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

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: 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: Nematode data

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

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

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

en: Fungi data

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

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

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

en: 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: 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: 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

biodiversity

display_name: biodiversity

fungi

display_name: fungi

grassland

display_name: grassland

nematodes

display_name: nematodes

plants

display_name: plants

restoration

display_name: restoration

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

topsoil-removal

display_name: topsoil-removal

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


en: forest-dynamics temperate-forests forest tree-mortality ecological-niche forest-inventory mortality-rate

state: active

type: dataset

format: OCTET

en:
These datasets and script document the analyses and the plot generation for the article **“Trends in background mortality in unmanaged forests across Europe over the last century”**.
We used a comprehensive network of permanent plots with repeated censuses spanning from 1936 to 2020 in 299 unmanaged forest reserves across Europe. The database includes 1.5 million tree records over successive censuses covering a total sampled area of 853.7 ha.

It consists of the following files:

- **Mortality_script.Rmd**: R markdown document to reproduce the analysis and the plot generation.
- **plot_level.RDS**: This dataset contains annual mortality rate values for each permanent plot at each census, of plots with at least three censuses. This dataset is used to calculate the weighted mean annual background mortality rates by forest types, and the series of models to study the trends of annual mortality rates, and then to create Figure 1 and Figure 2.
- **tree_level.RDS**: This dataset contains tree census data for each permanent plot at each census, of plots with at least two censuses. This dataset is used to model species-specific size-dependent mortality probabilities of the studied tree species, and then to create Figure 3.

ecological-niche

display_name: ecological-niche

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-inventory

display_name: forest-inventory

mortality-rate

display_name: mortality-rate

temperate-forests

display_name: temperate-forests

tree-mortality

display_name: tree-mortality

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester


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

format: XLS

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

bryophytes

display_name: bryophytes

conservation-priority

display_name: conservation-priority

endemic

display_name: endemic

europe

display_name: europe

range-size

display_name: range-size

rarity

display_name: rarity

red-list

display_name: red-list

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


maintainer: EnviDat Support

en: food-preferences food-taste-similarity national-dishes agro-food-trade latent-semantic-analysis

en: dyadic food similarity measures, pairwise similarity and dissimilarity (chord diagram)

en:
The figures show the distribution of two dyadic food tastes similarity measures on the base of
the national dishes: (i) the Manhattan distance and the (ii) latent semantic analysis. The chord diagram
shows the results for country pairs with most and less similarity on basis of the LSA-approach.

format: SERVICE

en: information on ingredients of national dishes

en: shows information of ingredients of all national dishes of 171 countries and their names

en: National dishes, migration data and data for bilateral food trade flows

en:
The data include information on national dishes, migration data and data for bilateral food
trade flows for 5 years as well as importer and exporter GDP, population, distance between
most populated cities, bilateral information on Preferential Trade Agreements, common
official primary langauge, common colonizer post-1945, colonial relationship for overall
171 countries. The final data set contains 145,350 observations (29,070 country pairs for
5 years)

en: The data described in this article were collected daily over the period 4 June 2018 to 23 August 2018 and contains information of several data sources. The database includes information on national recipes and their ingredients for 171 countries, measures for food taste similarities between all 171 countries as well as bilateral migration and agro-food trade data for 5 years. The database can be used for analyzing e.g., the relation between food preferences and international trade or food preferences and health outcomes (e.g., obesity) across countries.

agro-food-trade

display_name: agro-food-trade

food-preferences

display_name: food-preferences

food-taste-similarity

display_name: food-taste-similarity

latent-semantic-analysis

display_name: latent-semantic-analysis

national-dishes

display_name: national-dishes

en: Data of national dishes, their similarity and trade flows


backward-trajectories

maintainer: Michael Sprenger

title_for_slug: backward-trajectories

en: complex-topopgraphy backward-trajectories raclets alps atmospheric-dynamics lagrangian-analysis cosmo

en: Backward Trajectories

media_type: application/zip

en: The trajectories are started from surface up to 5000 m altitude, with an equidistant vertical spacing of 200 m, and calculated 120 h backward in time. As long as the trajectories stay in the COSMO1 domain (essentially the Alpine region), as run operationally at MeteoSwiss, the COSMO analysis wind fields are used. When the trajectories leave this domain, they are extended further backward in time based on ECMWF analysis data. The following fields are traced along the trajectories: longitude, latitude, geopotential height, pressure, specific humidity, temperature and surface topography. Furthermore, it is indicated for each trajectory time instance on which model data, COSMO or ECMWF, the trajectory time is based. Finally, the trajectories are started every hour in the specified time period, and positions and fields are available every 10 min. Each day is saved as a separate file. The last 8 digits of the file names indicate the starting date of the trajectories.

format: ZIP

mimetype: application/zip

en: Backward trajectories were calculated from two positions: Davos Wolfgang (LON: 9.85361, LAT: 46.83551) and Weissfluhjoch (LON: 9.80646 LAT: 46.83304) for the time period February 2 until March 27 2019 using COSMO or ECMWF, respectively.

alps

display_name: alps

atmospheric-dynamics

display_name: atmospheric-dynamics

backward-trajectories

display_name: backward-trajectories

complex-topopgraphy

display_name: complex-topopgraphy

cosmo

display_name: cosmo

lagrangian-analysis

display_name: lagrangian-analysis

raclets

display_name: raclets

en: Backward  Trajectories


bats-in-beaver-systems

title_for_slug: bats-in-beaver-systems

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

media_type: text/csv

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

format: CSV

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

en: R script bats_data_cleaning to clean the raw data

media_type: text/plain

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

format: TXT

en: sampling dates.xlsx

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

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

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

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

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

en: bats_raw_values_overview to produce raw value summaries and tables

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

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

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

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

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

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

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

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

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

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

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

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

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

beaver

display_name: beaver

castor

display_name: castor

chiroptera

display_name: chiroptera

deadwood

display_name: deadwood

ecosystem-engineer

display_name: ecosystem-engineer

flying-arthropods

display_name: flying-arthropods

habitat-heterogeneity

display_name: habitat-heterogeneity

nature-conservation

display_name: nature-conservation

wetland

display_name: wetland

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: 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: Bats in beaver systems


