leerwohnungen-total-anz

title_for_slug: leerwohnungen-total-anz

de: bezirke kantonzuerich gemeinden zimmerzahl wohnungen wohnungsbestand

de: Leerwohnungen Total [Anz.]

de: Leerstehende Wohnungen am 1. Juni des Erhebungsjahres

wohnungen

display_name: wohnungen

wohnungsbestand

display_name: wohnungsbestand

zimmerzahl

display_name: zimmerzahl

construction

de: Bau- und Wohnungswesen


neu-gegrundete-unternehmen-total-anz

maintainer: Statistisches Amt des Kantons Zürich, Data Shop

title_for_slug: neu-gegrundete-unternehmen-total-anz

de: bezirke gemeinden unternehmen kantonzuerich unternehmensstruktur

state: active

type: dataset

de: Neu gegründete Unternehmen Total [Anz.]

media_type: text/csv

format: CSV

mimetype: text/csv

de: Neu gegründete Unternehmen (2. und 3. Sektor). Die Statistik der Neugründungen erfasst nur tatsächlich - also «ex nihilo» - neu entstandene Unternehmen mit marktwirtschaftlicher Tätigkeit. Aus Fusionen, Übernahmen, Spaltungen oder Restrukturierungen hervorgegangene Unternehmen werden folglich nicht berücksichtigt. Werte < 4 werden nicht ausgewiesen.

bezirke

display_name: bezirke

gemeinden

display_name: gemeinden

kantonzuerich

display_name: kantonzuerich

unternehmen

display_name: unternehmen

unternehmensstruktur

display_name: unternehmensstruktur

work

fr: Travail, rémunération

de: Arbeit, Erwerb

en: Work and income

it: Lavoro e reddito

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.

political_level: canton

kanton-zuerich

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: ZH-Kt Statistisches Amt - DCAT Harvester


nettoaufwand-soziale-sicherheit-fr-einw

title_for_slug: nettoaufwand-soziale-sicherheit-fr-einw

name: Statistisches Amt des Kantons Zürich, Data Shop

de: oeffentlichefinanzen bezirke sozialewohlfahrt nettoaufwendungen gemeinden gemeindefinanzen kantonzuerich

de: Nettoaufwand soziale Sicherheit [Fr./Einw.]

de: Bruttoaufwand - Bruttoertrag (der Laufenden Rechnung). Aussage: Durch Steuern zu finanzierender Anteil des betreffenden Hauptaufgabenbereichs. Grundlage ist die Konsolidierte Gemeinde. Der Haushalt der Schulgemeinde wird dabei anteilsmässig in den Haushalt der poltischen Gemeinden umgelegt. Umstellung des Rechnungsmodells von HRM1 auf HRM2 ab Rechnungsjahr 2019.

gemeindefinanzen

display_name: gemeindefinanzen

nettoaufwendungen

display_name: nettoaufwendungen

oeffentlichefinanzen

display_name: oeffentlichefinanzen

sozialewohlfahrt

display_name: sozialewohlfahrt

national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia


sterbefalle-pers

title_for_slug: sterbefalle-pers

de: gemeinden bevoelkerungsbewegungen bezirke sterbefaelle kantonzuerich bevoelkerung

de: Sterbefälle [Pers.]

de: Anzahl Sterbefälle pro Jahr

bevoelkerung

display_name: bevoelkerung

bevoelkerungsbewegungen

display_name: bevoelkerungsbewegungen

sterbefaelle

display_name: sterbefaelle

population

fr: Population

de: Bevölkerung

en: Population

it: Popolazione


jugendquotient

title_for_slug: jugendquotient

de: kantonzuerich altersstruktur gemeinden bevoelkerung bezirke alter

de: Jugendquotient [%]

de: Quantitatives Verhältnis zwischen den 0- bis 19-Jährigen und den 20- bis 64-Jährigen.

alter

display_name: alter

altersstruktur

display_name: altersstruktur


neuzulassungen-anderer-antrieb

title_for_slug: neuzulassungen-anderer-antrieb

de: personenwagen gemeinden fahrzeuge motorfahrzeuge kantonzuerich motorfahrzeugbestand bezirke

de: Neuzulassungen anderer Antrieb [%]

de: Anteil der Personenwagen (PW) mit anderem Antrieb an allen Personenwagen, die zwischen dem 1. Oktober des Vorjahrs und dem 30. September erstmals in Verkehr gesetzt wurden. Hinweis: Der Wert für den ganzen Kanton umfasst alle Autos mit einem Zürcher Kennzeichen; diese können auch Personen oder Unternehmen gehören, die ihr Domizil nicht im Kanton Zürich haben.

fahrzeuge

display_name: fahrzeuge

motorfahrzeugbestand

display_name: motorfahrzeugbestand

motorfahrzeuge

display_name: motorfahrzeuge

personenwagen

display_name: personenwagen

mobility

fr: Mobilité et transports

de: Mobilität und Verkehr

en: Mobility and Transport

it: Mobilità e trasporti


de: haushaltsgroesse bezirke haushalte kantonzuerich haushalt-typ gemeinden bevoelkerung

de: Anteil 5-Personenhaushalte [%]

de: Anteil Fünfpersonenhaushalte an allen Privathaushalten

bezirke

display_name: bezirke

gemeinden

display_name: gemeinden

haushalt-typ

display_name: haushalt-typ

haushalte

display_name: haushalte

haushaltsgroesse

display_name: haushaltsgroesse

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich


arbeitslose-anz

title_for_slug: arbeitslose-anz

de: bezirke arbeitslosigkeit arbeitsmarkt arbeitslose gemeinden kantonzuerich

de: Arbeitslose [Anz.]

de: Jahresmittel der beim kantonalen Arbeitsamt eingeschriebenen Arbeitslosen. Aus Datenschutzgründen dürfen Werte von Gemeinden mit weniger als 6 Arbeitslosen (Jahresmittel) nicht ausgewiesen werden.

arbeitslose

display_name: arbeitslose

arbeitslosigkeit

display_name: arbeitslosigkeit

arbeitsmarkt

display_name: arbeitsmarkt

kantonzuerich

display_name: kantonzuerich

work

de: Arbeit, Erwerb

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.


geb-vol-nebengebaude-zunahme

title_for_slug: geb-vol-nebengebaude-zunahme

de: kantonzuerich gebaeudeart bautaetigkeit gebaeude gemeinden bezirke

de: Geb.Vol. Nebengebäude: Zunahme [%]

de: Veränderung des Gebäudevolumens gegenüber Vorjahr (1995: jährliche Veränderung im Zeitraum 1990-2000)

bautaetigkeit

display_name: bautaetigkeit

gebaeude

display_name: gebaeude

gebaeudeart

display_name: gebaeudeart

construction

fr: Construction, logement

de: Bau- und Wohnungswesen

en: Construction and housing

it: Costruzione e abitazione


krw-wahleranteil-cvp

title_for_slug: krw-wahleranteil-cvp

de: kantonzuerich parteien gemeinden politik kantonsratswahlen bezirke wahlen

de: KRW Wähleranteil CVP [%]

de: Wähleranteil CVP bei Kantonsratswahlen

kantonsratswahlen

display_name: kantonsratswahlen

parteien

display_name: parteien

politik

display_name: politik

wahlen

display_name: wahlen

politics

fr: Politique

de: Politik

en: Politics

it: Politica


maintainer: Statistisches Amt des Kantons Zürich, Wahlen & Abstimmungen

de: politik gemeinden abstimmungsergebnisse eidgenoessischevolksabstimmungen kantonalevolksabstimmungen politischegemeinden abstimmungen

label: Abstimmungsarchiv

de: Abstimmungsarchiv des Kantons Zürich, Ergebnisse pro Gemeinde

de: Abstimmungsarchiv des Kantons Zürich, Ergebnisse Kanton Zürich

de: Alle Ergebnisse der kantonalen und eidgenössischen Abstimmungen des Kantons Zürich seit 1831.

abstimmungen

display_name: abstimmungen

abstimmungsergebnisse

display_name: abstimmungsergebnisse

eidgenoessischevolksabstimmungen

display_name: eidgenoessischevolksabstimmungen

kantonalevolksabstimmungen

display_name: kantonalevolksabstimmungen

politischegemeinden

display_name: politischegemeinden

de: Abstimmungsarchiv des Kantons Zürich ab 1831


o-reingewinn-steuerpfl-jur-pers-fr

title_for_slug: o-reingewinn-steuerpfl-jur-pers-fr

de: reingewinn bezirke kantonzuerich gemeinden juristischepersonen

de: Ø Reingewinn steuerpfl. jur. Pers. [Fr.]

de: Durchschnittlicher Reingewinn (ehem. Ertrag) steuerpflichtiger juristischer Personen. Hinweis: Sehr hohe Beträge einiger weniger Steuerpflichtigen wirken sich entsprechend stark auf den Mittelwert aus.

bezirke

display_name: bezirke

gemeinden

display_name: gemeinden

juristischepersonen

display_name: juristischepersonen

reingewinn

display_name: reingewinn

national-economy

de: Volkswirtschaft

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich


neuerstellte-efh-whg

title_for_slug: neuerstellte-efh-whg

de: gemeinden wohnbautaetigkeit kantonzuerich bezirke einfamilienhaus immobilien

de: Neuerstellte EFH [Whg.]

de: Neu erstellte Einfamilienhäuser

einfamilienhaus

display_name: einfamilienhaus

immobilien

display_name: immobilien

kantonzuerich

display_name: kantonzuerich

wohnbautaetigkeit

display_name: wohnbautaetigkeit

statistisches-amt-kanton-zuerich

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.


de: gemeinden bevoelkerung kantonzuerich bezirke altersstruktur alter

de: Bevölkerung: Anteil 20-39-Jährige [%]

de: Anteil 20-39-Jähriger an der Gesamtbevölkerung nach zivilrechtlichem Wohnsitz(ab 2010 inkl. vorläufig Aufgenommene, die seit mehr als einem Jahr in der Gemeinde leben, aber ohne Wochenaufenthalter und Asylbewerber).

