from: Does one model fit all? patterns of beech mortality in natural forests of three European regions


en: fagus-orientalis-lipsky potassium calcium magnesium biogeochemical-cycling

en: Concentrations of Ca, Mg, K, and Na in throughfall, litterflow and soil solution of a Hyrcanian forst in Northern Iran

en:
Throughfall, litterflow and soil solution were sampled during one whole year under five Oriental beech trees in a mixed Hyrcanian beech forest. The amounts of Ca2+, Mg2+, K+ and Na+ in these fluxes were calculated based on their concentrations and the sampled volumes, and subsequently compared with the respective fluxes in the rainfall and soil solution of an adjacent forest gap.
In addition six soil profiles, one close to every single tree and one in the forest gap, were analyzed for pH, CaCO3, organic matter and texture.

biogeochemical-cycling

display_name: biogeochemical-cycling

calcium

display_name: calcium

fagus-orientalis-lipsky

display_name: fagus-orientalis-lipsky

magnesium

display_name: magnesium

potassium

display_name: potassium

educ

en: Education, culture and sport

en: Base cation dynamics in an Oriental beech forest


maintainer: Lorenz Walthert

en: anisohydric root-distribution water-potential stomatal-stringency water-use-strategy

en: The data file contains the data shown in the Figures 2-7 in Walthert et al. (2024).

en:
This dataset represents the data shown in the Figures 2 - 7 of Walthert et al. (2024): Coordination between degree of isohydricity and depth of root water uptake in temperate tree species. Science of the Total Environment (https://doi.org/10.1016/j.scitotenv.2024.174346). A detailed methodical description of the data can be found in the Methods section of the paper.

Abstract
In an increasingly dry environment, it is crucial to understand how tree species use soil water and cope with drought. However, there is still a knowledge gap regarding the relationships between species-specific stomatal behaviour, spatial root distribution, and root water uptake (RWU) dynamics.
Our study aimed to investigate above- and below-ground aspects of water use during soil drying periods in four temperate tree species that differ in stomatal behaviour: two isohydric tracheid-bearing conifers, Scots pine and Norway spruce, and two more anisohydric deciduous species, the diffuse-porous European beech, and the ring-porous Downy oak. From 2015 to 2020, soil-tree-atmosphere-continuum parameters were measured for each species in monospecific forests where trees had no access to groundwater. The hourly time series included data on air temperature, vapour pressure deficit, soil water potential, soil hydraulic conductivity, and RWU to a depth of 2 m. Analysis of drought responses included data on stem radius, leaf water potential, estimated osmotically active compounds, and drought damage.
Our study reveals an inherent coordination between stomatal regulation, fine root distribution and water uptake. Compared to conifers, the more anisohydric water use of oak and beech was associated with less strict stomatal closure, greater investment in deep roots, four times higher maximum RWU, a shift of RWU to deeper soil layers as the topsoil dried, and a more pronounced soil drying below 1 m depth. Soil hydraulic conductivity started to limit RWU when values fell below 10-3 to 10-5 cm/d, depending on the soil. As drought progressed, oak and beech may also have benefited from their leaf osmoregulatory capacity, but at the cost of xylem embolism with around 50% loss of hydraulic conductivity when soil water potential dropped below -1.25 MPa. Consideration of species-specific water use is crucial for forest management and vegetation modelling to improve forest resilience to drought.

anisohydric

display_name: anisohydric

root-distribution

display_name: root-distribution

stomatal-stringency

display_name: stomatal-stringency

water-potential

display_name: water-potential

water-use-strategy

display_name: water-use-strategy

envidat

en: EnviDat: the Environmental Data Portal

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

en: Water-use strategies of temperate tree species


maintainer: Frank Techel

en: machine-learning natural-avalanches snowpack-model avalanche-size avalanche

en: Data set AV2 used for training the avalanche-size estimator (Sections 2.1.2 and 3.2 in Mayer et al.)

en: Data set DL was used for validation of the avalanche-day classifier and the avalanche-size estimator. The data set contains the quality-checked danger levels merged with the respective SNOWPACK simulations (Sections 2.3 and 3.3(2) in Mayer et al.).

en: Data set AV3 was used for validation of the avalanche-day classifier (Sections 2.1.3 and 3.3(1) in Mayer et al.).

en: Data set AV1 was used for training the avalanche-day classifier (Sections 2.1.1, 2.2, and 3.1 in Mayer et al.).

en:
The data set contained in this repository was used in the analysis by Mayer et al. (2023):
Mayer, S. I., Techel, F., Schweizer, J., and van Herwijnen, A.: Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations, EGUsphere, https://doi.org/10.5194/egusphere-2023-646, 2023.

avalanche

display_name: avalanche

avalanche-size

display_name: avalanche-size

machine-learning

display_name: machine-learning

natural-avalanches

display_name: natural-avalanches

snowpack-model

display_name: snowpack-model

en: Data-set for prediction of natural dry-snow avalanche activity and avalanche size using physics-based snowpack simulations


maintainer: Axel Volkwein

en: mass-movements rockfall experiment field-test field-data trajectory experiments field-tests natural-hazards

en: Block trajectories recorded from local positioning system

en: This file contains more than 80 rockfall trajectories projected onto the terrain, i.e. the positions of the single blocks over time during the single tests that were recorded by the local positioning system with about 10Hz. It contains the detected positions as well as a smoothened path (see Volkwein et al., Geosciences, 2018). The table also contains the derived velocities and accelerations along the slope (attention: these accelerations are the once that occur during an impact).