maintainer: Florian Lustenberger

en: flood decision-making engineering hydrology estimation-methods

media_type: application/pdf

format: PDF

en:
(English version below)
Ziel dieser Faktenblätter ist es Informationen zu den Schätzmethoden, Hinweise zur Parametrisierung und eine grobe Einschätzung der Güte der Resultate in übersichtlicher Form bereitzustellen. Ein zeitaufwändiges Zusammensuchen dieser Informationen aus unterschiedlichen Quellen entfällt daher weitgehend. Die Bearbeitenden sollen damit in der Lage sein, sich innerhalb weniger Minuten die wichtigsten Hintergrundinformationen zu jeder der vorgestellten Methoden in Erinnerung zu rufen. Der Leitfaden unterstützt bei der Plausibilisierung und bei der Mittelung der Ergebnisse der einzelnen Schätzmethoden zu einem repräsentativen Endergebnis. Vorausgesetzt wird Grundwissen und Erfahrung in der Thematik.
Der Leitfaden führt die Fachperson anhand eines Entscheidungsbaums in acht Schritten durch den Arbeitsablauf und unterstützt mit einem Fragenkatalog bei der Plausibilisierung der Ergebnisse.
Auf den Faktenblättern sind für jede Schätzmethode alle relevanten Informationen stichwortartig und grafisch übersichtlich auf einer DIN-A4 Seite zusammengefasst. Jedes Faktenblatt ist einheitlich gegliedert.
The aim of these fact sheets is to provide information on the flood estimation methods, notes on their parameterization and a rough assessment of the quality of the results in a clear form. A time-consuming research for this information from different sources is therefore largely eliminated. The users should be able to recall the most important background information on each of the methods presented within a few minutes. The guide provides support in checking plausibility and averaging the results of the individual estimation methods to produce a representative final result. Basic knowledge and experience in the subject are assumed.
The guide leads the expert through the workflow in eight steps using a decision tree and supports the plausibility check of the results with a list of questions.
On the fact sheets, all relevant information for each estimation method is summarized with key words and graphics on one DIN A4 page. Each fact sheet has a standardized structure.

decision-making

display_name: decision-making

engineering

display_name: engineering

estimation-methods

display_name: estimation-methods

flood

display_name: flood

hydrology

display_name: hydrology

en: Faktenblätter und Leitfaden für Hochwasserschätzmethoden in der Praxis


maintainer: Giulia Mazzotti

en: snow-energy-balance forest-snow forest-structure snow-distribution snow-modelling

en: Canopy data

en: Includes all canopy structure metrics computed at all surveyed locations

en: Snow depth data forest sites

en: Contains all snow depth data acquired at all forest sites during winters 2012/13, 2013714 and 2014/15.

en: Meteo data to drive FSM2 model

en: Includes meteorological driving data used to run the forest snow model FSM2, processed and parsed in text files that match the format required by FSM2. Separate files are available for each field area (Laret, Drusatscha and Ischlag) and season (2012/13, 2013/14, 2014/15). Each file includes data for the period from October 1st to May 31st at hourly resolution.

en: Coordinates data

en: Includes coordinate information of each surveyed point (7 x 276 = 1932 forest points, 2 open areas)

en: Snow depth data open sites

en: Contains all snow detph data accquired at open sites nearby the forest stands during the same period.

en: Grid point nomenclature

en: The grid structure of each forest site and the respective nomenclature applied to label individual points within each grid are outlined here.  Each point marks the location of snow depth measurements and co-located canopy structure parameters.

en:
This dataset contains all snow, canopy and meteorological data presented and used in the publication: Mazzotti, G., Essery, R., Moeser, D. & Jonas T. (2020) 'Resolving spatial variability of forest snow using an energy-balance model with a 1-layer canopy'. Water Resources Research, https://doi.org/10.1029/2019WR026129.
This publication must be cited when using this dataset.

forest-snow

display_name: forest-snow

forest-structure

display_name: forest-structure

snow-distribution

display_name: snow-distribution

snow-energy-balance

display_name: snow-energy-balance

snow-modelling

display_name: snow-modelling

en: Snow depth, canopy structure and meterorological datasets from the Davos area, Switzerland, Winters 2012/13-2014/15, used for high-resolution forest snow modelling


high-resolution-climate-data-for-europe

title_for_slug: high-resolution-climate-data-for-europe

en: air-temperature maximum-temperature eur11 cordex daily-climate minimum-temperature europe precipitation

en: Data Access

en: CHELSA_EUR11 technical documentation

en: Technical documentation of the data with variable names and methodology.

en:
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present downscaled climate data for the CORDEX EUR11 domain at a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperature lapse rates. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height. The resulting data consist of a daily temperature and precipitation timeseries.

The data is distributed under a: Creative Commons: Attribution 4.0 International (CC BY 4.0) license.

air-temperature

display_name: air-temperature

cordex

display_name: cordex

daily-climate

display_name: daily-climate

europe

display_name: europe

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

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: High resolution climate data for Europe


maintainer: Matthias Haeni

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

en: Data plot of the forest station (VSB)

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

mimetype: application/pdf

en: Site photo of the forest station (VSB)

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

mimetype: image/jpeg

en: Forest station data (VSB)

en: Data file.

mimetype: text/csv

en: Field station data (VSF)

en: Data plot of the field station (VSF)

en: Site aerial photo (Visp)

en: Site aerial photo from both Visp stations. Source: Google. 2016. Site aerial photo from both Visp stations. Retrieved November 11, 2016, from https://www.google.ch/maps

en: Site photo of the field station (VSF)

en: Site photo of the field station from the side.

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Visp in Switzerland where one station is located within a natural mixed forest stand (VSB) with Scots pine (_Pinus sylvestris_; 40-80 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VSF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Visp is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

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


en: anthophila urban-ecosystems sads wildbees cities urban-beekeeping dominance honeybees

en: Dataset proportion honeybees and wild bees

en:
The dataset provide the main features for each included dataset from published and unpublished research works. Each row represents a dataset, which provides bee community data in one city / urban aggregation. Overall, it contains 68 datasets from 46 cities in 15 countries and five continents. The data is organised in the following columns:
- Article_ID: id used in the study
- DOI: doi of the publication, when available
- Authors
- Year: of publication
- Duration sampling (years)
- Start_sampling: month of the year
- End_sampling: month of the year
- Continent
- Country
- N. cities: number of cities included in the publication
- City
- Sampling type: active, passive or both
- Sampling methodology: within each sampling type.
- Habitats: list of sampling habitats
- N_habitats: number of sampling habitats
- Parks_similar: binnary (1 = yes; 0 = no)
- Agriculture: binnary (1 = yes; 0 = no)
- Private_gardens: binnary (1 = yes; 0 = no)
- Green_roofs: binnary (1 = yes; 0 = no)
- seminatural_natural_wasteland: binnary (1 = yes; 0 = no)
- other_unspecified: binnary (1 = yes; 0 = no)
- Number sites
- Abundance honeybees
- Abundance wild bees
- Total bees
- Proportion honeybees
- Proportion wildbees
- Area:. of the studied city/urban aggregation

en: R script for analyses and plots

en: Dataset species abundance

en:
This data resource provides the individual species abundance per city / urban aggregation. It contains data from 41 datasets from 37 studies collected in 33 cities / urban aggregations. It contains the following columns:
 - Article_ID
- DOI
- Authors
- Year
- Continent
- Country
- City
- Bee genus
- Bee species
- Bee merged
- Bee full
- Abundance
- Bee_ID_per_study: ID for the study

en: Dataset coordinates study cities

en:
The data contains the latitude and longitude of the studied cities / urban aggregations. It contains the following columns:
- Continent
-Country
- City
- Lat: Latitude
- Lon: Longitude

en:
The following repository contains the data and code to reproduce the figures analyses of the paper "Examining honeybee (Apis mellifera) dominance patterns within urban bee communities worldwide". It contains three datasets and one .R file

---

The data results from a literature review on abundance distribution of urban bee communities worldwide, aiming at obtaining studies reporting total abundances of honeybees and wildbees in a given city, and species abundances of wildbees and honeybees in a given city.