alter

display_name: alter

altersstruktur

display_name: altersstruktur

bevoelkerung

display_name: bevoelkerung

population

de: Bevölkerung


de: kantonzuerich unternehmensstruktur bezirke gemeinden betriebe

de: Grosse Betriebe (250+ VZÄ) [Anz.]

de: Anzahl Grosse Betriebe mit einer Beschäftigung von 250 Vollzeitäquivalenten oder mehr. Aktuellstes Jahr provisorisch.

betriebe

display_name: betriebe

unternehmensstruktur

display_name: unternehmensstruktur

work

de: Arbeit, Erwerb


scheidungen-anz

title_for_slug: scheidungen-anz

de: gemeinden bevoelkerung ehe scheidung bezirke ehescheidung kantonzuerich

de: Scheidungen [Anz.]

de: Anzahl Scheidungen. Hinweise: Am 1.Januar 2000 trat das neue Scheidungsrecht in Kraft. Ab 2011 Wechsel der Datenquelle: Es werden nicht mehr alle Scheidungen von zwei ausländischen Personen ausgewiesen.

ehe

display_name: ehe

ehescheidung

display_name: ehescheidung

scheidung

display_name: scheidung


de: gemeinden kantonzuerich bezirke bodenpreise bauland

label: Infoseite zu Bodenpreise inkl. interaktivem Rechner

de: Baulandpreis Modell (Median) [Fr./m2]

de: Bodenpreismodell: Median der Modellpreise von unbebautem Land der Wohn- und Mischzone inkl. Abbruchliegenschaften. Die Modellpreise werden jährlich für den gesamten ausgewiesenen Zeitraum neu berechnet und aktualisiert.

bauland

display_name: bauland

bodenpreise

display_name: bodenpreise

prices

fr: Prix

de: Preise

en: Prices

it: Prezzi


schafe-anz

title_for_slug: schafe-anz

de: landwirtschaft kantonzuerich bezirke viehbestand gemeinden nutztiere

de: Schafe [Anz.]

de: Anzahl Schafe. Hinweis: In der Stadt Zürich wurde ein grosser Schafbetrieb mit knapp 2000 Tieren per 2008 aufgelöst und die Flächen an andere Landwirte verpachtet.

bezirke

display_name: bezirke

gemeinden

display_name: gemeinden

landwirtschaft

display_name: landwirtschaft

nutztiere

display_name: nutztiere

viehbestand

display_name: viehbestand

agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

it: Agricoltura, selvicoltura

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich


schul-oberstufe-anz

title_for_slug: schul-oberstufe-anz

de: gemeinden schulen schuelerinnen kantonzuerich bezirke schueler sekundarschule

de: Schül. Oberstufe [Anz.]

de: Anzahl OberstufenschülerInnen (exklusiv LangzeitgymnasiastInnen; nach Wohnort)

kantonzuerich

display_name: kantonzuerich

schueler

display_name: schueler

schuelerinnen

display_name: schuelerinnen

schulen

display_name: schulen

sekundarschule

display_name: sekundarschule

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

statistisches-amt-kanton-zuerich

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.


de: sterbefaelle bezirke bevoelkerungsbewegungen bevoelkerung gemeinden kantonzuerich

de: Sterberate [pro 1000 Einw.]

de: Sterbefälle in Bezug auf die Einwohnerzahl (gemäss Kantonaler Erhebung) Ende des vorhergehenden Jahres. Beispiel: Die "Sterbefälle [pro 1000 Einw.] 2003" beziehen sich auf die Einwohnerzahl von Ende 2002.

bevoelkerung

display_name: bevoelkerung

bevoelkerungsbewegungen

display_name: bevoelkerungsbewegungen

sterbefaelle

display_name: sterbefaelle

population

de: Bevölkerung


de: kantonzuerich gemeinden steuerpflichtige oeffentlichefinanzen kirchensteuer bezirke

de:
Christkatholische Kirchensteuerpflichtige.
Bis 2019 nur Daten für die Stadt Zürich verfügbar. Ab 2020 für alle Gemeinden. Werte < 4 werden nicht ausgewiesen.

kirchensteuer

display_name: kirchensteuer

oeffentlichefinanzen

display_name: oeffentlichefinanzen

steuerpflichtige

display_name: steuerpflichtige

national-economy

de: Volkswirtschaft


motorrad-neuzulassungen-anz

title_for_slug: motorrad-neuzulassungen-anz

de: gemeinden fahrzeuge motorfahrzeuge kantonzuerich motorraeder motorfahrzeugbestand bezirke

de: Motorrad-Neuzulassungen [Anz.]

de: Zwischen dem 1. Oktober des Vorjahres und dem 30. September erstmals in Verkehr gesetzte Motorräder. Hinweis: Der Wert für den ganzen Kanton umfasst alle Motorräder mit einem Zürcher Kennzeichen; diese können auch Personen oder Unternehmen gehören, die ihr Domizil nicht im Kanton Zürich haben.

fahrzeuge

display_name: fahrzeuge

motorfahrzeugbestand

display_name: motorfahrzeugbestand

motorfahrzeuge

display_name: motorfahrzeuge

motorraeder

display_name: motorraeder

mobility

de: Mobilität und Verkehr


bauinvestitionen-fr-einw

title_for_slug: bauinvestitionen-fr-einw

de: bauinvestitionen bautaetigkeit bezirke gemeinden kantonzuerich

de: Bauinvestitionen  [Fr./Einw.]

de: Bauinvestitionen von Bautätigkeiten mit Investitionscharakter, die einer behördlichen Bewilligung bedürfen, in Franken pro Einwohner

bauinvestitionen

display_name: bauinvestitionen

bautaetigkeit

display_name: bautaetigkeit

bezirke

display_name: bezirke

gemeinden

display_name: gemeinden

construction

de: Bau- und Wohnungswesen

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich


de: bevoelkerung gemeinden kantonzuerich ehescheidung scheidung ehe bezirke

de: Scheidungen [pro 1000 Einw.]

de: Anzahl Scheidungen pro 1000 Einwohner (Rohe Scheidungsziffer). Hinweise: Am 1.Januar 2000 trat das neue Scheidungsrecht in Kraft. Ab 2011 Wechsel der Datenquelle: Es werden nicht mehr alle Scheidungen von zwei ausländischen Personen ausgewiesen.

ehe

display_name: ehe

ehescheidung

display_name: ehescheidung

kantonzuerich

display_name: kantonzuerich

scheidung

display_name: scheidung

statistisches-amt-kanton-zuerich

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.


anteil-gk-b

title_for_slug: anteil-gk-b

de: Anteil GK B [%]

de: Anteil Einwohner pro ÖV-Güteklasse B: Die ÖV-Güteklassen geben lagesscharf Aufschluss über die Erschliessungsqualität einer Gemeinde mit dem ÖV. In einem ersten Schritt werden Haltestellenkategorien definiert, in Abhängigkeit von einem an der Haltestelle verfügbaren ÖV-Verkehrsmittel und dem Kursintervall.  Danach wird in Abhängigkeit von Haltestellenkategorie und Luftliniendistanz die ÖV-Güteklasse ermittelt. Für jede Gemeinde werden die Einwohner pro Güteklasse in Prozent berechnet. (http://www.afv.zh.ch/internet/volkswirtschaftsdirektion/afv/de/verkehrsgrundlagen/verkehrsangebot/erreichbarkeit_und_erschliessung/_jcr_content/contentPar/downloadlist/downloaditems/infoblatt_v_g_teklas.spooler.download.1436273189233.pdf/KTZH_AFV_Broschuere_OeV-Gueteklassen.pdf)

oeffentlicherverkehr

display_name: oeffentlicherverkehr

verkehr

display_name: verkehr


krw-wahleranteil-edu

title_for_slug: krw-wahleranteil-edu

de: bezirke wahlen gemeinden parteien kantonzuerich kantonsratswahlen politik

de: KRW Wähleranteil EDU [%]

de: Wähleranteil EDU bei Kantonsratswahlen

kantonsratswahlen

display_name: kantonsratswahlen

parteien

display_name: parteien

politik

display_name: politik

wahlen

display_name: wahlen

politics

de: Politik


ov-anteil-modal-split

title_for_slug: ov-anteil-modal-split

de: verkehrsmittelwahl bezirke gemeinden oeffentlicherverkehr kantonzuerich

de: ÖV-Anteil (Modal Split) [%]

de: Grundlage für die Berechnung des ÖV-Anteils sind die ÖV-Wege des Quell-, Ziel- und Binnenverkehrs des jeweiligen Gebiets an einem durchschnittlichen Werktag (DWV). Mit Prognosezustand.

verkehrsmittelwahl

display_name: verkehrsmittelwahl


bauzonen-nicht-uberbaut-ha

title_for_slug: bauzonen-nicht-uberbaut-ha

de: nutzungsreserven bauzonenverbrauch gemeinden zonenart kantonzuerich bezirke bodennutzung bauzonen

de: Bauzonen nicht überbaut [ha]

de: Total nicht überbaute Bauzonenfläche

bauzonen

display_name: bauzonen

bauzonenverbrauch

display_name: bauzonenverbrauch

bodennutzung

display_name: bodennutzung

nutzungsreserven

display_name: nutzungsreserven

zonenart

display_name: zonenart


nrw-wahleranteil-al

title_for_slug: nrw-wahleranteil-al

de: kantonzuerich parteien politik gemeinden nationalratswahlen bezirke wahlen

de: NRW Wähleranteil AL [%]

de: Wähleranteil AL bei Nationalratswahlen. Hinweis: 2015 werden alle Auslandschweizer zur Stadt Zürich gezählt.

bezirke

display_name: bezirke

gemeinden

display_name: gemeinden

nationalratswahlen

display_name: nationalratswahlen

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich


aerzte-anz

title_for_slug: aerzte-anz

de: gesundheit bezirke aerzte kantonzuerich aerztinnen gemeinden

de: Aerzte [Anz.]

de: Aerzte mit Hauptberufstätigkeit im Ambulanten Sektor. Bis 2008: Ärzte mit eigener Praxis und Spitalärzte, die Privatpatienten behandeln, jeweils am Anfang eines Kalenderjahres. Für 1993 und 2003 keine Zahlen vorhanden. Aktuellstes Jahr provisorisch.

