maintainer: Marcia Phillips

en: temperature gemsstock permos permafrost borehole

en: DATASET & PLOTS

media_type: URL

en: Data visualiser and download service provided by PERMOS.

en: PERMOS DATA POLICY

en: Map of PERMOS borehole network

en:
Processed ground temperature measurements at the Gemsstock permafrost borehole in canton Uri, Switzerland. The borehole is located at 2940 m asl on a steep (50°) North-West slope (315°). The surface material is bedrock and borehole depth is 40 m. Thermistors used YSI 44008. Year of drilling 2006. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.

Publications

1. A. Haberkorn, M. Phillips, R. Kenner, H. Rhyner, M. Bavay, S.P. Galos, M. Hoelzle. Thermal regime of rock and its relation to snow cover in steep Alpine rock walls: Gemsstock, central Swiss Alps. 2015. Geografiska Annaler: Series A, Physical Geography. Volume 97. Issue 3. 579–597. http://dx.doi.org/10.1111/geoa.12101. 10.1111/geoa.12101.

2. R. Kenner, M. Phillips, C. Danioth, C. Denier, P. Thee, A. Zgraggen. Investigation of rock and ice loss in a recently deglaciated mountain rock wall using terrestrial laser scanning: Gemsstock, Swiss Alps. 2011. Cold Regions Science and Technology. Volume 67. Issue 3. 157–164. http://dx.doi.org/10.1016/j.coldregions.2011.04.006. 10.1016/j.coldregions.2011.04.006.

borehole

display_name: borehole

gemsstock

display_name: gemsstock

permafrost

display_name: permafrost

permos

display_name: permos

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: Processed permafrost borehole data (2940 m asl), Gemsstock, Switzerland


maintainer: Fachstelle Open Government Data des Kantons Zürich

state: active

type: dataset

de: ZH Datasets Visits - 2020 Mai

media_type: text/csv

de: Ausgenommen sind die Datensätze, die von der Geoinformation des Kantons Zürich auf opendata.swiss verwaltet werden.

format: CSV

mimetype: text/csv

de: ZH Datasets Visits - 2018 August

de: ZH Datasets Visits - 2021 Februar

de: Ausgenommen sind die Datensätze, die von der Geoinformation des Kanton Zürich auf opendata.swiss verwaltet werden.

de: ZH Datasets Visits - 2018 Februar

de: ZH Datasets Visits - 2020 August

de: ZH Datasets Visits - 2019 November

de: ZH Datasets Visits - 2018 Dezember

de: ZH Datasets Visits - 2019 Januar

de: ZH Datasets Visits - 2019 Oktober

de: ZH Datasets Visits - 2018 November

de: ZH Datasets Visits - 2020 Juni

de: ZH Datasets Visits - 2019 Dezember

de: ZH Datasets Visits - 2018 Oktober

de: ZH Datasets Visits - 2018 Mai

de: Monatliche Nutzungsstatistiken (Anzahl Besuche) der Open Government Data (OGD) Metadatensätze von Verwaltungseinheiten und Organen des Kantons Zürich, die auf dem zentralen Katalog für offene Behördendaten opendata.swiss findbar sind. Hinweise: Ab Januar 2019 sind die Web Analytics um weitere Metadateninformationen erweitert bzw. wurden Metadatenanpassungen vorgenommen. Ab März 2021 sind die monatlichen Aktualisierungen aufgrund technischer Herausforderungen pausiert. Variabelndefinitionen: Column 'name' = dataset slug; 'issued' = first publication of dataset; 'organization_name' = publisher slug; 'organization_url' = publisher URL; 'E' up to 'AB' = thematic categories according to DCAT AP Switzerland.

fachstelle-ogd-kanton-zuerich

de: Fach- und Koordinationsstelle Open Government Data Kanton Zürich

de: Die Fach- und Koordinationsstelle Open Government Data (OGD) ist das kantonale Kompetenzzentrum für offene Behördendaten im Statistischen Amt. Wir unterstützen die Organisationseinheiten und Organe des Kantons bei der Bereitstellung ihrer nicht schützenswerten Daten als Open Data.

de: Web Analytics der Open Government Data des Kantons Zürich auf opendata.swiss von Februar 2018 bis Februar 2021


envidat-supports-open-science

maintainer: Ionut Iosifescu Enescu

title_for_slug: envidat-supports-open-science

en: juypter-notebooks envidat open-science research-data-management wsl

en: Article as PDF

media_type: application/pdf

format: PDF

mimetype: application/pdf

en:
The article "EnviDat Supports Open Science" originally appeared in WSLintern No. 3 (2020), page 14-15 and it is republished here with permission from the WSLintern editorial team. It contains guidelines for WSL scientists about the main issues behind Open Science and how to pragmatically approach the complexities of doing Open Science with EnviDat’s support.

License: This article is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

envidat

display_name: envidat

juypter-notebooks

display_name: juypter-notebooks

open-science

display_name: open-science

research-data-management

display_name: research-data-management

wsl

display_name: wsl

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

it: EnviDat: il portale dei dati ambientali

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: EnviDat Supports Open Science


maintainer: M. Carol Resch

en: long-term-experiment vegetation grassland restoration nematodes topsoil-removal diversity

en: Nematode families

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

column 3-41: Nematode abundance (individuals per 100g dry soil) per family; nomenclature according to Bongers 1988

format: XLS

en: Coordinates of one research plot were wrong. This was corrected in the current version of the file.

en: Nematode feeding types

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

He.ecto.a: Herbivorous ectoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

He.endo.a: Herbivorous endoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ba.a: Bacterivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Fu.a: Fungivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Om.a: Omnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ca.a: Carnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

en: Nematode colonizer-persister classes

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

CP1.a: C-P (colonizer-persister) class 1 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to enrichment indicators according to Ferris, Bongers, & de Goede 2001
CP2.a: C-P (colonizer-persister) class 2 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to stress indicators according to Ferris, Bongers, & de Goede 2001
CP3.a: C-P (colonizer-persister) class 3 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990
CP4.a: C-P (colonizer-persister) class 4 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990
CP5.a: C-P (colonizer-persister) class 5 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

en: Nemadotes and vegetation

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

X: X-Coordinate

Y: Y-Coordinate

Ri.veg: Total number of plant species

Div.veg: Shannon diversity of plant species

Ri.nem: Total number of nematode families

Div.nem: Shannon diversity of nematode families

abund: Total nematode abundance (individuals per 100g dry soil)

MI: Maturity index according to Bongers 1990; calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

PPI: Plant parasite index according to Bongers 1990; calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

PPI.MI: Plant parasite to Maturity index according to Bongers, van der Meulen, & Korthals 1997; calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

CI: Channel index according to Ferris, Bongers, & de Goede 2001 (%); calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

EI: Enrichment index according to Ferris, Bongers, & de Goede 2001 (%); calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

SI: Structure index according to Ferris, Bongers, & de Goede 2001 (%); calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

He.a: Herbivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

He.ecto.a: Herbivorous ectoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

He.endo.a: Herbivorous endoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ba.a: Bacterivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Fu.a: Fungivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Om.a: Omnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ca.a: Carnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Om.Ca.a	Sum of omnivorous and carnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014
CP1.a: C-P (colonizer-persister) class 1 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to enrichment indicators according to Ferris, Bongers, & de Goede 2001

CP2.a: C-P (colonizer-persister) class 2 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to stress indicators according to Ferris, Bongers, & de Goede 2001

CP3.a: C-P (colonizer-persister) class 3 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

CP4.a: C-P (colonizer-persister) class 4 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

CP5.a: C-P (colonizer-persister) class 5 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

CP3-5.a: Sum of C-P3, C-P4 and C-P5 nematode abundance (individuals per 100g dry soil) equivalent to structure indicators according to Ferris, Bongers, & de Goede 2001

protect.sp.pro: plant species categorized into species of concern based on their red list status in Switzerland as well as their protection status in Switzerland and the Canton Zurich (Moser, Gygax, Bäumler, Wyler, & Palese, 2002)

tar.sp: Number of target species (all species recorded in the eleven target plots plus propagules of species applied by hand, resulting in a total of 143 species)

Indi.moist: Plant indicator values for soil moisture according to Landolt et al. 2010