---

1. **Data**:
- *20240308_cities_coords.csv*: the data contains the latitude and longitude of the included cities in the study. It is used for making the world map with the cities.
- *20241201_proportion_HB_WB.csv*: the data contains the total wild bee and honeybee abundances per city/urban aggregation, and the proportions.
- *20241201_rad_bees.csv*: the data contains the species abundances per city/urban aggregation

2. **Script**:
- *HoneybeeDominance_Analyses_Figures.R*: the script to run the analyses and make the plots. Analyses were done in R version 4.2.1, using the packages ggplot2, glmmTMB, multcomp, wesanderson, terra.

anthophila

display_name: anthophila

cities

display_name: cities

dominance

display_name: dominance

honeybees

display_name: honeybees

sads

display_name: sads

urban-beekeeping

display_name: urban-beekeeping

urban-ecosystems

display_name: urban-ecosystems

wildbees

display_name: wildbees

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 supporting ‘Examining honeybee (Apis mellifera) dominance patterns within urban bee communities worldwide’


en: soil-texture lwc matric-potential soil alpine measurements glide-snow-avalanche soil-classification

en: Daily averaged values used for the direct fitting

en: Hydrus files used for the inverse fitting

en: Complete list of the van Genuchten parameter values for each soil and method

en: Detailed information of each file in the dataset

en: Coordinates of each soil location, location of the snow profiles, and location of the snow liquid water content measurements

en: Texture data of the soil locations

en: The dataset contains measured values of soil liquid water content, matric potential, and soil texture of 40 soil samples in Davos, Switzerland. The measurements were used for determining van Genuchten parameter values through fitting of water retention curves, pedotransfer functions, and inverse fitting with Hydrus-1D.

alpine

display_name: alpine

glide-snow-avalanche

display_name: glide-snow-avalanche

lwc

display_name: lwc

matric-potential

display_name: matric-potential

measurements

display_name: measurements

soil

display_name: soil

soil-classification

display_name: soil-classification

soil-texture

display_name: soil-texture

en: Soil measurements of Seewer Berg and Davos


forest-canopy-layer-code

title_for_slug: forest-canopy-layer-code

en: itd forest protective-forest lidar forest-management mountain-forest vhm

en:
R script for large-scale mapping of forest canopy layering. The approach combines tree-based and area-based methods, using airborne laser scanning point clouds together with individual tree detection (ITD) information (tree height and position) to distinguish between single- and multi-layered forests. The output is a raster layer at 10m x 10m resolution, encoded as follows:
1: single-layered (class 1): Both point cloud and ITD approaches classify the forest as single-layered.
2: probably single-layered (class 2): ITD classifies the forest as single-layered, while the point cloud suggests multi-layered.
3: probably multi-layered (class 3): ITD classifies the forest as multi-layered, while the point cloud suggests single-layered.
4: multi-layered (class 4): Both point cloud and ITD approaches classify the forest as multi-layered.
Next to the R script, we also provide an example data set. The LAZ files in the example dataset are from the nationwide ALS data and are provided by the Federal Office of Topography swisstopo in accordance with its open data policy (swisstopo, 2022a, 2019). The detected individual trees were identified using Dalponte and Coomes' (2016) algorithm on a spike-free vegetation height model (cell size 0.5m). The provided forest mask corresponds to the NFI forest layer definition by Waser et al. (2015). For details and mentioned references, we refer to the related publication by Bast et al. (under revision; JAG).

forest

display_name: forest

forest-management

display_name: forest-management

itd

display_name: itd

lidar

display_name: lidar

mountain-forest

display_name: mountain-forest

protective-forest

display_name: protective-forest

vhm

display_name: vhm

en: Forest Canopy Layer Code


maintainer: Merin Reji Chacko

en: biodiversity-assessment spatial-ecology environmental-dna biomonitoring dna-metabarcoding

en: Genomic data

en: Fastq files, primer descriptions and the final output dataset.

en: Analysis and Figures

en: This zipped file includes R scripts and the datasets required to run the analyses. A README describes the columns in each dataset.

en: Field Metadata

en: Information concerning the locations and times of sampling, as well as the identity of the samplers and relevant comments.

en: This dataset contains the results of a five-day field excursion which the extent to which eDNA sampling can capture the diversity of a region with highly heterogeneous habitat patches across a wide elevation gradient through multiple hydrological catchments of the Swiss Alps. Using peristaltic pumps, we filtered 60 L of water at five sites per catchment for a total volume of 1 800 L. Using an eDNA metabarcoding approach focusing on vertebrates and plants, we detected 86 vertebrate taxa spanning 41 families and 263 plant taxa spanning 79 families across ten catchments. This dataset includes two sets of data. The first (Genomic data) includes all the necessary data for the bioinformatic pipeline, whereas the second (Analysis Figures) contains tidied data and scripts for the reproduction of all figures/analyses in the article describing this study.

biodiversity-assessment

display_name: biodiversity-assessment

biomonitoring

display_name: biomonitoring

dna-metabarcoding

display_name: dna-metabarcoding

environmental-dna

display_name: environmental-dna

spatial-ecology

display_name: spatial-ecology

en: Vertebrate and plant taxa recovered from 10 catchments in Vaud using an eDNA-metabarcoding approach


maintainer: Anita C. Risch

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

en:
site.code: Site code

Continent: Continent

sampleID: Sample ID

nn.latitude: Lattitude

nn.longitude: Longitude

bioclim.T.ann: Mean annual temperature (°C)

bioclim.T.var: Temperature variability (°C)

bioclim.T.q.wet: Temperature of the wettest quarter (°C)

bioclim.T.q.dry: Temperature of the driest quarter (°C)

bioclim.T.q.warm: Temperature of the warmest quarter (°C)

bioclim.P.ann: Mean annual preciptiation (mm)

bioclim.P.q.wet: Precipitation of the wettest quarter (mm)

ch.pH: pH

ch.WSK.vol: Water holding capacity (vol%)

ch.bulkdensity.gcm3: Bulk density (g cm-3)

ch.porespace: Pore space (vol%)

ch.sand.perc: Soil texture: sand (%)

ch.silt.perc: Soil texture: silt (%)

ch.clay.perc: Soil texture: clay (%)

ch.NO3start.mgg: NO3 at the beginning of the incubation (mgkg)

ch.Ntot.perc: Total soil N (%)

ch.Ctot.perc: Total soil C (%)

ch.Corg.perc: Soil organic C (%)

ch.netNminfield.mgkgd: Realised soil net N mineralisation (mg kg-1 d-1)

ch.netNminlab.mgkgd: Potential soil ne N mineralisation (mg kg-1 d-1)

archaea.cpg.log: Ammonia oxidising archaea (archaeal amoA gene copies g-1 soil)

bacteria.cpg.log: Ammonia oxidising bacteria (bacterial amoA gene copies g-1 soil)

micr.biom: Microbial biomass (ug g-1 soil)

ch.CtoN: C to N ratio

ch.netNnitriffield.mgkgd.sqrt: Realised soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniffield.mgkgd.sqrt: Realised soil net ammonification, transformed (mg kg-1 d-1)

ch.netNnitriflab.mgkgd.sqrt: Potential soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniflab.mgkgd.sqrt: Potential soil net ammonifiation, transformed (mg kg-1 d-1)