aerzte

display_name: aerzte

aerztinnen

display_name: aerztinnen

gesundheit

display_name: gesundheit

kantonzuerich

display_name: kantonzuerich

health

fr: Santé

de: Gesundheit

en: Health

it: Sanità

statistisches-amt-kanton-zuerich

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.


de: gemeinden apotheken kantonzuerich bezirke gewerbe

de: Apotheken [pro 1000 Einw.]

de: Definition: Alle öffentlichen Apotheken im Kanton Zürich. Aktuellstes Jahr provisorisch. Stand: Anfangs Folgejahr.

apotheken

display_name: apotheken

gewerbe

display_name: gewerbe


bestand-benzinmotor

title_for_slug: bestand-benzinmotor

de: personenwagen gemeinden fahrzeuge motorfahrzeuge kantonzuerich motorfahrzeugbestand bezirke

de: Bestand Benzinmotor [%]

de: Anteil der Personenwagen (PW) mit Benzinmotor an allen am 30. September eingelösten Personenwagen. Hinweis: Der Wert für den ganzen Kanton umfasst alle Autos mit einem Zürcher Kennzeichen; diese können auch Personen oder Unternehmen gehören, die ihr Domizil nicht im Kanton Zürich haben.

fahrzeuge

display_name: fahrzeuge

motorfahrzeugbestand

display_name: motorfahrzeugbestand

motorfahrzeuge

display_name: motorfahrzeuge

personenwagen

display_name: personenwagen

mobility

de: Mobilität und Verkehr


nrw-wahleranteil-fdp

title_for_slug: nrw-wahleranteil-fdp

de: politik bezirke nationalratswahlen kantonzuerich wahlen gemeinden parteien

de: NRW Wähleranteil FDP [%]

de: Wähleranteil FDP bei Nationalratswahlen. Hinweis: 2015 werden alle Auslandschweizer zur Stadt Zürich gezählt.

parteien

display_name: parteien

politik

display_name: politik

wahlen

display_name: wahlen

politics

de: Politik


maintainer: Anita Risch

en: exclusion subalpine ecosystem-functions ecosystem-multifunctionality vertebrates grassland invertebrates ecosystem-coupling swiss-national-park grazing

en: Datafile

en:
ID: Plot ID composited of Fence & Treatment number (90 plots total)

Grassland: Name of the six subalpine grasslands where the exclosure setups were erected. In large grasslands (GRIM, MING, STAB), we established four hierarchical exclosure setups (two in short- and two in tall-grass vegetation), whereas in the small grasslands (BOTS, DADA, MARG) only one setup was erected per vegetation type. BOTS = Val dal Botsch, GRIM = Grimmels, MING = Val Mingér, STAB = Alp Stabelchod, DADA = Stabelchod da daint, MARG = Margunet

Fence: Number of exclosure setup (1 - 18). Note that uneven numbers (1,3,5,7,9,11,13,15,17) represent setups established on short-grass vegetation, even numbers (2,4,6,8,10,12,14,16,18) setups established in tall-grass vegetation
Vegetation	Vegetation type, 1 = short-grass vegetation, 2 = tall-grass vegetation; (9 replicate fences on each vegetation type)
Treatment	Progressive animal exclusion, numbers indicate which animals had access to the plot: 1 = Large/medium/small mammals, and invertebrates have access ("L/M/S/I"), 2 = Medium/small mammals, and invertebrates have access (“M/S/I”), 3 = Small mammals and invertebrates have access (“S/I”), 4 = Invertebrates have access (“I”), 5 = No animals have access (“None”)

Bulkden:	Fine fraction soil bulk density (g cm-3)

Soil.moist	: Soil moisture (%)

Soil.org.C: Rhizosphere soil C content (%)

pH: Soil pH

Nema.PC1: First principal component (PC1) of principal component analysis (PCA) of the soil nematode community composition

Nema.PC2: Second principal component (PC2) of principal component analysis (PCA) of the soil nematode community composition

Micro.PC1: 	First principal component (PC1) of principal component analysis (PCA) of the soil microbial community composition

Micro.PC2: Second principal component (PC2) of principal component analysis (PCA) of the soil microbial community composition

Abv.Inv.PC1: First principal component (PC1) of principal component analysis (PCA) of the aboveground-dwelling invertebrate community composition

Abv.Inv.PC2: Second principal component (PC2) of principal component analysis (PCA) of the aboveground-dwelling invertebrate community composition

Arthr.PC1: First principal component (PC1) of principal component analysis (PCA) of the soil arthropod community composition

Arthr.PC2: Second principal component (PC2) of principal component analysis (PCA) of the soil arthropod community composition

Plant.PC1: First principal component (PC1) of principal component analysis (PCA) of the plant community composition

Plant.PC2: Second principal component (PC2) of principal component analysis (PCA) of the plant community composition

Root.biomass: Root biomass (g m-2)

Micro.biomass	: Microbial biomass carbon (mg kg-1)

Plant.richness: Plant species richness

Plant.N: Plant tissue nitrogen content (%)

Soil.N.min: Soil net nitrogen mineralisation (kg ha-1 season)

Soil.resp: Soil carbon dioxide emissions (soil respiration; g m-2 h-1)

format: XLS

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

Paper Citation:
 > Risch AC, Ochoa-Hueso R, van der Putten WH, Bump JK, Busse MD, Frey B, Gwiazdowicz DJ, Page-Dumroese DS, Vandegehuchte ML, Zimmermann S, Schütz M. Size-dependent loss of aboveground animals differentially affects grassland ecosystem coupling and functions. 2018. Nature Communications 9: 3684. [doi: 10.1038/s41467-018-06105-4](https://doi.org/10.1038/s41467-018-06105-4).