Indi.nutri: Plant indicator values for soil nutrients according to Landolt et al. 2010

Management: level of mowing and manuring frequency (35-40 kg N ha-1 application-1); intensity1 = mowing once and no manure; intensity2 = 2-3 times mowing and no manure; intensity3 = 2-3 times mowing and manuring; intensity4 = 4-5 times mowing and manuring

Tillage: last time of tillage

Land.use.history: for Initial plots = recent agricultural use; for all restored sites = agricultural use before restoration
Mowing	earliest date of biomass harvesting (excluding “Initial”) based on contracts between farmers and the Nature Protection Agency of Zurich

Soil.type: based on soil mapping survey 1991 (before restoration)

Soil.texture: determined by the sediment method (Gee & Bauder 1986) and classified into soil textural classes according to FAO (1990)

pH: soil pH (CaCl2)

Slope: slope gradient classes according to FAO (1990; %)

en: Plant community composition (plant species)

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

column 3-252: Plant abundance per species (in total 252 plant species) expressed as cover percentage estimation according to van der Maarel (vdM 1979; 7 classes: 1-7); nomenclature according to Lauber & Wagner (1996)

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

Paper Citation:
 > Resch, M.C., Schütz, M., Graf, U., Wagenaar, R., van der Putten, W.H., Risch, A.C. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56: 1782-1793.

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

 Methods
 Study area and experimental settings
The study was conducted in a nature reserve (Eigental: 47° 27’ to 47° 29’ N, 8° 37’ E, 461 to 507 m a.s.l.) that is located on the Swiss Central plateau close to Zurich airport (Canton Zurich, Switzerland). The mean annual temperature in this area ranges from 8.9 to 10.6 °C, mean annual precipitation from 910 to 1260 mm [10-year average (2007-2017); MeteoSchweiz, 2018]. The main soil types are calcaric to gleyic Cambisol and Gleysols. The reserve was established in 1967 to protect small remnants of oligotrophic semi-natural grasslands (roughly 12 ha). The plant community can be characterized as Molinion and Mesobromion (semi-wet to semi-dry), depending on the site-specific groundwater level and slope inclination (Delarze, Gonseth, Eggenberg, & Vust, 2015). These remnants represent species-rich islands in an otherwise intensively managed agricultural landscape. Semi-natural grasslands covered an area of 60,000 ha in the Canton Zurich in 1939, however, by 2005 only roughly 600 ha remained (Baudirektion Kanton Zürich, 2007). In 1990, the government of Canton Zurich decided to enlarge the nature reserve Eigental. The goal was to incorporate eleven patches of 20 ha adjacent intensively farmed land and transform these patches into semi-natural grasslands. The patches had a different agricultural history, ranging from permanent (no tillage for >50 years) to temporary grassland (as part of crop rotation; last tillage <5 years). On all freshly integrated patches fertilization was stopped in 1992 and from then on biomass was harvested three times a year and removed. After 5 years without noticeable effects on vegetation composition, the Nature Conservation Agency of Canton Zurich decided to increase the restoration efforts. In 1995, a large-scale experiment was initialized to evaluate if certain treatments can facilitate restoration within a reasonable timeframe of 5 to 10 years after treatment implementation.
The three restoration treatments used were:
i. “Harvest only”: Plots are being mowed two to three times a year and the biomass is removed.
ii. “Topsoil”: Topsoil was removed to a depth of 10 to 20 cm, depending on the depth of the O and A horizon, in four randomly selected areas within each of the eleven patches in late autumn 1995. The size of each topsoil removal area depended on individual patch size and was between 2700 and 7000 m2.
iii. “Topsoil+Propagules”: Propagules from target vegetation were added on half of the area where topsoil was removed, using fresh, seed-containing hay originating from a mixture of semi-dry to semi-wet species-rich grasslands of local provenance (within a radius of 7 km). Hay applications were conducted twice in 1995 and 1996. Repeated applications were chosen to account for the low quantity of available plant material per transfer, since area ratio between receptor and donor sites was roughly 1:1. In addition, hand-collected propagules from 15 selected target species of regional provenance (within a radius of 30 km) were equally applied in 1996 and 1997.
“Topsoil” and “Topsoil+Propagules” plots are mowed once a year, and the biomass is removed. Mowing on these plots started five years after the treatment was implemented.
Eleven permanent plots of 5 m x 5 m were randomly established in each treatment to monitor the vegetation development. The experiment was complemented with 11 control plots that represent the initial state of intensively managed grasslands, further referred to as “Initial”, and 11 control plots that represent the targeted state of donor sites for “Topsoil+Propagules”, further referred to as “Target”. Consequently, the experiment consists of 55 plots (5 treatments x 11 replicates). Management of intensively used grasslands includes mowing and fertilizing (manure) between two to five times a year, as well as different tillage regimes (no tillage for >50 years; last time of tillage <5 years).

 Nematode and plant sampling
Soil nematodes were sampled in 2 m x 2 m plots, randomly established at least 2 m away from the vegetation plots. We collected eight soil cores with a 2.2 cm diameter soil core sampler (Giddings Machine Company, Windsor, CO, USA) to a depth of 12 cm (representing the majority of the plant rooting system) in each plot at the beginning of July 2017. The eight cores within each replicate plot were combined, gently homogenized, placed in coolers and transported to the laboratory of NIOO in Wageningen, the Netherlands, within one week. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink, 1960) and concentrated, resulting in 6 mL nematode solution. The nematode solution was subdivided into three subsamples, two for morphological identification and quantification, and one for molecular work (not used in this study). For morphological identification and quantification, nematodes were heat-killed at 90 °C and fixed in 4 % formaldehyde solution (final volume 10 mL per subsample). All nematodes in 1 mL of formaldehyde solution were counted, and a minimum of 150 individuals per 1 mL sample (or all if less nematodes were present) were identified to family level using Bongers (1988). We then extrapolated the numbers of each nematode taxa identified to the entire sample and expressed them per 100 g dry soil for further analyses.
We calculated number of nematode taxa and Shannon diversity and assessed nematode community composition. In addition, we classified the nematode taxa into feeding types (herbivores, bacterivores, fungivores, omni-carnivores), structural and functional guilds (Table S4). Structural guilds assign nematode taxa according to life-history traits into five colonizer-persister (C-P) classes, ranging from one (early colonizers of new resources) to five (persisters in undisturbed habitats; Bongers 1990). C-P classes can be categorized as indicators for nutrient-enriched (C-P1), stressed (C-P2) and structured (C-P3 + C-P4 + C-P5) soil conditions (Ferris, Bongers, & de Goede, 2001). Functional guilds assign nematode taxa according to their C-P classification combined with their feeding habits (Ferris, Bongers, & de Goede, 2001). Based on the structural and functional guild classification we calculated five additional indices to assess soil nutrient status, disturbance and food web characteristics using NINJA (Sieriebriennikov, Ferris, & de Goede, 2014). 1) The Maturity index indicates the degree of different environmental perturbations (e.g., tillage, nutrient enrichment, pollution) and is used to monitor colonization and subsequent succession after disturbances (Bongers, 1990). 2) The ratio between the Plant Parasite (C-P of herbivorous nematodes only) to Maturity index is used to monitor the recovery of disturbed habitats incorporating information of life-history traits for all feeding types (Bongers, van der Meulen, & Korthals, 1997). 3) The Enrichment index indicates nutrient-enriched soils and agricultural management practices (Ferris, Bongers, & de Goede, 2001). 4) The Structure index provides information about the succession stage of the soil food web and therefore correlates with the degree of maturity of an ecosystem (Ferris, Bongers, & de Goede, 2001). 5) The Channel index provides information about the predominant decomposition pathways, where higher values stand for a higher proportion of energy transformed through the slow fungal decomposition channel (Ferris, Bongers, & de Goede, 2001). In addition, the Structure and Enrichment indices can be displayed in a biplot where nematode assemblages are plotted along a structure (x-axis) and enrichment (y-axis) trajectory (increasing index values). Each biplot quadrat reflects different levels of disturbance, soil nutrient pools and decomposition pathways (Ferris, Bongers, & de Goede, 2001).
The plant surveys were conducted on the 25 m2 permanent plots in June 2017. Plant species cover was visually assessed according to the semi-quantitative cover-abundance scale of Braun-Blanquet (1964; nomenclature: Lauber & Wagner, 1996). We calculated number of species and Shannon diversity, and assessed plant community composition. We also counted the number of target species (all species recorded in the eleven target plots plus propagules of species applied by hand, resulting in a total of 143 species) and categorized plant species into species of concern based on their red list status in Switzerland as well as their protection status in Switzerland and the Canton Zurich (Moser, Gygax, Bäumler, Wyler, & Palese, 2002). Furthermore, we calculated indicator values for soil moisture and soil nutrients for each species according to Landolt et al. (2010).