en: Overview of all tests

media_type: application/zip

en:
This table gives an overview on all tests performed including
- block used
- start & end position
- number of jumps
- etc.
-

format: ZIP

en: These data contain the point clouds obtained from laser scanning the test slope and the six test blocks used for the experiments. The coordinates of the slope are given in CH-1903 format. The coordinates of the block point clouds are kept in a lokal x/y/z coordinate system.

mimetype: application/zip

en: Jumps and impacts

en: This table contains data of more than 2200 of each jumps and impacts of the test blocks on a grassy slope. It gives there durations in combination with acting accerlartions and the rotational speed of the blocks. The data were retrieved using the block internal accelerometer and rotation sensors. The combination of these data with the measurement from the local positioning system allows the deduction of jump lengths, slope gradient etc.

en:
In summer 2014, 6 rock blocks between 20 and 80kg have been thrown in total 111 times down a slope at the Swiss Oberalppass close to the village Tschamut. The slope was mainly covered by grass and its lower part was flat and large enough to provide natural runouts of the single trajectories.

An extensive measurement program has been set up to measure the block trajectories: With surveyor's instruments the slope and the six used rock blocks were scanned and the start and end positions of each test were recorded. During the single events two cameras filmed the trajectories. A special sensor device located within the blocks recorded the acting accelerations and rotational speeds over time. Further, the device emitted a Wifi signal that got detected from eight receivers around the slope. Based on this signal the block position has been recorded over time.

The dataset contains all data that were gathered through above field campaign.

experiment

display_name: experiment

experiments

display_name: experiments

field-data

display_name: field-data

field-test

display_name: field-test

field-tests

display_name: field-tests

mass-movements

display_name: mass-movements

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

trajectory

display_name: trajectory

en: Repetitive trajectory testing in Tschamut 2014


maintainer: Mountain Hydrology and Mass Movements

en: windstorm-fatalities lightning-fatalities debris-flow-fatalities earthquake-fatalities natural-hazard-fatalities landslide-fatalities rockfall-fatalities ice-avalanche-fatalities flood-fatalities

en: Natural hazard fatalities per year in Swizterland since 1946.

en:
This dataset contains the number of fatalities due to flood, debris flow, landslide, rockfall, windstorm, lightning, ice avalanche, earthquake and other processes like roof avalanche or lacustrine tsunami for each year since 1946.
The following information is contained (by column and column title):

* year
* total number of hazard fatalities
* number of fatalities by flood (German: Hochwasser, Überschwemmung). Flood includes people drowned in flooded or inundated areas or carried away in streams under high-water conditions.
* number of fatalities by debris flow (German: Murgang).
* number of fatalities by landslide (German: Erdrutsch). Landslide includes people killed by landslides and hillslope debris flows (German: Hangmure).
* number of fatalities by rockfall (German: Steinschlag, Fels- und Bergsturz).
* number of fatalities by windstorm (German: Sturm). Windstorm includes people killed by falling objects or trees during very strong wind conditions and people who drowned in lakes because their boat capsized during such conditions.
* number of fatalities by lightning (German: Blitz).
* number of fatalities by ice avalanche (German: Eislawine).
* number of fatalities by earthquake (German: Erdbeben).
* number of fatalities by other processes like roof avalanche, lacustrine tsunami (German: andere Prozesse wie Dachlawine, Tsunami im See).

The data was collected based on newspaper research. For more information please refer to

_Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747-2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016._

The data collection is financed by the FOEN (with exception of the collection of the avalanche fatalities). The data contains the official statistics of the FOEN on fatalities due to flood, debris flow, landslide, rock fall and avalanche.

Restrictions: The data set is not complete. Only fatalities in or around settlements and on open transportation routes are included. More precisely, fatalities were not collected, when persons exposed themselves to a great danger on purpose. Or fatalities during leisure activities which are connected to a higher risk were not included (this includes e.g. canoeing or river surfing during flood, canyoning, mountaineering, climbing, walking or driving on a closed road).

Fatalities by avalanches are collected at the WSL Institute for Snow and Avalanche Research SLF. You can download the avalanche fatalities per hydrological year [here](https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936) and per calendar year [here](https://www.envidat.ch/dataset/avalanche-fatalities-per-calendar-year-since-1936). For a direct comparison with the fatalities presented here, please download the data set with the calendar years and do not consider fatalities in the backcountry (tour) or in terrain close to ski areas (offpiste).

debris-flow-fatalities

display_name: debris-flow-fatalities

earthquake-fatalities

display_name: earthquake-fatalities

flood-fatalities

display_name: flood-fatalities

ice-avalanche-fatalities

display_name: ice-avalanche-fatalities

landslide-fatalities

display_name: landslide-fatalities

lightning-fatalities

display_name: lightning-fatalities

natural-hazard-fatalities

display_name: natural-hazard-fatalities

rockfall-fatalities

display_name: rockfall-fatalities

windstorm-fatalities

display_name: windstorm-fatalities

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Number of natural hazard fatalities per year in Switzerland since 1946


en: COST European Snow Booklet (ESB)

media_type: application/pdf

en: Please find here the web version of the COST European Snow Booklet (ESB).

