{"data":[{"id":"10.5061/dryad.k98sf7mmr","type":"dois","attributes":{"doi":"10.5061/dryad.k98sf7mmr","identifiers":[],"creators":[{"name":"Grundler, Maggie","nameType":"Personal","givenName":"Maggie","familyName":"Grundler","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7104-0494","nameIdentifierScheme":"ORCID"}]},{"name":"Rosenblum, Erica Bree","nameType":"Personal","givenName":"Erica Bree","familyName":"Rosenblum","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]}],"titles":[{"title":"Data from: Preservation of stable isotope niche dynamics in squamate museum specimens"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Ecology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Stable isotopes","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Squamates","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Museum collections","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2025-12-04T18:54:05Z","dateType":"Created"},{"date":"2025-12-04T18:54:06Z","dateType":"Submitted"},{"date":"2026-05-15T00:00:00Z","dateType":"Issued"},{"date":"2026-05-15T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/1365-2656.70212","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["51096 bytes"],"formats":[],"version":"5","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Natural history museums are invaluable resources for large-scale\n ecological and evolutionary studies, but certain ecological traits can be\n challenging to recover, particularly from fluid preserved specimens.\n Stable isotope analysis is an elegant method for reconstructing the\n dietary niche over integrated timescales, and recovering this information\n from museum specimens can provide a critical axis of ecological\n information for studies of population dynamics through time and space.\n However, isotope ratios of tissues are known to be altered by extended\n contact with formalin and ethanol. Here, we assess whether intra- and\n interspecific variation in isotopic signature, which represent critical\n data used to assess metrics of niche diversity, can be reliably recovered\n following fluid preservation. We use a broad taxonomic distribution of\n squamates to compare niche metrics prior to and eight weeks following a\n standard museum preservation process. We could not recover intraindividual\n metrics of niche diversity but found that between-individual variation was\n not significantly altered, allowing for the reconstruction of community\n niche characteristics. We present an example isotopic analysis from museum\n specimens representing generalist and specialist Thamnophis garter snake\n populations that aligns with empirical estimates of niche width. We also\n present several additional analyses on tissue-specific effects,\n delipification, and buffer storage, with useful insights for field\n collection and downstream analysis decisions.","descriptionType":"Abstract"},{"description":"# Data from: Preservation of stable isotope niche dynamics in squamate\n museum specimens Dataset DOI:\n [10.5061/dryad.k98sf7mmr](https://doi.org/10.5061/dryad.k98sf7mmr) ##\n Description of the data and file structure The provided data files\n represent carbon and nitrogen stable isotope ratios from animal tissues\n that were flash frozen or stored in RNAlater salt buffer. ### Files and\n variables #### File: ebv_museum_metaData.csv **Description:** Metadata for\n East Bay Vivarium squamate specimens including Museum of Vertebrate\n Zoology accession numbers. ##### Variables * sample_ID: identification\n given to the specimen for use in the study * museum_ID: accession number\n given to the specimen for housing at the Museum of Vertebrate Zoology *\n genus: genus of the specimen * species: species of the specimen *\n ebv_diet: diet items fed to specimen while alive at the East Bay Vivarium\n * SVL (mm): snout-vent length of specimen in millimeters * tail (mm): tail\n length of specimen in millimeters * weight (g): weight of specimen in\n grams following thawing * donation_date: date specimen was donated to the\n MVZ (format: MM/DD/YYYY or YYYY) * location_found: location data used by\n MVZ * date_found: date range used by MVZ (format: Mon-Year or YYYY) *\n circumstances: origin of specimen upon arrival to EBV * fixed: date\n specimen was placed in formalin (format: MMDDYYYY) * etoh_transfer: date\n specimen was transferred to ethanol (format: MMDDYYYY) * days_in_formalin:\n days specimen spent in formalin to ensure fixation #### File:\n RNAlater_bias_correction_data.csv **Description:** Stable isotope ratios\n from paired samples for analysis of effect of RNAlater storage buffer.\n ##### Variables * sample: identification given to the specimen for use in\n the study * genus: genus of specimen * tissue: tissue type used for stable\n isotope analysis * d15N: delta nitrogen signature of flash frozen tissue *\n d15N_treatment: delta nitrogen signature of tissue stored in RNAlater *\n d13C: delta carbon signature of flash frozen tissue *\n d13C_treatment: delta carbon signature of tissue stored in RNAlater *\n bias_N: nitrogen bias between flash frozen and buffer-stored tissue *\n bias_C: carbon bias between flash frozen and buffer-stored tissue ####\n File: MVZ_thamnophis_isotopes.csv **Description:** Stable isotope ratios\n from historical MVZ *Thamnophis* specimens. ##### Variables * sample_id:\n sample_name plus tissue type code * sample_name: name of specimen based on\n MVZ accession number * N: percent nitrogen * d15N: delta nitrogen\n signature * C: percent carbon * d13C: delta carbon signature * species:\n species of specimen * tissue: tissue type used for stable isotope analysis\n #### File: ebv_isotope_data_ALL.csv **Description:** Stable isotope ratios\n from East Bay Vivarium squamate specimens representing time series and\n effect of delipification analysis. ##### Variables * sample_id:\n sample_name plus timepoint and tissue type code * timepoint: timepoint\n sample was taken during preservation process * tissue: tissue type used\n for stable isotope analysis * delipified: TRUE if tissue was subjected to\n delipification * flashfrozen: TRUE if tissue was flash frozen (vs. stored\n in RNAlater buffer) * 15N: delta nitrogen signature * 13C: delta carbon\n signature * genus: genus of specimen * species: species of specimen  ##\n Missing Values Values represented by NA indicate missing data (i.e.\n metadata not available for specimens or samples that failed during isotope\n analysis) ## Code/software All data were analyzed in the R programming\n environment.","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","awardTitle":"Graduate Research Fellowship","funderName":"U.S. National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.k98sf7mmr","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-15T23:20:11Z","registered":"2026-05-15T23:20:12Z","published":null,"updated":"2026-05-15T23:20:12Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.16904/envidat.40","type":"dois","attributes":{"doi":"10.16904/envidat.40","identifiers":[{"identifier":"https://www.envidat.ch/#/metadata/3735a11a-34a5-4a83-b2db-ed22484589d8","identifierType":"URL"}],"creators":[{"name":"Reuter, Benjamin","givenName":"Benjamin","familyName":"Reuter","affiliation":["WSL Institute for Snow and Avalanche Research SLF"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7302-3858","nameIdentifierScheme":"ORCID"}]},{"name":"van Herwijnen, Alec","givenName":"Alec","familyName":"van Herwijnen","affiliation":["WSL Institute for Snow and Avalanche Research SLF"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5637-6486","nameIdentifierScheme":"ORCID"}]},{"name":"Bellaire, Sascha","givenName":"Sascha","familyName":"Bellaire","affiliation":["WSL Institute for Snow and Avalanche Research SLF"],"nameIdentifiers":[]},{"name":"Schweizer, Jürg","givenName":"Jürg","familyName":"Schweizer","affiliation":["WSL Institute for Snow and Avalanche Research SLF"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5076-2968","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Data set on snow instability"}],"publisher":"EnviDat","container":{},"publicationYear":2018,"subjects":[{"lang":"en","subject":"AVALANCHE FORCASTING"},{"lang":"en","subject":"CRACK PROPAGATION"},{"lang":"en","subject":"FAILURE INITIATION"},{"lang":"en","subject":"PROPGATOIN SAW TEST"},{"lang":"en","subject":"RUTSCHBLOCK"},{"lang":"en","subject":"SLAB"},{"lang":"en","subject":"SLAB TENSILE SUPPORT"},{"lang":"en","subject":"SNOW AVALANCHE"},{"lang":"en","subject":"SNOW STABILITY EVALUATION"},{"lang":"en","subject":"WEAK LAYER"}],"contributors":[{"name":"Reuter, Benjamin","givenName":"Benjamin","familyName":"Reuter","affiliation":["WSL Institute for Snow and Avalanche Research SLF"],"contributorType":"ContactPerson","nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7302-3858","nameIdentifierScheme":"ORCID"}]},{"name":"Snow Avalanches and 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Describing snow instability by failure initiation, crack propagation and slab tensile support. Geophys. Res. Lett., 45, doi: 10.1029/2018GL078069.","descriptionType":"Abstract"},{"lang":"en","description":"Resource Title: data set on snow instability\n Size: 10332 bytes\n Format: text/csv\n URL: https://www.envidat.ch/dataset/3735a11a-34a5-4a83-b2db-ed22484589d8/resource/25cec3bc-4c79-411b-b04f-5bee43114c42/download/snow_instability_data.csv\n Description:","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Read-me file on snow instability data\n Size: 172308 bytes\n Format: application/pdf\n URL: https://www.envidat.ch/dataset/3735a11a-34a5-4a83-b2db-ed22484589d8/resource/7ca3bf86-f927-43d0-bc49-56b444d42dcb/download/read-me_data_sets_snow_instability.pdf\n 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Belgium","Department of Environmental Systems Science, ETH Zurich"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4316-2013","nameIdentifierScheme":"ORCID"}]},{"name":"Kirchner, James","givenName":"James","familyName":"Kirchner","affiliation":["ETH Zurich","Swiss Federal Institute for Forest, Snow and Landscape Research WSL"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6577-3619","nameIdentifierScheme":"ORCID"}]},{"name":"Zappa, Massimiliano","givenName":"Massimiliano","familyName":"Zappa","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research WSL"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2837-8190","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Average precipitation and PET over Switzerland at 500m resolution"}],"publisher":"EnviDat","container":{},"publicationYear":2020,"subjects":[{"lang":"en","subject":"ARIDITY"},{"lang":"en","subject":"CLIMATE"},{"lang":"en","subject":"POTENTIAL EVAPOTRANSPIRATION"},{"lang":"en","subject":"PRECIPITATION"},{"lang":"en","subject":"WEATHER"}],"contributors":[{"name":"Kirchner, James","givenName":"James","familyName":"Kirchner","affiliation":["ETH Zurich"],"contributorType":"ContactPerson","nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6577-3619","nameIdentifierScheme":"ORCID"}]},{"name":"Physics of Environmental 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average precipitation over Switzerland at 500m resolution"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/475208bd-42d0-4a12-a1b0-e322f64fce5f/resource/02355670-67a9-4236-ae0f-1bab203db336/download/pcp_ch_longterm_yr_avg.tif","relatedItemIdentifierType":"URL"}},{"titles":[{"title":"1980-2011 average potential evapotranspiration (in mm per year) at 500m resolution over Switzerland"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/475208bd-42d0-4a12-a1b0-e322f64fce5f/resource/f6c57849-bf7a-4083-b65c-930d13e8e18a/download/pet_ch_longterm_yr_avg.tif","relatedItemIdentifierType":"URL"}}],"sizes":["1197880 bytes","1200978 