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

#Methods
Study sites
The experimental exclosure setups were installed within the SNP (IUCN category Ia preserve; Dudley 2008), in south-eastern Switzerland. The park covers 172 km2 of forests and subalpine and alpine grasslands along with scattered rock outcrops and scree slopes. The entire area has been protected from human impact (no hunting, fishing, camping or off-trail hiking) since 1914. Large, fairly homogenous patches of short- and tall-grass vegetation, which originate from different historical management and grazing regimes, cover the park’s subalpine grasslands entirely. Short-grass vegetation developed in areas where cattle used to rest (nutrient input) prior to the park’s foundation (14th century to 1914) (Schütz and others 2003, 2006) and is dominated by lawn grass species such as Festuca rubra L., Briza media L. and Agrostis capillaris L. (Schütz and others 2003, 2006). Today, this vegetation type is intensively grazed by diverse vertebrate and invertebrate communities that inhabit the park and consume up to 60% of the available biomass (Risch and others 2013). Tall-grass vegetation developed where cattle formerly grazed, but did not rest, and is dominated by rather nutrient-poor tussocks of Carex sempervirens Vill. and Nardus stricta L. (Schütz and others 2003, 2006). This vegetation type receives considerably less grazing, with only roughly 20% of the biomass consumed (Risch and others 2013). Consequently, the two vegetation types together represent a long-term trajectory of changes in grazing regimes. Underlying bedrock of all grasslands is dolomite, which renders these grasslands rather poor in nutrients regardless of former and current land-use regimes.
Experimental design
To progressively exclude aboveground vertebrate and invertebrate animals, we established 18 size-selective exclosure setups (nine in short-grass, nine in tall-grass vegetation) distributed over six subalpine grasslands across the SNP (Risch and others 2013, 2015). Elevation differences of exclosure locations did not exceed 350 m (between 1975 and 2300 m a.s.l.). The exclosures were established immediately after snowmelt in spring 2009 and were left in place for five consecutive growing seasons (until end of 2013). They were, however, temporarily dismantled every fall (late October after first snowfall) to protect them from avalanches. They were re-established in the same location every spring immediately after snowmelt. Each size-selective exclosure setup consisted of five plots (2 x 3 m) that progressively excluded aboveground vertebrates and invertebrates from large to small. The plots are labelled according to the guilds that had access to them “L/M/S/I”, “M/S/I”, “S/I”, “I”, “None”; L = large mammals, M = medium mammals, S = small mammals, I = invertebrates, None = no animals had access. As we only had permission to have the experimental setup in place for five consecutive growing seasons, the experiment had to be completely dismantled in the late fall of 2013 and all material removed from the SNP.
Our exclosure design was aimed at excluding mammalian herbivores, but naturally also excluded the few medium and small mammalian predators, as well as the entire aboveground invertebrate food web. A total of 26 large to small mammal species can be found in the SNP, but large apex predators are missing (wolf, bear, lynx) . Reptiles, amphibians and birds are scarce to absent in the subalpine grasslands under study. Only two reptile species occur in the park and they are confined to rocky areas that warm up enough for them to survive. One frog species spawns in an isolated pond far from our grasslands. Only three bird species occasionally feed on the subalpine grasslands. Using game cameras (Moultrie 6MP Game Spy I-60 Infrared Digital Game Camera, Moultrie Feeders, Alabaster, AL, USA), we did observe that the medium- and small-sized mammals (marmot/hares and mice) were not afraid to enter the fences and feed on their designated plots. We never spotted reptiles, amphibians or birds on camera. We distinguished between 59 higher aboveground-dwelling invertebrate taxa that our size-selective exclosures excluded (see also methods for aboveground-dwelling invertebrates below).
The “L/M/S/I” plot (not fenced) was located at least 5 m from the 2.1 m tall and 7 x 9 m large main electrical fence that enclosed the other four plots. The bottom wire of this fence was mounted at 0.5 m height and was not electrified to enable safe access for medium and small mammals, while fencing out the large ones. Within each main fence, we randomly established four 2 x 3 m plots separated by 1-m wide walkways from one another and from the main fence line: 1) the “M/S/I” plots were unfenced, allowing access to all but the large mammals; 2) the “S/I” plots (10 x 10 cm electrical mesh fence) excluded all medium-sized mammals. Note that the bottom 10 cm of this fence remained non-electrified to enable safe access for small mammals; 3) the “I” plots (2 x 2 cm metal mesh fence) excluded all mammals. We double-folded the mesh at the bottom 50 cm to reduce the mesh size to smaller than 1 x 1 cm openings; and 4) the “None” plots were surrounded by a 1 m tall mosquito net (1.5 x 2 mm) to exclude all animals. The top of the plot was covered with a mosquito-meshed wooden frame mounted to the corner posts (roof). We treated these plots a few times with biocompatible insecticide (Clean kill original, Eco Belle GmbH, Waldshut-Tiengen, Germany) to remove insects that might have entered during data collection or that hatched from the soil, but amounts were negligible and did not impact soil moisture conditions within these plots.
To assess whether the design of the “None” exclosure (mesh and roof) affected the response variables within the plots and, therefore, influenced the results, we established an additional six “micro-climate control” exclosures (one in each of the six grasslands) (Risch and others 2013, 2015). These exclosures were built as the “None” exclosures but were open at the bottom (20 cm) of the 3-m side of the fence facing away from the prevailing wind direction to allow invertebrates to enter. A 20-cm high and 3-m long strip of metal mesh was used to block access to small mammals. Thus, this construction allowed a comparable micro-climate to the “None” plots, but also a comparable feeding pressure by invertebrates to the “I” plots. We compared various properties within these exclosures against one another to assess if our construction altered the conditions in the “None” plots. We showed that differences in plant (e.g., vegetation height, aboveground biomass) and soil properties (e.g., soil temperature, moisture) found between the “I” and the “None” treatments were not due to the construction of the “None” exclosure, but a function of animal exclusions, although the amount of UV light reaching the plant canopy was significantly reduced (Risch and others 2013).
Aboveground invertebrate sampling
Aboveground invertebrates were sampled with two different methods to capture both ground- and plant-dwelling organisms: 1) we randomly placed two pitfall traps (67 mm in diameter, covered with a roof) filled with 20% propylene glycol in one 1 x 1 m subplot of the 2 x 3 m treatment plots in spring 2013 (May) and emptied them every two weeks until late September 2013 (Vandegehuchte and others 2017b, 2018). A pitfall trap consisted of a plastic cylinder (13 cm depth, 6.75 cm diameter). Within each cylinder we placed a 100 ml plastic vial with outer diameter 6.70 cm and on top of the cylinder we placed a plastic funnel to guide the invertebrates into the vials. Each trap was cover with a cone-shaped and transparent plastic roof to protect the trap from rain (Vandegehuchte and others 2017b, 2018). Note that in the “None” plots only one trap was placed as control to check for effectiveness of the exclosure. 2) We vacuumed all invertebrates from a 60 x 60 cm area on another 1 x 1 m subplot with a suction sampler (Vortis, Burkhard manufacturing CO, Ltd., Rickmansworth, Hertfordshire, UK) every month from June to September 2013 (Vandegehuchte and others 2017b, 2018). For this purpose, we quickly placed a square plastic frame (60 x 60 x 40 cm) with a closable mosquito mesh sleeve attached to the top edge into the plot from the outside. The suction sample was then inserted into through the sleeve and operated for 45 s to collect the invertebrates (Vandegehuchte and others 2017b, 2018).
We sorted the ≈100 000 individuals collected with both methods by hand and identified each individual morphologically to the lowest taxonomic level feasible (59 taxa, including orders, suborders, subfamilies, families; phylum for Mollusca). These taxa belonged to the following feeding types: 19 herbivores, 16 detritivores, 9 predators, 8 mixed feeders, 5 omnivores and 2 non-classified feeders (or not feeding as adults) (Vandegehuchte and others 2017b). We summed the numbers from the two pitfall traps and the suction sampling over the course of the 2013 season to represent the aboveground invertebrate abundance and community composition of a plot. Note: we did not specifically attempt to catch flying invertebrates with e.g., sticky traps, thus a few flying insects may have been missed with our vacuum sampling approach.
Sampling of plant properties
The vascular plant species composition was assessed at peak biomass every summer (July) by estimating the frequency of occurrence of each species with the pin count method in each plot (Frank and McNaughton 1990). A total of 172 taxa occurred within our 90 plots and we calculated plant species richness for each plot separately. We used the 2013 data in this study. Plant quality was assessed every year in July and September; here we use plant quality at the end of the experiment (September 2013). Two 10 x 100 cm wide strips of vegetation per plot were clipped, combined, dried at 65°C, and ground (Pulverisette 16, Fritsch, Idar-Oberstein, Germany) to pass through a 0.5 mm sieve. Twenty randomly selected samples across all treatments were analysed for N (Leco TruSpec Analyser, Leco, St. Joseph, Michigan, USA) (Vandegehuchte and others 2015). Nitrogen concentrations of the other samples were then estimated from models established for the experiment and the entire SNP relating Fourier transform-near infrared reflectance (FT-NIR) spectra to the measured values of N using a multi-purpose FT-NIR spectrometer (Bruker Optics, Fällanden, Switzerland) (Vandegehuchte and others 2015). Root biomass was sampled every fall by collecting five 2.2 cm diameter x 10 cm deep soil samples (Giddings Machine Company, Windsor, CO, USA) per plot (450 samples year-1). The samples were dried at 30 °C and roots were sorted from the sample by hand. We sorted each sample for 1 h which allowed to retrieve over 90% of all roots present in the samples (Risch and others 2013). The roots were then dried at 65 °C for 48 and weighed to the nearest mg. We averaged the values per plot and used the 2013 data only in this study.
Sampling of edaphic communities
In 2009, 2010, and 2011 we collected three composited soil samples (5 cm diameter x 10 cm depth; AMS Samplers, American Falls, ID, USA) and assessed bacterial community structure using T-RFLP profiling (Liu and others 1997; Blackwood and others 2003; Hodel and others 2014). We detected a total of 89 operational taxonomic units (OTUs). These values are in accordance with other studies reporting OTU richness (Wirthner and others 2011; Zumsteg and others 2012; Meola and others 2014) using T-RFLP profiling, a method that detects the most abundant, and thus likely, the most relevant, taxa. We averaged the data over the three years of collections for our calculations. Microbial biomass carbon (MBC) was determined with the substrate-induced method (Anderson and Domsch 1978) every fall (September) between 2009 and 2013 by collecting three mineral soil samples (5 cm diameter × 10 cm mineral soil core, AMS Samplers, American Falls, ID, USA). The three samples were combined (90 samples for each sampling year), immediately put on ice, taken to the laboratory, passed through a 2-mm sieve and stored at 4°C. Again, we only used the 2013 data in this study.
Soil samples (5 cm diameter x 10 cm depth) to extract soil arthropods were collected in June, July, and August 2011 with a soil corer lined with a plastic sleeve to ensure an undisturbed sample (total of 270 samples). The plastic line core was immediately sealed on both ends using cling film and put into a cooler. All plots were sampled within three days and the extraction of arthropods started the evening of the sampling day using a high-gradient Tullgren funnel apparatus (Crossley and Blair 1991; Vandegehuchte and others 2015). Samples were kept in the extractor for four days and the soil arthropods were collected in 95% ethanol. All individuals were counted and each individual was identified morphologically to the lowest level feasible [76 taxa, including orders, suborders, subfamilies, families (Protura, Thysanoptera, Aphidina, Psylina, Coleoptera, Brachycera, Nematocera, Auchenorryncha, Heteroptera, Formicidae); sub-phylum for Myriapoda, for Acari and Collembola also including morpho-species). Note that we also included larval stages (nine of the 76 taxa) (Vandegehuchte and others 2015). All data were summed over the season. A detailed species list for mites and collembolans is published (Vandegehuchte and others 2017a) [https://doi.org/10.1371/journal.pone.0118679.s001]. Earthworms are rare in the SNP and therefore were not included. We collected eight random 2.2 cm diameter x 10 cm deep soil cores from each plot in September 2013 to determine the soil nematode community composition. The samples were mixed and the nematodes were extracted from 100 ml of fresh soil using Oostenbrink elutriators (Oostenbrink 1960). All nematodes in a 1 ml of the 10 ml extract were counted, a minimum of 150 individuals sample-1 were identified to genus or family level using (Bongers 1988), the numbers of all nematodes were extrapolated to the entire sample and expressed for a 100 g dry sample. In total we identified 63 genus or family levels (Vandegehuchte and others 2015). The list of all the nematodes found is published (Vandegehuchte and others 2015) [http://www.oikosjournal.org/appendix/oik-03341] or DOI: [doi: 10.1111/oik.03341].
We are aware that sampling soil microbes from 2009 to 2011 and soil arthropods in 2011 was not ideal, but we are positive that this does not bias the results. Most of the parameters measured in our experiment either already showed a treatment response after the first growing season (e.g., plant biomass) or did not respond over the entire time experiment (e.g., microbial biomass C). The microbial community composition (2009 – 2011) was highly influenced by inter-annual differences in temperature and precipitation, but did not differ between treatments or vegetation types (Hodel and others 2014). We therefore felt comfortable using the 2009 through 2011 data for describing the soil microbial community in our experimental treatments. Similarly, we are positive that our soil arthropod data are representative. We did assess soil arthropods in August 2012 and found no differences to the August 2011 data. However, we did not feel comfortable combining the 2011 June, July, August data with only August data for 2012 for our analyses.
Sampling of soil properties
We collected three soil samples (5 cm diameter x 10 cm depth) in each plot in September 2013 after removing the vegetation. First, we collected the top layer of mineral soil rich in organic matter, the surface organic layer or rhizosphere, typically 1 to 3 cm in depth with a soil corer (AMS Samples, American Falls, Idaho, USA). Second, we collected a 10 cm mineral soil core beneath this surface layer. The cores for each layer were composited, dried at 65 °C for 48 h and fine-ground to pass a 0.5 mm screen. We then analysed all samples for total C using a Leco TruSpec Analyser (Leco, St. Joseph, Michigan, USA). Mineral soil pH was measured potentiometrically in 1:2 soil:CaCl2 solution with an equilibration time of 30 min.
Soil net N mineralisation was assessed during the 2013 growing season (Risch and others 2015). For this purpose, we randomly collected a 5 cm diameter x10 cm deep soil sample with a soil corer (AMS Samples, American Falls, Idaho, USA) after clipping the vegetation in June 2013. After weighing and sieving (4 mm mesh) the soil, we extracted a 20 g subsample in 1 mol l-1 KCl for 1.5 h on an end-over-end shaker and thereafter filtered it through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnenmühle FineArt GmbH, Dassel, Germany). From these filtrates NO3- concentrations were measured colorimetrically (Norman and Stucki 1981) and NH4+with flow injection analysis (FIAS 300, Perkin Elmer, Waltham Massachusetts, USA) (Risch and others 2015). We dried the rest of the sample 105 °C to constant mass to determine fine,fraction bulk density. A second soil sample was collected within each plot in June 2013 with a corer lined with a 5 x 13 cm aluminium cylinder. The corer was driven 11.5 cm deep into the soil so that the top 1.5 cm of the cylinder remained empty. Into this space we placed a polyester bag (250 µm) filled an ion-exchanger resin to capture the incoming N. The bag was filled with a 1:1 mixture of acidic and alkaline exchanger resin (ion-exchanger I KA/ion exchanger IIIAA, Merck AG, Darmstadt, Germany). We then removed 1.5 cm soil at the bottom of the cylinder and placed a second resin exchanger bag into this space to capture the N leached from the soil column. To assure that the exchange resin was saturated with H+ and Cl- prior to filling the bags, the mixture was stirred with 1.2 ml l-1 HCl for 1 h and then rinsed with demineralized water until the electrical conductivity of the water reached 5 µm cm-1. The cylinder with the resin bags in place was reinserted into the soil with the top flush to the soil surface and incubated for three months. We recollected the cylinders in September 2013. Each resin bag and 20 g of sieved soil (4 mm mesh) from each cylinder were then separately extracted with KCl and NO3- and NH4+ concentrations were measured. Nitrate and NH4+ concentrations of all samples were then converted to a content basis by multiplying their values with fine fraction bulk density. Net N mineralisation was thereafter calculated as the difference between the N content of the samples collected at the end of the three-month incubation (including the N extracted from the bottom resin bag) and the N content at the beginning of the incubation (Risch and others 2015).
Soil CO2 emissions were measured every two weeks between 0900 and 1700 hrs from early May through late September 2013 with a PP-Systems SRC-1 soil respiration chamber (15 cm high, 10 cm diameter; closed circuit) attached to a PP-Systems EGM-4 infrared gas analyser (PP-Systems, Amesbury, MA, USA) on two locations per plot (Risch and others 2013). The chamber was placed on randomly placed, permanently installed PVC collars (10 cm diameter) driven 5 cm into the soil at the beginning of the study (Risch and others 2013). Freshly germinated plants growing within the collars were removed prior to each measurement to avoid measuring plant respiration or photosynthesis. The two measurements collected per plot and sampling date were averaged.
Soil moisture (with time domain reflectometry; Field-Scout TDR-100, Spectrum Technologies, Plainfield, Illionois, USA) and temperature (with a waterproof digital pocket thermometer; Barnstead International, Dubuque, Iowa, USA) were measured at five random locations per plot every two weeks during the growing seasons during the experiment for the 0 to 10 cm depth (Risch and others 2013, 2015). As soil moisture and soil temperature were highly negatively correlated (Risch and others 2013), we only used soil moisture for this study. We used plot-level averages of all values available to capture soil moisture variability during the five years of the experiment. The results remained unchanged when we only used soil moisture from the 2013 growing season.
Numeral calculations and statistical analyses
Ecosystem coupling. We conducted principal component analyses (PCAs; unscaled) at the complete dataset level using the abundances of each taxonomical entity to describe each of the five different communities used in this study: aboveground-dwelling invertebrates, vascular plants, soil microorganisms, soil arthropods and soil nematodes. We retained the first two components (PCA axis 1 and PCA axis 2) of each analysis as we found them to adequately represent the temporal and spatial variability of our 90 treatment plots in previous studies55,67. Together they explained a total of 71.70% of the variation for aboveground invertebrates, 44.36% for plants, 44.85% for soil microorganisms, 61.85% for soil arthropods and 77.19% for soil nematodes. In addition, we used soil pH and soil organic C content as a proxy for soil chemical properties, soil bulk density as a proxy for soil physical properties and soil moisture (negatively correlated with soil temperature) as a proxy for soil micro-climatic conditions for an overall total of fourteen constituents.
We calculated ecosystem coupling9 for each exclosure treatment within each vegetation type (i.e., 2  5 treatment combinations in total) as an integrated measure of pairwise ecological interactions between ecosystem constituents representing ecological communities and the soil abiotic environment. These ecological interactions are defined by non-parametric Spearman rank correlation analyses between two constituents, excluding interactions involving two abiotic constituents (e.g., soil pH vs. soil moisture) and interactions between the first (PC1) and second (PC2) component of each community type, as these are orthogonal by definition. Interactions between abiotic constituents were excluded from the analyses because the focus of our study was on communities and how they interact with one another and their surrounding environment; therefore, including abiotic-abiotic interactions was not of interest here. Given that the effectiveness of our experimental design resulted in that no community composition data of aboveground-dwelling invertebrates was available for the “None” plots (all animals excluded), only thirteen instead of fourteen constituents were included in the ecosystem coupling calculations for this treatment. The complete absence of aboveground invertebrates represents the most extreme case of disturbance between aboveground animal communities and the rest of the ecosystem constituents. This may have resulted in a slight overestimation of ecosystem coupling for these plots.
	Average ecosystem coupling was calculated as follows:
Ecosystem coupling=
where Xi is the absolute Coupling was calculated value of the Spearman’s rho coefficient of the ith correlation for each treatment within each vegetation type (i.e., based on nine replicates each), considering and n is the number of pairwise comparisons considered (n = a total of 80; interactions (56 in the case of the “None” treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the “None” treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the “None” treatment).	Coupling was calculated for each treatment within each vegetation type (i.e., based on nine replicates each), considering a total of 80 interactions (56 in the case of the “None” treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the “None” treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the “None” treatment).
To establish whether constituents were significantly and positively coupled within treatments (i.e., the average of their correlation coefficients were greater than in a null model where correlation only happens by chance), we calculated one-tailed p-values based on permutation tests with 999 permutations.