 References
Baudirektion Kanton Zürich (2007). 10 Jahre Naturschutz-Gesamtkonzept für den Kanton Zürich 1995-2005 – Stand der Umsetzung. Zürich: Baudirektion Kanton Zürich.
Bongers, T. (1988). De nematoden van Nederland. Utrecht: Stichting Uitgeverij Koninklijke Nederlandse Natuurhistorische Vereniging.
Bongers, T. (1990). The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia, 83, 14-19. doi:10.1007/BF00324627
Bongers, T., van der Meulen, H., & Korthals, G. (1997). Inverse relationship between the nematode maturity index and plant parasite index under enriched nutrient conditions. Applied Soil Ecology, 6, 195-199. doi:10.1016/S0929-1393(96)00136-9
Braun-Blanquet, J. (1964). Pflanzensoziologie, Grundzüge der Vegetationskunde (3rd ed.). Wien: Springer.
Delarze, R., Gonseth, Y., Eggenberg, S., & Vust, M. (2015). Lebensräume der Schweiz: Ökologie - Gefährdung - Kennarten (3rd ed.). Bern: Ott.
Ferris, H., Bongers, T., & de Goede, R.G.M. (2001). A framework for soil food web diagnostics: extension of the nematode faunal analysis concept. Applied Soil Ecology, 18, 13-29. doi:10.1016/S0929-1393(01)00152-4
Landolt, E., Bäumler, B., Erhardt, A., Hegg, O., Klötzli, F., Lämmler, W., … Wohlgemuth, T. (2010). Flora indicativa. Ecological indicator values and biological attributes of the Flora of Switzerland and the Alps (2nd ed.). Bern: Haupt.
Lauber, K., & Wagner, G. (1996). Flora Helvetica. Flora der Schweiz. Bern: Haupt.
MeteoSchweiz (2018). Klimabulletin Jahr 2017, Zürich: MeteoSchweiz.
Moser, D., Gygax, A., Bäumler, B., Wyler, N., & Palese, R. (2002) Rote Liste der gefährteten Farn- und Blütenpflanzen der Schweiz. Bern: BUWAL.
Oostenbrink, M. (1960). Estimating nematode populations by some selected methods. In N.J. Sasser & W.R. Jenkins (Eds.), Nematology (pp. 85-101). Chapel Hill: University of North Carolina Press.
Sieriebriennikov, B., Ferris, H., & de Goede, R.G.M (2014). NINJA: An automated calculation system for nematode-based biological monitoring. European Journal of Soil Biology, 61, 90-93. doi:10.1016/j.ejsobi.2014.02.004

diversity

display_name: diversity

grassland

display_name: grassland

long-term-experiment

display_name: long-term-experiment

nematodes

display_name: nematodes

restoration

display_name: restoration

topsoil-removal

display_name: topsoil-removal

vegetation

display_name: vegetation

en: Grassland restoration: nematodes and plant communities


maintainer: Christoph Marty

en: Dataset

en: Datasets are grouped under experiment "Aare" and "Graubunden".Data is also made available in netCDF format. A range of tools are available to [read netCDF](http://www.unidata.ucar.edu/software/netcdf/software.html).

format: SERVICE

en: Graubunden input

media_type: application/x-tar

en: Grid input data for the 'Graubunden' CH2014 simulation.

mimetype: application/x-tar

en: Aare input

en: Grid input data for the 'Aare' CH2014 simulation.

en: CH2014 REPORT

en:
CH2014-Impacts: Toward Quantitative Scenarios of Climate Change Impacts in Switzerland

The CH2014-Impacts report presents the results of a concerted effort of 15 Swiss research institutions to describe impacts of climate change in Switzerland quantitatively. This dataset contributes to the Crosphere/Snowcover section.

en:
 Overview

The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change.

 Experimental Setup

Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graubünden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m × 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999–2012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085).

 Data

Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5–9 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400–800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average.

climate-change

display_name: climate-change

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

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: Alpine3D simulations of future climate scenarios CH2014


maintainer: Frank Techel

en: rutschblock-test avalanche-danger switzerland extended-column-test avalanche-observations norway

en: avalanche occurrence data (Norway, Sitzerland) - avalanche sizes per day and warning region

en: ECT data (Norway, Switzerland)

en: Rutschblock data (Switzerland)

en: avalanche occurrence data (Norway, Sitzerland) - largest avalanche size per day and warning region

en:
Observational data used to quantitatively describe the key elements describing avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size.
The data set consists of
- Rutschblock test results (Switzerland)
- Extended Column Test results (Switzerland, Norway)
- Avalanche occurrence data (Switzerland, Norway).
The data were extracted from the respective operational databases of the national avalanche warning services in Switzerland (WSL Institute for Snow and Avalanche Research SLF Davos, Switzerland) and Norway (The Norwegian Water Resources and Energy Directorate NVE).
For further information regarding the data, please refer to the publication or contact the author.

avalanche-danger

display_name: avalanche-danger

avalanche-observations

display_name: avalanche-observations

extended-column-test

display_name: extended-column-test

norway

display_name: norway

rutschblock-test

display_name: rutschblock-test

switzerland

display_name: switzerland

education

en: Education and science

en: Stability tests, avalanche observations, Switzerland, Norway


maintainer: EnviDat Support

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

en:
Site	ID for different carcasses across the Yellowstone Landscape
Location	Sampling location, either under carcass or control plot
Species	Indicating whether carcass was from bison or elk
Gender	Gener of carcass; F = female, M = male, ? = unknown
Lat	Latitude
Long	Longitude
DSK	Days since kill; in days
Soil.N	Soil N concentration (%)
Soil.C	Soil C concentration (%)
Veg.C	Vegetation C concentration (%); dominant grasses sampled
Veg.N	Vegetation N concentration (%); dominant grasses sampled
Veg.CtoN	Vegetation C to N ratio; dominant grasses sampled
Soil.resp	Soil respiration (g m-2 h-1)
soil.temp	Soil temperature (°C)
Soil.moist	Soil moisture (volumentric; %)
CRR	Cotton rotting rate
bacteria.richness	Bacteria richness
bacteria.diversity	Bacteria diverstiy
fungi.richness	Fungi richness
fungi.diversity	Fungi diversity
bacteria.abundance	Bacteria abundance
fungi.abundance	Fungi abundance

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

Paper Citation:

Risch, A.C., Frossard, A., Schütz, M., Frey, B., Morris, A.W., Bump, J.K. (accepted) Effects of elk and bison carcasses on soil microbial communities and ecosystem functions in Yellowstone, USA. (accepted). Functional Ecology
doi: ...