bytes"],"formats":["image/tiff","image/tiff"],"version":"1.0","rightsList":[{"lang":"en","rights":"WSL Data 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Units are mm per year. Files are GeoTIFF rasters, and can be read in R using the command raster(\"pcp_ch_longterm_yr_avg.tif), after installing packages \"raster\" and \"rgdal\".","descriptionType":"Abstract"},{"lang":"en","description":"Resource Title: 1980-2011 average precipitation over Switzerland at 500m resolution\n Size: 1197880 bytes\n Format: image/tiff\n URL: https://www.envidat.ch/dataset/475208bd-42d0-4a12-a1b0-e322f64fce5f/resource/02355670-67a9-4236-ae0f-1bab203db336/download/pcp_ch_longterm_yr_avg.tif\n Description: Long-term (1980-2011) average precipitation (in mm per year) at 500m resolution over Switzerland. GeoTIFF raster file can be read in R using the command raster(“pet_ch_longterm_yr_avg.tif”), after installing packages \"raster\" and \"rgdal\".","descriptionType":"Other"},{"lang":"en","description":"Resource Title: 1980-2011 average potential evapotranspiration (in mm per year) at 500m resolution over Switzerland\n Size: 1200978 bytes\n Format: image/tiff\n URL: https://www.envidat.ch/dataset/475208bd-42d0-4a12-a1b0-e322f64fce5f/resource/f6c57849-bf7a-4083-b65c-930d13e8e18a/download/pet_ch_longterm_yr_avg.tif\n Description: Long-term (1980-2011) average potential evapotranspiration (in mm per year) at 500m resolution over Switzerland. GeoTIFF raster file can be read in R using the command raster(“pet_ch_longterm_yr_avg.tif”), after installing packages \"raster\" and \"rgdal\".","descriptionType":"Other"}],"geoLocations":[{"geoLocationPolygon":[{"polygonPoint":{"pointLatitude":45.81802,"pointLongitude":5.95587}},{"polygonPoint":{"pointLatitude":47.80838,"pointLongitude":5.95587}},{"polygonPoint":{"pointLatitude":47.80838,"pointLongitude":10.49203}},{"polygonPoint":{"pointLatitude":45.81802,"pointLongitude":10.49203}},{"polygonPoint":{"pointLatitude":45.81802,"pointLongitude":5.95587}}]},{"geoLocationPlace":"Switzerland"}],"fundingReferences":[{"funderName":"Swiss National Science 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Research"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0531-8336","nameIdentifierScheme":"ORCID"}]},{"name":"Frossard, Aline","givenName":"Aline","familyName":"Frossard","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research"],"nameIdentifiers":[]},{"name":"Schütz, Martin","givenName":"Martin","familyName":"Schütz","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research"],"nameIdentifiers":[]},{"name":"Frey, Beat","givenName":"Beat","familyName":"Frey","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research"],"nameIdentifiers":[]},{"name":"Morris, Aaron W.","givenName":"Aaron W.","familyName":"Morris","affiliation":["University of Minnesota, Department of Fisheries, Wildlife and Conservation Biology"],"nameIdentifiers":[]},{"name":"Bump, Joseph K.","givenName":"Joseph K.","familyName":"Bump","affiliation":["University of Minnesota, Department of Fisheries, Wildlife and Conservation Biology"],"nameIdentifiers":[]}],"titles":[{"lang":"en","title":"Elk and bison carcasses in Yellowstone, USA"}],"publisher":"EnviDat","container":{},"publicationYear":2020,"subjects":[{"lang":"en","subject":"BACTERIA"},{"lang":"en","subject":"BIOGEOCHEMICAL CYCLING"},{"lang":"en","subject":"CARCASS"},{"lang":"en","subject":"CARRION"},{"lang":"en","subject":"DECOMPOSITION"},{"lang":"en","subject":"DETRITUS"},{"lang":"en","subject":"FUNGI"},{"lang":"en","subject":"SOIL BIODIVERSITY"},{"lang":"en","subject":"WOLF"},{"lang":"en","subject":"YELLOWSTONE"}],"contributors":[{"name":"Risch, Anita C.","givenName":"Anita C.","familyName":"Risch","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research"],"contributorType":"ContactPerson","nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0531-8336","nameIdentifierScheme":"ORCID"}]},{"name":"Plant-Animal Interactions","nameType":"Organizational","affiliation":[],"contributorType":"ResearchGroup","nameIdentifiers":[]}],"dates":[{"date":"2017-06-20/2017-12-31","dateType":"Collected"},{"date":"2020","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsRequiredBy","relatedIdentifier":"https://www.envidat.ch/dataset/0610ae35-071b-4711-98dc-0f750d1f0ab4/resource/ee8e69e2-865e-4210-8fb5-5325c240bf04/download/risch_et_al_fe_2020.xlsx","relatedIdentifierType":"URL"}],"relatedItems":[{"titles":[{"title":"elk-and-bison-carcasses-in-yellowstone-usa"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/0610ae35-071b-4711-98dc-0f750d1f0ab4/resource/ee8e69e2-865e-4210-8fb5-5325c240bf04/download/risch_et_al_fe_2020.xlsx","relatedItemIdentifierType":"URL"}}],"sizes":["17955 bytes"],"formats":["application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"],"version":"1.0","rightsList":[{"lang":"en","rights":"ODC Open Database License v1.0","rightsUri":"http://www.opendatacommons.org/licenses/odbl/1.0/","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"odbl-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"lang":"en","description":"This dataset contains all data on which the following publication below is based.\nPaper Citation:\nRisch, 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 \ndoi: ...\nMethods\nStudy area and study sites\nThis 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, \u0026amp; 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. \nFor 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 \u0026amp; Johnson, 1951). \nThe 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 \u0026amp; 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, \u0026amp; 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). \nEcosystem functions and soil properties\nWe 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).\nQuality 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 \u0026amp; Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, \u0026amp; 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, \u0026amp; 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. \nAbundances 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, \u0026amp; Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight).\nThe 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.\nIn addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter \u0026amp; Howson, 1977; Latter \u0026amp; 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 \u0026amp; Walton, 1988; Risch, Jurgensen, \u0026amp; Frank, 2007; Withington \u0026amp; 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 \u0026amp; 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.\nStatistical analyses\nUnivariate analyses for ecosystem functions, soil biotic and abiotic properties\nWe 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, \u0026amp; 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 \u0026amp; 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.\nWe 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). \nMultivariate analyses\nRare OTUs, defined as OTUs with a low abundance of reads, were retained in multivariate methods because they only marginally influence these analyses (Gobet, Quince, \u0026amp; 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). \nIndicator species analyses were performed using the multipatt function implemented in the “indicspecies” package version 1.7.6 with 100000 permutations (De Caceres \u0026amp; 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, \u0026amp; 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).\nReferences\nAbarenkov, 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\nBengtsson-Palme, J., Hartmann, M., Eriksson, K. M., Pal, C., Thorell, K., Larsson, D. G. J., \u0026amp; 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\nBengtsson-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\nBivand, 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.\nBump, J. K., Peterson, R. O., \u0026amp; Vucetich, J. A. (2009). Wolves modulate soil nutrient heterogeneity and foliar nitrogen by configuring the distribution of ungulate carcasses. Ecology, 90(11), 3159–3167.\nBump, J. K., Webster, C. R., Vucetich, J. A., Peterson, R. O., Shields, J. M., \u0026amp; 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\nDanell, K., Berteaux, D., \u0026amp; Brathen, K. A. (2002). Effect of muskox carcasses on nitrogen concentration in tundra vegetation. Arctic, 55(4), 389392.\nDe Caceres, M., \u0026amp; Jansen, F. (2016). indicspecies: relationship between species and groups of species. R package version 1.7.6.\nDe Cáceres, M., Legendre, P., \u0026amp; 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\nEdgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10, 996.\nEdgar, R. C., \u0026amp; 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\nFrey, B., Niklaus, P. A., Kremer, J., Lüscher, P., \u0026amp; 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\nFrey, B., Rime, T., Phillips, M., Stierli, B., Hajdas, I., Widmer, F., \u0026amp; Hartmann, M. (2016). Microbial diversity in European alpine permafrost and active layers. FEMS Microbial Ecology, 92(3), fiw018.\nFrossard, A., Donhauser, J., Mestrot, A., Gygax, S., Bååth, E., \u0026amp; 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\nGeremia, C., Wallen, R., \u0026amp; 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.\nGobet, A., Quince, C., \u0026amp; 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\nLatter, P., \u0026amp; Howson, G. (1977). The use of cotton strips to indicate cellulose decomposition in the field. Pedobiologia, (17), 145–155.\nLatter, P., \u0026amp; 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.\nLenth, R., \u0026amp; Love, J. (2018). lsmeans: least-squares means. R package version 2.27-62.\nMartin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal, 17(1), 10–12.\nMeagher, M. M. (1973). The bison of Yellowstone National Park. NPS Scientific Monograph (Vol. 1). National Park Service, Yellowstone Center for Resources.\nMelis, C., Selva, N., Teurlings, I., Skarpe, C., Linnell, J. D. C., \u0026amp; 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\nNikolenko, S. I., Korobeynikov, A. I., \u0026amp; 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\nOksanen, 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.\nPinheiro, J., Bates, D., DebRoy, S., \u0026amp; Sarkar, D. (2018). nlme: Linear and nonlinear mixed effect models. R package version 3.1-131.1.\nQuast, 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\nQuimby, D. C., \u0026amp; Johnson, D. E. (1951). Weights and measurements of Rocky Mountain elk. Journal of Wildlife Management, 15, 57–62.\nR Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Zurich, Switzerland.\nR Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.\nRime, T., Hartmann, M., Brunner, I., Widmer, F., Zeyer, J., \u0026amp; 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\nRisch, A. C., Jurgensen, M. F., \u0026amp; 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\nSchloss, 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\nSmith, 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.\nWang, Q., Garrity, G. M., Tiedje, J. M., \u0026amp; 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\nWithington, C., \u0026amp; 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.