format: PDF

mimetype: application/pdf

en: European Snow Booklet public metadata

en:
The core of the European Snow Booklet (ESB), the country reports of 38 European countries, are based on information and metadata on snow measurements collected through a comprehensive survey, the ESB Questionnaire, between August 2017 and March 2018. The station metadata (for example location, elevation, measurement start date) from each country was provided by one or several institutions responsible for snow observations. By answering the questionnaire the contacts agreed that the information and metadata provided could be presented in the ESB. For cases where institutions indicated that information on station locations could be made publicly available, the metadata for the variables snow depth (HS), depth of snowfall (HN) and water equivalent of the snow cover (SWE), is additionally provided here for download (METADATA.xlsx).
Please note that not all institutions gave permission to publish their station metadata. This metadata has to be requested from the non-personal institution contacts provided in the METADATA.xlsx file. In some cases, however, no institutional contact is available and thus the contact of the country representative who filled in the questionnaire is used.
For current, updated station metadata we refer to the (non-personal) institutional contacts provided in METADATA.xlsx and/or the institution webpages. Note, however, that we cannot guarantee for completeness of the dataset for all countries because operational networks are prone to changes.

en: COST European Snow Booklet (ESB) - DORA Link

media_type: text/html

en: DORA Link of the COST European Snow Booklet (ESB).

format: HTML

en: List of authors and list of the editorial board of the European Snow Booklet

en:
Here you can find the list of all lead authors of the different chapters of the European Snow Booklet (ESB).
In addition a list of the editorial board that reviewed the ESB is given.

en:
The European Snow Booklet (ESB) is a book of reference for snow measurements that has been produced through collaboration with many European snow practitioners and snow scientists in the framework of the European Cooperation in Science and Technology (COST) Action ES1404 “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction (HarmoSnow)”. The ESB provides a unique collection of information about operational snow observations in the European countries and the methods used to perform basic measurements of snow on the ground: snow depth (HS), depth of snowfall (HN), water equivalent of the snow cover (SWE) and presence of snow on the ground (PSG). Information and station metadata (for example location, elevation) for these basic snow variables were collected through a comprehensive survey, the ESB questionnaire between August 2017 and March 2018. Numerous institutions of 38 European countries provided detailed information describing the status of the operational snow observations and the methods used at the time of the survey. Based on the information provided, a country report was written for each European country. Similarities and differences among the countries, that is, the choice of snow variables to be measured, the measurement principles applied, the number of stations, or the spatial and elevational station distribution are pointed out. Thus the collection of country reports demonstrates the relevance of snow measurements for each country. Thus, the intention of the ESB is to foster better knowledge transfer regarding snow measurements between the snow science and operational communities and to improve the communication of information to the general public.

For detailed information on the European countries, we refer to the ESB, which can be downloaded here (envidat.ch). Please note that the ESB is not a living document and information and station metadata are from August 2017 till March 2018, except for Latvia (metadata updated in December 2018).

depth-of-snowfall

display_name: depth-of-snowfall

harmonisation-of-snow-measurements

display_name: harmonisation-of-snow-measurements

snow-depth

display_name: snow-depth

snow-measurement-methods

display_name: snow-measurement-methods

water-equivalent-of-the-snow-cover

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

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


maintainer: Beat Frey

en: info-fauna fungi eco-fauna ecosystem-protection red-list fauna lichens soil-quality soil-organisms soil-dependency database conservation soil-biodiversity

en: Raw dataset of species and their soil dependency as well as endangerment status.

en: Proportion of endangered species grouped by the four soil dependency classes.

en: Overview over the soil dependency and endangerment status of different groups of organisms.

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

Paper Citation:
Frey B., Maurer C., Schneider K. (2023) Informationen zum Boden anhand der Gebundenheit der Rote-Liste Arten BAFU. Bundesamt für Umwelt BAFU. 26 p.

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

Soil is the habitat and basis of life for countless creatures. The great importance of soil organisms for the fertility of our soils is widely recognized, but information is sparse on their distribution and possible endangerment in Switzerland. In the present project, target species, i.e. species that are closely linked to the soil, were identified, and for the the first time, information was gathered on the degree of endangerment of the soil ecosystem in Switzerland on the basis of Red List species. For eleven Red List groups, the species' soil dependency was determined, i.e. whether their life cycle includes stages in or on the soil. The assessment of soil dependency was made using four classes, which enabled the classification not only of insects but also of vertebrates, land snails, lichens and fungi.

conservation

display_name: conservation

database

display_name: database

eco-fauna

display_name: eco-fauna

ecosystem-protection

display_name: ecosystem-protection

fauna

display_name: fauna

fungi

display_name: fungi

info-fauna

display_name: info-fauna

lichens

display_name: lichens

red-list

display_name: red-list

soil-biodiversity

display_name: soil-biodiversity

soil-dependency

display_name: soil-dependency

soil-organisms

display_name: soil-organisms

soil-quality

display_name: soil-quality

en: Information on soil based on the soil dependency of the FOEN red list species


en: robustness metaweb network-analysis habitat-fragmentation trophic-networks trophich food-webs

en:
This resource is broken down into two sections: 01 simulations and 02 analyses
In the 01 simulations section, the program which run the simulations for the paper is provided. It is prepared such that it can be easily run on a cluster. Please note that the results folder is empty, as they would have included 108,000 csv files. Instead, the summarised version is available in the next section.
In the 02 analyses section, all code needed to recreate the analyses and figures from the paper are provided.
Please refer to the separate README.txt files for each section for further information.