Methods
Study area and study sites
This study was conducted in YNP’s Northern Range (NR), located in north-western Wyoming and south-western Montana, USA (~44.9163° N, 110.4169° W). The NR expands over ~1000 km2 and features long cold winters and short dry summers. Grasslands and shrublands dominate the NR that is the home of large migratory herds of bison (winter counts 2017: ~3919 individuals; Geremia, Wallen, & White, 2017) and elk (~5349 individuals) as well as their main predators, approximately five packs of wolves with a total of 33 individuals (Smith et al., 2017). As part of a long-term research program within YNP, wolf predation has been studied since their reintroduction in 1995.
For our study, we received ground-truthed coordinates of bison and elk carcasses from winter 2016/17 (November 2016 through April 2017) from the YNP Wolf Project. Between June 20 and July 1, 2017, we visited 24 carcasses in total. At five sites, we could not sample as the carcasses were no longer found. In total we located remains (hairmats, rumen content, bones, teeth) of 19 adult male and female carcasses (7 bison, 12 elk; Supplementary Table 1). Live body weights of adult bison and elk are approximately 730 kg (male bison), 450 kg (female bison), 330 kg (male elk), and 235 kg (female elk, Meagher, 1973; Quimby & Johnson, 1951).
The kills and subsequent consumption happened between 34 and 173 days prior to our sampling (hereafter “days since kill”, DSK), for which we accounted in our statistics. Note that wolves and other scavengers consumed the soft tissue of the carcasses quickly, hence, there is close to no soft tissue left for decomposition as compared to an intact body left on the soil surface. The 19 carcass sites covered the extent of YNP’s NR, with both bison and elk carcasses showing similar distributions; elevation ranged from 1703 to 2884 m a.s.l. (Supplementary Fig 1 & Supplementary Table 1). The carcasses were all located in grassland or sage-brush shrubland, with or without sparsely scattered trees, and both bison and elk carcasses showed the same distribution of DSK. At each study site, we selected a reference plot (hereafter “control”) that was of comparable size, slope aspect and vegetation to the carcass location (hereafter “carcass”). The control was at least 10 m away (Danell, Berteaux, & Brathen, 2002; Melis et al., 2007) from the carcass itself to ensure the absence of potential direct and indirect carcass effects (paired design; (Bump, Webster, et al., 2009; Bump, Peterson, et al., 2009).

Ecosystem functions and soil properties
We randomly collected 50 g of mineral soil from three locations on both control and carcass plots to a depth of 5 cm with sterile techniques and gently mixed the material to obtain a composite sample. Half the soil sample was immediately bagged in plastic bags (whirl packs), stored in a cooler with ice packs (~5 ºC), sieved (2-mm) and frozen within 4-6 hours of collection to assess soil microbial communities. For this purpose, we extracted total genomic DNA from 0.5 g soil using the PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany). DNA concentrations were measured using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplifications of partial bacterial small-subunit ribosomal RNA genes (region V3–V4 of 16S rRNA) and fungal ribosomal internal transcribed spacers (region ITS2) were performed as described previously (Frey et al., 2016). Each sample consisting of 40 ng DNA was amplified in triplicate and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantified with the Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were sent to the Genome Quebec Innovation Center (Montreal, Canada) for barcoding using the Fluidigm Access Array technology and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA).
Quality control of bacterial and fungal reads was performed using a customized pipeline (Supplementary Table 2; Frey et al., 2016). Paired-ends reads were matched with USEARCH (Edgar & Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, & Alekseyev, 2013) and PCR primers were trimmed (allowing for 1 mismatch, read length >300 bp for 16S and >200 bp for ITS primers) using Cutadapt (M. Martin, 2011). Sequences were dereplicated and singleton reads removed prior to clustering into operational taxonomic units (OTUs) at 97% identity using USEARCH (Edgar, 2013). The remaining centroid sequences were tested for the presence of ribosomal signatures using Metaxa2 (Bengtsson-Palme et al., 2015) or ITSx (Bengtsson-Palme et al., 2013). Taxonomic assignments of the OTUs were obtained using Bayesian classifier (Wang, Garrity, Tiedje, & Cole, 2007) with a minimum bootstrap support of 60% implemented in mothur (Schloss et al., 2009) by querying the bacterial and fungal reads against the SILVA Release 128 (Quast et al., 2013) and UNITE 8.0 (Abarenkov et al., 2010) reference databases for 16S and ITS OTUs, respectively.
Abundances of the bacterial 16S rRNA gene and fungal ITS amplicon were determined by quantitative real-time PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) as described previously (Frossard et al., 2018). The same primers (without barcodes) and cycling conditions as for the sequencing approach were used for the 16S and ITS qPCR. Three standard curves per target region were obtained using tenfold serial dilutions of plasmids generated from cloned targets (Frey, Niklaus, Kremer, Lüscher, & Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight).
The other half of the soil sample was bagged in paper, dried to constant weight at 60°C, passed through a 2 mm sieve and analyzed for total C and N concentration with a CE Instruments NC 2100 soil analyzer (CE Elantech Inc., Lakewood NJ, USA). We also collected 20 mature and undamaged leaves of the dominant grass species growing on control and carcass sites, but taxa were not recorded. The plant material was dried at 60°C, finely ground till homogenized and also analyzed to obtain total C and N concentrations. Soil temperature (10 cm depth) was measured with a waterproof digital thermometer (Barnstead International, Dubuque IA, USA) at three locations each at the control and carcass site. Soil moisture (0 – 10 cm depth) was measured with time domain reflectometry (Field-Scout TDR-100; Spectrum Technologies, Plainfield IL, USA) at five randomly chosen points on control and carcass sites. We measured soil respiration at five randomly chosen points at both control and carcass sites with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA). For each measurement the soil chamber (15 cm high; 10 cm diameter) was tightly placed on the soil surface, after clipping plants to avoid measuring plant respiration or photosynthesis. Measurements were conducted over 120 s.
In addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter & Howson, 1977; Latter & Walton, 1988). Cotton cloth tensile strength loss (CTSL) is a measure of decomposition, and an index to express the combined effect of soil microclimatic, physical, chemical and biological properties on decomposition while accounting for OM quality (Latter & Walton, 1988; Risch, Jurgensen, & Frank, 2007; Withington & Sanford Jr., 2007). We placed five 20 cm wide x 13 cm long sheets of 100% unbleached cotton cloth (American Type SM 1/18’’, Warp: 34/1, Weft: 20/1, Weave plain, 29.5 picks/cm warp, 22 picks/cm weft, 237 g/m2; Daniel Jenny & Co., Switzerland;) at each carcass and control site vertically into the soil by making slits with a flat spade to a depth of 12 cm. We inserted each cloth with the spade, and then pushed the slit closed to assure tight contact with the soil. The cloths were retrieved after 18 to 27 days. After retrieval, the cloths were air-dried, remaining soil gently removed by hand, and 1.5 cm wide strips were cut at the 3.5-5.0 cm (top) and the 9-10.5 cm (bottom) soil depth. The strips were equilibrated at 50 % relative humidity and 20°C for 48 hours (climate chamber) prior to strength testing (Scanpro Awetron TH-1 tensile strength tester; AB Lorentzen and Wettre, Kista, Sweden). Cotton rotting rate (CRR) = (CTScontrol - CTSfinal/CTSfinal)1/3 * (365/t), where CTScontrol is the cotton tensile strength of a control cloth and CTSfinal the cotton tensile strength of the incubated sample, t is the incubation period in days. Control cloths were inserted into the ground and immediately retrieved to account for tensile strength loss associated with cloth insertion. We averaged the CRR of top and bottom strips for further analyses as no difference was found between the two. All sampling and cloth insertion took place between June 20 and July 1, 2017, cloths were retrieved between July 17 and 20, 2017. Soil respiration, average CRR, vegetation N concentration and vegetation C:N ratio are defined as ecosystem functions, soil C and N concentration, soil temperature and moisture as soil abiotic properties, and bacterial and fungal richness (number of taxa), diversity (Shannon) and abundance as soil biotic properties.