\nPlease cite this paper together with the citation for the datafile.","descriptionType":"Abstract"},{"lang":"en","description":"Resource Title: elk-and-bison-carcasses-in-yellowstone-usa\n Size: 17955 bytes\n Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet\n URL: https://www.envidat.ch/dataset/0610ae35-071b-4711-98dc-0f750d1f0ab4/resource/ee8e69e2-865e-4210-8fb5-5325c240bf04/download/risch_et_al_fe_2020.xlsx\n Description: Site ID for different carcasses across the Yellowstone Landscape\nLocation Sampling location, either under carcass or control plot\nSpecies Indicating whether carcass was from bison or elk\nGender Gener of carcass; F = female, M = male, ? = unknown\nLat Latitude\nLong Longitude\nDSK Days since kill; in days\nSoil.N Soil N concentration (%)\nSoil.C Soil C concentration (%)\nVeg.C Vegetation C concentration (%); dominant grasses sampled\nVeg.N Vegetation N concentration (%); dominant grasses sampled\nVeg.CtoN Vegetation C to N ratio; dominant grasses sampled\nSoil.resp Soil respiration (g m-2 h-1)\nsoil.temp Soil temperature (°C)\nSoil.moist Soil moisture (volumentric; %)\nCRR Cotton rotting rate\nbacteria.richness Bacteria richness\nbacteria.diversity Bacteria diverstiy\nfungi.richness Fungi richness\nfungi.diversity Fungi diversity\nbacteria.abundance Bacteria abundance\nfungi.abundance Fungi abundance","descriptionType":"Other"}],"geoLocations":[{"geoLocationPolygon":[{"polygonPoint":{"pointLatitude":45.81802,"pointLongitude":5.95587}},{"polygonPoint":{"pointLatitude":47.80838,"pointLongitude":5.95587}},{"polygonPoint":{"pointLatitude":47.80838,"pointLongitude":10.49203}},{"polygonPoint":{"pointLatitude":45.81802,"pointLongitude":10.49203}},{"polygonPoint":{"pointLatitude":45.81802,"pointLongitude":5.95587}}]},{"geoLocationPlace":"Yellowstone National Park, United States"}],"fundingReferences":[{"funderName":"United States National Science Foundation","awardNumber":"NSF ID#1545611"},{"funderName":"United States National Science Foundation","awardNumber":"NSF ID#1556676"}],"url":"https://www.envidat.ch/#/metadata/elk-and-bison-carcasses-in-yellowstone-usa","contentUrl":null,"metadataVersion":5,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-06-03T05:55:12Z","registered":"2020-06-03T05:55:12Z","published":null,"updated":"2026-05-15T23:13:46Z"},"relationships":{"client":{"data":{"id":"ethz.wsl","type":"clients"}}}},{"id":"10.16904/envidat.155","type":"dois","attributes":{"doi":"10.16904/envidat.155","identifiers":[{"identifier":"https://www.envidat.ch/#/metadata/e9a393f6-f3fe-4917-b533-249eeefbde5b","identifierType":"URL"}],"creators":[{"name":"Kirchner, James W.","givenName":"James W.","familyName":"Kirchner","affiliation":["ETH Zurich","Swiss Federal Research Institute WSL","University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6577-3619","nameIdentifierScheme":"ORCID"}]},{"name":"Godsey, Sarah E.","givenName":"Sarah E.","familyName":"Godsey","affiliation":["Idaho State University","University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6529-7886","nameIdentifierScheme":"ORCID"}]},{"name":"Solomon, Madeline","givenName":"Madeline","familyName":"Solomon","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Osterhuber, Randall","givenName":"Randall","familyName":"Osterhuber","affiliation":["Central Sierra Snow Laboratory","University of California, Berkeley"],"nameIdentifiers":[]},{"name":"McConnell, Joseph R.","givenName":"Joseph R.","familyName":"McConnell","affiliation":["Desert Research 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FLOW"},{"lang":"en","subject":"SNOWMELT"},{"lang":"en","subject":"STREAMFLOW"}],"contributors":[{"name":"Kirchner, James W.","givenName":"James W.","familyName":"Kirchner","affiliation":["ETH Zurich"],"contributorType":"ContactPerson","nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6577-3619","nameIdentifierScheme":"ORCID"}]},{"name":"Physics of Environmental 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Raw data for publication \"Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties\""}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/14280e45-7eee-4f1c-93cd-9f00083ddcc8/resource/13089b78-5a54-47a5-abe2-243a1e32772d/download/risch-et-al.-gcb_2020_raw.data.xlsx","relatedItemIdentifierType":"URL"}}],"sizes":["39920 bytes"],"formats":["application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"],"version":"1.0","rightsList":[{"lang":"en","rights":"ODC Open Database License v1.0","rightsUri":"http://www.opendatacommons.org/licenses/odbl/1.0/","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"odbl-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"lang":"en","description":"This dataset contains all data on which the following publication below is based.\nPaper Citation:\nRisch Anita C., Zimmermann, Stefan, Moser, Barbara, Schütz, Martin, Hagedorn, Frank, Firn, Jennifer, Fay, Philip A., Adler, Peter B., Biederman, Lori A., Blair, John M., Borer, Elizabeth T., Broadbent, Arthur A.D., Brown, Cynthia S., Cadotte, Marc W., Caldeira, Maria C., Davies, Kendi F., di Virgilio, Augustina, Eisenhauer, Nico, Eskelinen, Anu, Knops, Johannes M.H., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Prober, Suzanne M., Seabloom, Eric W., Siebert, Julia, Silveira, Maria L. , Speziale, Karina L., Stevens, Carly J., Tognetti, Pedro M., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties. Global Change Biology\nPlease cite this paper together with the citation for the datafile.\nWe assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth’s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).","descriptionType":"Abstract"},{"lang":"en","description":"Resource Title: Risch et al. 2020 - Raw data for publication \"Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties\"\n Size: 39920 bytes\n Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet\n URL: https://www.envidat.ch/dataset/14280e45-7eee-4f1c-93cd-9f00083ddcc8/resource/13089b78-5a54-47a5-abe2-243a1e32772d/download/risch-et-al.-gcb_2020_raw.data.xlsx\n Description: we assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth’s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).\n\nStudy sites and experimental design\nThe 22 sites contributing to this project are part of the Nutrient Network Global Research Cooperative (NutNet, https://nutnet.umn.edu/). Mean annual temperature across our 22 sites ranged from -4 to 22°C, mean annual precipitation from 252 to 1,592 mm, and elevations from 6 to 4,261 m above sea level (Fig. 1, Supplementary Table S1). Soil organic C varied from 0.8 to 7.8%, soil total N from 0.1 to 0.6%, and the soil C:N ratio from 9.1 to 21.5. Soil clay content spanned from 3.0 to 35%, and soil pH from 3.4 to 7.6 (Supplementary Table S2). Thus, the sites covered a wide range of environments in which grasslands occur (Fig. 1, Supplementary Table S1 \u0026amp; S2). \nAt each site, the effects of nutrient addition and herbivore removal were tested via a randomized-block design (Borer et al., 2014; Supplementary Fig. S1a). Three replicate blocks with 10 treatment plots each were established at each site, with the exception of the site at bldr.us, where only two blocks were established (Supplementary Fig. S1a). The 10 plots were randomly assigned to a nutrient or fencing treatment, but only a subset of four plots was used in the current study, each with a different treatment (see below; Supplementary Fig. S1a). All plots were 5 x 5 m and divided into four 2.5 x 2.5 m subplots (Supplementary Fig. S1b). Each subplot was further divided into four 1 x 1 m square sampling plots, one of which was set aside for soil sampling (Borer et al., 2014; Supplementary Fig. S1b). Plots were separated by at least 1 m wide walkways. \nIn this study, we collected data from the following four treatments: (i) untreated control plots (Control), (ii) herbivore removal plots (Fence), (iii) plots fertilized with N, P, K, plus nine essential macro and micronutrients (NPK), and (iv) plots with simultaneous fertilizer addition and herbivore removal (NPK+Fence; Supplementary Fig. S1a). The number of years of treatment differed among sites (2 – 9 years since start of treatment; Supplementary Table S1). For the nutrient additions, all sites applied 10 g N m-2 yr-1 as time-release urea; 10 g P m-2 yr-1 as triple-super phosphate; 10 g K m-2 yr-1 as potassium sulfate. A micro-nutrient mix (Fe, S, Mg, Mn, Cu, Zn, B, Mo, Ca) was applied at 100 g m-2 together with K in the first year of treatments but not thereafter. \nThe vertebrate herbivore removal treatment (Fence) was established by fencing two plots, one control and one NPK plot, within each of the blocks (Supplementary Fig. S1a). We designed the fences so that they would effectively exclude aboveground mammalian herbivores with a body mass of over 50 g (Borer et al., 2014). At the majority of sites, the height of the fences was 180 cm, and the fence design included wire mesh (1 cm holes) on the first 90 cm along with a 30 cm outward-facing flange stapled to the ground to exclude burrowing animals; climbing and subterranean animals may potentially still access these plots (Borer et al., 2014). For slight modifications in fence design at a few sites see Supplementary Table S3. While most sites only had native herbivores, a few sites (4) were also grazed by domestic animals (Supplementary Table S1).\nPotential and realized soil net N mineralization, ammonification, nitrification and other soil properties \nEach site participating in the study received a package containing identical material from the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) to be used for sampling and on-site N incubations. For the field incubation, we followed the protocol by Risch et al. (2015, 2019). Briefly, we drove a 5 x 15 cm (diameter x depth) steel cylinder 13.5 cm deep into the soil after clipping the vegetation at randomized locations in each plot. The top 1.5 cm of the cylinder remained empty to capture incoming N from run-off or deposition with a polyester mesh bag (mesh-size 250 µm) filled with 13.2 ± 0.9 g of acidic and alkaline exchanger resin (1:1 mixture; ion-exchanger I KA/ion-exchanger III AA, Merck AG, Darmstadt). The bag was fixed in place with a metal Seeger ring (Bruetsch-Rüegger Holding, Urdorf, Switzerland). We then removed 1.5 cm soil at the bottom of the cylinder and placed another resin bag to capture N leached from the soil column. The exchange resin was saturated with H+ and Cl- prior to filling the bags by stirring the mixture in 1.2M HCl for 1 h and then rinsing it with demineralized water until the electrical conductivity of the water reached 5 μS/cm. The cylinders were then re-inserted into the cored hole, level with the soil surface, and incubated for an average of 42 days (range 40 to 57days). The individual site coordinators chose the timing of incubation to start approximately six weeks prior to peak plant biomass production. All incubations were completed between February 2015 and January 2016 accounting for differences in growing season between northern and southern hemispheres. At the end of the incubation, the cylinders were re-collected and immediately shipped to WSL in an insulated box together with cold packs to halt further mineralization. Gloves were worn at all times to avoid contamination of the samples. Upon arrival at WSL, we extracted the resin bags and a 20 g subsample of sieved soil (4 mm) separately in a 100 ml PE-bottle with 80 ml 1 M KCl for 1.5 h on an end-over-end shaker and filtered through ashless folded filter paper (DF 5895 150, ALBET LabScience). We measured NO3- (colorimetrically; Norman \u0026amp; Stucki, 1981) and NH4+concentrations (flow injection analysis; FIAS 300, Perkin Elmer) on these filtrates. \nAt the start of the field incubation, we additionally collected two soil cores of 5 x 12 cm (diameter x depth) in each sampling plot and composited them to measure potential soil net Nmin, soil chemical and biological properties (see below). We also collected an additional sample (5 x 12 cm) to assess soil physical properties, which remained within the steel cylinder. Both ends were tightly closed with plastic caps. Cores were carefully packed to avoid further disturbance, and together with the composited soil samples, were shipped to the laboratory at WSL within a few days after collection. \nFrom the composited samples, we