We considered six ecosystem functions and process rates commonly used to assess ecosystem functioning (Meyer and others 2015; Manning and others 2018). Plant N content represents a measure of forage quality, while plant richness has been shown to stabilise biomass production, thus allowing the system to respond to changes in herbivory. Soil net N mineralisation, soil respiration, root biomass, and microbial biomass represent fluxes or stocks of energy. For all functions and processes higher values represent higher functioning (Manning and others 2018). All these variables were measured in the last year of the experiment (2013). We then quantified ecosystem multifunctionality using the multiple threshold approach (Byrnes and others 2014; Manning and others 2018), which considers the number of functions that are above a certain threshold, over a series of threshold values (typically 10-99%) that are defined based on the maximum value of each function. We weighted all our functions equally for these calculations (Manning and others 2018). The number of functions in a plot with values higher than a given threshold value for the respective function is summed up. The sum represents ecosystem multifunctionality for that plot. Given that choosing any particular threshold as a measure of ecosystem multifunctionality is arbitrary, we calculated the average of thresholds from 10-90% (in 10% intervals) as a more integrated representation of ecosystem multifunctionality.
We used Pearson correlations to explore the relationships between ecosystem coupling (all interactions, biotic-biotic interactions, abiotic-biotic interactions involving above- and belowground constituents, and all interactions, biotic-biotic interactions, abiotic-biotic interactions involving belowground constituents only) and ecosystem multifunctionality by calculating the slopes of all relationships between ecosystem coupling and multifunctionality for all thresholds between 10 and 99%. We also related ecosystem coupling with the average of multifunctionality at thresholds between 30-80% as explained before and considered this correlation as a robust indication of the type of association between these two variables. In addition, we explored the relationships between ecosystem coupling (all interactions, biotic-biotic interactions, abiotic-biotic interactions involving above- and belowground constituents, and all interactions, biotic-biotic interactions, abiotic-biotic interactions involving belowground constituents only) and individual ecosystem functions. The effects of exclosures and vegetation type on individual functions and multifunctionality were evaluated using linear mixed effects models ('lme' function of the nlme package), with exclosure and vegetation type as fixed effects and fence as a random factor. All statistical analyses and numerical calculations were done in R version 3.4.0 (R Core Team 2016).

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- Risch AC, Haynes AG, Busse MD, Filli F, Schütz M. 2013. The response of soil CO2 fluxes to progressively excluding vertebrate and invertebrate herbivores depends on ecosystem type. Ecosystems 16:1192–202.
- Risch AC, Schütz M, Vandegehuchte ML, Van Der Putten WH, Duyts H, Raschein U, Gwiazdowicz DJ, Busse MD, Page-Dumroese DS, Zimmermann S. 2015. Aboveground vertebrate and invertebrate herbivore impact on net N mineralization in subalpine grasslands. Ecology 96:3312–22.
- Schütz M, Risch AC, Achermann G, Thiel-Egenter C, Page-Dumroese DS, Jurgensen MF, Edwards PJ. 2006. Phosphorus translocation by red deer on a subalpine grassland in the Central European Alps. Ecosystems 9:624–633.
- Schütz M, Risch AC, Leuzinger E, Krüsi BO, Achermann G. 2003. Impact of herbivory by red deer (Cervus elaphus L.) on patterns and processes in subalpine grasslands in the Swiss National Park. For Ecol Manage 181:177–88.
- Vandegehuchte ML, van der Putten WH, Duyts H, Schütz M, Risch AC. 2017a. Aboveground mammal and invertebrate exclusions cause consistent changes in soil food webs of two subalpine grassland types, but mechanisms are system-speciﬁc. Oikos 126:212–23.
- Vandegehuchte ML, Raschein U, Schütz M, Gwiazdowicz DJ, Risch AC. 2015. Indirect short- and long-term effects of aboveground invertebrate and vertebrate herbivores on soil microarthropod communities. PLoS One 10:e0118679.
- Vandegehuchte ML, Schütz M, de Schaetzen F, Risch AC. 2017b. Mammal-induced trophic cascades in invertebrate food webs are modulated by grazing intensity in subalpine grassland. J Anim Ecol 86:1434–46.
- Vandegehuchte ML, Trivellone V, Schütz M, Firn J, de Schaetzen F, Risch AC. 2018. Mammalian herbivores affect leafhoppers associated with specific plant functional types at different timescales. Funct Ecol 32:545–55.
- Wirthner S, Frey B, Busse MD, Schütz M, Risch AC. 2011. Effects of wild boar (Sus scrofa L.) rooting on the bacterial community structure in mixed-hardwood forest soils in Switzerland. Eur J Soil Biol 47:296–302. http://dx.doi.org/10.1016/j.ejsobi.2011.07.003
- Zumsteg A, Luster J, Göransson H, Smittenberg RH, Brunner I, Bernasconi SM, Zeyer J, Frey B. 2012. Bacterial, Archaeal and Fungal Succession in the Forefield of a Receding Glacier. Microb Ecol 63:552–64. https://doi.org/10.1007/s00248-011-9991-8