Statistical analyses
Univariate analyses for ecosystem functions, soil biotic and abiotic properties
We tested whether individual ecosystem functions, soil biotic and abiotic properties differed between carcass and control (“Location”), bison and elk (“Species”) and days since kill (“DSK”). For this purpose, we used linear mixed effect models (LMM, “nlme” package v 3.1 – 131.1 in R v 3.4.4; Pinheiro, Bates, DebRoy, & Sarkar, 2018; R Core Team, 2019) with Location, Species, Location x Species and DSK as fixed effects. Site was included as random effect to account for the paired design. We developed a separate model for all dependent variables. All but bacterial richness, fungal richness, fungal diversity and vegetation N concentration were natural-log transformed to meet model assumptions. For each LMM, we calculated contrasts to assess the specific comparisons we were interested in with the “lsmeans” package v 2.27-62 (Lenth & Love, 2018): 1) carcass vs control, 2) carcass bison vs control bison, and 3) carcass elk vs control elk. We also tested whether we had differences between bison and elk carcasses or the sites where bison and elk were killed and included contrasts 4) carcass bison vs carcass elk and 5) control bison vs control elk.
We calculated the log response ratio (LRR = ln[carcass/control]) to obtain carcass effects for all variables for both species separately. LRR < 0 indicates higher value at control compared to carcass, LRR > 0 indicates higher values at carcass compared control. We used LRRs for visualization and to assess spatial patterns in carcass effects across YNP. For this purpose we calculated the Moran’s I statistic for each ecosystem function, soil biotic and abiotic property based on a latitude-longitude matrix with the “moran.test” function in the “spdep” package version 1.1-3 (Bivand et al., 2019).
Multivariate analyses
Rare OTUs, defined as OTUs with a low abundance of reads, were retained in multivariate methods because they only marginally influence these analyses (Gobet, Quince, & Ramette, 2010). Bray–Curtis dissimilarity matrices were generated based on square-root-transformed matrices. We used Principal Coordinate Analyses (PCoA) to assess how soil bacterial and fungal communities differed between control and carcass of bison and elk (“vegan” package v 2.5-4, Oksanen et al., 2019). We then extracted PCoA axes scores 1 and 2 and used LMM (“nlme” package) with Location, Species, Location x Species and DSK as fixed effects. Site was, again, included as random effect. We again calculated the contrasts as described above using the “lsmeans” package. We also assessed how ecosystem functions, and soil abiotic and biotic properties were related to the soil bacteria and fungi community structure associated with bison and elk control and carcasses using the “envfit” function in the “vegan” package (Oksanen et al., 2019).
Indicator species analyses were performed using the multipatt function implemented in the “indicspecies” package version 1.7.6 with 100000 permutations (De Caceres & Jansen, 2016). This step allowed to identify OTUs that led to changes in multivariate patterns between control and carcass of both bison and elk separately (De Cáceres, Legendre, & Moretti, 2010). The multipatt function uses a point biserial correlation coefficient statistical test. Indicator OTUs were defined as bacterial and fungal OTUs with more than 50 sequences, i.e., removing rare taxa and taxa with low abundances containing little indicator information (Rime et al., 2015) and that were significantly correlated with Location (p < 0.05, correlation coefficient > 0.3). A heatmap of these OTUs were generated with the vegan and ggplot2 packages. The indicator analyses were performed in R version 3.3.3 (R Core Team, 2017).

References
Abarenkov, K., Henrik Nilsson, R., Larsson, K.-H., Alexander, I. J., Eberhardt, U., Erland, S., … Kõljalg, U. (2010). The UNITE database for molecular identification of fungi – recent updates and future perspectives. New Phytologist, 186(2), 281–285. doi:10.1111/j.1469-8137.2009.03160.x
Bengtsson-Palme, J., Hartmann, M., Eriksson, K. M., Pal, C., Thorell, K., Larsson, D. G. J., & Nilsson, R. H. (2015). metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15(6), 1403–1414. doi:10.1111/1755-0998.12399
Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., … Nilsson, R. H. (2013). Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods in Ecology and Evolution, 4(10), 914–919. doi:10.1111/2041-210X.12073
Bivand, R., Altman, M., Anselin, L., Assuncao, R., Berke, O., Blanchet, G., … Yu, D. (2019). spdep: Spatial dependence, weighthing schemes, statistics. R package version 1.1-3.
Bump, J. K., Peterson, R. O., & Vucetich, J. A. (2009). Wolves modulate soil nutrient heterogeneity and foliar nitrogen by configuring the distribution of ungulate carcasses. Ecology, 90(11), 3159–3167.
Bump, J. K., Webster, C. R., Vucetich, J. A., Peterson, R. O., Shields, J. M., & Powers, M. D. (2009). Ungulate carcasses perforate ecological filters and create biogeochemical hotspots in forest herbaceous layers allowing trees a competitive advantage. Ecosystems, 12(6), 996–1007. doi:10.1007/s10021-009-9274-0
Danell, K., Berteaux, D., & Brathen, K. A. (2002). Effect of muskox carcasses on nitrogen concentration in tundra vegetation. Arctic, 55(4), 389392.
De Caceres, M., & Jansen, F. (2016). indicspecies: relationship between species and groups of species. R package version 1.7.6.
De Cáceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by combining groups of sites. Oikos, 119(10), 1674–1684. doi:10.1111/j.1600-0706.2010.18334.x
Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10, 996.
Edgar, R. C., & Flyvbjerg, H. (2015). Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics, 31(21), 3476–3482. doi:10.1093/bioinformatics/btv401
Frey, B., Niklaus, P. A., Kremer, J., Lüscher, P., & Zimmermann, S. (2011). Heavy-machinery traffic impacts methane emissions as well as methanogen abundance and community structure in oxic forest soils. Applied and Environmental Microbiology, 77(17), 6060–6068. doi:10.1128/AEM.05206-11
Frey, B., Rime, T., Phillips, M., Stierli, B., Hajdas, I., Widmer, F., & Hartmann, M. (2016). Microbial diversity in European alpine permafrost and active layers. FEMS Microbial Ecology, 92(3), fiw018.
Frossard, A., Donhauser, J., Mestrot, A., Gygax, S., Bååth, E., & Frey, B. (2018). Long- and short-term effects of mercury pollution on the soil microbiome. Soil Biology and Biochemistry, 120, 191–199. doi:https://doi.org/10.1016/j.soilbio.2018.01.028
Geremia, C., Wallen, R., & White, P. J. (2017). Status report of the Yellowstone bison population, September 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center for Resources.
Gobet, A., Quince, C., & Ramette, A. (2010). Multivariate cutoff level analysis (MultiCoLA) of large community data sets. Nucleic Acids Research, 38(15), e155–e155. doi:10.1093/nar/gkq545
Latter, P., & Howson, G. (1977). The use of cotton strips to indicate cellulose decomposition in the field. Pedobiologia, (17), 145–155.
Latter, P., & Walton, D. (1988). The cotton strip assay for cellulose decomposition studies in soil: history of the assay and development. In Cotton strip assay: an index for decomposition in soils (pp. 7–9). ITE Symposium, Institute of Terrestrial Ecology, Natural Environment Research Council, UK.
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Meagher, M. M. (1973). The bison of Yellowstone National Park. NPS Scientific Monograph (Vol. 1). National Park Service, Yellowstone Center for Resources.
Melis, C., Selva, N., Teurlings, I., Skarpe, C., Linnell, J. D. C., & Andersen, R. (2007). Soil and vegetation nutrient response to bison carcasses in Białowieża Primeval Forest, Poland. Ecological Research, 22(5), 807–813. doi:10.1007/s11284-006-0321-4
Nikolenko, S. I., Korobeynikov, A. I., & Alekseyev, M. A. (2013). BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics, 14(1), S7. doi:10.1186/1471-2164-14-S1-S7
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., … Wagner, H. H. (2019). vegan: community ecology package. R package version 2.5-4.
Pinheiro, J., Bates, D., DebRoy, S., & Sarkar, D. (2018). nlme: Linear and nonlinear mixed effect models. R package version 3.1-131.1.
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., … Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research, 41(Database issue), D590–D596. doi:10.1093/nar/gks1219
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R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
Rime, T., Hartmann, M., Brunner, I., Widmer, F., Zeyer, J., & Frey, B. (2015). Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield. Molecular Ecology, 24(5), 1091–1108. doi:10.1111/mec.13051
Risch, A. C., Jurgensen, M. F., & Frank, D. A. (2007). Effects of grazing and soil micro-climate on decomposition rates in a spatio-temporally heterogeneous grassland. Plant and Soil, 298(1–2), 191–201. doi:10.1007/s11104-007-9354-x
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., … Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537–7541. doi:10.1128/AEM.01541-09
Smith, D., Stahler, D., Cassidy, K., Stahler, E., Metz, M., Cassidy, B., … Cato, E. (2018). Yellowstone National Park wolf project annual report 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center of Resources.
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267. doi:10.1128/AEM.00062-07
Withington, C., & Sanford Jr., R. (2007). Decomposition rates of buried substances increase with altitude in a forest-alpine tundra ecotone. Soil Biology and Biochemistry, (39), 68–75.