extracted an equivalent of 20 g dry soil with KCl, as described above, and measured NO3- and NH4+concentrations. Realized soil net Nmin was then calculated as the difference between the inorganic N content of samples collected at the end of the incubation (plus N extracted from the bottom resin bag) and the N content at the beginning of the incubation; values were scaled to represent daily mineralization rates (mg N kg-1soil d-1; Risch et al., 2015). Realized soil net Nmin values represent an average period of 42 days prior to peak biomass, typically the highest period of biological activity, and not the entire year (Risch et al., 2019). \nA second subsample of the composited sample was used to determine potential soil net Nmin in the laboratory (Risch et al., 2019). Briefly, we weighed duplicate samples (8 g dry soil) into 50-ml Falcon tubes. Soil moisture was brought to 60% of the field capacity of each plot, the Falcon tubes tightly closed and then incubated at 20°C for 42 days in a dark room. Every week the Falcon tubes were opened and ventilated. At the end of the incubation, the soil samples were extracted the same way as described above and NO3- and NH4+ was determined. Potential soil net Nmin was calculated as the difference between the N content before and after the incubation and scaled to represent daily values (mg N kg-1soil d-1). Using our NO3- and NH4+ measures we also calculated potential and realized soil net nitrification and soil net ammonification to be able to better understand the drivers of fertilization and herbivore removal effects on potential and realized soil net Nmin. \nA third subsample of the composite soil sample was sieved (2 mm mesh) and microbial biomass (μg Cmic g-1 soil dry weight) was estimated by measuring the maximal respiratory response to the addition of glucose solution (4 mg glucose per g soil dry weight dissolved in distilled water; substrate-induced respiration method) on approximately 5.5 g of soil (Anderson \u0026amp; Domsch, 1978). The rest of the composited sample was dried at 65°C for 48 h, ground and sieved (2 mm mesh) to assess a series of soil chemical properties (Risch et al., 2019). We measured the percentage of clay as an indicator of soil texture (Gee \u0026amp; Bauder, 1986; Risch et al., 2019). \nStatistical analyses\nPotential and realized soil net Nmin were square root transformed to account for a highly skewed data distribution (yt = sign(y)*sqrt|y|; negative values in the data set impeded log transformation). To assess treatment effects on potential and realized soil net Nmin, we used linear mixed effects models (LMMs) fitted by maximum likelihood using the lme function from the nlme package (version 3.131.1; Pinheiro et al., 2016), R version 3.6.1; R Foundation for Statistical Computing. Treatment (Control, Fence, NPK, NPK+Fence) was a fixed factor, with site and block as random factors, where block was nested within site. We also tested for effects of time since start of treatments in preliminary analyses by adding total treatment years as an additional fixed factor. We did not find a significant effect of years of treatment, and thus dropped this variable from the models. The LMMs were corrected using varIdent if the homogeneity of variance criterion was not met (Pinheiro et al., 2016). To visualize our results, we calculated treatment effects using Cohens’ d statistic (Cohen, 1977; Koricheva et al., 2013). Note that calculating response ratios (or log response ratios) was not possible with our data, as we have both negative and positive values. We also fitted LMMs for potential and realized soil net ammonification and nitrification to gain more insight into how global change affects the processes underpinning potential and realized soil net Nmin. We also sqrt-transformed (see above) these dependent variables. Treatment was included as a fixed factor with random factors as described above. In addition, we assessed how potential and realized soil net Nmin were related to potential and realized soil net ammonification and nitrification, respectively. For this, we calculated site averages for each treatment separately. We then ran LMMs, with potential and realized soil net Nmin as the dependent variable, potential and realized soil net ammonification/nitrification as the independent ones. Site was included as a random factor. \nBased on our previous work (Risch et al., 2019) and the existing literature (Schimel \u0026amp; Bennett, 2004; Liu et al., 2017), we developed a priori causal conceptual models of relationships among treatments, environmental drivers, and potential and realized soil net Nmin (Supplementary Fig. S2) to test with structural equation modelling (SEM) using a d-sep approach (Shipley, 2009; Lefcheck, 2016). The variables included in the model were long-term climatic conditions, specifically, site-level mean annual precipitation (MAP) and temperature of the wettest quarter (T.q.wet), plot-level soil texture (clay content) and soil microbial biomass. Mean annual precipitation and T.q.wet were obtained from WorldClim (Hijmans et al., 2005) (http://www.worldclim.org/) and together with the experimental treatments were predicted to directly affect soil properties and soil net Nmin (Supplementary Fig. S2). Soil clay content was, in turn, predicted to affect microbial biomass and soil net Nmin. Because we determined microbial biomass prior to incubating the samples in the laboratory or field, we assumed that the abundance of these microbes would be responsible for N process rates and not vice versa (Supplementary Fig. S2). We tested our conceptual model (Supplementary Fig. S2) using the piecewiseSEM package (version 2.0.2; Lefcheck, 2016) in R 3.4.0, in which a structured set of linear models are fitted individually. This approach allowed us to account for the nested experimental design, and overcome some of the limitations of standard structural equation models, such as small sample sizes (Shipley, 2009; Lefcheck, 2016). We used the lme function of the nlme package to model response variables, including site as a random factor. Good fit of the SEM was assumed when Fisher’s C values were non-significant (p \u0026gt; 0.05). For all significant interactions between covariates and experimental treatments detected in the SEMs, we calculated treatment effect sizes, i.e. the differences in potential or realized soil net Nmin between Control and treatments (Fence, NPK, NPK+Fence) and plotted these values against the climate or soil covariates. Finally, we fitted LMMs for the soil variables included in our SEMs, with treatment as the fixed factor, and with site and block as random factors, where block was nested within site.","descriptionType":"Other"}],"geoLocations":[{"geoLocationPolygon":[{"polygonPoint":{"pointLatitude":-54.97761367069625,"pointLongitude":-132.1875}},{"polygonPoint":{"pointLatitude":61.270232790000634,"pointLongitude":-132.1875}},{"polygonPoint":{"pointLatitude":61.270232790000634,"pointLongitude":158.90625}},{"polygonPoint":{"pointLatitude":-54.97761367069625,"pointLongitude":158.90625}},{"polygonPoint":{"pointLatitude":-54.97761367069625,"pointLongitude":-132.1875}}]},{"geoLocationPlace":"Global"}],"fundingReferences":[{"funderName":"WSL","awardNumber":"two internal competitive WSL grants"},{"funderName":"National Science Foundation Research Coordination Network","awardNumber":"NSF-DEB-1042132"},{"funderName":"Long-Term Ecological Research 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C.","familyName":"Risch","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research, Switzerland"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0531-8336","nameIdentifierScheme":"ORCID"}]},{"name":"Báez Jacome, Selene V.","givenName":"Selene V.","familyName":"Báez Jacome","affiliation":["Escuela Politécnica Nacional del Ecuador, Ecuador."],"nameIdentifiers":[]},{"name":"Biederman, Lori A.","givenName":"Lori A.","familyName":"Biederman","affiliation":["Iowa State University, USA"],"nameIdentifiers":[]},{"name":"Zimmermmann, Stephan","givenName":"Stephan","familyName":"Zimmermmann","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research, Switzerland"],"nameIdentifiers":[]},{"name":"Blair, John M.","givenName":"John M.","familyName":"Blair","affiliation":["Kansas State University, USA"],"nameIdentifiers":[]},{"name":"Brown, Cynthia S.","givenName":"Cynthia S.","familyName":"Brown","affiliation":["Colorado State University, USA"],"nameIdentifiers":[]},{"name":"Caldeira, Maria C.","givenName":"Maria C.","familyName":"Caldeira","affiliation":["Universidade de Lisboa, Portugal"],"nameIdentifiers":[]},{"name":"Collins, Scott L.","givenName":"Scott L.","familyName":"Collins","affiliation":["University of New Mexico, USA"],"nameIdentifiers":[]},{"name":"Daleo, Pedro","givenName":"Pedro","familyName":"Daleo","affiliation":["Universidad Nacional de Mar del Plata, Argentina"],"nameIdentifiers":[]},{"name":"di Virgilio, Agustina","givenName":"Agustina","familyName":"di Virgilio","affiliation":["Universidad Nacional del Comahue, Argentina"],"nameIdentifiers":[]},{"name":"Ebeling, Anne","givenName":"Anne","familyName":"Ebeling","affiliation":["University Jena, Germany"],"nameIdentifiers":[]},{"name":"Seabloom, Eric W.","givenName":"Eric W.","familyName":"Seabloom","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research"],"nameIdentifiers":[]},{"name":"Eisenhauer, Nico","givenName":"Nico","familyName":"Eisenhauer","affiliation":["German Centre for Integrative Biodiversity Research (iDiv) , Germany"],"nameIdentifiers":[]},{"name":"Esch, Ellen","givenName":"Ellen","familyName":"Esch","affiliation":["University of California San Diego, USA"],"nameIdentifiers":[]},{"name":"Eskelinen, Anu","givenName":"Anu","familyName":"Eskelinen","affiliation":["Helmholtz Center for Environmental Research – UFZ, Germany"],"nameIdentifiers":[]},{"name":"Ochoa-Hueso, Raul","givenName":"Raul","familyName":"Ochoa-Hueso","affiliation":["University of Cádiz, Spain"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-1839-6926","nameIdentifierScheme":"ORCID"}]},{"name":"Hagenah, Nicole","givenName":"Nicole","familyName":"Hagenah","affiliation":["University of Pretoria, South Africa"],"nameIdentifiers":[]},{"name":"Hautier, Yann","givenName":"Yann","familyName":"Hautier","affiliation":["Utrecht University, The Netherlands."],"nameIdentifiers":[]},{"name":"Kirkman, Kevin P.","givenName":"Kevin P.","familyName":"Kirkman","affiliation":["University of KwaZulu-Natal, South Africa"],"nameIdentifiers":[]},{"name":"MacDougall, Andrew S.","givenName":"Andrew S.","familyName":"MacDougall","affiliation":["University of Guelph, Canada"],"nameIdentifiers":[]},{"name":"Moore, Joslin L.","givenName":"Joslin L.","familyName":"Moore","affiliation":["Monash University, Australia"],"nameIdentifiers":[]},{"name":"Power, Sally A.","givenName":"Sally A.","familyName":"Power","affiliation":["Western Sydney University, Australia."],"nameIdentifiers":[]},{"name":"Harpole, W. Stanley","givenName":"W. Stanley","familyName":"Harpole","affiliation":["Helmholtz Center for Environmental Research – UFZ, Germany"],"nameIdentifiers":[]},{"name":"Prober, Suzanne M.","givenName":"Suzanne M.","familyName":"Prober","affiliation":["CSIRO Land and Water, Australia"],"nameIdentifiers":[]},{"name":"Roscher, Christiane","givenName":"Christiane","familyName":"Roscher","affiliation":["Helmholtz Center for Environmental Research – UFZ, Germany"],"nameIdentifiers":[]},{"name":"Sankaran, Mahesh","givenName":"Mahesh","familyName":"Sankaran","affiliation":["National Centre for Biological Sciences, India"],"nameIdentifiers":[]},{"name":"Siebert, Julia","givenName":"Julia","familyName":"Siebert","affiliation":["German Centre for Integrative Biodiversity Research (iDiv) , Germany"],"nameIdentifiers":[]},{"name":"Schütz, Martin","givenName":"Martin","familyName":"Schütz","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research, Switzerland"],"nameIdentifiers":[]},{"name":"Speziale, Karina L.","givenName":"Karina L.","familyName":"Speziale","affiliation":["Universidad Nacional del Comahue, Argentina"],"nameIdentifiers":[]},{"name":"Tognetti, Pedro","givenName":"Pedro","familyName":"Tognetti","affiliation":["Universidad de Buenos Aires, Argentina."],"nameIdentifiers":[]},{"name":"Virtanen, Risto","givenName":"Risto","familyName":"Virtanen","affiliation":["Helmholtz Center for Environmental Research – UFZ, Germany"],"nameIdentifiers":[]},{"name":"Yahdjian, Laura","givenName":"Laura","familyName":"Yahdjian","affiliation":["Universidad de Buenos Aires, Argentina"],"nameIdentifiers":[]},{"name":"Moser, Barbara","givenName":"Barbara","familyName":"Moser","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research, Switzerland"],"nameIdentifiers":[]},{"name":"Knops, Johannes M.H.","givenName":"Johannes M.H.","familyName":"Knops","affiliation":["University of Nebraska, USA"],"nameIdentifiers":[]},{"name":"Frey, Beat","givenName":"Beat","familyName":"Frey","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research, Switzerland"],"nameIdentifiers":[]},{"name":"Firn, Jennifer","givenName":"Jennifer","familyName":"Firn","affiliation":["Queensland University of Technology, Australia"],"nameIdentifiers":[]},{"name":"Fay, Philip A.","givenName":"Philip A.","familyName":"Fay","affiliation":["USDA-ARS Grassland, Soil, and Water Research Laborator, USA"],"nameIdentifiers":[]},{"name":"Hagedorn, Frank","givenName":"Frank","familyName":"Hagedorn","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research, Switzerland"],"nameIdentifiers":[]},{"name":"Borer, Elizabeth T.","givenName":"Elizabeth T.","familyName":"Borer","affiliation":["University of Minnesota, USA"],"nameIdentifiers":[]},{"name":"McCulley, Rebecca L.","givenName":"Rebecca L.","familyName":"McCulley","affiliation":["University of Kentucky, USA"],"nameIdentifiers":[]},{"name":"Broadbent, Arthur A.D.","givenName":"Arthur A.D.","familyName":"Broadbent","affiliation":["The University of Manchester, United Kingdom"],"nameIdentifiers":[]},{"name":"Stevens, Carly J.","givenName":"Carly J.","familyName":"Stevens","affiliation":["Lancaster University, United Kingdom"],"nameIdentifiers":[]},{"name":"Silveira, Maria L.","givenName":"Maria L.","familyName":"Silveira","affiliation":["University of Florida, USA"],"nameIdentifiers":[]},{"name":"Adler, Peter B.","givenName":"Peter B.","familyName":"Adler","affiliation":["Utah State University, USA"],"nameIdentifiers":[]}],"titles":[{"lang":"en","title":"Soil net nitrogen mineralisation across global grasslands"}],"publisher":"EnviDat","container":{},"publicationYear":2019,"subjects":[{"lang":"en","subject":"GLOBAL"},{"lang":"en","subject":"GRASSLAND"},{"lang":"en","subject":"MINERALIZATION"},{"lang":"en","subject":"NUTRIENT NETWORK"},{"lang":"en","subject":"SOIL"},{"lang":"en","subject":"SOIL BIOLOGY"},{"lang":"en","subject":"SOIL CHEMISTRY"},{"lang":"en","subject":"WORLDWIDE"}],"contributors":[{"name":"Risch, Anita C.","givenName":"Anita C.","familyName":"Risch","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research, Switzerland"],"contributorType":"ContactPerson","nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0531-8336","nameIdentifierScheme":"ORCID"}]},{"name":"Plant-Animal Interactions","nameType":"Organizational","affiliation":[],"contributorType":"ResearchGroup","nameIdentifiers":[]}],"dates":[{"date":"2015-01-01/2016-12-31","dateType":"Created"},{"date":"2019","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsRequiredBy","relatedIdentifier":"https://www.envidat.ch/dataset/109ef559-0459-4056-aff5-282374d01ff9/resource/24108011-8b7b-4da0-9742-2f05576ecbc1/download/risch-et-al._soil-net-nitrogen-mineralisation-aross-global-grasslands.xlsx","relatedIdentifierType":"URL"}],"relatedItems":[{"titles":[{"title":"soil-net-nitrogen-mineralisation-across-global-grasslands"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/109ef559-0459-4056-aff5-282374d01ff9/resource/24108011-8b7b-4da0-9742-2f05576ecbc1/download/risch-et-al._soil-net-nitrogen-mineralisation-aross-global-grasslands.xlsx","relatedItemIdentifierType":"URL"}}],"sizes":["33527 bytes"],"formats":["application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"],"version":"1.0","rightsList":[{"lang":"en","rights":"ODC Open Database License v1.0","rightsUri":"http://www.opendatacommons.org/licenses/odbl/1.0/","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"odbl-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"lang":"en","description":"This dataset contains all data on which the following publication below is based.\nPaper Citation:\nRisch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Schütz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2\nPlease cite this paper together with the citation for the datafile.\nWe conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/","descriptionType":"Abstract"},{"lang":"en","description":"Resource Title: soil-net-nitrogen-mineralisation-across-global-grasslands\n Size: 33527 bytes\n Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet\n URL: https://www.envidat.ch/dataset/109ef559-0459-4056-aff5-282374d01ff9/resource/24108011-8b7b-4da0-9742-2f05576ecbc1/download/risch-et-al._soil-net-nitrogen-mineralisation-aross-global-grasslands.xlsx\n Description: site.code: Site code\n\nContinent: Continent\n\nsampleID: Sample ID\n\nnn.latitude: Lattitude\n\nnn.longitude: Longitude\n\nbioclim.T.ann: Mean annual temperature (°C)\n\nbioclim.T.var: Temperature variability (°C)\n\nbioclim.T.q.wet: Temperature of the wettest quarter (°C)\n\nbioclim.T.q.dry: Temperature of the driest quarter (°C)\n\nbioclim.T.q.warm: Temperature of the warmest quarter (°C)\n\nbioclim.P.ann: Mean annual preciptiation 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They combine the modelling of uni- or bimodal diameter distributions and species compositions, i.e. the shares of stems of individual species. Generalised stand descriptions may be used to interpret tree species diversity, regeneration and harvest potentials on a plot-level basis, and to initialise forest models with representative stand data.\nThe data stored here were derived from the fourth campaigns of the Swiss National Forest Inventory (NFI). The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php).\n---------------------------------------\nThe file 'Data Figures 2 and 4' is publicly available and contains the data used to produce the Figures 2 and 4 published in the paper.\nThe files 'Data diameter modelling' and 'Data species modelling' contain all the data required to reproduce the diameter and species model building. The access to these two files is restricted as they contain raw data from the fourth Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement.\nThe files 'Script diameter and species modelling' and 'Functions diameter modelling' are publicly available and provide the R code used to derive the generalised stand descriptions from the Swiss NFI data.","descriptionType":"Abstract"},{"lang":"en","description":"Resource Title: Data Figures 2 and 4\n Size: 744 bytes\n Format: text/csv\n URL: https://www.envidat.ch/dataset/f8fe4971-0538-4d8e-b866-8d1cfd4b1584/resource/2e6a5bbe-6836-47b2-86bb-be16ba2fe81e/download/data_figures_2_4.csv\n Description: This csv file contains the data required to reproduce Figure 2 and Figure 4.\n\n* 'stand_type' states whether a row corresponds to NFI4 average summary statistics describing a generalised beech-dominated, spruce-dominated or an unspecified forest stand in Switzerland in its unimodal or bimodal form (e.g. 'beech.uni' refers to a generalised unimodal beech-dominated stand)\n* 'md' refers to the mean diameter\n* 'dq10' refers to the 10% quantile of the diameter\n* 'dq90' refers to the 90% quantile of the diameter\n* 'spr' refers to the spread between minimal and maximal diameter\n* 'nph' refers to the number of stems per hectare\n* 'o.qmd' refers to the overstorey quadratic mean diameter\n* 'rho.emp' refers to proportion parameter (rho) determined by breakpoint setting\n* 'ba' refers to the basal area per hectare\n* 'drel' refers to the relative diameter ('drel' = (d - min(d)) / (max(d) - min(d)))\n* 'temp' refers to the mean winter temperature (1988-2017, Dec-Feb)\n* 'dev' refers to the development stage of the stand (e.g. 'Young growth')\n* 'ftyp' refers to the potential climax forest type (e.g. 'Beech' refers to a beech-dominated stand)\n* 'str' refers to the stand structure\n* 'tillering' indicates whether the observed tree species on the NFI plot correspond to 'ftyp' ('TRUE') or not ('FALSE')\n* 'prodreg' refers to the Swiss production region (ecoregion)\n* 'fowner' refers to the forest ownership ('Public' or ' Private')","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Data diameter modelling\n Size: 243750 bytes\n Format: application/x-rds\n URL: https://www.envidat.ch/dataset/f8fe4971-0538-4d8e-b866-8d1cfd4b1584/resource/9cadbc57-7eab-4415-bf97-229d52cd412b/download/data_nfi4_diameter_modelling.rds\n Description: This rds file contains a list of 5085 data frames required to reproduce the uni- and bimodal diameter model. Each data frame comprises single-tree diameters ('dbh') and assigned representation factors ('repfa') at a single Swiss NFI4 plot.\n\n* 'dbh' refers to the diameter at breast height (130 cm)\n* 'repfa' refers to the representation factor used to scale up to one hectare","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Data species modelling\n Size: 450665 bytes\n Format: application/x-rds\n URL: https://www.envidat.ch/dataset/f8fe4971-0538-4d8e-b866-8d1cfd4b1584/resource/03162eba-0bd2-400a-b0f7-75c7cf117269/download/data_nfi4_species_modelling.rds\n Description: This rds file contains a data frame with single-tree and stand data required to reproduce the species composition model:\n\n* 'clnr' refers to the plot number\n* 'spec' refers to the main tree species group (12 groups)\n* 'ba' refers to the basal area per hectare\n* 'nph' refers to the number of stems per hectare\n* 'drel' refers to the relative diameter ('drel' = (d - min(d)) / (max(d) - min(d)))\n* 'temp' refers to the mean winter temperature (1988-2017, Dec-Feb)\n* 'dev' refers to the development stage of the stand (e.g. 'Young growth')\n* 'ftyp' refers to the potential climax forest type (e.g. 'Beech' refers to a beech-dominated stand)\n* 'str' refers to the stand structure\n* 'tillering' indicates whether the observed tree species on the NFI plot correspond to 'ftyp' ('TRUE') or not ('FALSE')\n* 'prodreg' refers to the Swiss production region (ecoregion)\n* 'fowner' refers to the forest ownership ('Public' or ' Private')","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Script diameter and species modelling\n Size: 16141 bytes\n Format: text/x-r\n URL: https://www.envidat.ch/dataset/f8fe4971-0538-4d8e-b866-8d1cfd4b1584/resource/0e6e2a50-fb05-4236-b53f-a5bafaf7a069/download/script_diameter_and_species_modelling.r\n Description: This R script can be used to derive the uni- and bimodal Weibull log-likelihood regression models (in short: uni- and bimodal model), the species composition model and the main results presented in Mey et al. 2021 using data from the fourth Swiss NFI ('data_NFI4_diameter_modelling.rds' and 'data_NFI4_species_modelling.rds') and the R functions stored in 'functions_diameter_modelling.R'.","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Functions diameter modelling\n Size: 20702 bytes\n Format: text/x-r\n URL: https://www.envidat.ch/dataset/f8fe4971-0538-4d8e-b866-8d1cfd4b1584/resource/e3f02601-e820-4df9-bcc9-c21bea82dc38/download/functions_diameter_modelling.r\n Description: This R file contains the functions used to produce the generalised diameter distributions in Mey et al. 