ecosystem-coupling

display_name: ecosystem-coupling

ecosystem-functions

display_name: ecosystem-functions

ecosystem-multifunctionality

display_name: ecosystem-multifunctionality

exclusion

display_name: exclusion

grassland

display_name: grassland

grazing

display_name: grazing

invertebrates

display_name: invertebrates

subalpine

display_name: subalpine

swiss-national-park

display_name: swiss-national-park

vertebrates

display_name: vertebrates

education

de: Bildung, Wissenschaft

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

value: Envidat Harvester

en: Ecosystem coupling and multifunctionality - exclosure experiment


maintainer: Michael Lehning

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

en: ADS datasets and simulation inputs

media_type: application/zip

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

format: ZIP

mimetype: application/zip

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

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

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

Please cite:

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

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

alpine-precipitation

display_name: alpine-precipitation

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-distribution

display_name: snow-distribution

snow-hydrology

display_name: snow-hydrology

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


maintainer: Sven-Eric Hopf

en: monitoring forest ash-dieback iap fraxinus

en: Monitoring of ash trees as part of the Intercantonal Forest Observation Programme

media_type: .pdf

en: Packgroun information on data for the publication: Spread and Severity of Ash Dieback in Switzerland – Tree Characteristics and Landscape Features Explain Varying Mortality Probability, (Klesse et al. 2021, Front. For. Glob. Change 4:645920., doi: 10.3389/ffgc.2021.645920)

format: SERVICE

en:
In 2013, the Institute for Applied Plant Biology (IAP) started a monitoring programme to study the development and the spatial variation of the ash dieback disease with the aim to find some partially resistant European ash trees (Fraxinus excelsior).
We collaborate as co-authors for the publication: Spread and Severity of Ash Dieback in Switzerland - Tree Characteristics and Landscape Features Explain Varying Mortality Probability (Klesse et al. 2021 in frontiers)

ash-dieback

display_name: ash-dieback

forest

display_name: forest

fraxinus

display_name: fraxinus

iap

display_name: iap

monitoring

display_name: monitoring


maintainer: EnviDat Support

en: humidity shortwave-radiation air-temperature precipitation-amount snow-depth longwave-radiation

en: Site photo 3

media_type: JPEG

en: DATA ACCESS

media_type: SMET

en: Meteorological station data 1975-2016. Outlier analysis and general quality control has been carried out. Station was completely renovated in 2007 and instruments changed. A complete air temperature timeseries can be obtained by combining parameters "TA" and "AIR_TEMP_FUEHLER".

en: Site photo 2

en: STB2 CSV

en: Stillberg meteo as CSV (see smet for metadata header)

en: Overview plot

media_type: PNG

en: Overview plots of all variables in timeseries.

format: PNG

en: Latest data from Stillberg climate station

en: This dataset represents the latest data from the Stillberg climate station updated daily. Automated quality control procedures are applied but no manual checks as in the main dataset.

en: README

media_type: TXT

en: Description of station parameters and history.

format: TXT

en: Site photo 1

en: Snow sensor perpendicular to slope.

en: Longterm meteorological and snow station on a steep (c. 40 degrees) North-East exposed slope at 2090 m a.s.l above Davos, Switzerland. Established 1975 and ongoing measurements made, although instruments have changed during this period. The station is located within a experimental plantation that serves as avalanche protection to downslope communities as well as a unique site for treeline dynamics research.

air-temperature

display_name: air-temperature

humidity

display_name: humidity

longwave-radiation

display_name: longwave-radiation

precipitation-amount

display_name: precipitation-amount

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

education

en: Education and science

en: Long-term meteorological and snow station at 2090 m a.s.l., Stillberg, Davos, Switzerland (1975 - present)


en: landscape-services land-use-change statistical-model land-use modeling urbanization switzerland

en: Data for Assessing potential landscape service trade-offs driven by urbanization in Switzerland

en:
The provided folder structure follows the set up of the scripts.
Data is only provided when it has been fully produced by us, otherwise dummies are given.
For data that will be produced by one of the scripts, no dummies are given.
The folders may contain sub-folders, which would be filled if the scripts were all run

 00_Analysis_Data
Data produced throughout the process of the calibration and projection that is later needed for analysis

 01_Raw_Data
Primary data from external sources that were used to compile the rasters and numbers used during the calibration and projection of landscape transitions as well as service calculation

 02_Prepared_Data
Data that will be prepared for further use by scripts are put in here
Additionally it contains the parameter sheets for the transition predictors that we compiled

 03_Model_Data
 Model outputs
Folder were the projection models statistics will be saved
 Models
Folder into which the actual transition models will be saved
 Residuals
Folder into which data on models residuals will be saved for future use

 04_Projected_Data
The output of the projections will be saved into this folder

 05_Service_Data
Any service calculations will be saved in here

 06_Results_Analysis
Any Analysis plots and tables as well as intermediate data will be saved here in the respective sub-folder

en: Scripts for Assessing potential landscape service trade-offs driven by urbanization in Switzerland

en:
Scripts ordered by their main purpose within sequential folders in this ReadMe marked with

Within the folders the capital letters refer to the parallel or sequential use of the scripts; scripts here in ReadMe are marked with

 00_Function Scripts
 Functions.r
Script containing several functions for analyzing statistical models: cohen.kappa, meva.table, D2, max-Tss, plot-TSS

 Service_functions.R
Script containing functions for calculating all services and returning sums (and median for bd and rec) as well as rasters

 01_Data Preparation
 A_AS_raster_preparation.R
Preparation of the land use rasters for the years 1985, 1997, and 2009 based on the original Arealstatistik (AS/ASCH); Assigning our categories and assining new values to all "construction sites" for calibrating the models
For model projection starting point (2009) and for service calculations (all ASCH years) the "other" categories were assigned to the land use categories based on dominant neighbor, so that there were no empty cells
Creation of water & mountainous area raster that stayed stable throughout the projections
Creation of MFH raster (2009), to have as base for all new projection years, as this category never becames any other

 B_Factors_preparation.R
Preparation of the factor rasters that were used to calculate service values per cell (see Table 2): LWfactor, Transportation, Vol, Rivers, Lakes, slope, TWW, Hazard_risk

 B_Predictors_preparation.R
Preparation of the raster of the predictors used for calibrating (ASCH years) and projecting (stable) the transition models that do not depend on the land use categories: Temperature (ASCH years), solar radiation (stable), Public transportation access (stable), Elevation (stable), Workers density (ASCH years), Population change (ASCH transitions)

 B_Regions_preparation.R
Preparation of bioregions as raster to be used to mask asxxxx later to calculated services and land use area just for one region

 C_Pred_proj.R
Projecting the dynamic predictors into the future for projecting the land use transitions with the model: Linear extrapolations for workers densities and population; more detailed extrapolation of temperature (based on data for all stations that matched between the temp table we used for the calibration period and the projections from CS2M, and then making a spatial interpolation for all years based on the interpolation of temperature for all years per station)

 02_Model calibration
 A_Bioreg_pred_corr.R
Calculating correlation between the predictors for each bioregion and land use category, Output are those with too high correlation so that one predictor can be removed from transitions were both predictors occur

 A_trans_numbers_bioreg.R
Calculate transition numbers for each bioregion to decide which transitions to primarily consider

 B_Bioreg_ext_eval.R
Calculating TSS based on the external evaluation for the bioregion transitions that happened often enough to try to model them (calibration data: 85-97, evaluation data: 97-09)

 B_Bioreg_int_eval.R
Calculating TSS based on the internal evaluation for the bioregion transitions that happened often enough to try to model them (calibration data: 70%, evaluation data: 30%)

 C_Bioreg_TSS_analysis.R
Extracting TSS numbers from internal and external evaluation, calculating mean and median for each transition for each region for each evaluation method and saving as csv per region to be able to select only the transitions with high enough model performance

 D_Bioreg_Models_for_prediction.R
Calibration of the models that were then used to project land use transitions into the future; calibrated separately for each bioregion

 E_Residuals_trans_models.R
Calculation of Moran's I statistics to analyze the residuals of the land use transition models

 03_Projections
 A_Bioreg_Projections_run.R
Master script for: Extrapolaitng into the future with the regional models and then assembling for each time step to calculate new AS predictors; Several Outputs: txt file to test for NAs in Predictors, txt file with random seed number for sampling, txt file with times for calculation steps (check progress of script while running), csv sheet with projected transition frequencies, tif-rasters for every region and time step, pdf with raster plot for every region and time step

 04_Service calculation
 A_Bioreg_as_services.R
Calculate services per bioregion for years 1985, 1997, 2009

 A_Service_calculation_bioregions.R
Extracting service numbers(sums) per bioregions for all projection runs and storing them in a list of matrixes to be available for further plotting; can be run for swiss wide projections or regional ones

 05_Analysis
 A_Bioreg_ASCH_development.R
Extracting the occurrence of each category per region for all runs (first part), then for ASCH years, and then plotting them nicely; only works for regional projections

 A_Bioreg_Predictors_importanceD2.R
Calculating D2 for each transition and each considered predictor from bivariate models to get value of importance

 B_Bioreg_Pred_imp_analysis.R
Using calculated D2 to sort Predictors according to their importance for each transition

 A_Bioreg_Spatial_results_analysis.R
Plotting randomly sampled runs for ASCH and services to analyses their spatial development

 A_Bioreg_Transition_numbers_check_v1.R
Analyzing the frequency for all transitions for all regions and producing the point plots for each transition separately

 A_Bioreg_Transition_numbers_check_v2.R
Analyzing percentage and numbers of all transitions but plotting them per region and originating ASCH category

 B_Transition_Analysis.R
Further Analysis of transition numbers

 A_Service_analysis.R
Plotting of the service development for bioregions in several ways for analyzing

en:
Data and scripts of publication:

Madleina Gerecke, Oskar Hagen, Janine Bolliger, Anna M. Hersperger, Felix Kienast, Bronwyn Price, Loïc Pellissier (2019) Assessing potential landscape service trade-offs driven by urbanization in Switzerland. Palgrave communications.