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

bacteria

display_name: bacteria

biogeochemical-cycling

display_name: biogeochemical-cycling

carcass

display_name: carcass

carrion

display_name: carrion

decomposition

display_name: decomposition

detritus

display_name: detritus

fungi

display_name: fungi

soil-biodiversity

display_name: soil-biodiversity

wolf

display_name: wolf

yellowstone

display_name: yellowstone

en: Elk and bison carcasses in Yellowstone, USA


maintainer: Peter Bebi

en: soil-temperature climate-change carbon-dioxide vegetation experiment

en: FACE experimental design

media_type: PNG

format: PNG

en: FACE DATASETS

media_type: application/zip

format: ZIP

mimetype: application/zip

en: Project description

en:
 Overview
High elevation ecosystems are important in research about environmental change because shifts in climate associated with anthropogenic greenhouse gas emissions are predicted to be more pronounced in these areas than in most other regions of the world. This project involves a Free Air CO2 Enrichment (FACE) and soil warming experiment located in a natural treeline environment near Davos, Switzerland (Stillberg, 2200 m a.s.l.). Elevated atmospheric CO2 concentrations (+200 ppm) were applied from 2001 to 2009, and a soil warming treatment (+4 K) was added in 2007 (ongoing). The combined CO2 enrichment and warming treatment reflects conditions expected to occur in this region in approximately 2050. A broad range of ecological and biogeochemical research is carried out as part of this environmental change project.

#Experimental setup
The experiment consists of 40 hexagonal 1.1 m2 plots, 20 with a Pinus mugo ssp. uncinata (mountain pine, evergreen) individual in the centre and 20 with a Larix decidua (European larch, deciduous) individual in the centre. A dense cover of understorey vegetation surrounds the tree in each plot, including the dominant dwarf shrub species Vaccinium myrtillus (bilberry), Vaccinium gaultherioides (group V. uliginosum agg., northern bilberry) and Empetrum nigrum ssp. hermaphroditum (crowberry) plus several herbaceous and non-vascular species.

At the beginning of the experimental period, the 40 plots were assigned to 10 groups of four neighbouring plots (two larch and two pine trees per group) in order to facilitate the logistics of CO2 distribution and regulation. Half of these groups were randomly assigned to an elevated CO2 treatment while the remaining groups served as controls and received no additional CO2. In spring 2007, one plot of each tree species identity was randomly selected from each of the 10 CO2 treatment groups and assigned a soil warming treatment, yielding a balanced design with a replication of five individual plots for each combination of CO2 level, warming treatment and tree species.

#Data
Soil and air conditions have been monitored closely throughout the study period, with most measurements made during the combined CO2 x warming experiment (2007-2009).

carbon-dioxide

display_name: carbon-dioxide

climate-change

display_name: climate-change

experiment

display_name: experiment

soil-temperature

display_name: soil-temperature

vegetation

display_name: vegetation

en: FACE: Stillberg CO2 enrichment and soil warming study


biogas-aus-hofdunger-in-der-schweiz

title_for_slug: biogas-aus-hofdunger-in-der-schweiz

en: review biogas manure bioenergy switzerland

en: Biogas aus Hofdünger in der Schweiz

media_type: PDF

en: Ziel dieses Whitepapers ist es, Entscheidungsträgern, Verwaltungen und Stakeholdern die aktuellsten Forschungsergebnisse zur Verfügung zu stellen, um die optimale Nutzung von Bioenergie aus Hofdünger in der Schweizer Energiewende zu fördern. Zu diesem Zweck werden die Ergebnisse des Schweizer Kompetenzzentrums für Bioenergieforschung - SCCER BIOSWEET - zusammengefasst und in einem breiteren Kontext dargestellt. Wenn nichts anderes erwähnt wird, beziehen sich die Ergebnisse auf die Schweiz und im Falle der Ressourcen auf die heimischen Biomassepotenziale.

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

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.


maintainer: Yves Bühler

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

en: RAMMS simulations NHESS

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

en: Potential Release  Area  NHESS

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

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

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

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

avalanche

display_name: avalanche

hazard-indication-mapping

display_name: hazard-indication-mapping

hazard-mapping

display_name: hazard-mapping

ramms

display_name: ramms

release

display_name: release

snow

display_name: snow

education

en: Education and science

en: Automated Avalanche Release Area (PRA) Delineation Davos


maintainer: Leo Gallus Bont

en: seilaplan openstreetmap forest geoprocessing accessible-forest qgis

en: Juypter Notebook for Data Preparation (online)

media_type: ipynb

en: Digitised sample perimeter

en: Juypter Notebook for Data Preparation

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

en: Forest vector data (for sample forest range)

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

media_type: image/tiff

format: TIFF

mimetype: image/tiff

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

en: Preview

media_type: image/png

mimetype: image/png

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

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

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

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

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

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

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

accessible-forest

display_name: accessible-forest

forest

display_name: forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan

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


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

en: Forest station data (VOB)

en: Data file.

en: Site aerial photo (Vordemwald)

media_type: image/jpeg

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

mimetype: image/jpeg

en: Site photo of the forest station (VOB)

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

en: Data plot of the forest station (VOB)

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

en: Data plot of the field station (VOF)

en: Site photo of the field station (VOF)

en: Site photo of the field stationfrom the side.

en: Field station data (VOF)

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 Vordemwald in Switzerland where one station is located within a natural mixed forest stand (VOB) with European silver fir (_Abies alba_; 110 yrs) and oak trees (_Quercus sp._; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Vordemwald 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

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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: Vordemwald, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards


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

en: Herunterladen des DHM mit SwissGeoDownloader

en: View video on youtube!

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. The plugin ‘Swiss Geo Downloader’, which is available for the open source geoinformation software QGIS, allows freely available Swiss geodata to be downloaded and displayed directly within QGIS. It was developed in 2021 by Patricia Moll in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. In this tutorial we describe how to download the high accuracy elevation model ‘swissALTI3D’ with the help of the ‘Swiss Geo Downloader’ and how to use it for digital planning of a cable line with the plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to the Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

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

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

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Das Plugin Swiss Geo Downloader, welches für das Open Source Geoinformationssystem QGIS zur Verfügung steht, ermöglicht frei verfügbare Schweizer Geodaten direkt innerhalb von QGIS herunterzuladen und anzuzeigen. Es wurde 2021 von Patricia Moll in Zusammenarbeit mit der eidgenössischen Forschungsanstalt Wald, Schnee und Landschaft WSL entwickelt. In diesem Tutorial beschreiben wir, wie man mit Hilfe des Swiss Geo Downloaders das hochgenaue Höhenmodell swissALTI3D herunterladen und für die Seillinienplanung mit dem Plugin Seilaplan verwenden kann.

Link zum Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

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

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

swiss-geo-downloader

display_name: swiss-geo-downloader

swiss-geodata

display_name: swiss-geodata

education

en: Education and science

en: Seilaplan Tutorial: DTM download with SwissGeoDownloader


maintainer: Michael Lehning

en: snow-cover alpine-precipitation snow alpine3d-simulations snow-distribution alpine3d snow-hydrology

en: ADS datasets and simulation inputs

en: The file contains the ADS datasets of snow depth and input data used for simulations.

en:
Dataset (Model input, snow distribution and validation) for the precipitation scaling paper, which should be cited along with the data set citation.

This data is useful for distributed hydrological modelling or other tasks that involve the study of snow distribution and precipitation in the high Alpine.

The format of the data is for Alpine3D (models.slf.ch) model runs but other models could be used, too.