en:
This dataset contains all processed data, simulation outputs, and R/Python scripts used to assess the robustness of regional multi-habitat food webs in Switzerland under various non-random species extinction scenarios. It accompanies the manuscript submitted to Communications Biology, and available as a preprint here:
Reji Chacko, M, Albouy, C., Altermatt, F., et al. Decreases in the robustness of regional food webs to sequential species extinctions following habitat loss, 04 December 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-5225132/v1]
Please note that the most up-to-date version of the simulations code is available here: https://github.com/mrejichacko/casCHades

food-webs

display_name: food-webs

habitat-fragmentation

display_name: habitat-fragmentation

metaweb

display_name: metaweb

network-analysis

display_name: network-analysis

robustness

display_name: robustness

trophic-networks

display_name: trophic-networks

trophich

display_name: trophich

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Simulation data and analyses of regional food web robustness under habitat loss


data-interactive-avalanche-segmentation

maintainer: Elisabeth D. Hafner

title_for_slug: data-interactive-avalanche-segmentation

en: deep-learning interactive avalanche segmentation snow

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

avalanche

display_name: avalanche

deep-learning

display_name: deep-learning

interactive

display_name: interactive

segmentation

display_name: segmentation

snow

display_name: snow

en: Data interactive avalanche segmentation


maintainer: Michael Eisenring

en: drought-legacy phytochemistry fagus-sylvatica herbivory climate-change

en:
What are the research data files about:
Raw data on various beech (Fagus sylvatica) leaf traits. Beech leaf chemistry (primary and specialized metabolites), leaf damage measurements from various herbivore feeding guilds and crown health assessment

Which methods were used:
Field surveys on trees that showed either prematured defoliation due to drought-stress or showed not pre-mature defoliation. .

When and where was the data collected/analyzed:
Field data was collected in 2019, 2020
Data was analyzed in 2021-2023

climate-change

display_name: climate-change

drought-legacy

display_name: drought-legacy

fagus-sylvatica

display_name: fagus-sylvatica

herbivory

display_name: herbivory

phytochemistry

display_name: phytochemistry

en: Legacy effects of premature defoliation in response to an extreme drought event modulate phytochemical profiles with subtle consequences for leaf herbivory in European beech


en: drifting-snow splash snow-saltation saltation-parameterizations aerodynamic-entrainment large-eddy-simulation

en: Code and input files to reproduce the simulations in Melo et al. (2024), as well as the post-processing scripts used in the analysis.

en:
This dataset includes the necessary files to reproduce the results presented in Melo et al. (2024):
- code version used in the simulations (also available at the institutional [GitLab repository](https://gitlabext.wsl.ch/atmospheric-models/les-lsm) - tag 'r2024')
- input files to run the simulations
- post-processing scripts (MATLAB and FORTRAN)
- selected raw output files
- post-processed results (MATLAB data files).
 	**Note:** Direct model outputs are not provided because of their size, but they can be shared upon request. They can be reproduced by running the code. The post-processed results allow you to reproduce all figures in Melo et al. (2024).

aerodynamic-entrainment

display_name: aerodynamic-entrainment

drifting-snow

display_name: drifting-snow

large-eddy-simulation

display_name: large-eddy-simulation

saltation-parameterizations

display_name: saltation-parameterizations

snow-saltation

display_name: snow-saltation

splash

display_name: splash

en: Understanding snow saltation parameterizations - Reproducibility


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

en: Site photo of the forest station (LEB)

en: Site photo of the forest station from below (viewing upwards the slope)

mimetype: image/jpeg

en: Forest station data (LEB)

en: Data file.

en: Data plot of the forest station (LEB)

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

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

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


maintainer: Benjamin Reuter

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

en: 1999-2017 Avalanche problem types from Crocus

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

en: 2015-2016 Avalanche problem types from SNOWPACK

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

en: 1999-2017 Avalanche problem types from SNOWPACK

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

en: 2015-2016 Seismic avalanche acitivity catalogue

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

en: 2015-2016 Avalanche problem types from Crocus

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

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

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

avalanche-danger

display_name: avalanche-danger

avalanche-problem-type

display_name: avalanche-problem-type

crocus

display_name: crocus

snow

display_name: snow

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability

snowpack

display_name: snowpack

weissfluhjoch

display_name: weissfluhjoch

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Yaquan Chang

en: hengduan-mountains model-residuals habitat-heterogeneity phytodiversity hypervolume

en: HDM heterogeneity scales

en: The Heterogeneity matrics in the Hengduan mountain scale

en: Hengduan plant family richness map

en: This is the family level plant richness map in the Hengduan mountain scale at 1km resoution

en: Eurasia SR and heteorgeneity

en: This table is used for the Eurasia scale analyses, including both family level species richness map and different heterogeneity predictors

en: Eurasia plant family richness map

en: This is the plant family level richness map at the Eurasia scale calculated from https://www.nature.com/articles/s41597-021-00997-6

en: Plant range map at Hengduan scale

en: This is the plant range map at the Hengduan mountain scale based on species distribution model, elevation, and county level distribution information

en: This research data is used to re includes 1 km resolution seed plant species range maps for the Hengduan mountain region (southwest China), elevation information for each species, family-level plant richness data, and habitat heterogeneity data at both Eurasia and Hengduan mountain regions. This research data includes 1 km resolution seed plant species range maps for the Hengduan mountain region (southwest China), elevation information for each species, family-level plant richness data, and habitat heterogeneity data at both Eurasia and Hengduan mountain regions.

habitat-heterogeneity

display_name: habitat-heterogeneity

hengduan-mountains

display_name: hengduan-mountains

hypervolume

display_name: hypervolume

model-residuals

display_name: model-residuals

phytodiversity

display_name: phytodiversity

en: Phytodiversity is associated with habitat heterogeneity from Eurasia to Hengduan


en: long-term-experiment mountain-forest treeline afforestation stillberg

en: Dataset V1

en: This dataset is outdated and has been replaced with an updated version (see description).