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

Paper Citation:

Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Schütz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2

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

We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/

global

display_name: global

grassland

display_name: grassland

mineralization

display_name: mineralization

nutrient-network

display_name: nutrient-network

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide

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: meteo-station snow-height longwave-radiation gemstock shortwave-radiation

en: DATA ACCESS

en:
Meteo data at Gemsstock [Level 2 dataset](http://models.slf.ch/p/dataset-processing/).

fields      = timestamp TSS HS VW DW VW_MAX OSWR ISWR ILWR RECORD AUX_VOLTAGE LOGGER_VOLTAGE LW_SENSOR_TEMP VENTILATION_FLAG OLWR

en:
Meteorological station at Gemstock (3021 m asl) in Canton Uri. The station includes in/out LW/SW and a snow height sensor.

Data from this station is managed by the permos.ch project. More information: https://www.permos.ch/permafrost-monitoring/field-sites

gemstock

display_name: gemstock

longwave-radiation

display_name: longwave-radiation

meteo-station

display_name: meteo-station

shortwave-radiation

display_name: shortwave-radiation

snow-height

display_name: snow-height

en: GEM2: Meteorological and snow station at Gemsstock (3021 m asl), Canton Uri, Switzerland


en: antarctica uav drone irra sea-ice snow-temperature snow-roughness snow

en:
This dataset accompanies the publication "How Flat is Flat? Investigating the spatial variability of snow surface temperature and roughness on landfast sea ice using UAVs in McMurdo Sound, Antarctica".
The dataset consists of 1) processed orthomosaic of RGB values, snow topography (digital elevation model), and surface temperature, 2) topography-dependent irradiance map, and 3) point data of measured radiation and atmospheric parameters in the McMurdo Sound, October-December 2022. The data was collected as part of the New Zealand Marsden Fund Research Grant 21-VUW-103 "Can Snow Change the Fate of Antarctic Sea Ice?". Unprocessed data associated with this field campaign are published in 10.16904/envidat.633.
For the airborne measurements, we used a DJI Matrice 30T, a multi-rotor (quadcopter) UAV with self-heating batteries, suitable for operations in polar conditions (temperature range from -20 to +50 °C). The UAV has a wide-angle camera (12 megapixels) for RGB images and a thermal camera (Uncooled Vox Microbolometer, long-wave infrared spectrum 8-14 µm, 1.31 megapixels) for TIR images with an accuracy of ±2 °C or ± 2 %.
GNSS data includes high-precision location information (latitude, longitude, ellipsoidal height, receiver clock offset, and timestamps formatted in decimal hours and day-of-year notation).
WE calculated the distribution of received solar irradiance over the sea-ice surface using a terrain-corrected radiative transfer model and measured shortwave radiation components, accounting for surface roughness, aspect, slope, and solar position during the flight, while applying terrain shading and measured diffuse radiation.
The broadband radiation station data consists of shortwave (Sw) and longwave (Lw) radiation measurements (Kipp & Zonen CMP22 pyranometers and CGR4 pyrgeometers).
Surface temperature readings from cold, sediment, and hot targets were recorded using Apogee SI-121-SS sensors.
This dataset also contains automated weather station (AWS) data (air and snow surface temperatures, relative humidity, wind direction, and wind speed).
For detailed information, please use the README file.

antarctica

display_name: antarctica

drone

display_name: drone

irra

display_name: irra

sea-ice

display_name: sea-ice

snow

display_name: snow

snow-roughness

display_name: snow-roughness

snow-temperature

display_name: snow-temperature

uav

display_name: uav

en: Snow On Antarctic Sea Ice - McMurdoSound 2022 - UAV Retrievals of Snow Topography and Snow Surface Temperature


en: soil-water-matrix-potential soil-temperature soil-moisture landslide-early-warning soil-water-content

en: In-situ soil moisture measurements, Langnau i E, since 2021

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each. Meteorological variables, such as air temperature or precipitation can be received from MeteoSwiss.
Time intervall: 1 hour. Measurement period: since June 2021.
Location: flat terrain, meadow (measurement site of MeteoSwiss)
For a detailed description of site, soil and vegetation: see attached document.

en: Detailed description of soil moisture measurment sites, soil properties and sensor installations (in German)

en: In-situ soil moisture measurements, Wasen i.E., flat location, 2019-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, and two additional soil temperature measurements (oC), air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: April 2019-Dec 2023.
Location: flat terrain, meadow
For a detailed description of site, soil and vegetation: see attached document.

en: In-situ soil moisture measurements, Trub, since 2021

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, as well as air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: since April 2021.
Location: slope, meadow
For a detailed description of site, soil and vegetation: see attached document.

en: In-situ soil moisture measurements, Wasen i.E., flat location, since 2019

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, and two additional soil temperature measurements (oC), air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: since April 2019.
Location: flat terrain, meadow
For a detailed description of site, soil and vegetation: see attached document.
Updates 2025:
The “precipitation” column was set to not available (NA) for periods when the pluviometer was identified as clogged. These periods were assigned (i) based on regular maintenance of the measurement station and (ii) irregular behavior of the measurement series compared to the precipitation signal from the other nearby measurement stations.
Column “VWC_0.7m_right” and “ST_5TE_0.7m_right” were set to not available (NA) for the period after the 2023-06-19T14:00:00Z due to irregular behavior of the ECH2O - 5TE sensor.

en: In-situ soil moisture measurements, Wasen i.E., steep slope, since 2019

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, and two additional soil temperature measurements (oC), air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: since April 2019.
Location: steep hillslope, meadow
For a detailed description of site, soil and vegetation: see attached document.

en: In-situ soil moisture measurements, Ranflüh, since 2021

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, as well as air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: since April 2021.
Location: hillslope, meadow
For a detailed description of site, soil and vegetation: see attached document.
Update 2025:
The “precipitation” column was set to not available (NA) for periods when the pluviometer was identified as clogged. These periods were assigned (i) based on regular maintenance of the measurement station and (ii) irregular behavior of the measurement series compared to the precipitation signal from the other nearby measurement stations.

en: In-situ soil moisture measurements, Napf, since 2021

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each. Meteorological variables such as air temperature or precipitation can be received from MeteoSwiss.
Time intervall: 1 hour.
Measurement period: since June 2021
Location: flat terrain, meadow (official MeteoSwiss station)
For a detailed description of site, soil and vegetation: see attached document.

en: In-situ soil moisture measurements, Ranflüh, 2021-2023

en:
Volumetric soil water content (m3 m-3), soil water potential (mbar) and soil temperatures (oC) measured in two profiles and four depths each, as well as air temperature (oC) and precipitation (mm). Time intervall: 1 hour. Measurement period: April 2021-Dec 2023.
Location: hillslope, meadow
For a detailed description of site, soil and vegetation: see attached document.