Please cite:

_Vögeli, C., Lehning, M., Wever, N., Bavay M., 2016: Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution., Front. Earth Sci. 4: 108. doi: 10.3389/feart.2016.00108._

Dataset is provided as a single zip file.
The archive contains two directories, the valuable distributed snow depth maps for the landscape Davos and the simulation input.
The archive also contains the file: "ReadMeMetadataDataSetPrecipitationScaling" which explains the data structure.

alpine-precipitation

display_name: alpine-precipitation

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-distribution

display_name: snow-distribution

snow-hydrology

display_name: snow-hydrology

en: Precipitation Scaling Data Set (Vögeli et al., Frontiers)


root-traits

maintainer: Ivano Brunner

title_for_slug: root-traits

en: drought soil-coring fine-root-traits belowground-biomass ingrowth-cores

en: Root-traits

en:
Fine-root traits of Scots pine in response to enhanced soil water availability deriving from long-term irrigation in the Pfynwald

Data_Fig.1.xlsx
Fine-root biomass of the topsoil (0-10 cm) in the dry and irrigated treatment of the Scots pine forest of the years 2003 to 2016 recorded by soil coring

Data_Tab1+2_2005.xlsx
Fine-root traits from roots of ingrowth cores from 2005 after two years of growth in the dry and irrigated treatment of the Scots pine forest

Data_Tab1+2_2016.xlsx
Fine-root traits from roots of ingrowth cores from 2016 after two years of growth, and from roots of the soil-coring sampling from 2016 in the dry and irrigated treatment of the Scots pine forest

belowground-biomass

display_name: belowground-biomass

drought

display_name: drought

fine-root-traits

display_name: fine-root-traits

ingrowth-cores

display_name: ingrowth-cores

soil-coring

display_name: soil-coring


aerosol-data-davos-wolfgang

title_for_slug: aerosol-data-davos-wolfgang

en: drincz inp aerosol ice-nucleation particles raclets

en: APS Dataset Davos Wolfgang

media_type: application/x-netcdf

en: The APS file contains aerosol data (total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) for aerodynamic diameters between 0.5 μm and ~20 μm as well as instrument characteristics, in a temporal resolution of 20 seconds, with time in UTC, measured at Davos Wolfgang.

mimetype: application/x-netcdf

en: OPC GT-526S Dataset Davos Wolfgang

en: Particle counts were recorded in six size channels (0.3, 0.5, 0.8, 1.5, 2.5 and 5 μm) in a resolution of 20 seconds (time in UTC) by a particle counter (GT-526S, Met One Instruments, Inc.).

en: DRINCZ Dataset Davos Wolfgang

en: The DRINCZ file contains information on temperature (in °C) dependent frozen fractions, number of Ice Nuleating Particles (INP, per standard liter) and differential INP concentrations (per standard liter and Kelvin).

en: OPC-N3 Dataset Davos Wolfgang

en: Particle concentrations in a size range of 0.35 μm to 40 μm were measured with an OPC-N3 (Alphasense, Inc.) in a resolution of 20 seconds (time in UTC). PM₁, PM₂,₅ and PM₁₀ were directly derived.

en: SMPS Dataset Davos Wolfgang

en: The SMPS file contains aerosol data (particle concentration, total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) in a size range between 12 and 460 nm (electrical mobility diameter) as well as instrument characteristics (sample temperature, pressure, relative humidity, mean free path and gas viscosity), in a temporal resolution of 2 minutes, with time in UTC, measured at Davos Wolfgang.

en:
Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.

Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0).

Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³).

Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³).

Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.

aerosol

display_name: aerosol

drincz

display_name: drincz

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

particles

display_name: particles

raclets

display_name: raclets

envidat

en: EnviDat: the Environmental Data Portal

en: Aerosol Data Davos Wolfgang


maintainer: Roksolana Petrovska

en: lar acer dominance mortality-rate fagus regeneration shade-tolerance

media_type: r script (flat-ascii text)

en: The R-script of survival analysis for accelerated failure time model

en: Mortality of regeneration

media_type: CSV

en: Acer platanoides, Acer pseudoplatanus and Fagus sylvatica living and dead juveniles (0-500 cm in height) grown in the deep shade of a prime beech-dominated forest.

en:
 One individual per species, vitality class (low and high) and height class (eight classes: 0–10, 11–20, 21–35, 36–60, 61–90, 91–130, 131–200 and 201–500 cm) was randomly selected and harvested in each of the six plots. This resulted in a sample of 82, 80 and 89 living individuals of A. platanoides, A. pseudoplatanus and F. sylvatica, respectively.

 Additionally stems of dead Acer spp. and F. sylvatica trees that had died within the last three years (2015–2018) were randomly harvested, matching the height classes of the harvested living trees wherever possible. In total, 179 dead young trees (60 A. platanoides, 72 A. pseudoplatanus and 47 F. sylvatica) were collected.

 Variables:
* species_code: a_pla - Acer platanoides, a_pse - Acer pseudoplatanus, f_syl - Fagus sylvatica
* species: as above
* dummy: 0 - living individual, 1 - dead individual
* LAR_cm2_g: leaf area ratio or ratio of leaf area to total plant biomass, [cm2/g]
* tree_age: in years
* avg_ring_micron: average width of the last 5 rings in tree life excluding the last ring
* dry_mass_g: aboveground and belowground biomass
* DLI: direct light index (measured only under living individuals)
* BLI: diffuse light index (measured only under living individuals)
* GLI: global light index

acer

display_name: acer

dominance

display_name: dominance

fagus

display_name: fagus

lar

display_name: lar

mortality-rate

display_name: mortality-rate

regeneration

display_name: regeneration

shade-tolerance

display_name: shade-tolerance

education

en: Education and science

envidat

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

en: Mortality of regeneration: Acer spp. and Fagus sylvatica


weather-station-davos-wolfgang

maintainer: Jörg Wieder

title_for_slug: weather-station-davos-wolfgang

en: temperature pressure weather relative-humidity raclets

en: Weather Station Davos Wolfgang

en: The data file contains date and time (YYYY-MM-DD hh:mm:ss) in UTC, temperature (°C), relative humidity (%), pressure (hPa) at 10 m above ground and above sea level. The data was averaged over 10 minutes.

en: The dataset contains weather parameters measured at Davos Wolfgang (LON: 9.853594, LAT: 46.835577).

pressure

display_name: pressure

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

weather

display_name: weather


irkis-soil-moisture-measurements-davos

maintainer: Nander Wever

title_for_slug: irkis-soil-moisture-measurements-davos

en: dischma-catchment meteorological-measurements soil-moisture davos meteo-station

en: IRKIS soil moisture data (zip file)

en: Compressed archive (zip) containing soil moisture measurements, as well as meteorological measurements and interpolated meteorological time series.

en:
Meteorological and soil moisture measurements from soil moisture stations installed from October 2010 - October 2013 in the area surrounding Davos, in particular in the Dischma catchment.

There are in total 7 stations: 1202, 1203, 1204, 1205, 222, 333 and SLF2. For each of the stations, there is a:

* vwc_[stn].smet: containing the soil moisture measurements

* station_[stn].smet: in-situ measured meteorlogical parameters. Note, the quality of these measurements for stations 1202, 1203, 1204 and 1205 is very low, with data gaps. Use this data with care. For stations 222, 333 and SLF2, data quality is high and only the default cautiousness should be applied.

* interpolatedmeteo_[stn].smet contains per stations a dataset derived by interpolating from several stations in the Davos area to the stations location. This dataset was generated from the output of the Alpine3D model, of which simulations are presented in the Wever et al. (2017) manuscript.

At the soil moisture measurement sites, Decagon 10HS sensors were installed, at 10, 30, 50, 80 and 120 cm depth. Per depth 2 sensors were installed, labelled A and B in the datafiles. Note that at stations 1203, 1204 and 1205, sensors were only installed at 10, 30 and 50 cm depth.

The files follow the SMET format: https://models.slf.ch/docserver/meteoio/SMET_specifications.pdf and metadata for the stations can be found in the header of the smet files.

Please cite the Wever et al. (2017) reference when using this data in publications.

For a more detailed description, please refer to: Wever, N., Comola, F., Bavay, M., and Lehning, M.: Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment, Hydrol. Earth Syst. Sci., 21, 4053-4071, https://doi.org/10.5194/hess-21-4053-2017, 2017.

davos

display_name: davos

dischma-catchment

display_name: dischma-catchment

meteo-station

display_name: meteo-station

meteorological-measurements

display_name: meteorological-measurements

soil-moisture

display_name: soil-moisture

en: IRKIS Soil moisture measurements Davos


en: spatial-planning land-use-change land-use-transformations europe strategic-projects large-scale-urban-development-projects urban-transformations strategic-spatial-planning

en: QCA data set large-scale urban development projects

media_type: TXT

en: Please refer to the sections: 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalization in the Metadata for further details on this resource.

format: TXT

mimetype: text/plain

en: QCA R script large-scale urban development projects Europe

media_type: R

en:
For the set up of this R script, we used the “QCA” (Duşa, 2019) and “SetMethods” (Oana and Schneider, 2018) R packages.