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evaluations"}],"publisher":"EnviDat","container":{},"publicationYear":2021,"subjects":[{"lang":"en","subject":"ENERGY"},{"lang":"en","subject":"HYBRID CHOICE MODEL"},{"lang":"en","subject":"LANDSCAPE"},{"lang":"en","subject":"LANDSCAPE MEANINGS"},{"lang":"en","subject":"LANDSCAPE-TECHNOLOGY FIT"},{"lang":"en","subject":"MIXED RENEWABLE ENERGY LANDSCAPES"},{"lang":"en","subject":"PERCEIVED LANDSCAPE QUALITY"},{"lang":"en","subject":"RENEWABLE ENERGY"},{"lang":"en","subject":"RENEWABLE ENERGY MEANINGS"},{"lang":"en","subject":"STATED CHOICE MODEL"},{"lang":"en","subject":"SURVEY"}],"contributors":[{"name":"Salak, Boris","givenName":"Boris","familyName":"Salak","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research WSL"],"contributorType":"ContactPerson","nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4929-8376","nameIdentifierScheme":"ORCID"}]},{"name":"Economics and Social 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The data was collected between November 2018 to March 2019 using an online questionnaire and resulted on 1026 responses. The online questionnaire consisted of two main parts – (1) questions covering meanings related to landscapes, nature and renewable energy infrastructure, including the “fit” of landscape/REI combinations and (2) online choice experiment. While in the first part of the questionnaire we asked respondents about their personal connection to certain landscapes, to nature and to specific renewable energy infrastructures, we also asked them to evaluate the fitting of seven different Swiss landscapes (near natural alpine areas, northern alps, touristic alpine areas, agricultural plateau, urban plateau, jura ridges, urban alpine valley) with five different REI (wind, PV ground, PV roof, power lines) combinations. In the second part of the questionnaire, the stated choice experiment confronted respondents with 15 consecutive choice tasks, with each task involving a choice between two “energy system transformation” options and an opt-out option (none). Each choice option (beside the opt-out option) included four unlabeled attributes (landscape, wind energy infrastructure, photovoltaic energy infrastructure, high voltage overhead power line infrastructure) with varying levels. Due to data cleaning procedures (item nonresponse) the number of responses used within hybrid choice modelling and analysis was n=844 (12660 choice observations). An analysis of the hybrid choice model and further insights are presented in the article “How landscape-technology fit affects public evaluations of renewable energy infrastructure scenarios. A hybrid choice model.”","descriptionType":"Abstract"},{"lang":"en","description":"Resource Title: Energyscape RAW data\n Size: 277161 bytes\n Format: text/csv\n URL: https://www.envidat.ch/dataset/e408aaea-2ba5-47e8-8be4-2053c9528b0c/resource/7b8aea81-5547-4940-a463-5eb862955145/download/energyscape_questionnaire_dataset.csv\n Description: This dataset contains the raw data of the questionnaire including headers (column names). One line represents one respondent. Details to each variable (including scales) can be found in an attached PDF file.","descriptionType":"Other"},{"lang":"en","description":"Resource Title: ENERGYSCAPE base data variable description \n Size: 85200 bytes\n Format: application/pdf\n URL: https://www.envidat.ch/dataset/e408aaea-2ba5-47e8-8be4-2053c9528b0c/resource/b3362724-a5d3-445f-9692-60c7fdf9806e/download/energyscape_questionnaire_dataset_description.pdf\n Description: This file contains descriptions (variable name, variable description, scale) of all variables of the base data.","descriptionType":"Other"},{"lang":"en","description":"Resource Title: ENERGYSCAPE choice model attributes and tasks \n Size: 36812 bytes\n Format: application/pdf\n URL: https://www.envidat.ch/dataset/e408aaea-2ba5-47e8-8be4-2053c9528b0c/resource/967dc54a-0e06-4b63-afc4-34b4d518fa94/download/energyscape_choice_tasks_and_attributes.pdf\n Description: This data provides overview about the choice model design, the choice tasks and the choice attributes.","descriptionType":"Other"},{"lang":"en","description":"Resource Title: ENERGYSCAPE Landscape attribute of choice model\n Size: 498857 bytes\n Format: application/pdf\n URL: https://www.envidat.ch/dataset/e408aaea-2ba5-47e8-8be4-2053c9528b0c/resource/2615b640-9347-4c8e-890f-28c4b484900d/download/energyscape_attribute_landscape.pdf\n Description: This file illustrated the 7 landscapes applied in the choice model. 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4"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/c9ce4b0e-a00a-4a96-811c-99dd23d2f9a5/download/d_fig4.csv","relatedItemIdentifierType":"URL"}},{"titles":[{"title":"Data figure 5"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/322cf1b9-04b6-4c9f-8060-83341a2719cf/download/d_fig5.csv","relatedItemIdentifierType":"URL"}},{"titles":[{"title":"Data figure 6"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/c01ebb60-0184-4392-a562-4c3555e901fb/download/d_fig6.csv","relatedItemIdentifierType":"URL"}},{"titles":[{"title":"Data figure 7"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/4adfbe81-302b-4654-a07c-87827098b257/download/d_fig7.csv","relatedItemIdentifierType":"URL"}},{"titles":[{"title":"Data figure 8"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/24e1a8c9-6380-432a-8a0b-ca979da75b2d/download/d_fig8.csv","relatedItemIdentifierType":"URL"}},{"titles":[{"title":"Plot-level data for characterisation of 'latent reserves'"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/3ce25df8-138f-4abd-a2da-4afea2df26c1/download/d_plotlevel.csv","relatedItemIdentifierType":"URL"}},{"titles":[{"title":"Tree-level data for characterisation of 'latent reserves'"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/1bb1a8e4-0581-451e-b9bf-1dbbac120f4a/download/d_treelevel.csv","relatedItemIdentifierType":"URL"}},{"titles":[{"title":"Data for mortality analyses"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/5e3cd66f-bc2f-49ce-b300-0468eeee420c/download/d_mortality.csv","relatedItemIdentifierType":"URL"}}],"sizes":["10362 bytes","5508 bytes","198106 bytes","16424 bytes","5834 bytes","62372 bytes","2258862 bytes","1535133 bytes","7664896 bytes","851216 bytes"],"formats":["text/csv","text/csv","text/csv","text/csv","text/csv","text/csv","text/csv","text/csv","text/csv","text/csv"],"version":"1.0","rightsList":[{"lang":"en","rights":"WSL Data Policy","rightsUri":"https://www.wsl.ch/en/about-wsl/programmes-and-initiatives/envidat.html"}],"descriptions":[{"lang":"en","description":"The files refer to the data used in Portier et al. \"‘Latent reserves’: a hidden treasure in National Forest Inventories\" (2020) *Journal of Ecology*.\n \n**'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for -40 to -70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database: \n \n* Shrub forests were excluded.\n* Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested.\n* To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg \u0026amp; Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, \u0026amp; Wildi (1994).\n* Plots had to originate from natural regeneration. \n* Intensive livestock grazing must never have occurred on the plots.\n \nThe tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php).\n****\nThe files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. \nThe files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement.","descriptionType":"Abstract"},{"lang":"en","description":"Resource Title: Data figure 2\n Size: 10362 bytes\n Format: text/csv\n URL: https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/d40bae83-10d7-4545-80f0-29d6d138571e/download/d_fig2.csv\n Description: This file contains the data required to reproduce Figure 2: \n \n- \"FOREST_TYPE\" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention) \n- \"ELEV_VEG_ZONE\" refers to the elevation vegetation zone (colline and submontane, montane or subalpine) \n- \"SPECIES_GROUP\" refers to all the trees of a given species group (spruce, fir, pine, larch, stone pine, other conifers, beech, maple, ash, oak, chestnut, other broadleaves) \n- \"DENSITY\" refers to the proportion of trees (corrected for the inclusion probability of each tree) within each combination of forest type, elevation vegetation zone and species group. \n \nYoung-growth trees are not included in the density calculation.","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Data figure 3\n Size: 5508 bytes\n Format: text/csv\n URL: https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/744ed206-605b-43a0-a560-bd0f27777735/download/d_fig3.csv\n Description: This file contains the data required to reproduce Figure 3: \n \n- \"FOREST_TYPE\" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention) \n- \"ELEV_VEG_ZONE\" refers to the elevation vegetation zone (colline and submontane, montane or subalpine) \n- \"VARIABLE\" refers to the indicators described in the figure: Ellenberg indicators for light, temperature and moisture, as well as species richness. \n- \"mean\" refers to the mean value of each indicator for each combination of forest type and elevation vegetation zone (corrected for the inclusion probability of each tree). \n- \"se\" corresponds to the standard error.\n\nFor these calculations, site index was constrained to the range observed in 'latent reserves' with at least 40 years without intervention.","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Data figure 4\n Size: 198106 bytes\n Format: text/csv\n URL: https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/c9ce4b0e-a00a-4a96-811c-99dd23d2f9a5/download/d_fig4.csv\n Description: This file contains the data required to reproduce Figure 4: \n \n- \"FOREST_TYPE\" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention) \n- \"ELEV_VEG_ZONE\" refers to the elevation vegetation zone (colline and submontane, montane or subalpine) \n- \"SPECIES_GROUP\" refers to all the trees of a given species group (spruce, fir, pine, larch, stone pine, other conifers, beech, maple, ash, oak, chestnut, other broadleaves) \n- \"DBH_CL\" refers to the DBH (diameter at breast height) class to which a tree belongs to, in centimetres. \n- \"DENSITY\" refers to the proportion of trees (corrected for the inclusion probability of each tree) within each combination of forest type, elevation vegetation zone, species group and DBH class. \n \nThe inclusion probability of each tree was accounted for in the density calculation.","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Data figure 5\n Size: 16424 bytes\n Format: text/csv\n URL: https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/322cf1b9-04b6-4c9f-8060-83341a2719cf/download/d_fig5.csv\n Description: This file contains the data required to reproduce Figure 5: \n \n- \"FOREST_TYPE\" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention) \n- \"DOMINANT_SPECIES\" refers to the dominant species in a given plot (spruce, fir, other conifers, beech, other broadleaves) \n- \"VARIABLE\" refers to the attributes described in the figure: basal area in squared meters per hectare, median DBH in cm, stand density in number of stems per hectare, deadwood in tons per hectare, site index in tons per hectare per year. Basal area, median DBH and stand density are given both with and without including young-growth trees. \n- \"mean\" refers to the mean value of each attribute for each combination of forest type and dominant species (corrected for the inclusion probability of each tree). \n- \"se\" corresponds to the standard error. \n \nFor these calculations, site index was constrained to the range observed in ‘latent reserves’ with at least 40 years without intervention. Additionally, only NFI sample plots from the montane and subalpine zones were used as colline and submontane NFI plots from ‘latent reserves’ and managed forests are located in different geographical regions.","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Data figure 6\n Size: 5834 bytes\n Format: text/csv\n URL: https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/c01ebb60-0184-4392-a562-4c3555e901fb/download/d_fig6.csv\n Description: This file contains the data required to reproduce Figure 6: \n \n- \"FOREST_TYPE\" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention) \n- \"SPECIES_GROUP\" refers to all the trees of a given species group (spruce, fir, pine, larch, stone pine, other conifers, beech, maple, ash, oak, chestnut, other broadleaves) \n- \"MORTALITY_RATE\" refers to the mean annual mortality rate of each species group calculated according to Sheil and May (1996) \n- \"LOWER_CI\" and \"UPPER_CI\" refer to the lower and upper confidence intervals on the mortality rates, calculated according to Sheil and May (1996). \n\nReference \nSheil, D., \u0026amp; May, R. M. (1996). Mortality and recruitment rate evaluations in heterogeneous tropical forests. Journal of Ecology, 84(1), 91–100. doi: 10.2307/2261703","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Data figure 7\n Size: 62372 bytes\n Format: text/csv\n URL: https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/4adfbe81-302b-4654-a07c-87827098b257/download/d_fig7.csv\n Description: This file contains the data required to reproduce Figure 7: \n \n- \"SPECIES_GROUP\" refers to the species groups used in the mortality analyses (spruce, fir, pine, larch, stone pine, beech, maple and ash, oak, chestnut, other broadleaves) \n- \"VARIABLE\" refers to the explanatory variables used in the model and presented in the figure: DBH (diameter at breast height) and relBAI (relative basal area increment). \n- \"VALUE\" refers to the the values of the variables for which predictions were made. \n- \"PRED_MORTALITY_PROB\" refers to the predicted mortality probability for a given species group and value of either DBH or relBAI.","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Data figure 8\n Size: 2258862 bytes\n Format: text/csv\n URL: https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/24e1a8c9-6380-432a-8a0b-ca979da75b2d/download/d_fig8.csv\n Description: This file contains the data required to reproduce Figure 8: \n \n* \"SPECIES_GROUP\" refers to the species groups used in the mortality analyses (spruce, fir, pine, larch, stone pine, beech, maple and ash, oak, chestnut, other broadleaves) \n* \"BASAL_AREA\" refers to the values of the basal area in squared meters per hectare for which predictions were made \n* \"STAND_DENSITY\" refers to the values of stand density in number of stems per hectare for which predictions were made \n* \"NORM_PRED_MORTALITY_PROB\" refers to the normalised predicted mortality probability corresponding to a given species group and values of basal area and stand density.","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Plot-level data for characterisation of 'latent reserves'\n Size: 1535133 bytes\n Format: text/csv\n URL: https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/3ce25df8-138f-4abd-a2da-4afea2df26c1/download/d_plotlevel.csv\n Description: This table contains the plot-level information required to reproduce the charaterisation of 'latent reserves' and comparison to managed forests: \n \n* \"FOREST_TYPE\" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)\n* \"ELEV_VEG_ZONE\" refers to the elevation vegetation zone (colline and submontane, montane or subalpine) \n* \"DOMINANT_SPECIES\" refers to the dominant species in a given plot (spruce, fir, other conifers, beech, other broadleaves) \n* \"TIME_SINCE_INTERVENTION\" refers to the numbers of years since the last human intervention on the plot. This variable should be used to set the threshold of time since last intervention one wants to use to define 'latent reserves'.\n* \"VEGYEAR\" refers to the number of vegetation years between the measurements of a plot for NFI2 and NFI3.\n* \"BA\" provides the basal area in squared meters per hectare of a plot calculated from trees having a DBH of at least 12 cm.\n* \"BA_WITH_YG\" provides the basal area in squared meters per hectare of a plot calculated from trees having a DBH of at least 12 cm as well as young growth trees. \n* \"STAND_DENSITY\" provides the stand density in number of stems per hectare of a plot calculated from trees having a DBH of at least 12 cm.\n* \"STAND_DENSITY_WITH_YG\" provides the stand density in number of stems per hectare of a plot calculated from trees having a DBH of at least 12 cm as well as young growth trees. \n* \"SITE_INDEX\" refers to the site index of a plot, i.e. the total biomass productivity calculated according to Keller (1976).\n* \"DEADWOOD\" refers to the amount of deadwood of a plot in tons per hectare. \n\n******\n\nReference\nKeller, W. (1976). Einfacher ertragskundlicher Bonitätsschlüssel für Waldbestände in der Schweiz. Zurich, Switzerland: ETH Zurich.","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Tree-level data for characterisation of 'latent reserves'\n Size: 7664896 bytes\n Format: text/csv\n URL: https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/1bb1a8e4-0581-451e-b9bf-1dbbac120f4a/download/d_treelevel.csv\n Description: This table contains the tree-level information required to reproduce the charaterisation of 'latent reserves' and comparison to managed forests: \n \n* \"FOREST_TYPE\" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)\n* \"SOURCE\" differentiate inventoried trees (\"W\", DBH greater or equal to 12 cm) and young-growth trees measured on subplots (\"JW\"). \n* \"SPECIES\" refers to the species code of the Swiss NFI. \n* \"SPECIES_GROUP\" refers to which species group a tree belongs to (spruce, fir, pine, larch, stone pine, other conifers, beech, maple, ash, oak, chestnut, other broadleaves)\n* \"ALIVE_NFI2\" takes the value '1' when the tree was alive at the previous inventory (NFI2). Trees with no value (NA) were not measured at NFI2. This variable should be used to calculate mortality rates, as only trees that were present at NFI2 can be used to calculate the mortality rate between NFI2 and NFI3. \n* \"DEAD\" takes the value '1\u0026amp; when a tree was observed dead at NFI3, and the value '0' when it was observed alive. \n* \"INCLUSION_PROB\" represents the inclusion probability of a tree i.e. the number of trees that a given tree represents per hectare. This number depends on the DBH of the tree and on whether the plot area was reduced by a boundary (e.g. a forest edge). This variable should be used to weight the trees in some calculations. \n* \"DBH\" is the diameter at breast height of a tree in centimeters. \n* \"LIGHT\", \"TEMPERATURE\", \"MOISTURE\" refer to the value of the Ellenberg indicators for light, temperature and moisture that are species-specific. 'NA' is attributed when the value of an indicator is not available for a given species.","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Data for mortality analyses\n Size: 851216 bytes\n Format: text/csv\n URL: https://www.envidat.ch/dataset/702645a2-8965-40df-ae9d-8b81df220bd1/resource/5e3cd66f-bc2f-49ce-b300-0468eeee420c/download/d_mortality.csv\n Description: This table contains the tree- and plot-level data necessary to conduct the tree mortality analyses: \n \n* \"DEAD\" takes the value '0' when the tree was alive at NFI3 and '1' when it was observed dead\n* \"SPECIES\" refers to which species group the tree belongs to\n* \"relBAL\" is the relative basal area of larger trees\n* \"DBH\" is the diameter at breast height\n* \"relBAI\" is the relative basal area increment of the tree\n* \"BA\" is the basal area in squared meters per hectare (stand-level)\n* \"STAND_DENSITY_LOG\" is the logarithm of the stand density in number of stems per hectare\n* \"BEERS_ASPECT_CORR\" is the Beers aspect corrected for slope of the plot\n* \"SLOPE_SQRT\" is the squared root of the slope of the plot in percentage\n* \"ELEVATION\" is the elevation of the plot in meters above sea level\n* \"AWC\" is the available soil water-holding capacity in millimeters\n* \"VEGYEAR\" is the number of vegetation years between the measurements of the plots at NFI2 and NFI3.","descriptionType":"Other"}],"geoLocations":[{"geoLocationPolygon":[{"polygonPoint":{"pointLatitude":45.81802,"pointLongitude":5.95587}},{"polygonPoint":{"pointLatitude":47.80838,"pointLongitude":5.95587}},{"polygonPoint":{"pointLatitude":47.80838,"pointLongitude":10.49203}},{"polygonPoint":{"pointLatitude":45.81802,"pointLongitude":10.49203}},{"polygonPoint":{"pointLatitude":45.81802,"pointLongitude":5.95587}}]},{"geoLocationPlace":"Switzerland"}],"fundingReferences":[{"awardUri":"http://www.snf.ch/en/researchinFocus/nrp/nfp-73/Pages/default.aspx","funderName":"Swiss National Science Foundation (program NRP 73)","awardNumber":"project 407340_172372"}],"url":"https://www.envidat.ch/#/metadata/latent-reserves-in-the-swiss-nfi","contentUrl":null,"metadataVersion":5,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-08-07T10:28:37Z","registered":"2020-08-07T10:28:38Z","published":null,"updated":"2026-05-15T23:13:20Z"},"relationships":{"client":{"data":{"id":"ethz.wsl","type":"clients"}}}},{"id":"10.16904/envidat.209","type":"dois","attributes":{"doi":"10.16904/envidat.209","identifiers":[{"identifier":"https://www.envidat.ch/#/metadata/c512e1bb-7de2-4c15-ac25-6f2da84d866e","identifierType":"URL"}],"creators":[{"name":"Hagedorn, Frank","givenName":"Frank","familyName":"Hagedorn","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research WSL","Forest Soils and Biogeochemistry"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5218-7776","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Drought alters C footprint of trees in soil: tracking the spatio-temporal fate of 13C labelled assimilates in the soil"}],"publisher":"EnviDat","container":{},"publicationYear":2021,"subjects":[{"lang":"en","subject":"ALLOCATION"},{"lang":"en","subject":"DROUGHT"},{"lang":"en","subject":"FOREST"},{"lang":"en","subject":"MICROBIAL BIOMASS"},{"lang":"en","subject":"PFYNWALD"},{"lang":"en","subject":"RHIZOSPHERE"},{"lang":"en","subject":"SOIL"}],"contributors":[{"name":"Hagedorn, Frank","givenName":"Frank","familyName":"Hagedorn","affiliation":["Swiss Federal Institute for Forest, Snow and Landscape Research 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General context of the data set \"LUSzoning”; 2. Background and aims of the study using the data set LUSzoning; 3. The data set LUSzoning.\n1. General context of the data set \"LUSzoning\". \nThe data set \"LUSzoning\" stands for Land-use simulations integrating zoning regulations in Spanish functional urban areas. The data set 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 2016-2021. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, digital zoning plans) into quantitative land-change modelling approaches at the urban regional level. \n2. Background and aims of the study using the data set “LUSzoning”. \nAs part of the CONCUR project, a specific task was to integrate planning spatial policies in land-change modelling. Planning can be implemented in modelling using either hard or gradual restrictions. Different studies have addressed the inclusion of spatial planning policies in land-use change modelling. However, the integration of zoning constraints is generally established as hard or Boolean-based restrictions (e.g., whether urban development is allowed or not), while not accounting for the spatial heterogeneity or gradual characteristics within planning zones (e.g., whether planning regulations allow low, medium or high urban density), though these could improve real patterns simulations in urban areas. We assume Spanish General Zoning plans were suitable to explore the integration of planning into land-change modelling as soft constrains because they define land-use intensities in the buildable zoning areas. In light of the above considerations, the overall aim of the study was to model urban land-use changes using a multi-scenario approach that integrates digitized zoning plans for the Functional Urban Areas (FUAs) of Madrid, Barcelona, Valencia, and Zaragoza. The following specific objectives were addressed: i) to analyse the role of planning by defining three future scenarios that integrate digitized zoning plans and one scenario that assumes almost no planning intervention; ii) to introduce zoning constraints that reflect different degrees of urban densities; iii) to generate a transferable spatially-explicit modelling framework to integrate planning into land-use change simulations. \nFour future land-use demands scenarios were defined for the FUAs. Storylines were created considering probable development scenarios related to zoning plans, current Spanish legislation and sustainability goals defined along two axes: a high market-oriented vs. high planning-intervention axis, and an axis of short-term economic growth vs. long-term sustainable growth. The sustainable development scenario (S1) is characterized by low gross floor area (GFA) growth that is limited to areas that are currently under development according to zoning plans. The business-as-usual scenario (S2) is characterized by medium GFA growth in the range of on-going trends. The strong development scenario (S3) is characterized by high GFA growth rates. Growth is restricted to buildable areas without urbanization project designated in zoning plans. The unrestricted development scenario (S4) prioritizes a high degree of market liberalization characterized by high GFA growth that surpasses population demands. S4 follows a rapid economic growth pattern with almost no planning intervention.\n3. The data set “LUSzoning”. \nThe dataset includes 16 .asc raster layers providing the simulated land-uses under four defined scenarios for Barcelona, Madrid, Valencia and Zaragoza Functional Urban Areas (FUAs) for 2030. The simulated raster layers were created using CLUMondo simulation framework and have a spatial resolution of 30m. The .asc layers name include the name of the FUA and scenario number. For example, the output from simulating the urban growth for the city of Zaragoza under Scenario 2 is named “Zaragoza_S2.tif”. Furthermore, a .txt file named “Legend.txt” includes the numeric value of the land-use and the category of land-use that represents to interpret the .asc raster layers. 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Grenoble Alpes, CNRS, ISTerre Grenoble, FR","Univ. Grenoble Alpes, CNRS, ISTerre Grenoble, FR"],"nameIdentifiers":[]},{"name":"Moreau, Ludovic","givenName":"Ludovic","familyName":"Moreau","affiliation":["Univ. Grenoble Alpes, CNRS, ISTerre Grenoble, FR"],"nameIdentifiers":[]},{"name":"Trottet, Bertil","givenName":"Bertil","familyName":"Trottet","affiliation":["EPFL, SLAB Snow and Avalanche Simulation Laboratory Lausanne, VD, CH"],"nameIdentifiers":[]},{"name":"Gaume, Johan","givenName":"Johan","familyName":"Gaume","affiliation":["EPFL, SLAB Snow and Avalanche Simulation Laboratory Lausanne, VD, CH"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8931-752X","nameIdentifierScheme":"ORCID"}]},{"name":"Cathomen, Janic","givenName":"Janic","familyName":"Cathomen","affiliation":["WSL Institute for Snow and Avalanche Research SLF, Snow Avalanches and Prevention Davos Dorf, CH"],"nameIdentifiers":[]},{"name":"Dual, Jürg","givenName":"Jürg","familyName":"Dual","affiliation":["Institute for Mechanical Systems, ETH Zurich, Zurich, Switzerland","ETH Zürich"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5861-9058","nameIdentifierScheme":"ORCID"}]},{"name":"Schweizer, Jürg","givenName":"Jürg","familyName":"Schweizer","affiliation":["WSL Institute for Snow and Avalanche Research SLF, Snow Avalanches and Prevention Davos Dorf, CH"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5076-2968","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Crack propagation speeds in weak snowpack layers from three events: PST, whumpf and slab avalanche"}],"publisher":"EnviDat","container":{},"publicationYear":2021,"subjects":[{"lang":"en","subject":"CRACK SPEED"},{"lang":"en","subject":"DYNAMIC CRACK PROPAGATION"},{"lang":"en","subject":"PROPAGATION SAW TEST"},{"lang":"en","subject":"SLAB AVALANCHE"},{"lang":"en","subject":"SNOW"},{"lang":"en","subject":"WEAK LAYER"},{"lang":"en","subject":"WHUMPF"}],"contributors":[{"name":"Bergfeld, Bastian","givenName":"Bastian","familyName":"Bergfeld","affiliation":["WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland"],"contributorType":"ContactPerson","nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3316-1922","nameIdentifierScheme":"ORCID"}]},{"name":"Avalanche Formation and Dynamics","nameType":"Organizational","affiliation":[],"contributorType":"ResearchGroup","nameIdentifiers":[]}],"dates":[{"date":"2021-10-11","dateType":"Created"},{"date":"2021","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsRequiredBy","relatedIdentifier":"https://www.envidat.ch/dataset/ad08d108-113f-4236-b141-a16edc3e4e1c/resource/cbe26adb-d263-488d-beb3-e98eabd6c95d/download/data.zip","relatedIdentifierType":"URL"},{"relationType":"IsRequiredBy","relatedIdentifier":"https://www.envidat.ch/dataset/ad08d108-113f-4236-b141-a16edc3e4e1c/resource/a900aa3c-ab2b-4d31-bf35-d45906ebf869/download/readme.txt","relatedIdentifierType":"URL"}],"relatedItems":[{"titles":[{"title":"crack propagation speeds"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/ad08d108-113f-4236-b141-a16edc3e4e1c/resource/cbe26adb-d263-488d-beb3-e98eabd6c95d/download/data.zip","relatedItemIdentifierType":"URL"}},{"titles":[{"title":"Readme"}],"creators":[],"contributors":[],"relationType":"References","relatedItemType":"Other","relatedItemIdentifier":{"relatedItemIdentifier":"https://www.envidat.ch/dataset/ad08d108-113f-4236-b141-a16edc3e4e1c/resource/a900aa3c-ab2b-4d31-bf35-d45906ebf869/download/readme.txt","relatedItemIdentifierType":"URL"}}],"sizes":["200147125 bytes","2507 bytes"],"formats":["application/zip","text/plain"],"version":"1.0","rightsList":[{"lang":"en","rights":"WSL Data Policy","rightsUri":"https://www.wsl.ch/en/about-wsl/programmes-and-initiatives/envidat.html"}],"descriptions":[{"lang":"en","description":"For the release of a slab avalanche, crack propagation within a weak snowpack layer below a cohesive snow slab is required. As crack speed measurements can give insight into the underlying processes, we analysed three crack propagation events that occurred in similar snowpacks and covered all scales relevant for avalanche release. For the largest scale, up to 400 m, we estimated crack speed from an avalanche movie, for scales between 5 and 25 meters, we used accelerometers placed on the snow surface, and for scales below 5 meters, we performed a Propagation Saw Test. The mean crack speeds ranged from 36 ± 6 to 49 ± 5 m s^{-1}, and did not exhibit scale dependence. Using the Discrete Element Method and the Material Point Method, we reproduced the measured crack speeds reasonably well, in particular the terminal crack speed observed at smaller scales.\nThis dataset includes raw data as well as crack speed estimates from the three crack propagation events. \nWhere possible, we reproduced these field experiments with numerical models based on Discrete Element Method (DEM, Bobillier and others, 2020 and 2021) and Material Point Method (MPM. Gaume and others, 2018 and Trottet and others, 2021). The input parameters of the models were estimated from the corresponding snow profiles conducted at each test site.\n The raw data include:\n* Propagation Saw Test movie with mechanical fields derived from Digital image Correlation analysis of the recording\n* Acceleration data recorded with wireless time synchronized accelerometers placed on the snow surface during crack propagation in a whumpf.\n*Video of an artificially triggered avalanche with widespread crack propagation. The video was used to georeference surface cracks in order to estimate crack propagation time and distance, providing crack propagation speed estimates.\n* Snow profile recorded at each test site\n Experimental crack speed estimates include:\n* Crack speed evolution within the first meters derived from the Propagation Saw Test.\n* Crack speeds estimated from the time delay of the collapse, observed between different accelerometers during crack propagation of a whumpf.\n* Crack speed estimates from video analysis of the artificially triggered avalanche.\n Reproduced crack speeds using the DEM an MPM model:\n* Modelled Propagation Saw Test using MPM (2D and 3D system) and DEM.\n* Modelled whumpf using MPM (beam and areal configuration)\n* Modelled avalanche using MPM (beam and areal configuration)\nBeside the movies (mp4 format), all data is either provided as netCDF files or excel sheets (see readme file), depending on the amount of data. A detailed description of the three crack propagation events and how crack speed was derived, can be found in the related publication:\n References for applied models: \nBobillier, G., B. Bergfeld, A. Capelli, J. Dual, J. Gaume, A. van Herwijnen and J. Schweizer 2020. Micromechanical modeling of snow failure. The Cryosphere, 14(1): 39-49.\nBobillier, G., B. Bergfeld, J. Dual, J. Gaume, A. van Herwijnen and J. Schweizer 2021. Micro-mechanical insights into the dynamics of crack propagation in snow fracture experiments. Scientific Reports, 11: 11711.\nGaume, J., T. Gast, J. Teran, A. van Herwijnen and C. Jiang 2018. Dynamic anticrack propagation in snow. Nature Communications, 9(1): 3047.\nTrottet, B., R. Simenhois, G. Bobillier, A. van Herwijnen, C. Jiang and J. Gaume 2021. From sub-Rayleigh to intersonic crack propagation in snow slab avalanche release. EGU General Assembly 2021, Online, 19-30 Apr 2021, EGU21-8253.","descriptionType":"Abstract"},{"lang":"en","description":"Resource Title: crack propagation speeds\n Size: 200147125 bytes\n Format: application/zip\n URL: https://www.envidat.ch/dataset/ad08d108-113f-4236-b141-a16edc3e4e1c/resource/cbe26adb-d263-488d-beb3-e98eabd6c95d/download/data.zip\n Description: Please find the readme file.","descriptionType":"Other"},{"lang":"en","description":"Resource Title: Readme\n Size: 2507 bytes\n Format: text/plain\n URL: https://www.envidat.ch/dataset/ad08d108-113f-4236-b141-a16edc3e4e1c/resource/a900aa3c-ab2b-4d31-bf35-d45906ebf869/download/readme.txt\n Description: Information how to interpret the data in \"crack propagation speeds.zip\".","descriptionType":"Other"}],"geoLocations":[{"geoLocationPoint":{"pointLatitude":46.80772203292321,"pointLongitude":9.870043694972992}},{"geoLocationPlace":"Switzerland"}],"fundingReferences":[{"funderName":"Swiss National Science Foundation","awardNumber":"200021_169424"}],"url":"https://www.envidat.ch/#/metadata/crack-propagation-speeds-in-weak-snowpack-layers","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-10-12T07:28:22Z","registered":"2021-10-12T07:28:23Z","published":null,"updated":"2026-05-15T23:13:02Z"},"relationships":{"client":{"data":{"id":"ethz.wsl","type":"clients"}}}}],"meta":{"total":77870,"totalPages":400,"page":1},"links":{"self":"https://api.datacite.org/dois?query=fundingReferences.funderName%3A%22National+Science+Foundation%22","next":"https://api.datacite.org/dois?page%5Bnumber%5D=2\u0026page%5Bsize%5D=25\u0026query=fundingReferences.funderName%3A%22National+Science+Foundation%22"}}