en: In the context of landslide early warning research, soil moisture (e.g. volumetric soil water content and soil water potential) has been measured at six locations in the Napf-region (Emmental, central Switzerland) since April 2019. Here, we provide the hourly mean values of all sensors from all 6 locations after a comprehensive data control, filtering and aggregation (from 10-min to 1-hour values). A sub-set of these data (for the locations Wasen i.E.) has previously been published in envidat.ch at https://www.doi.org/10.16904/envidat.369 applying the same data post-processing method. The dataset is complemented with meteorological data (locally measured air temperature and precipitation) as well as soil temperature. A detailed description of the sites and measurement installations can be found in a separate report (attached with the data).

landslide-early-warning

display_name: landslide-early-warning

soil-moisture

display_name: soil-moisture

soil-temperature

display_name: soil-temperature

soil-water-content

display_name: soil-water-content

soil-water-matrix-potential

display_name: soil-water-matrix-potential

en: In-situ soil moisture measurements Napf-region


en: snow-modelling avalanche snowdrift snowpack source-code

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

avalanche

display_name: avalanche

snow-modelling

display_name: snow-modelling

snowdrift

display_name: snowdrift

snowpack

display_name: snowpack

source-code

display_name: source-code

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: FSM2trans code


maintainer: Camille Albouy

en: bay-biscay actinopterygii atlantic-region teleo edna dna-metabarcoding fish-composition chondrichthyes

en: Data access

en:
Environmental DNA complements scientific trawling in surveys of marine fish biodiversity (Dataset 2020)

In October 2020 we chose 16 sites from the 2020 EVHOE survey for eDNA sampling. The French international EVHOE bottom trawl survey is carried out annually during autumn in the BoB to monitor demersal fish resources. All sites were located on the continental shelf (26–170 m depth), except one on the upper slope with a depth of 1045 m. To perform eDNA sampling, we collected water samples at 15 sites (Figure 1a). At each site, we sampled seawater using Niskin bottles deployed with a circular rosette. There were nine bottles on the rosette, each of them able to hold ∼5 l of water. At each site, we first cleaned the circular rosette and bottles with freshwater, then lowered the rosette (with bottles open) to 5 m above the sea bottom, and finally closed the bottles remotely from the boat. The 45 l of sampled water was transferred to four disposable and sterilized plastic bags of 11.25 l each to perform the filtration on-board in a laboratory dedicated to the processing of eDNA samples. To speed up the filtration process, we used two identical filtration devices, each composed of an Athena® peristaltic pump (Proactive Environmental Products LLC, Bradenton, Florida, USA; nominal flow of 1.0 l min–1 ), a VigiDNA 0.20 μm filtration capsule (SPYGEN, le Bourget du Lac, France), and disposable sterile tubing. Each filtration device filtered the water contained in two plastic bags (22.5 l), which represent two replicates per sampling site. We followed a rigorous protocol to avoid contamination during fieldwork, using disposable gloves and single-use filtration equipment and plastic bags to process each water sample. At the end of each filtration, we emptied the water inside the capsule that we replaced by 80 ml of CL1 conservation buffer and stored the samples at room temperature following the specifications of the manufacturer (SPYGEN, Le Bourget du Lac, France). We processed the eDNA capsules at SPYGEN, following the protocol proposed by Polanco-Fernández et al., (2020). We performed library preparation and sequencing at Fasteris (Geneva, Switzerland). Specifically, we prepared four libraries using the MetaFast protocol (a ligation-based method) and sequenced them separately. We carried out paired-end sequencing using a MiSeq sequencer (2 × 125 bp, Illumina, San Diego, CA, USA) on two MiSeq Flow Cell Kits (v3; Illumina), following the manufacturer’s instructions. We analysed the sequence reads using the OBITools package (http:
//metabarcoding.org/obitools; Boyer et al., 2016), following the protocol described by Valentini et al. (2016).

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

actinopterygii

display_name: actinopterygii

atlantic-region

display_name: atlantic-region

bay-biscay

display_name: bay-biscay

chondrichthyes

display_name: chondrichthyes

dna-metabarcoding

display_name: dna-metabarcoding

edna

display_name: edna

fish-composition

display_name: fish-composition

teleo

display_name: teleo

en: Environmental DNA Marine France Evhoe 2020


maintainer: Dirk Nikolaus Karger

en: interannual-climate-variability reptiles chelsa amphibians niche-models range-maps species-distribution-models sdm random-forests mammals generalized-additive-models iucn generalized-linear-models climate-variablity

en: Readme file containing the file naming convention, file dimensions, and variable names

en: File containing the climate predictors as SAGA grid files (.sgrd)

en: File containing the record dimension, genus, and species of the respective netcdf files

en: Amphibians

en: Species distribution models based on different climate predictor groups

en: Reptiles

en: Mammals

en: We modelled the global distribution of 730 amphibian, 1276 reptile, and 1961 mammal species globally as a function of current climate at a 0.5° spatial resolution using four different predictor groups composed of different combinations of input variables: mean climatic conditions, spatial climatic variability, and temporal (interannual) climatic variability.

amphibians

display_name: amphibians

chelsa

display_name: chelsa

climate-variablity

display_name: climate-variablity

generalized-additive-models

display_name: generalized-additive-models

generalized-linear-models

display_name: generalized-linear-models

interannual-climate-variability

display_name: interannual-climate-variability

iucn

display_name: iucn

mammals

display_name: mammals

niche-models

display_name: niche-models

random-forests

display_name: random-forests

range-maps

display_name: range-maps

reptiles

display_name: reptiles

sdm

display_name: sdm

species-distribution-models

display_name: species-distribution-models

en: Global species distributions for mammals, reptiles, and amphibians


maintainer: Roman Zweifel

en: forest icos carbon-storage sap-flow forest-dynamics forest-ecosystems ecosystem-functioning carbon-cycle water-relations ecophysiology picea-abies eddy-covariance-measurements dbh seehornwald forest-biomass carbon-sequestration forest-function icp-forests flux open-data climate-growth co2 forest-canopy carbon-dioxide ilter tree-stem stem-growth subalpine dendrometer metadata eddy-covariance forest-monitoring forest-area climate-change treenet davos twd lwf forest-characteristics biomass automated-monitoring atmospheric-conditions

en: Readme

en: Collection of files with information about the research site Seehornwald Davos CH-DAV

en: Site description of the Seehornwald Davos (CH-DAV) research site. The material provided offers information on the biomass and growth of trees in CH-DAV, including supporting materials such as maps, geolocations and analyses. CH-DAV is part of networks such as ICOS, LWF, TreeNet, ICPForests and is dedicated to forest monitoring and research.