format: GDB

en: Site photo

en: This site photo is outdated and has been replaced with an updated version (see description).

en: Documentation

en: This data documentation is outdated and has been replaced with an updated version (see description).

mimetype: application/msword

en:
 Important
This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry "Long-term afforestation experiment at the Alpine treeline site Stillberg, Switzerland." The entry can be found under this link <https://www.envidat.ch/dataset/afforestation-stillberg> and with this DOI 10.16904/envidat.397.

afforestation

display_name: afforestation

long-term-experiment

display_name: long-term-experiment

mountain-forest

display_name: mountain-forest

stillberg

display_name: stillberg

treeline

display_name: treeline

educ

en: Education, culture and sport

en: Long-term treeline research dataset at Stillberg, Davos


maintainer: Thorsten Bartels-Rausch

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

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

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

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

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

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

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

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

antartic

display_name: antartic

arctic

display_name: arctic

cryosphere

display_name: cryosphere

laboratory-study

display_name: laboratory-study

polar

display_name: polar

sea-salt-aerosol

display_name: sea-salt-aerosol

envidat

en: EnviDat: the Environmental Data Portal

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

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


en: model hydrology discharge water-temperature climate-change-impact

en: Data access

en:
This work presents the first extensive study of climate change impacts on rivers temperature in Switzerland. Results show that even for low emissions scenarios, water temperature increase will lead to adverse effect for both ecosystems and socioeconomic sectors (such as nuclear plant cooling) throughout the 21st century. For high emissions scenarios, the effect will be worsen. This study also shows that water warming in summer will be more important in Alpine regions than in lowlands.
This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).

climate-change-impact

display_name: climate-change-impact

discharge

display_name: discharge

hydrology

display_name: hydrology

model

display_name: model

water-temperature

display_name: water-temperature

en: Dataset for: Future water temperature of rivers in Switzerland under climate change investigated with physics-based models


en: tree-ring dendrochronology beech network-analysis fagus-sylvatica dendroecology

en:
R code to run the model behind the results of the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".
Requires beech_rawtrw.txt.

format: OCTET

en:
This dataset contains the raw ring-width and climate data (1952-2020) used in the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".
RW= ring width
Year= Calendar year
CN = core number
ID3= plot identifier
longitude = longitude (°)
latitude = latitude (°)
elevation = elevation (m)
age= estimated age in calendar year
radius= estimated radius in calendar year (mm)
MAT= mean annual temperature (°C)
MAP= mean annual precipitation (mm)
MCWB= mean climatic water balance (P-PET; mm)
amplitude= simple continentality measure (July - January mean maximum temperatures)
MATspline1 and 2, MCWBspline1 and 2, radiusspline4 and 5= natural spline transformed values of MAT, MCWB and radius ns(x,2)
CWD_pMay_Jun= Climatic Water Balance of previous year May to June !!!#please note CWD here is NOT Climatic Water Deficit, this was a labeling error in the creation of the dataset headers
CWD_May_Jun= Climatic Water Balance of current year May to June
Tmax_pMay_Jun= mean monthly maximum temperature of previous year May to June
Tmax_May_Jun= mean monthly maximum temperature of current year May to June
weight = site replication-based weighting input for model
SOM= membership of regional cluster 1-8 based on Supervised Organized Mapping

en: Model output

en: Output of the Mixed-effects model

en: This dataset contains the raw ring-width and climate data (1952-2020), as well as the R code to run the model and the model output used in the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".

beech

display_name: beech

dendrochronology

display_name: dendrochronology

dendroecology

display_name: dendroecology

fagus-sylvatica

display_name: fagus-sylvatica

network-analysis

display_name: network-analysis

tree-ring

display_name: tree-ring

en: European Beech tree-ring dataset 1952-2020


maintainer: Michael Lombardo

en: transport measurements snow soil water pore-water

en: Descriptions of the nomenclature, Hydrus files, and videos.

en: The Hydrus files used for the inverse fitting of the hydraulic conductivity.

en: Videos of the capillary rise experiments

en: Description of the videos

en: This dataset consists of data related to capillary rise experiments performed with neutron radiography. There are 4 videos of capillary rise experiments as well as the files used to perform the inverse fitting with Hydrus. The videos show the upward flow of water in glass columns filled with sand and snow or sand, gravel, and snow. The videos show the 2D evolution of the unitless optical density with time. The Hydrus files were used to fit the parameter values of the Mualem-van Genuchten model. The experiments were performed at the Paul Scherrer Institute (PSI) in Villigen, Switzerland.

measurements

display_name: measurements

pore-water

display_name: pore-water

snow

display_name: snow

soil

display_name: soil

transport

display_name: transport

water

display_name: water

educ

en: Education, culture and sport

en: Capillary rise rise experiments in snow using neutron radiography


en: biodiversity insect-herbivores biodiversity-experiment urban-biodiversity urban birds periurban pest-invasion urban-ecology

en:
We established our study sites in the cities of Basel, Lugano and Zurich, and their peri-urban forests. We characterised urban tree cover of each city using a rectangular grid with squares of 100x100 m. Within each square, we measured the area covered by urban trees using the European Union's Copernicus Land Monitoring Service information, Urban Atlas Street Tree Layer 2018 [https://doi.org/10.2909/205691b3-7ae9-41dd-abf1-1fbf60d72c8c](https://doi.org/10.2909/205691b3-7ae9-41dd-abf1-1fbf60d72c8c).
Then we assigned each square to four categories of urban tree cover that roughly represented the main types of urban uses:
1) low cover, industrial/commercial areas, 0-20% tree cover;
2) intermediate cover, residential areas, 20-40% tree cover;
3) high cover, urban parks and cemeteries, 40-60% tree cover;
4) peri-urban forests (natural/semi-natural forests), 60-100% tree cover.