Contains land use projections for Switzerland and scripts and data for these projections as well as the calculation of landscape services.

Data Folder:
Contains sub-folder with the data necessary for this study (provided were no copyright issues, otherwise placeholders with descriptions), and folders where produced data may be stored

Scripts Folder:
Contains scripts organized into subfolders depending on their purpose

Note: Some abbreviations within the scripts and data are derived from German words and not English.

land-use

display_name: land-use

land-use-change

display_name: land-use-change

landscape-services

display_name: landscape-services

modeling

display_name: modeling

statistical-model

display_name: statistical-model

switzerland

display_name: switzerland

urbanization

display_name: urbanization

envidat

en: EnviDat: the Environmental Data Portal

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

en: Land use projections and services for Switzerland


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

en: Site aerial photo (Schänis)

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

en: Site photo of the forest station (SCB)

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

en: Data plot of the field station (SCF)

media_type: PDF

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

format: PDF

en: Data plot of the forest station (SCB)

en: Further details on the data repository

en: Meta data documentation.

en: Forest station data (SCB)

media_type: CSV

en: Data file.

en: Field station data (SCF)

en: Site photo of the field station (SCF)

en: Site photo of the field station from above (viewing downwards the slope).

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Schänis in Switzerland where one station is located within a natural mixed forest stand (SCB) with European beech (_Fagus sylvatica_; 130-150 yrs), European silver fir (_Abies alba_; 130-150 yrs) and European ash (_Fraxinus excelsior_; 130-150 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, SCF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Schänis is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

en: Schänis, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1998 onwards


maintainer: Stephan Zimmermann

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

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

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

biogeochemical-cycling

display_name: biogeochemical-cycling

calcium

display_name: calcium

fagus-orientalis-lipsky

display_name: fagus-orientalis-lipsky

magnesium

display_name: magnesium

potassium

display_name: potassium

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Base cation dynamics in an Oriental beech forest


en: drifting-snow mass-flux eddy-covariance-measurements meteorological-data blowing-snow antarctica turbulence large-eddy-simulations micro-rain-radar

en: Measurement data from micro-meteorological station

media_type: zip-compressed folder of ASCII files

en: Automatic in-situ measurements of micro-meteorology and snow transport

en: Measurement data from Micro Rain Radar (MRR)

media_type: NetCDF

en: Remote sensing data of blowing snow from a Micro Rain Radar (MRR).

en: Simulation data for a situation with negligible snow transport

media_type: zip-compressed folder of csv

en: Results from two LES-LSM simulations called NoDrift_1 and NoDrift_2, which reproduce the conditions observed at the S17 site on 12th January 2019, 05:40 to 05:50 UTC.

media_type: zip-compressed folder of text files (f90)

en: Source code of the large-eddy simulations coupled with a Lagrangian stochastic model (LES-LSM), available on GitLab.

en: Readme

en: Meta data

en: Simulation data for a situation with significant snow transport

en: Data from two LES-LSM simulations called Drift_1 and Drift_2, which reproduce the conditions observed at the S17 site on 11th January 2019, 00:40 to 00:50 UTC.

en: Measurement data from snow particle counter at MRR site

en: Near-surface observations of drifting snow from a Snow Particle Counter (SPC) installed at the location of the Micro Rain Radar.

en:
This dataset contains measurement and simulation data. The measurements characterize the standard meteorology, turbulence, and snow transport at the S17 site near Syowa Station in East Antarctica during an expedition in austral summer 2018/2019. Large-eddy simulations with sublimating particles provide additional insight into the latent and sensible heat exchange between snow and air in two example situations observed at the S17 site.

A part of the measurement data was recorded by an automatic measurement station from 10th January 2019 to 26th January 2019. This measurement station was equipped with standard meteorological sensors, a three-dimensional ultrasonic anemometer, an open-path infrared gas analyzer, a snow particle counter, an infrared radiometer for measurements of the surface temperature, and a sonic ranging sensor measuring changes in snow surface elevation.

At a horizontal distance of approximately 500 m, a Micro Rain Radar (MRR) was installed in a tilted configuration with an elevation angle of 7° for remote sensing of blowing snow between 25th December 2018 and 24th January 2019. In addition, near-surface in-situ measurements of snow transport were performed at the location of the MRR by deploying a snow particle counter from 27th December 2018 to 24th January 2019.

The simulations cover a 18 x 18 x 6 m³ domain and reproduce the steady-state conditions during a 10-min interval with significant snow transport and another 10-min interval with negligible snow transport. We provide the model source code and the post-processed simulation data, i.e., horizontally averaged quantities as a function of height and time.

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

drifting-snow

display_name: drifting-snow

eddy-covariance-measurements

display_name: eddy-covariance-measurements

large-eddy-simulations

display_name: large-eddy-simulations

mass-flux

display_name: mass-flux

meteorological-data

display_name: meteorological-data

micro-rain-radar

display_name: micro-rain-radar

turbulence

display_name: turbulence

en: Meteorology and snow transport at S17 near Syowa, Antarctica, in austral summer 2018/2019


lwf-tea-bag-sites

maintainer: Marcus Schaub

title_for_slug: lwf-tea-bag-sites

en: verification forest decomposition tea-bag modeling roiboos litter carbon green-tea

en: Site Information

en: Descriptive information on sites

en:
Decomposition of plant litter is a key process for the transfer of carbon and nutrients in ecosystems. Carbon contained in the decaying biomass is released to the atmosphere as respired CO2, and may contribute to global warming. Litterbag studies have been used to improve our knowledge of the drivers of litter decomposition, but they lack comparability because litter quality is plant species-specific. The use of commercial tea bags as a standard substrate was suggested in order to harmonize studies, where green tea and rooibos represent more labile and more recalcitrant C compounds as surrogates of local litter.
The tea bag approach was implemented on eight sites of the Swiss long-term Forest Ecosystem Research (LWF) network (https://www.wsl.ch/LWF). This allowed us to take advantage from the existing infrastructure and data from a previous litterbag study with local litter. In Beatenberg and Schaenis, additional elevation transects were established (1200-1800 m and 540-1150 m, respectively) to examine particularly the effect of temperature on decomposition. In Pfynwald (https://www.wsl.ch/de/ueber-die-wsl/versuchsanlagen-und-labors/flaechen-im-wald/pfynwald.html) and Salgesch, infrastructure of running projects was used to examine the effect of drought and understory removal, respectively. In Novaggio, tea bags were incubated in summer and winter to study the effect of seasonality particularly precipitation. Tea bags are collected after 3, 12, 24, and 36 months; for the two time-shifted experiments additionally after 6 and 9 months.
The study has two primary objectives. Firstly, it contributes to TeaComposition initiative (http://teacomposition.org/) which aims at investigating long-term litter decomposition and its key drivers at present as well as under different future climate scenarios using a common protocol and standard litter (tea) across nine terrestrial biomes. Secondly, the data are used to further develop decomposition models such as Yasso (http://en.ilmatieteenlaitos.fi/yasso) which is used by several countries, including Switzerland to estimate the annual carbon fluxes in dead wood, litter, and soil for reporting in National Greenhouse Gas Inventories under the United Nations Framework Convention on Climate Change and the Kyoto Protocol.

carbon

display_name: carbon

decomposition

display_name: decomposition

forest

display_name: forest

green-tea

display_name: green-tea

litter

display_name: litter

modeling

display_name: modeling

roiboos

display_name: roiboos

tea-bag

display_name: tea-bag

verification

display_name: verification

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: LWF-Tea bag sites


maintainer: Sebastian Schlögl

en: snow atmospheric-turbulence atmosphere turbulent-sensible-heat-fluxes stable-boundary-layer

en: Turbulence data AA00

en: sensor in 25 m height

en: Turbulence data GR00

en: sensor in 2 m height

en: sensor in 0.2 m height

en: Turbulence data WFJ07

en: lower sensor

en: sensor in 1 m height

en: Turbulence data PM07

en: SEE

en: NWW

en: upper sensor

en: Turbulence data WFJ11

en:
We used five different atmospheric turbulence datasets from four test sites, with these sites showing differences in their topographical characteristics. We chose one typical alpine test site with high topographical complexity (Weissfluhjoch, Davos, Switzerland) and three test sites consisting of one glacier site (Plaine Morte, Crans-Montana, Switzerland) and two polar sites (Greenland and Antarctica) representing a quasi-ideal site with homogeneous surface and quasi infinite fetch in all directions.
The turbulent sensible heat flux was calculated using the eddy-covariance method. Note that the sonic temperature fluctuations have been converted into virtual temperature fluctuations.
Three-dimensional wind velocity and air temperature were processed using a linear detrending (Rannik and Vesala, 1999) and a planar fit approach (Massmann and Lee, 2002) to rotate the coordinate system. Air temperature, relative humidity and air pressure from weather stations were used to calculate air properties, which are required for the data processing. The weather stations are located in the immediate vicinity of the turbulence tower and are affected by the same air masses. Turbulence data were averaged to 30-min intervals, whilst changing to a 15-min time interval marginally affects the heat fluxes at the Weissfluhjoch test site (Mott et al., 2011). Note that we define a negative sensible heat flux as being directed towards the snow surface and a positive sensible heat flux as being directed upwards.
The selected datasets and corresponding test sites are briefly introduced below:

Weissfluhjoch 2007 (WFJ07): A vertical set-up of two three-dimensional ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) was used at the traditional field site Weissfluhjoch (2540 m asl.) to measure three-dimensional wind velocity and air temperature at a frequency of 20 Hz. The sensors were mounted 3 m and 5 m above the ground and provided reliable data for 50 days between 11 February 2007 and 24 April 2007. Further information on the field campaign can be found in Stössel et al. (2010) and Mott et al. (2011).