atmospheric-conditions

display_name: atmospheric-conditions

automated-monitoring

display_name: automated-monitoring

biomass

display_name: biomass

carbon-cycle

display_name: carbon-cycle

carbon-dioxide

display_name: carbon-dioxide

carbon-sequestration

display_name: carbon-sequestration

carbon-storage

display_name: carbon-storage

climate-change

display_name: climate-change

climate-growth

display_name: climate-growth

davos

display_name: davos

dbh

display_name: dbh

dendrometer

display_name: dendrometer

ecophysiology

display_name: ecophysiology

ecosystem-functioning

display_name: ecosystem-functioning

eddy-covariance

display_name: eddy-covariance

eddy-covariance-measurements

display_name: eddy-covariance-measurements

flux

display_name: flux

forest

display_name: forest

forest-area

display_name: forest-area

forest-biomass

display_name: forest-biomass

forest-canopy

display_name: forest-canopy

forest-characteristics

display_name: forest-characteristics

forest-dynamics

display_name: forest-dynamics

forest-ecosystems

display_name: forest-ecosystems

forest-function

display_name: forest-function

forest-monitoring

display_name: forest-monitoring

icos

display_name: icos

icp-forests

display_name: icp-forests

ilter

display_name: ilter

lwf

display_name: lwf

metadata

display_name: metadata

open-data

display_name: open-data

picea-abies

display_name: picea-abies

sap-flow

display_name: sap-flow

seehornwald

display_name: seehornwald

stem-growth

display_name: stem-growth

subalpine

display_name: subalpine

tree-stem

display_name: tree-stem

treenet

display_name: treenet

twd

display_name: twd

water-relations

display_name: water-relations

educ

en: Education, culture and sport

en: Site description Davos Seehornwald CH-DAV


en: urban-landscape traffic-noise restoration forest field-data

en: This document includes the questionnaire for Papers 2 and 3, administered at the third measurement time point (10 days after participation on site).

en: 03a_Studyprotocol_Data on evaluation of acoustic impression of the test settings_Studyprotocol.sav

en: This data file includes information for an additional post hoc classification of the sound environment of the test sites. This evaluation was conducted in the afternoons of the same week in 2023 at two locations within each test site by a civil servant who was not familiar with the study's research questions.

en: 06_Studyprotocol_GPS tracks of the walking routes.zip

en: This folder contains the GPS tracks of the walking routes in the test sites.

en: This file contains the informed consent document that participants signed to participate in the study.

en: 07_Studyprotocol_Test site pictures.zip

en: This folder includes two pictures of each test site. The locations for the two pictures on each route were chosen to be as characteristic as possible in terms of the visual and acoustical impressions of the walk.

en: 03b_Studyprotocol_Script_Analysis stationary noise evaluation.sps.sps

en: This SPSS syntax file contains a script for analyzing data related to the stationary evaluation of the acoustic impressions at the test sites, which is numbered 03a in this repository.

en: 11_P4_S1_Instructions for individual mindful walking.pdf

en: This document contains the booklet with instructions for mindful walking, which participants in the mindfulness condition were encouraged to take with them and use during their individual walks over the 10 days following on-site participation.

en: This SPSS data file includes the data used for analysis in paper 2 (Psychophysiological effects of walking in forests and urban built environments with disparate road traffic noise exposure: A randomized controlled trial) and paper 3 ("The effects of walking in urban forests and urban built environments with disparate road traffic noise exposure on repetitive negative thinking and connectedness with the non-human world: A randomized controlled trial").

en: 13_P4_Data mindful walking.sav

en: This file contains data for the manuscript "Effects of a mindful walking intervention in urban forests for healthy adults".

en: 09_P2_Fotos Noise ROS paper.zip

en: This folder included the example pictures of the conditions included in paper 2 ("Psychophysiological effects of walking in forests and urban built environments with disparate road traffic noise exposure: A randomized controlled trial“).

en: 02_Studyprotocol_Data on Laeq from noise recordings of walking routes_Studyprotocol.sav

en: This data file contains information on the LAeq levels recorded along the final walking routes between September 2021 and March 2022, prior to the commencement of data collection.

en: This document includes the questionnaire for Papers 2 and 3, administered at the second measurement time point (after the walk).

en: This document includes the questionnaire for Papers 2 and 3, administered at the first measurement time point (before the walk began).

en: This file includes the information sheet provided to participants before they consented to take part in the study.

en: 01_Studyprotocol_Locations of the starting points of the walking routes in the test settings.pdf

en: This document includes GPS coordinates of the starting points of the walking routes in the test sites.

en: 12_P4_S2_Description of mindful walking intervention on site.pdf

en: This file includes the guided mindful walking instructions provided to participants in the mindfulness condition during the on-site walks.

en:
This repository contains data related to the field experiments of the RESTORE project. This project is a collaboration between the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and the Swiss Federal Laboratories for Materials Science & Technology (EMPA). It has received funding from the Swiss National Science Foundation. The overall objective of the RESTORE project was to assess the effects of green spaces as facilitators and noise as impediment to recover from stress in people's daily environments across Switzerland.
We conducted a randomized, controlled field study to compare the psychophysiological benefits of exposure to forests and urban built environments with different levels of road traffic noise in healthy adults during 30-minute walks in Zürich, Switzerland to explore:
1) The psychophysiological effects of walking in forests and urban built environments with disparate road traffic noise exposure
2) The effects of walking in urban forests and urban built environments with disparate road traffic noise exposure on repetitive negative thinking and connectedness with the non-human world
3) The effects of a mindful walking intervention in urban forests for healthy adults

Accordingly, this repository includes data and documentation for the 3 scientific articles:

Scientific article 1. “Psychophysiological effects of walking in forests and urban built environments with disparate road traffic noise exposure: study protocol of a randomized controlled trial", BMC Psychology, 2024. https://doi.org/10.1186/s40359-024-01720-x

Scientific article 2. "Psychophysiological effects of walking in forests and urban built environments with disparate road traffic noise exposure: A randomized controlled trial“. Journal of Environmental Psychology, 2025.

Scientific article 3. “The effects of walking in urban forests and urban built environments with disparate road traffic noise exposure on repetitive negative thinking and connectedness with the non-human world: A randomized controlled trial". To be submitted to PLOS One, 2025.

Scientific article 4: "Effects of a mindful walking intervention in urban forests for healthy adults". To be submitted to Current Psychology, 2025.

field-data

display_name: field-data

forest

display_name: forest

restoration

display_name: restoration

traffic-noise

display_name: traffic-noise

urban-landscape

display_name: urban-landscape

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: pmcamx-uf nucleation total-number modeling cloud-droplets

en: data_JRL_New particle formation events can reduce cloud droplets in boundary layer clouds at the continental scale.Paper_2023GL106182.zip

en: This repository contains PMCMx-UF model outputs used for the paper: D. Patoulias K. Florou S. N. Pandis and A. Nenes: New particle formation events can reduce cloud droplets in boundary layer clouds at the continental scale, Geophysical Research Letters, in review, 2023.

cloud-droplets

display_name: cloud-droplets

modeling

display_name: modeling

nucleation

display_name: nucleation

pmcamx-uf

display_name: pmcamx-uf

total-number

display_name: total-number

educ

en: Education, culture and sport

en: Dataset for the publication New particle formation events can reduce cloud droplets in boundary layer clouds at the continental scale


maintainer: Marco Moretti

en: resource-availability bettergardens urban-ecosystems pollinator-experiment urban-biodiversity urbanization zurich city4bees beekeeping gardens wild-bees honeybees

en: Response and predictors used for the SEMs

en:
The data contains the following columns:

  SiteID: numeric, the identity of the garden where the bee was sampled.
  Taxonname: character, species name
  Lecty: numeric discreet, a mesure of specialisation degree. Levels: 1 = monolectic; 2=oliolectic; 3=polilectic
  Pheno.start= numeric discreet, the starting week of the flying activity.
  Pheno.end= numeric discreet, the ending week of the flying activity.
  Pheno.duration=numeric discreet, Pheno.end - Pheno.start
  Daytime.activity = numeric continuous, individual-level measure of the day hour when the bee was sampled
  ITD = numeric continuous, individual-level measure of the InterTegular Distance, a metric of mobility in mm.
  Rel.tongue.length =numeric continuous, individual-level measure of the relative tongue length (Tongue length / ITD), a metric of specilaisation degree, in mm.

en: Metadata for the predictor and response dataset

en:
Mean, standard deviation , minimum and maximum values of six functional traits. The data has the following structure:
- Bee_species: bee scientific name
- mean_trait: mean value
- sd_trait: standard deviation
_min_trait: minimum value
- max_trait: maximum value
- trait: the six traits used. ITD = Intertegular distance (mm); Pheno.duration = duration of the phenology; Pheno.start = start of the phenology; Pheno.end = end of the phenology; Rel.tongue.length= relative tongue length (mm); Daytime.activity = daily activity (h)
DistNichePart: functional dissimilarity in a multidimentional trait space defined by five traits between wild bee individuals of a wild bee species and the honeybee individuals. Values are at the species level.

en: Metadata for the predictors and response dataset

en:
Cities are socio-ecological systems that filter and select species, thus establishing unique species assemblages and biotic interactions. Urban ecosystems can host richer wild bee communities than highly intensified agricultural areas, specifically in resource-rich urban green spaces such as allotment and family gardens. At the same time, urban beekeeping has boomed in many European cities, raising concerns that the fast addition of a large number of managed bees could deplete the existing floral resources, triggering competition between wild bees and honeybees. The data has been used to investigated the interplay between resource availability and the number of honeybees at local and landscape scales and how this relationship influences wild bee diversity.

This dataset contains the raw and processed data supporting the findings from the paper: "Low resource availability drives feeding niche partitioning between wild bees and honeybees in a European city".

The data contains:
1. Bee trait measurements at the species and individual-level of five functional traits.
2. The values of the feeding niche partitioning (functional dissimilarity to honeybees)
3. The predictors of resource availability and beekeeping intensity at local and landscape scales used in the modelling of the paper for the 23 experimental sites.

beekeeping

display_name: beekeeping

bettergardens

display_name: bettergardens

gardens

display_name: gardens

honeybees

display_name: honeybees

pollinator-experiment

display_name: pollinator-experiment

resource-availability

display_name: resource-availability

urban-biodiversity

display_name: urban-biodiversity

urban-ecosystems

display_name: urban-ecosystems

urbanization

display_name: urbanization

wild-bees

display_name: wild-bees

zurich

display_name: zurich

en: Niche partitioning between wild bees and honeybees


en: snow snow-density microstructure wfj-research-site snowmicropen weisfluhjoch weissfluhjoch snow-measurements snow-ssa snow-stratigraphy

en: WFJ_DailySMP data access

en: A huge thanks goes to the countless PhD students, without whom collecting this dataset would not be possible.

en:
The WFJ_DailySMP dataset contains daily SnowMicroPen (SMP) measurements conducted throughout the winter seasons 2015-2025 (ongoing) at the Weissfluhjoch research site, Davos, Switzerland. The measurement program DailySMP started in winter 2015/2016 during which the measurement protocol was established as part of the RHOSSA campaign, described in the related publication.
Since then, measurements have been repeated every winter as part of the standard snow monitoring program by the PhD students at SLF. All published SMP files have undergone manual quality control by an expert, including a supervised detection of the snow surface, which is included in the dataset.
In addition to the raw data, we provide a rudimentary post-processing workflow in which the derived SSA and density profiles are adjusted by the total snow height obtained from the adjacent IMIS snow station (https://www.slf.ch/en/avalanche-bulletin-and-snow-situation/measured-values/description-of-automated-stations/) and their evolution is plotted over time. Please refer to the README for detailed information on how to get started.

microstructure

display_name: microstructure

snow

display_name: snow

snow-density

display_name: snow-density

snow-measurements

display_name: snow-measurements

snow-ssa

display_name: snow-ssa

snow-stratigraphy

display_name: snow-stratigraphy

snowmicropen

display_name: snowmicropen

weisfluhjoch

display_name: weisfluhjoch

weissfluhjoch

display_name: weissfluhjoch

wfj-research-site

display_name: wfj-research-site

en: WFJ_DailySMP - High-Resolution Snow Stratigraphy at Weissfluhjoch -- Davos 2015-ongoing


functional-invertebrate-groups-dataset

title_for_slug: functional-invertebrate-groups-dataset

en: functional-groups led community-composition alan artificial-lighting invertebrates light-pollution

en: Raw data of invertebrate sampling at different streetlight characteristics (light temperature (K), light level (L_level), luminaire shape (Diff)) over two summers (2021 & 2022). Included are two pitfall trap positions (Center C (100% light intensity) and Periphery P (10% light intensity).

en: Collected Invertebrate data using pitfall traps at streetlights with different characteristics at three different sites in Switzerland (Birmensdorf, Lägern & Alpthal). LED characteristics are LED color temperature, light level and luminaire shape (K, L_level, Diff). Additionally, we had two pitfall trap positions: Center (C; 100% light intensity)) and Periphery (P; 10% light intensity). The traps were emptied weekly over a period of two summers (2021 & 2022). The samples were sorted into 40 different taxonomic invertebrate groups. We included weeks where we turned of the light for a different experiement, these weeks were excluded from this dataset.

alan

display_name: alan

artificial-lighting

display_name: artificial-lighting

community-composition

display_name: community-composition

functional-groups

display_name: functional-groups

invertebrates

display_name: invertebrates

led

display_name: led

light-pollution

display_name: light-pollution

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: Functional Invertebrate Groups - Dataset


maintainer: Rebecca Mott

en: tls terrestrial-laser-scanning patchy-snow-covers riegl-vz6000 snow-ablation-rates

en: Snowmask 2016

en: Digital Elevation Modell 2016

en: Digital Elevation Modell 2014

en: Snowmask 2015

en: Snowmask 2014

en: Digital Elevation Modell 2015

en: Gletschboden test site

en: We recorded snow ablation maps with a terrestrial laser scanner (TLS, Riegl-VZ6000) at the Gletschboden area. The TLS position is located approximately 30 vertical meters above the Gletschboden area at a northerly exposed slope. In total 44 TLS measurement sets have been conducted in three consecutive years 2014-2016 (2014: 13 measurements; 2015: 17 measurements; 2016: 14 measurements). The TLS system has a single-point measurement frequency of 300 kHz and a beam divergence of 0.007°. This set-up allows a horizontal resolution of approximately 0.01 m in 100 m distance to the TLS position. One scan of the Gletschboden area lasts approximately 15 minutes. The travel time from the laser scanner towards the surface is recorded and afterwards converted into a point cloud of distances. 5 reflectors located at the Gletschboden area and in the closer surroundings were additionally scanned during each measurement to transform the point cloud from the scanner own coordinate system into Swiss coordinates. Additionally, orthophotos have been created by using pictures recorded from the TLS in order to provide snow mask maps. Snow and bare ground can be distinguished by the RGB color information of the orthophoto. Cells with blue band information greater than 175 were categorized as snow and all cells with values smaller or equal 175 were categorized as bare ground.