Our study sites consisted of eight 1.2 km-long transects in each city, equally distributed across the four tree cover categories. We designed an experimental measurements of bird predation rate on herbivore insect larvae using artificial caterpillars. They mimicked a variety of common, cryptically-coloured herbivorous species, which are usually preyed by birds. We designed caterpillars with green water-resistant modelling clay (Staedtler Noris®), 4-6 cm long and approximately 0.5 cm wide. Caterpillars were threaded to a thin (Ø = 0.6 mm) zinc-plated iron wire and tethered to branches of trees or bushes at a height up to 2 m from the ground. We placed 2-4 caterpillar mimics every 200 m along each transect (five points per transect) or, when not possible due to absence of suitable branches, within 200 m from the point.
In total, we placed 270 caterpillar mimics at 115 points during the late breeding season (June-July 2023). Caterpillar mimics were checked after 10-14 days and replaced with new ones at new locations within 10 m from the previous ones. During each of the two visits, we counted the number of bird predation marks.

biodiversity

display_name: biodiversity

biodiversity-experiment

display_name: biodiversity-experiment

birds

display_name: birds

insect-herbivores

display_name: insect-herbivores

periurban

display_name: periurban

pest-invasion

display_name: pest-invasion

urban

display_name: urban

urban-biodiversity

display_name: urban-biodiversity

urban-ecology

display_name: urban-ecology

envidat

en: EnviDat: the Environmental Data Portal

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

en: Urban bird predation on artificial caterpillars


c-n-p-release-treeline

maintainer: Frank Hagedorn

title_for_slug: c-n-p-release-treeline

en: biogeochemical-cycling litter-decomposition treeline-ecosystems climate-change microbial-biomass carbon-cycle enzymatic-activities nutrient-fluxes

en: Litter layer characteristics, C-N-P-release during decomposition, microbial C-N-P, enzyme activity, mineralization of 13C-labeled glucose-6-phosphate from samples collected across two treelines

en:
1. Chemical characteristics of litter layer sampled along elevation gradient across treeline ecotones in the Khibiny mountains (birch) and South Urals (spruce)
2. Release of C-N-P during decomposition at 5°C and 15° studied in an incubation experiment with microlysimeters
3. Data on microbial biomass C-N-P, potential activity of C-N-P requiring enzymes in the litter layer
4. Mineralization of 13C labeled glucose-6 phosphate, microbial 13C uptake, and net P immobilization of released P to identify microbial processing of labile organic P

biogeochemical-cycling

display_name: biogeochemical-cycling

carbon-cycle

display_name: carbon-cycle

climate-change

display_name: climate-change

enzymatic-activities

display_name: enzymatic-activities

litter-decomposition

display_name: litter-decomposition

microbial-biomass

display_name: microbial-biomass

nutrient-fluxes

display_name: nutrient-fluxes

treeline-ecosystems

display_name: treeline-ecosystems

en: C-N-P-Release Treeline


maintainer: Alec van Herwijnen

en: slab-avalanche strength snow snow-mechanics microstructure depth-hoar

en:
CT Reference Data: Contains sample names, positions, evaluated regions and microstructural parameters based on data from the RHOSSA and MOSAiC campaigns.
MOSAiC:
https://mosaic-portal.org/data/open-mcs/vessel/polarstern/sn_microct/sn_microct/exdata/
https://doi.org/10.1594/PANGAEA.952794
RHOSSA:
https://doi.org/10.16904/envidat.151

en: Parent sample variability: Contains the CT data used to estimate the parent sample variability.

en: Time series data: Contains the CT data of upper slab, weak layer and lower slab used to evaluate the temporal evolution within the artificial samples.

en: Experimental Data: Contains results of the compression experiments and microstructural parameters.

en:
This repository hosts the experimental data accompanying our publication, “On the compressive strength of weak snow layers of depth hoar,” featured in the Journal of Glaciology.

-Compressive Strength Data:
Measurements of the compressive strength for 92 artificially grown weak snow samples.
-Microstructural CT Data:
CT-derived microstructural information for each sample, including:
Density
Specific surface area
Connectivity density
Correlation lengths
Anisotropy
-Additional CT Data:
Parent Sample Variability: CT data used to assess the variability of the parent samples.
Temporal Evolution: CT data capturing the evolution within the artificial weak layers.
Reference Data: Information on the reference CT data sourced from the RHOSSA and MOSAiC campaigns.

depth-hoar

display_name: depth-hoar

microstructure

display_name: microstructure

slab-avalanche

display_name: slab-avalanche

snow

display_name: snow

snow-mechanics

display_name: snow-mechanics

strength

display_name: strength


maintainer: EnviDat Support

en: ice antarctica sea-salt-aerosol snow arctic

en: This table lists all data files in chronological order. "SampleTemp" refers to the reading of the PT-1000 at the side of the sample holder. "cellPressure" is the water vapour pressure during the measurements. From this, in presence of ice, the temperature of the ice sample can be calculated which is given in the column "iceTemp". "dT" is the difference, or temperature offset, between sensor's reading and the ice samples temperature. The offset is due to the sensor being mounted away from the cooled spot of the sample were it experiences heating from warmer parts of the sample holder. The off-set is used to calibrate the temperature sensor and the resulting temperature during the experiments is given in the column "temperature". "relHum" is the relative humidity above (supercooled) liquid and "concBrine" is the concentration of a NaCl solution in mol/l in equilibrium with ice at the experimental temperature. "Area" is the integrated PE signal.