The following further resources were also used:
Dusa, A. (2019). QCA with R: A comprehensive resource. Springer: Berlin.
Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences. A guide to qualitative comparative analysis. Cambridge: Cambridge University Press.
Thomann, E., Oana, I.-E., & Wittwer, S. (2018). Performing fuzzy- and crisp set QCA with R. A user-oriented beginner's guide. Retrieved from http://www.evathomann.com/links/qca-r-manual

en:
Table of Content: 1. General context of the data set "lsUDPs" ; 2. Background and aims of the study using the data set lsUDPs; 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalisation

1. General context of the data set "lsUDPs"
The data set "lsUDPs" has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2020. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, strategic spatial plans) into quantitative land-change modelling approaches at the urban regional level. The first stage (2016-2017) of the CONCUR project focussed on 21 urban regions in Western Europe. The urban regions were selected through a multi-stage strategy for empirical research (see Hersperger, A. M., Grădinaru, S., Oliveira, E., Pagliarin, S., & Palka, G. (2019). Understanding strategic spatial planning to effectively guide development of urban regions. Cities, 94, 96–105. https://doi.org/10.1016/j.cities.2019.05.032 ).

2. Background and aims of the study using the data set lsUDPs
As part of the CONCUR project, a specific task was to examine the relationship between strategic spatial plans and the formulation and implementation (i.e. urban land change) of large-scale urban development projects in Western Europe.
Strategic urban projects are typically large-scale, prominent urban transformations implemented locally with the aim to stimulate urban growth, for instance in the form of urban renewals of deprived neighborhoods, waterfront renewals and transport infrastructures. While strategic urban projects are referred to in the literature with multiple terms, in the CONCOR project we call them large-scale urban development projects (lsUDPs).
Previous studies acknowledged both local and supra-local (or structural) factors impacting the context-specific implementation of lsUDPs. Local governance factors, such as institutional capacity, coordination among public and private actors and political leadership, intertwine with supra-local conditions, such as state re-scaling processes and devolution of state competencies in spatial planning, de-industrialisation and increasing social inequality. Hence, in implementing lsUDPs, multi-scalar factors act in combination.
Because the formulation and implementation of lsUDPs require multi-scalar coordination among coalitions of public and private actors over an extended period of time, they are generally linked to strategic spatial plans (SSPs). Strategic spatial plans convey collective visions and horizons of action negotiated among public and private actors at the local and/or regional level to steer future urban development, and can contain legally binding dispositions, but also indicative guidelines.
The key question remains as to what extent large-scale urban development projects and strategic spatial plans can be regarded as aligned. By alignment, or “concordance”, we mean that strategic projects are formulated and implemented as part of the strategic planning process (“high concordance”), or that the strategic role of projects is reconfirmed in (subsequent) strategic plans (“moderate concordance”). Lack of concordance is found when lsUDPs have been limitedly (or not at all) acknowledged in strategic spatial plans.
We assume that certain local and supra-local factors, characterising the development of the projects, foster (but not strictly “cause”) the degree of alignment between lsUPDs and SSPs. In this study, we empirically examine how, and to what extent, the concordance between 38 European large-scale urban development projects and strategic plans (outcome: CONCOR) has been enabled by five multi-scalar factors (or conditions): (i) the role of the national state (STATE), (ii) the role of (inter)national private actors (PRIVATE), (iii) the occurrence of supra-regional external events (EVENTS), (iv) the degree of transport connectivity (TRANSP), and (v) local resistance from civil society (RESIST). We adopted a (multi-data) case-based qualitative strategy for empirical research and applied the formalised procedure of within- and cross-case comparison offered by fuzzy-set Qualitative Comparative Analysis appropriate for the goal of this study. Based on set theory, QCA formally integrates contextual sensitivity to case specificities (within-case knowledge) with systematic comparative analysis (across-case knowledge).
The research question the data set has been created to reply to is the following: which conditions, and combinations of conditions, enable the concordance between large-scale urban development projects and strategic spatial plans?
The conditions (“independent variables”) considered are. STATE: the set of large-scale urban projects characterized by a high degree of state intervention and support in their formulation and implementation, PRIVATE: the set of large-scale urban projects characterized by a high degree of involvement of (inter)national private actors in their formulation and implementation, EVENTS: the set of large-scale strategic projects whose formulation and implementation have been strongly affected by unforeseen international events and/or global trends, TRANSP: the set of large-scale strategic projects with a high degree of road and/or transit connectivity, and RESIST: set of large-scale strategic projects whose realization has been characterized by resistances that have substantially delayed or modified the project implementation.
The outcome (“dependent variable”) under analysis is CONCOR: the set of large-scale urban projects having a high degree of concordance/alignment/integration with strategic spatial plans

3. The data set lsUDPs

   3.1 Selection of cases and data collection
To generate the current data set on large-scale urban development projects in European urban regions (data set "lsUDPs"), we identified 35 large-scale urban development projects in a sample of the 21 Western urban regions considered in the CONCUR project (see supra, Hersperger et al. 2019): Amsterdam, Barcelona, Copenhagen, Hamburg, Lyon, Manchester, Milan, Stockholm, Stuttgart. The criteria we followed to identify the 35 large-scale urban development projects are: geographical location, size (large-scale), site (located either in the city core or in the larger urban region) and urban function (e.g. housing, transportation infrastructures, service and knowledge economic functions). Employing these criteria facilitated the selection of diverse large-scale urban development projects while still ensuring sufficient comparability.
In 2016, we performed 47 in-depth interviews with experts in urban and regional planning and large-scale strategic projects and infrastructure (i.e. academics and practitioners) about the formulation, implementation and development (1990s–2010s) of each project in each of the 9 selected urban regions. On average, each interviewee answered questions on 3.1 large-scale urban development projects. Three cases were subdivided into two cases because a clear differentiation between specific implementation stages was identified by the interviewees (expansion of the Barcelona airport, cases “bcn_airport80-90” and “bcn_airport00-16”; realisation of Lyon Part-Dieu, cases “lyo_partdieu70-90” and “lyo_partdieu00-16”; MediaCityUK, cases “man_salfordquays80-00” and “man_mediacityuk00-16”). Therefore, from the initial 35 cases, the final number of analysed cases in the lsUDPs dataset is 38.

    3.2 The data set lsUDPs: Data management and operationalisation
Interviews were fully transcribed and analysed through MAXQDA (version 12.3, VERBI GmbH, Berlin, Germany), and intercoder agreement was evaluated on a sample of nine interviews. We also compiled “synthetic case descriptions” (SCD) for each case (totalling more than 160 SCDs) to spot potential inconsistencies among interviewees’ accounts and to facilitate completion of the “calibration table” for each case (see below). An online expert survey distributed to the interviewees (response rate 78%) helped systematise the information collected during the interviews. We also consulted both academic and gray literature on the case studies to check for possible ambiguity and inconsistencies in the interview data, and to solve discrepancies between our assigned set membership scores and questionnaire values. Site visits were also carried out to retrieve additional information on the selected cases.
For each case (i.e. each of the 38 selected large-scale urban development projects), we operationalised each condition (i.e. STATE, PRIVATE, EVENTS, TRANSP, RESIST) and the outcome (CONCOR) in terms of sets, for subsequent application of Qualitative Comparative Analysis. This process is called “calibration”; we used a number of indicators for each condition to qualitatively assess each large-scale project across the conditions. The case-based qualitative assessment was then transformed into fuzzy-set membership values. Fuzzy-set membership values range from 0 to 1, and should be conceived as “fundamentally interpretative tools” that “operationalize theoretical concepts in a way that enhances the dialogue between ideas and evidence” (Ragin 2000:162, in “Fuzzy-set Social Science”. Chicago: University Press). We employed a four-value fuzzy-set scale (0, 0.33, 0.67, 1) to “quantify” into set membership scores the individual histories of cases retrieved from interview data. Only the condition TRANSP was calibrated as a crisp-set (0, 1).
The translation of qualitative case-based information into numerical fuzzy-set membership values was iteratively performed by populating a calibration table following standard practices recently emerged in QCA when dealing with qualitative (interview) data.

europe

display_name: europe

land-use-change

display_name: land-use-change

land-use-transformations

display_name: land-use-transformations

large-scale-urban-development-projects

display_name: large-scale-urban-development-projects

spatial-planning

display_name: spatial-planning

strategic-projects

display_name: strategic-projects

strategic-spatial-planning

display_name: strategic-spatial-planning

urban-transformations

display_name: urban-transformations

envidat

en: EnviDat: the Environmental Data Portal

en: lsUDPS Large-scale urban development projects in European urban regions