patchy-snow-covers

display_name: patchy-snow-covers

snow-ablation-rates

display_name: snow-ablation-rates

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

tls

display_name: tls

educ

en: Education, culture and sport

en: DISCHMEX - High-resolution daily snow ablation rates in an Alpine environment


en: runoff land alptal topsoil-interflow overland-flow plot studibach time-series rainfall

en:
Time series of overland flow and topsoil interflow discharge at 14 runoff plot locations in the Studibach catchment over 27 rain events in 2022.
Format: NEAD
Metadata included in the file: latitude, longitude, data units, timezone.
Source for reading and writing NEAD format: https://www.doi.org/10.16904/envidat.187.

en:
The dataset comprises the time series of overland flow (OF) and topsoil interflow (TIF) discharges from 14 small runoff plots (1 x 3 m) over 27 rainfall events in 2022 across the Studibach in the Alptal, a typical pre-Alpine catchment in Switzerland.
The runoff plots were installed on different topographic indices and vegetation covers in order to study the spatio-temporal variability of OF and TIF.
The runoff was collected into self-made "Upwelling Bernoulli Tubes" and the water level inside these tubes was measured by pressure sensors (DCX-22-CTD, Keller Druck, Switzerland) at a 5-min resolution. The discharge was derived from the water level.
The precipitation was measured with a tipping bucket at the Erlenhöhe meteorological station and has a 10-min resolution.

alptal

display_name: alptal

land

display_name: land

overland-flow

display_name: overland-flow

plot

display_name: plot

rainfall

display_name: rainfall

runoff

display_name: runoff

studibach

display_name: studibach

time-series

display_name: time-series

topsoil-interflow

display_name: topsoil-interflow

en: Overland and topsoil interflow time series from the Studibach - summer 2022


en: climate imis grassland phenology monitoring long-term snow alpine snowmelt ultrasonic

en: Contains custom functions required for the scripts; this file must be sourced before running other scripts.

en: Generates the methods-related figures used in the publication.

en: Contains IMIS station location data

en: Temporary data. Contains annual unfiltered output of phenofit (i.e. the variable names are in line with the phenofit documentation), including all curve parametrization variables for plotting and additional variables, which are not necessarily used in the analysis.

media_type: image/png

en: Snow3.png and grass.png are needed for plotting the methods plot in 5_methods_plots.Rmd

format: PNG

en: Contains daily climate station data including daily median temperatures, snow depth, plant height, and annual melt-out timings.

en: implements the phenofit method. Note: This script is computationally intensive and may take a while to run. We recommend using the temporary data created from it (i.e. starting from 2_temperature.R). For a better description of the phenofit R package and the different methods to determine the SOG, see Kong et al. (2022): https://doi.org/10.1111/2041-210X.13870

en: Contains unfiltered annual phenological, temperature and snow-climatic data for all IMIS stations.

en: Contains filtered annual phenological data from 40 IMIS stations suitable and used for phenological analysis

en: Is the readme that documents all data and code in this repository

en: Contains annual phenological variables and phenofit outputs, sorted, ordered and renamed according with the readme in this repository

en: Documents and reproduces the statistical models used in the analysis.

en:
Long-term phenological data in alpine regions are often limited to a few locations and thus, little is known about climate-change induced plant phenological shifts above the treeline. Because plant growth initiation in seasonally snow-covered regions is largely driven by snowmelt timing and local temperature, it is essential to simultaneously track phenological shifts, snowmelt, and near-ground temperatures. In this study, we make use of ultrasonic snow height sensors installed at climate stations in the Swiss Alps to reveal phenological advance of grassland ecosystems and relate them to climatic changes over 25 years (1998 – 2023). When snow is absent, these snow height sensors additionally provide information on plant growth at a uniquely fine temporal scale. Daily temperatures and snow- / plant height values, annual phenological- and melt-out dates were derived from IMIS climate station data (doi.org/10.16904/envidat.406). We observed an advance of green-up by -2.4 days/decade coinciding with strong warming of up to +0.8°C/decade. Although the timing of melt-out has not changed significantly over the study period in this focal region, phenological responses to early melt-out years varied due to differing influences of photoperiodic and thermal constraints, which were not equally important across elevations and communities. Phenological shifts of alpine grasslands are thus likely to become even more pronounced if melt-out timing advances in the future as predicted.
This repository relates to the publication Zehnder et al. (2025). Snow height sensors reveal phenological advance in alpine grasslands. Global Change Biology. accepted. The model data and code that support our findings are deposited in this repository.

alpine

display_name: alpine

climate

display_name: climate

grassland

display_name: grassland

imis

display_name: imis

long-term

display_name: long-term

monitoring

display_name: monitoring

phenology

display_name: phenology

snow

display_name: snow

snowmelt

display_name: snowmelt

ultrasonic

display_name: ultrasonic

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: IMIS Phenology Data Since 1998


maintainer: Anna Braun

en: snow metamorphism microstructure numerical-simulations fem

author: EnviDat

en: Simulation parameters and outputs for "Towards a physically and microstructure-based equation for the evolution of the specific surface area in snow"

format: ODS

en:
In the associated study [1], evolution of randomly generated bi-continuous microstructures that resemble real snow microstructures has been investigated. The control of the ambient conditions (temperature and temperature gradient) and of the physical processes at play on the rate of change of the specific surface area have been evaluated. Based on the evaluation, a new physically-informed governing law for the coarsening of microstructures has been proposed. This law accurately predicts the simulation outputs under various conditions. Furthermore, a potential parametrization of newly introduced material properties has been proposed and evaluated on snow coarsening data.
This dataset contains the parameters used for the mesh generation and the finite-element simulations. The output parameters of simulations, such as the ice fraction, surface of the ice-air interface per total volume, mean curvature and growth rate are provided.
[1]: A.Braun, K. Fourteau, S. Frei, M. Lehning, H. Löwe: Towards a physically and microstructure-based equation for the evolution of the specific surface area in snow, Journal of Glaciology, accepted for publication in 2024

fem

display_name: fem

metamorphism

display_name: metamorphism

microstructure

display_name: microstructure

numerical-simulations

display_name: numerical-simulations

educ

en: Education, culture and sport

en: Simulation parameters and outputs for towards a physically and microstructure-based equation for the evolution of the specific surface area in snow