en: Code

en: Matlab code to read and plot the data

en: Fig 1C

en: Data to Figure 1C

en: Fib 1B_3

en: Individual data to mean shown in Figure 1B

en: Fig 1F_1

en: Individual data to mean shown in Figure 1F

en: Fig 1D

en: Data to Figure 1D

en: Fig 1F

en: Data to Figure 1F

en: Fig 1B_2

en: Fig 1A

en: Data Figure 1A

en: Fig 1E

en: Data to Figure 1E

en: Table 2

en: This table lists all NEXAFS data files in chronological order with PE carbon to oxygen signal intensity ratios -from which the atomic ratio were calculated in the paper - before ("preCtoO") and after ("postCtoO") each NEXAFS measurement. Also listed is the change in Na ("lossSodi"), O ("lossOxy"), Cl ("lossChlor"), and C ("lossCarb") signal intensity after the NEXAFS measurement compared to prior to the NEXAFS measurement.

en: Fig 1F_2

en: Fig 1B_1

en: Fig 1B

en: Data in Figure 1B

en: Laboratory experiments are presented on the phase change at the surface of sodium chloride – water mixtures at temperatures between 259 K and 240 K. High selectivity to the upper few nanometres of the frozen solution – air interface is achieved by using electron yield near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. We present the NEXAFS spectrum of the hydrohalite.

en: Data set on interfacial supercooling and the precipitation of hydrohalite in frozen NaCl solutions by X-ray absorption spectroscopy


maintainer: Yves Bühler

en: hazard-indication-mapping ramms snow avalanche release hazard-mapping

en: RAMMS simulations NHESS

en: Simulated maximum pressures for the scenarios frequent and extreme taking the forest layer into account

en: Potential Release  Area  NHESS

en: This folder contains the output of the different investigated algorithms plus the reference dataset, used for training and validation.

en:
This dataset contains the output and reference data published in the paper "Automated snow avalanche release area delineation - validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping" Yves Bühler, Daniel von Rickenbach, Andreas Stoffel, Stefan Margreth, Lukas Stoffel, Marc Christen (2018) Natural Hazards And Earth System Sciences.

Abstract:
Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche cadastre information, terrain analysis, climatological datasets and numerical modelling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such area-wide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-the-art numerical avalanche simulations.

In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche cadastre data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff mapped most avalanches in detail since many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference datasets. Because all tested algorithms do not provide meaningful delineation between individual potential release areas (PRA), we propose a new algorithm based on object-based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10 – 30 years return period) and extreme (100 – 300 years return period). This approach opens the door for large scale avalanche hazard indication mapping in all regions where high quality and resolution digital terrain models and snow data are available.

avalanche

display_name: avalanche

hazard-indication-mapping

display_name: hazard-indication-mapping

hazard-mapping

display_name: hazard-mapping

ramms

display_name: ramms

release

display_name: release

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Automated Avalanche Release Area (PRA) Delineation Davos


maintainer: Zhao

en: austin planning driving-factors land-dynamics urban-land-use

en: Potential driving factors of urban transformations of Austin over 25 years

en: In this study, the Austin metropolitan area, Texas, U.S., one of the fastest urban transformations and transformations regions, is selected to test the hypothesis that spatial planning and policies are important factors of urban transformations. Despite ample previous work in understanding the interactions between human and urban form transformation at specific areas, the actual interventions and outcomes of planning and policies (e.g., ‘smart growth’) on urban forms have been poorly measured. In this study, the potential influencing factors of urban transformations of Austin over 25 years were selected and collected.

austin

display_name: austin

driving-factors

display_name: driving-factors

land-dynamics

display_name: land-dynamics

planning

display_name: planning

urban-land-use

display_name: urban-land-use


figures-perspective-urban-beekeeping

maintainer: Joan Casanelles

title_for_slug: figures-perspective-urban-beekeeping

en: wildbees diversity-distributions hymenoptera urban-ecology insects urban-beekeeping

en: R script for the figures V2

en: R script for the figures

en: R script for the figures V1

en: Deprecated version of the R script

en: Coordinates studied cities

en: Coordinates (LatLon format) of the cities where data from urban bee ecology studies was used.

en: Distribution beehives among beekeepers

en: The dataset contains the number of beehives per registered beekeeper ID (annonymised) in four Swiss cities (Bern, Lausanne, Geneve, Zurich) and Paris.