Weissfluhjoch 2011-13 (WFJ11): Three-dimensional wind velocity and air temperature were recorded at 5 m above the ground at a frequency of 10 Hz with a three-dimensional ultrasonic anemometer (CSAT3). The analysis was conducted for data obtained between February and March in the years 2011-13.

Plaine Morte 2007 (PM07): Two three-dimensional ultrasonic anemometers (CSAT3) were installed on a horizontal boom facing opposite directions (west-north-west vs. east-south-east) at 3.75 m above the ground to measure air temperature and three-dimensional wind velocity at 20 Hz. The data were collected at the almost flat field site on the Plaine Morte glacier (2750 m asl.) near Crans-Montana, Switzerland from February to April 2007. High quality meteorological data were additionally recorded and used to force the model. A detailed description about the set-up at the Plaine Morte glacier can be found in Huwald et al. (2009) and Bou-Zeid et al. (2010).

Greenland 2000 (GR00): High-frequency three-dimensional ultrasonic anemometer measurements (CSAT3) were recorded at 50 Hz at the Summit Camp (72.3 °N, 38.8 °W, 3208 m asl.) located on the northern dome of the Greenland ice sheet. Data were collected at 1 m and 2 m above the snow surface during summer in 2000 and 2001. Additionally, meteorological measurements were obtained for the post processing and used to force the model. More information about the field campaign can be found in Cullen et al. (2007, 2014).

Antarctica 2000 (AA00): A set-up of three vertical three-dimensional ultrasonic anemometers (DA-600, Kaijo Denki) were installed at Mizuho Station (70°42' S, 44°20' E, 2230 m asl.) in Eastern Antarctica at 0.2, 1 and 25 m and recorded turbulence data at a frequency of 100 Hz from October to November 2000. Longwave and shortwave radiation, relative humidity, air and snow surface temperature were additionally measured and used to force the model. More information about the field campaign can be found in Nishimura and Nemoto (2005).

atmosphere

display_name: atmosphere

atmospheric-turbulence

display_name: atmospheric-turbulence

snow

display_name: snow

stable-boundary-layer

display_name: stable-boundary-layer

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

en: How do stability corrections perform in the stable boundary layer over snow?


en: snow-depth saltation boundary-layer-winds snow-cover vertical-wind-motion

en: Data Compilation

media_type: ZIP

en: Compressed TXT files

en:
The data correspond to the experiments presented and discussed in a paper regarding the interaction between turbulent wind fluctuations and snow saltation mass-fluxes (Paterna, 2016). Each of the nine data files corresponds to a different experiment presented in the paper and conducted in the winter 2014/2015 in the WSL/SLF cold wind tunnel in Davos. For each file the five columns indicate the time from the beginning of the experiment, the streamwise (u’) and the vertical (w’) wind velocity fluctuations, the streamwise (qx) and the vertical (qz) snow mass-flux components. From these time-series the scales of the snow saltation and of the turbulent flow are obtained with respect to the eddy-cycles and snow saltation cycles. From spectral analysis of the time-series a decoupling of the snow saltation from the turbulence forcing reveals two regimes of interaction: a turbulence-dependent regime occurring with weak saltation, and a turbulence-independent regime with strong saltation. Further details can be found at the link below.

Publication http://onlinelibrary.wiley.com/doi/10.1002/2016GL068171/abstract

boundary-layer-winds

display_name: boundary-layer-winds

saltation

display_name: saltation

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth

vertical-wind-motion

display_name: vertical-wind-motion

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Wind tunnel measurement data of drifting snow and turbulent wind fluctuations


data-set-on-snow-instability

title_for_slug: data-set-on-snow-instability

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

en: Read-me file on snow instability data

media_type: application/pdf

mimetype: application/pdf

en: data set on snow instability

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

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

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

avalanche-forcasting

display_name: avalanche-forcasting

crack-propagation

display_name: crack-propagation

failure-initiation

display_name: failure-initiation

propgatoin-saw-test

display_name: propgatoin-saw-test

rutschblock

display_name: rutschblock

slab

display_name: slab

slab-tensile-support

display_name: slab-tensile-support

snow-avalanche

display_name: snow-avalanche

snow-stability-evaluation

display_name: snow-stability-evaluation

weak-layer

display_name: weak-layer

en: Data set on snow instability


maintainer: Adam Winstral

en: jules-investigation-model snow-depth jim snow-simulation snow

media_type: text/plain

en: File/data descriptor

mimetype: text/plain

en: HS and SWE observations

en: Automated (HS) and manual (SWE) data used to evaluate model simulations

en: DEM

en: 1-km resolution DEM used for downscaling gridded NWP data to stations and for distributing BDE evaluations at HS-stations to SWE-stations

en: Station forcing data

en: All forcing data need to run JIM at HS and SWE stations. These are data downscaled directly from COSMO NWP data (WY14, WY15) and further enhanced by additional station observations of snow depth (HS) and air temperature (Ta) in WY17. Further information can be found in README file. Note that in WY14 and WY15, shortwave radiation was not split into direct and diffuse (not necessary for JIM) and that the default method in JIM for splitting precipitation phase was used. In WY 17, direct and diffuse were combined as input to JIM and JIM was forced with pre-processed liquid and solid precipitation phases.

en: These data were used to drive and evaluate Jules Investigation Model (JIM) snow simulations. The data provided are the forcing data used for the "deterministic" runs as described in Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 2019) used observed snow depths (HS) to detect biases in these deterministic simulations related to precipitation and energy inputs to JIM. Simulations that included the BDE evaluations substantially improved JIM simulations.

jim

display_name: jim

jules-investigation-model

display_name: jules-investigation-model

snow

display_name: snow

snow-simulation

display_name: snow-simulation

en: Meteorological data used to develop and validate the bias-detecting ensemble (BDE)


maintainer: Franziska Gerber

en: topography wrf cryowrf cosmo-wrf static-data landuse

media_type: .txt

en: Example namelist.wps to use with the 1s static data

en: Static data for WRF - Soiltype bottom

en:
Example GEOGRID.TBL to use with the 1s static data;
Note about naming:
* 121 -> 1-2-1 topography smoothing applied to dataset
* 1 -> one smoothing cycle applied to the data (needs to be changed depending on the chosen resolution to avoid too steep slope angles)

media_type: .zip

en: Static data for WRF - Topography

en: Static data for WRF - Landuse

en: Static data for WRF - Soiltype top

en: Documentation

en: Documentation of the dataset

en: Static input data (topography, landuse and soiltype) for the WRF preprocessing system WPS is provided for Switzerland and its neighboring countries between 45-49 N and 4-12 E. The data is provided at a resolution of 1 s. Topography is based on the Aster dataset, while landuse is extracted from the Corine landuse dataset. Soil type is set to silty clay loam for the entire domain. This static input data is valid for WRF and CRYOWRF.

cosmo-wrf

display_name: cosmo-wrf

cryowrf

display_name: cryowrf

landuse

display_name: landuse

static-data

display_name: static-data

topography

display_name: topography

wrf

display_name: wrf

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: High resolution static data for WRF over Switzerland


en: castanea-sativa pest-invasion dryocosmus-kuriphilus torymus-sinensis biological-control population-evolution southern-switzerland

media_type: text/csv

en:
Data has been collected from summer 2011 to 2016.

Main reasons for NA values:

-	D. kuriphilus still not arrived in the site.
-	No galls were found during the field sampling.
-	Site has not been sampled in the correspondent year because of time, weather or money constrains.
-    T. sinensis not found in the galls

Variable description:

Year = sampling year.

Coordinate, elevation, slope, aspect = average site characteristics.

ts_arrival = T. sinensis arrival year.

dk_arrival = D. kuriphilus arrival year.

dk_parasitism_by_ts = D. kuriphilus parasitism rate by T. sinensis. Formula: (no. of T. sinensis/ no. of D. kuriphilus chambers) * 100 at gall level.

dk_infestation = Percentage of attacked buds. Formula: no. of attacked buds / no. of total developed buds*100 (as presented in Kotobuki et al. 1985) at shoot level.

ts_per_bud = number of alive T. sinensis individual calculated at bud level.

dk_per_bud = number of alive D. kuriphilus individual calculated at bud level.

dk_pot_per_bud = overall number (dead and alive) D. kuriphilus individual calculated at bud level.

total_gall_nr_per_bud = average gall number per bud.

chamber_per_galls = average D. kuriphilus chamber number per gall.

For details see the submitted publication with the same name

References
Kotobuki, K., Mori, K. and Sato, Y. (1985) ‘2 methods to estimate the tree damage by chestnut gall wasp Dryocosmus-kuriphilus’, Bulletin of the fruit tree research station A (Yatabe), 2(12), pp. 29–36.

en:
This dataset contains the population evolution of a pest and its biocontrol agent in terms of presence proportion at gall level and absolute number of insects.

The study area extends from the Cuneo region (Piedmont, Italy) to southern Switzerland. In order to provide a complete range of data covering the entire process from the pest arrival to complete biological control by its natural enemy T. sinensis, a space-for-time substitution approach has been adopted so as to create a temporal gradient of the epidemic stages over the whole study area.

The southernmost Swiss sites roughly represent the arrival and establishment of the pest without the presence of the natural enemy, the central ones the early epidemic stage and the epidemic peak, whereas the northern ones the end of the epidemic with the beginning of the biocontrol. The Italian ones represent the beginning of the equilibrium between the two population as well as the situation with stable T. sinensis populations on the long term.

These data are used in the paper entitled: Torymus sinensis local and regional early population dynamics in the Insubrian and Piedmont regions

biological-control

display_name: biological-control

castanea-sativa

display_name: castanea-sativa

dryocosmus-kuriphilus

display_name: dryocosmus-kuriphilus

pest-invasion

display_name: pest-invasion

population-evolution

display_name: population-evolution

southern-switzerland

display_name: southern-switzerland

torymus-sinensis

display_name: torymus-sinensis