en: DEPRECATED. Data growth urban beekeeping

en: Proportion urban honeybees and wildbees

en:
The dataset contains the proportion of wildbees and honeybees in the different studies obtained from the literature review.
Article_ID: ID for each article
DOI
Authors
Year: publication year
Duration sampling (years): numerical
Start_sampling: numerical, date
End_sampling: numerical, date
Continent
Country
N. cities: number of cities sampled in the article. Numerical.
City
Sampling type: categorical. Levels: active (e.g., Hand-netting); passive (e.g., pantraps. trap-nests); both (combination of passive and active methods).
Sampling methodology: categorical. Levels: direct observations, hand-netting, pantraps, interception traps, vane traps.
Number sites: total number of study sites.
Abundance honeybees: number of honeybees recorded
Abundance wild bees: number of wildbees recorded
Total bees: total number of bees recorded.
Proportion honeybees: Abundance of honeybees / Total number of bees
Proportion wildbees: Abundance of wildbees / Total number of bees

en: NDVI values within 3 buffers  in hives in Zurich

en:
The dataset contains the average NDVI values around beehives in Zurich at three spatial scales (250, 500 and 1000 m). It contains 3 columns_
- buffer: indicating the spatial scale
- ID: hive ID (annonymised)
- mean.NDVI: mean NDVI value

en: Data rank-abundance distribution urban bees

en: Outdated, use the new resource

en: Local overheating values within 3 buffers in hives in Zurich

en:
The dataset contains the average overheating values around beehives in Zurich at three spatial scales (250, 500 and 1000 m). It contains 4 columns:
- buffer: indicating the spatial scale
-ID: hive ID (annonymised)
- mean.overwarming: mean overheating value, expressed in degrees Kelvin
-mena.overwarming.cor = transformed values do degrees Celsius

en: Species-abundance data

en:
Abundance data at the species level per used dataset for the rank abundance distribution (RAD).
40 datasets used.

en: Proportion honeybees and wildbees

en: Outdated, use the new resource "Propotion urban honeybees and wildbees"

en: Data growth urban beekeeping

en:
The dataset contains the data of the growth of urban beekeeping in different cities.
For each city in a given year, it contains the following:
Continent.
Country.
City: the name of the city
Year: the year when the information was collected
Area: of the city
Total N. beekeepers: when available, the estimated number of beekeepers in that city for that given year
Total N. hives: when available, the estimated number of beehives in that city for that given year
Density: the total number of hives divided by the city area
Reference: the source of the information.

en: DEPRECATED. Distirbution beehives among beekeepers

en: The dataset contains the number of beehives per registered beekeeper ID (annonymised) in four Swiss cities (Bern, Lausanne, Geneve, Zurich).

en: Results literature review

en:
 The file shows the results from the literature review. Briefly, we searched papers using google scholar search engine using the following query: (“WILD BEE OR WILDBEE”)(“URBAN” OR “CITY”), and limited the time span from 2010-2023. The search query returned 200 candidate papers. In addition, we complemented the candidate papers from the literature research performed by Ferrari & Polidori (2022), adding 22 additional candidate papers. To select papers, we checked that (1) were conducted in urban areas; (2) sampled the whole bee community; (3) provided data on abundance of wildbees; (4) provided data on honeybees; and (5) provided individual abundance data of each wild bee species.
The dataset contains the following:
-Article_ID: ID for the specific article in the form "A1", "A2"..."An"
- Title: of the article
- Year: of publication
- Journal: of publication
- Publisher:
- Link: link to the publication
- DOI: when applicable
- Total abundance wild bees reported: was the total number of collected bees reported? Logical (Y/N)
- Honey bee abundance reported: was thenumber of honeybees collected reported? 	Logical (Y/N)
- Abundance per wild bee species: was the number of wildbee species sampled provided? Logical (Y/N)
- Select proportion: paper selected to calculate the proportion of honeybees and wildbees.
- Select RAD: paper selected to compute the rank-abundance distribution diagram
Comment: comments on the papers, data availability, etc.

en:
Data and code from the perspective paper "When honeybees comes to town"
The .r file provides the code to generate the figures.
In addition, this repository contains the data for the figures 1-3.
For Figure 4, part of the data is confidential, Please, refer to the contacts provided:
- For the distribution of hives in Zurich (2018): https://www.zh.ch/de/gesundheitsdirektion/veterinaeramt.html
- For the overheating map of Zurich: Prof. Dr. Eberhard Parlow / Stadt Zurich (https://www.zh.ch/de/umwelt-tiere/umweltschutz/umweltpraxis/definitionsseite/2012/68/zup068_2012_a0030_klimaanalyse-pdf.html)

diversity-distributions

display_name: diversity-distributions

hymenoptera

display_name: hymenoptera

insects

display_name: insects

urban-beekeeping

display_name: urban-beekeeping

urban-ecology

display_name: urban-ecology

wildbees

display_name: wildbees


data-set-on-snow-instability

title_for_slug: data-set-on-snow-instability

en: slab slab-tensile-support crack-propagation weak-layer failure-initiation rutschblock propgatoin-saw-test avalanche-forcasting snow-stability-evaluation snow-avalanche

en: Read-me file on snow instability data

en: data set on snow instability

en:
These data on snow instability include three data subsets that were analyzed and the results published by Reuter and Schweizer (2018) who suggest a novel framework on how to describe snow instability by failure initiation, crack propagation and slab tensile support.

Please refer to the Read-me file for further details on the data.

These data are the basis of the following publication:
Reuter, B. and Schweizer, J., 2018. Describing snow instability by failure initiation, crack propagation and slab tensile support. Geophys. Res. Lett., 45, doi: 10.1029/2018GL078069.

avalanche-forcasting

display_name: avalanche-forcasting

crack-propagation

display_name: crack-propagation

failure-initiation

display_name: failure-initiation

propgatoin-saw-test

display_name: propgatoin-saw-test

rutschblock

display_name: rutschblock

slab

display_name: slab

slab-tensile-support

display_name: slab-tensile-support

snow-avalanche

display_name: snow-avalanche

snow-stability-evaluation

display_name: snow-stability-evaluation

weak-layer

display_name: weak-layer

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Data set on snow instability